diff --git a/reference/lehmann.rice.uniformity.html b/reference/lehmann.rice.uniformity.html
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+
+
Uniformity trial of rice in India — lehmann.rice.uniformity • agridat
+
Skip to contents
+
+
+
+
+
agridat
+
+
1.24
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uniformity trial of rice in India, 3 years on same land.
+
+
Usage
+
data ( "lehmann.rice.uniformity" )
+
+
+
A data frame with 153 observations on the following 5 variables.
yearyear
plotplot (row)
rangerange (column)
yieldgrain yield (pounds)
totaltotal crop yield (pounds)
+
Details
+
+
+
A uniformity experiment of paddy rice on the Experimental Farm at
+ Hebbal (near Bangalore). The plots were the same year-over-year.
+
The 6th report
+
P. 2: Plots are 1/10 acre, 50 links wide, 200 links long.
+
The 7th report
+
P. 6 table 1 has yield (pounds) of paddy produced on the wet area of
+ the farm for 1905-196. (No total weight weight is given).
+
The 9th report
+
P. 19 has commenets.
+ P. 47 tables 6 & 7 has grain/total yield for Range B and Range C
+ 1906-1908.
+
Field width: 3 plots * 200 links
+
Field length: 17 plots * 50 links
+
+
Source
+
Lehmann, A.
+ Ninth Annual Report of the Agricultural Chemist For the Year 1907-08.
+ Department of Agriculture, Mysore State.
+ [2nd-9th] Annual Report of the Agricultural Chemist.
+ https://books.google.com/books?id=u_dHAAAAYAAJ
+
+
References
+
Theodor Roemer (1920).
+ Der Feldversuch. Page 68, table 12.
+ https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ
+
+
Examples
+
if ( FALSE ) { # \dontrun{ library ( agridat ) data ( lehmann.rice.uniformity ) dat <- lehmann.rice.uniformity libs ( desplot ) dat $ year = factor ( dat $ year ) desplot ( dat , yield ~ range * plot | year , aspect= ( 17 * 50 ) / ( 2 * 200 ) , main= "lehmann.rice.uniformity" , flip= TRUE , tick= TRUE ) desplot ( dat , total ~ range * plot | year , aspect= ( 17 * 50 ) / ( 2 * 200 ) , main= "lehmann.rice.uniformity" , flip= TRUE , tick= TRUE ) # libs(dplyr) # group_by(dat, year) } # }
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+
diff --git a/reference/lehner.soybeanmold.html b/reference/lehner.soybeanmold.html
index b1727d9..ff62f73 100644
--- a/reference/lehner.soybeanmold.html
+++ b/reference/lehner.soybeanmold.html
@@ -1,5 +1,5 @@
-
Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold • agridat
+
Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold • agridat Skip to contents
diff --git a/reference/lessman.sorghum.uniformity.html b/reference/lessman.sorghum.uniformity.html
index 178c3a9..f323f3f 100644
--- a/reference/lessman.sorghum.uniformity.html
+++ b/reference/lessman.sorghum.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of sorghum — lessman.sorghum.uniformity • agridat
+
Uniformity trial of sorghum — lessman.sorghum.uniformity • agridat Skip to contents
diff --git a/reference/li.millet.uniformity.html b/reference/li.millet.uniformity.html
index d5e45e0..88e2b97 100644
--- a/reference/li.millet.uniformity.html
+++ b/reference/li.millet.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of millet — li.millet.uniformity • agridat
+
Uniformity trial of millet — li.millet.uniformity • agridat Skip to contents
diff --git a/reference/libs.html b/reference/libs.html
index 0e748e6..112fa2c 100644
--- a/reference/libs.html
+++ b/reference/libs.html
@@ -1,5 +1,5 @@
-
Load multiple packages and install if needed — libs • agridat
+
Load multiple packages and install if needed — libs • agridat Skip to contents
diff --git a/reference/lillemo.wheat.html b/reference/lillemo.wheat.html
index f262bb8..dafece9 100644
--- a/reference/lillemo.wheat.html
+++ b/reference/lillemo.wheat.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat • agridat
+
Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat • agridat Skip to contents
diff --git a/reference/lin.superiority.html b/reference/lin.superiority.html
index bbb3687..c8dec7e 100644
--- a/reference/lin.superiority.html
+++ b/reference/lin.superiority.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority • agridat
+
Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority • agridat Skip to contents
diff --git a/reference/lin.unbalanced.html b/reference/lin.unbalanced.html
index 4cd3e6e..f47af2e 100644
--- a/reference/lin.unbalanced.html
+++ b/reference/lin.unbalanced.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced • agridat
+
Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced • agridat Skip to contents
diff --git a/reference/linder.wheat.html b/reference/linder.wheat.html
index a1dfa54..88e0f36 100644
--- a/reference/linder.wheat.html
+++ b/reference/linder.wheat.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of wheat in Switzerland — linder.wheat • agridat
+
Multi-environment trial of wheat in Switzerland — linder.wheat • agridat Skip to contents
diff --git a/reference/little.splitblock.html b/reference/little.splitblock.html
index 8101643..9c2a5b2 100644
--- a/reference/little.splitblock.html
+++ b/reference/little.splitblock.html
@@ -1,5 +1,5 @@
-
Split-block experiment of sugar beets — little.splitblock • agridat
+
Split-block experiment of sugar beets — little.splitblock • agridat Skip to contents
diff --git a/reference/loesell.bean.uniformity.html b/reference/loesell.bean.uniformity.html
index c66709a..ba2e25d 100644
--- a/reference/loesell.bean.uniformity.html
+++ b/reference/loesell.bean.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of white pea beans — loesell.bean.uniformity • agridat
+
Uniformity trial of white pea beans — loesell.bean.uniformity • agridat Skip to contents
diff --git a/reference/lonnquist.maize.html b/reference/lonnquist.maize.html
index 40f57fe..65e51e8 100644
--- a/reference/lonnquist.maize.html
+++ b/reference/lonnquist.maize.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of maize, half diallel — lonnquist.maize • agridat
+
Multi-environment trial of maize, half diallel — lonnquist.maize • agridat Skip to contents
diff --git a/reference/lord.rice.uniformity.html b/reference/lord.rice.uniformity.html
index 49367b9..fca9996 100644
--- a/reference/lord.rice.uniformity.html
+++ b/reference/lord.rice.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of rice — lord.rice.uniformity • agridat
+
Uniformity trial of rice — lord.rice.uniformity • agridat Skip to contents
diff --git a/reference/love.cotton.uniformity.html b/reference/love.cotton.uniformity.html
index 9432a3a..d5fdbf1 100644
--- a/reference/love.cotton.uniformity.html
+++ b/reference/love.cotton.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of cotton — love.cotton.uniformity • agridat
+
Uniformity trial of cotton — love.cotton.uniformity • agridat Skip to contents
diff --git a/reference/lu.stability.html b/reference/lu.stability.html
index e33141a..3a4c18d 100644
--- a/reference/lu.stability.html
+++ b/reference/lu.stability.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of maize, to illustrate stability statistics — lu.stability • agridat
+
Multi-environment trial of maize, to illustrate stability statistics — lu.stability • agridat Skip to contents
diff --git a/reference/lucas.switchback.html b/reference/lucas.switchback.html
index a3bc22b..8c65e00 100644
--- a/reference/lucas.switchback.html
+++ b/reference/lucas.switchback.html
@@ -1,5 +1,5 @@
-
Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback • agridat
+
Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback • agridat Skip to contents
diff --git a/reference/lyon.potato.uniformity.html b/reference/lyon.potato.uniformity.html
index 5885864..88a43b4 100644
--- a/reference/lyon.potato.uniformity.html
+++ b/reference/lyon.potato.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of potatoes — lyon.potato.uniformity • agridat
+
Uniformity trial of potatoes — lyon.potato.uniformity • agridat Skip to contents
diff --git a/reference/lyons.wheat.html b/reference/lyons.wheat.html
index 6ffb6e4..a28096d 100644
--- a/reference/lyons.wheat.html
+++ b/reference/lyons.wheat.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat • agridat
+
Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat • agridat Skip to contents
diff --git a/reference/magistad.pineapple.uniformity.html b/reference/magistad.pineapple.uniformity.html
index 5770ff9..c489483 100644
--- a/reference/magistad.pineapple.uniformity.html
+++ b/reference/magistad.pineapple.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of pineapple — magistad.pineapple.uniformity • agridat
+
Uniformity trial of pineapple — magistad.pineapple.uniformity • agridat Skip to contents
diff --git a/reference/masood.rice.uniformity.html b/reference/masood.rice.uniformity.html
index b87c2ba..d9b514c 100644
--- a/reference/masood.rice.uniformity.html
+++ b/reference/masood.rice.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of rice — masood.rice.uniformity • agridat
+
Uniformity trial of rice — masood.rice.uniformity • agridat Skip to contents
diff --git a/reference/mcclelland.corn.uniformity.html b/reference/mcclelland.corn.uniformity.html
index fff7cd8..c57fea7 100644
--- a/reference/mcclelland.corn.uniformity.html
+++ b/reference/mcclelland.corn.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of corn — mcclelland.corn.uniformity • agridat
+
Uniformity trial of corn — mcclelland.corn.uniformity • agridat Skip to contents
diff --git a/reference/mcconway.turnip.html b/reference/mcconway.turnip.html
index a80fa47..c365f6e 100644
--- a/reference/mcconway.turnip.html
+++ b/reference/mcconway.turnip.html
@@ -1,5 +1,5 @@
-
RCB experiment of turnips — mcconway.turnip • agridat
+
RCB experiment of turnips — mcconway.turnip • agridat Skip to contents
diff --git a/reference/mckinstry.cotton.uniformity.html b/reference/mckinstry.cotton.uniformity.html
index 38a613a..bbcd23b 100644
--- a/reference/mckinstry.cotton.uniformity.html
+++ b/reference/mckinstry.cotton.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity • agridat
+
Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity • agridat Skip to contents
diff --git a/reference/mcleod.barley.html b/reference/mcleod.barley.html
index 3d271de..c121674 100644
--- a/reference/mcleod.barley.html
+++ b/reference/mcleod.barley.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley • agridat
+
Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley • agridat Skip to contents
diff --git a/reference/mead.cauliflower.html b/reference/mead.cauliflower.html
index 1a2d39b..ef60679 100644
--- a/reference/mead.cauliflower.html
+++ b/reference/mead.cauliflower.html
@@ -1,5 +1,5 @@
-
Leaves for cauliflower plants at different times — mead.cauliflower • agridat
+
Leaves for cauliflower plants at different times — mead.cauliflower • agridat Skip to contents
diff --git a/reference/mead.cowpea.maize.html b/reference/mead.cowpea.maize.html
index f07f872..380af6d 100644
--- a/reference/mead.cowpea.maize.html
+++ b/reference/mead.cowpea.maize.html
@@ -1,5 +1,5 @@
-
Intercropping experiment of maize/cowpea — mead.cowpea.maize • agridat
+
Intercropping experiment of maize/cowpea — mead.cowpea.maize • agridat Skip to contents
diff --git a/reference/mead.germination.html b/reference/mead.germination.html
index 346c0f1..47d2cfd 100644
--- a/reference/mead.germination.html
+++ b/reference/mead.germination.html
@@ -1,5 +1,5 @@
-
Seed germination with different temperatures/concentrations — mead.germination • agridat
+
Seed germination with different temperatures/concentrations — mead.germination • agridat Skip to contents
diff --git a/reference/mead.lamb.html b/reference/mead.lamb.html
index 3e8f7d5..ab2736d 100644
--- a/reference/mead.lamb.html
+++ b/reference/mead.lamb.html
@@ -1,5 +1,5 @@
-
Number of lambs born to 3 breeds on 3 farms — mead.lamb • agridat
+
Number of lambs born to 3 breeds on 3 farms — mead.lamb • agridat Skip to contents
diff --git a/reference/mead.strawberry.html b/reference/mead.strawberry.html
index cbed89f..fe55c67 100644
--- a/reference/mead.strawberry.html
+++ b/reference/mead.strawberry.html
@@ -1,5 +1,5 @@
-
RCB experiment of strawberry — mead.strawberry • agridat
+
RCB experiment of strawberry — mead.strawberry • agridat Skip to contents
diff --git a/reference/mead.turnip.html b/reference/mead.turnip.html
index bb15f4c..4356a07 100644
--- a/reference/mead.turnip.html
+++ b/reference/mead.turnip.html
@@ -1,5 +1,5 @@
-
Density/spacing experiment for turnips in 3 blocks. — mead.turnip • agridat
+
Density/spacing experiment for turnips in 3 blocks. — mead.turnip • agridat Skip to contents
diff --git a/reference/mercer.mangold.uniformity.html b/reference/mercer.mangold.uniformity.html
index 883d608..ac0f4c6 100644
--- a/reference/mercer.mangold.uniformity.html
+++ b/reference/mercer.mangold.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of mangolds — mercer.mangold.uniformity • agridat Uniformity trial of mangolds — mercer.mangold.uniformity • agridat Skip to contents
diff --git a/reference/mercer.wheat.uniformity.html b/reference/mercer.wheat.uniformity.html
index 044694a..8366480 100644
--- a/reference/mercer.wheat.uniformity.html
+++ b/reference/mercer.wheat.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of wheat — mercer.wheat.uniformity • agridat
+
Uniformity trial of wheat — mercer.wheat.uniformity • agridat Skip to contents
diff --git a/reference/miguez.biomass.html b/reference/miguez.biomass.html
index 6ebbecb..2115e4a 100644
--- a/reference/miguez.biomass.html
+++ b/reference/miguez.biomass.html
@@ -1,5 +1,5 @@
-
Biomass of 3 crops in Greece — miguez.biomass • agridat
+
Biomass of 3 crops in Greece — miguez.biomass • agridat Skip to contents
diff --git a/reference/minnesota.barley.weather.html b/reference/minnesota.barley.weather.html
index 44fa36f..b8979b7 100644
--- a/reference/minnesota.barley.weather.html
+++ b/reference/minnesota.barley.weather.html
@@ -1,5 +1,5 @@
-
Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather • agridat Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather • agridat Skip to contents
diff --git a/reference/minnesota.barley.yield.html b/reference/minnesota.barley.yield.html
index 83ed16e..b371425 100644
--- a/reference/minnesota.barley.yield.html
+++ b/reference/minnesota.barley.yield.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield • agridat Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield • agridat Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity • agridat
+
Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity • agridat Skip to contents
diff --git a/reference/moore.uniformity.html b/reference/moore.uniformity.html
index 6ddbb82..ba0e0bb 100644
--- a/reference/moore.uniformity.html
+++ b/reference/moore.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity • agridat Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity • agridat Skip to contents
diff --git a/reference/nagai.strawberry.uniformity.html b/reference/nagai.strawberry.uniformity.html
index 2f71ddb..33edd0a 100644
--- a/reference/nagai.strawberry.uniformity.html
+++ b/reference/nagai.strawberry.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of strawberry — nagai.strawberry.uniformity • agridat
+
Uniformity trial of strawberry — nagai.strawberry.uniformity • agridat Skip to contents
diff --git a/reference/nair.turmeric.uniformity.html b/reference/nair.turmeric.uniformity.html
index 22f8c57..c686cb0 100644
--- a/reference/nair.turmeric.uniformity.html
+++ b/reference/nair.turmeric.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of turmeric. — nair.turmeric.uniformity • agridat
+
Uniformity trial of turmeric. — nair.turmeric.uniformity • agridat Skip to contents
diff --git a/reference/narain.sorghum.uniformity.html b/reference/narain.sorghum.uniformity.html
index c82259b..bc36a0a 100644
--- a/reference/narain.sorghum.uniformity.html
+++ b/reference/narain.sorghum.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of sorghum — narain.sorghum.uniformity • agridat
+
Uniformity trial of sorghum — narain.sorghum.uniformity • agridat Skip to contents
diff --git a/reference/nass.corn.html b/reference/nass.corn.html
index bc55b7d..591239c 100644
--- a/reference/nass.corn.html
+++ b/reference/nass.corn.html
@@ -1,5 +1,5 @@
-
U.S. historical crop yields by state — nass.corn • agridat U.S. historical crop yields by state — nass.corn • agridat Nebraska farm income in 2007 by county — nebraska.farmincome • agridat
+
Nebraska farm income in 2007 by county — nebraska.farmincome • agridat Skip to contents
diff --git a/reference/nonnecke.peas.uniformity.html b/reference/nonnecke.peas.uniformity.html
index b2e5d0b..e08b3d6 100644
--- a/reference/nonnecke.peas.uniformity.html
+++ b/reference/nonnecke.peas.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of canning peas — nonnecke.peas.uniformity • agridat
+
Uniformity trial of canning peas — nonnecke.peas.uniformity • agridat Skip to contents
diff --git a/reference/nonnecke.sweetcorn.uniformity.html b/reference/nonnecke.sweetcorn.uniformity.html
index 616b30d..072eb33 100644
--- a/reference/nonnecke.sweetcorn.uniformity.html
+++ b/reference/nonnecke.sweetcorn.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity • agridat
+
Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity • agridat Skip to contents
diff --git a/reference/obsi.potato.uniformity.html b/reference/obsi.potato.uniformity.html
index d2f1202..75a8a8d 100644
--- a/reference/obsi.potato.uniformity.html
+++ b/reference/obsi.potato.uniformity.html
@@ -1,5 +1,5 @@
-
Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity • agridat
+
Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity • agridat Skip to contents
diff --git a/reference/odland.soybean.uniformity.html b/reference/odland.soybean.uniformity.html
index bb2fb54..345ad83 100644
--- a/reference/odland.soybean.uniformity.html
+++ b/reference/odland.soybean.uniformity.html
@@ -1,5 +1,5 @@
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Uniformity trials of soy hay and soybeans — odland.soybean.uniformity • agridat Uniformity trials of soy hay and soybeans — odland.soybean.uniformity • agridat Skip to contents
diff --git a/reference/omer.sorghum.html b/reference/omer.sorghum.html
index 92f6aff..924d6af 100644
--- a/reference/omer.sorghum.html
+++ b/reference/omer.sorghum.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of sorghum, 6 environments — omer.sorghum • agridat
+
Multi-environment trial of sorghum, 6 environments — omer.sorghum • agridat Skip to contents
diff --git a/reference/onofri.winterwheat.html b/reference/onofri.winterwheat.html
index 4060364..d72a5d0 100644
--- a/reference/onofri.winterwheat.html
+++ b/reference/onofri.winterwheat.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of winter wheat, 7 years — onofri.winterwheat • agridat
+
Multi-environment trial of winter wheat, 7 years — onofri.winterwheat • agridat Skip to contents
diff --git a/reference/ortiz.tomato.html b/reference/ortiz.tomato.html
index 1da50e5..54bafc3 100644
--- a/reference/ortiz.tomato.html
+++ b/reference/ortiz.tomato.html
@@ -1,5 +1,5 @@
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Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato • agridat
+
Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato • agridat Skip to contents
diff --git a/reference/pacheco.soybean.html b/reference/pacheco.soybean.html
index 8b504e4..6c40672 100644
--- a/reference/pacheco.soybean.html
+++ b/reference/pacheco.soybean.html
@@ -1,5 +1,5 @@
-
Multi-environment trial of soybean in Brazil. — pacheco.soybean • agridat
+
Multi-environment trial of soybean in Brazil. — pacheco.soybean • agridat Skip to contents
diff --git a/reference/paez.coffee.uniformity.html b/reference/paez.coffee.uniformity.html
index d5147aa..0267041 100644
--- a/reference/paez.coffee.uniformity.html
+++ b/reference/paez.coffee.uniformity.html
@@ -1,5 +1,5 @@
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Uniformity trial of coffee — paez.coffee.uniformity • agridat
+
Uniformity trial of coffee — paez.coffee.uniformity • agridat Skip to contents
diff --git a/reference/panse.cotton.uniformity.html b/reference/panse.cotton.uniformity.html
index 59e8a0e..103fd54 100644
--- a/reference/panse.cotton.uniformity.html
+++ b/reference/panse.cotton.uniformity.html
@@ -1,5 +1,5 @@
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Uniformity trial of cotton — panse.cotton.uniformity • agridat
+
Uniformity trial of cotton — panse.cotton.uniformity • agridat Skip to contents
diff --git a/reference/parker.orange.uniformity.html b/reference/parker.orange.uniformity.html
index 74126eb..f53ff65 100644
--- a/reference/parker.orange.uniformity.html
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Uniformity trial of oranges — parker.orange.uniformity • agridat
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diff --git a/reference/patterson.switchback.html b/reference/patterson.switchback.html
index 1397a1b..0f173c6 100644
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Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback • agridat
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diff --git a/reference/payne.wheat.html b/reference/payne.wheat.html
index 563d97d..312114b 100644
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Long term rotation experiment at Rothamsted — payne.wheat • agridat
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diff --git a/reference/pearce.apple.html b/reference/pearce.apple.html
index 1731846..c5077ff 100644
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Apple tree yields for 6 treatments with covariate — pearce.apple • agridat
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diff --git a/reference/pearl.kernels.html b/reference/pearl.kernels.html
index 179a80c..65f0500 100644
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Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels • agridat Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels • agridat Skip to contents
diff --git a/reference/pederson.lettuce.repeated.html b/reference/pederson.lettuce.repeated.html
index ab1842b..5bfcc60 100644
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Repeated measurements of lettuce growth — pederson.lettuce.repeated • agridat
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diff --git a/reference/perry.springwheat.html b/reference/perry.springwheat.html
index a6c6ca4..a8e87ec 100644
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Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat • agridat
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diff --git a/reference/petersen.sorghum.cowpea.html b/reference/petersen.sorghum.cowpea.html
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Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea • agridat
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Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea • agridat Skip to contents
diff --git a/reference/piepho.barley.uniformity.html b/reference/piepho.barley.uniformity.html
index ea242fa..1a196e3 100644
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Uniformity trial of barley — piepho.barley.uniformity • agridat
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diff --git a/reference/piepho.cocksfoot.html b/reference/piepho.cocksfoot.html
index 30e7925..c597a91 100644
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Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot • agridat
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Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot • agridat Skip to contents
diff --git a/reference/polson.safflower.uniformity.html b/reference/polson.safflower.uniformity.html
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Uniformity trial of safflower — polson.safflower.uniformity • agridat
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diff --git a/reference/ratkowsky.onions.html b/reference/ratkowsky.onions.html
index 3ef2374..47369ed 100644
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Onion yields for different densities at two locations — ratkowsky.onions • agridat
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diff --git a/reference/reid.grasses.html b/reference/reid.grasses.html
index a3069fd..1574fd7 100644
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Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses • agridat Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses • agridat Skip to contents
diff --git a/reference/riddle.wheat.html b/reference/riddle.wheat.html
index 7a3b5ab..adea4bb 100644
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Modified Latin Square experiments of wheat — riddle.wheat • agridat
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diff --git a/reference/ridout.appleshoots.html b/reference/ridout.appleshoots.html
index b0fe660..7def5b2 100644
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Root counts for propagated columnar apple shoots. — ridout.appleshoots • agridat
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diff --git a/reference/robinson.peanut.uniformity.html b/reference/robinson.peanut.uniformity.html
index 8d2f86d..cc5218a 100644
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Uniformity trial of peanuts — robinson.peanut.uniformity • agridat
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diff --git a/reference/roemer.sugarbeet.uniformity.html b/reference/roemer.sugarbeet.uniformity.html
index c2b55d6..2785e15 100644
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Uniformity trial of sugar beets — roemer.sugarbeet.uniformity • agridat
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diff --git a/reference/rothamsted.brussels.html b/reference/rothamsted.brussels.html
index 35b95b1..51a08ac 100644
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RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels • agridat
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diff --git a/reference/rothamsted.oats.html b/reference/rothamsted.oats.html
index 7918719..1f3f21e 100644
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RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats • agridat
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diff --git a/reference/ryder.groundnut.html b/reference/ryder.groundnut.html
index d2978c7..724606c 100644
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RCB experiment of groundut, wet and dry yields — ryder.groundnut • agridat
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diff --git a/reference/salmon.bunt.html b/reference/salmon.bunt.html
index e70f7a7..0b3227c 100644
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Fungus infection in varieties of wheat — salmon.bunt • agridat
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diff --git a/reference/saunders.maize.uniformity.html b/reference/saunders.maize.uniformity.html
index b7689a9..a786113 100644
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Uniformity trial of maize in South Africa — saunders.maize.uniformity • agridat
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diff --git a/reference/sawyer.multi.uniformity.html b/reference/sawyer.multi.uniformity.html
index 1d49dcd..e2edc28 100644
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Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity • agridat
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diff --git a/reference/sayer.sugarcane.uniformity.html b/reference/sayer.sugarcane.uniformity.html
index 0de8e45..113f690 100644
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Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity • agridat
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Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity • agridat Skip to contents
diff --git a/reference/senshu.rice.html b/reference/senshu.rice.html
index de3ac6b..13537a9 100644
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Multi-environment trial of rice, with solar radiation and temperature — senshu.rice • agridat
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diff --git a/reference/shafi.tomato.uniformity.html b/reference/shafi.tomato.uniformity.html
index 4a1bdd7..d9b5923 100644
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Uniformity trial of tomato — shafi.tomato.uniformity • agridat
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diff --git a/reference/shafii.rapeseed.html b/reference/shafii.rapeseed.html
index 5818565..ce3674e 100644
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Multi-environment trial of rapeseed in U.S. — shafii.rapeseed • agridat
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diff --git a/reference/sharma.met.html b/reference/sharma.met.html
index a1e96b4..0da4f2b 100644
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Multi-environment trial — sharma.met • agridat
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diff --git a/reference/shaw.oats.html b/reference/shaw.oats.html
index 01910f1..1beae2e 100644
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Multi-environment trial of oats in India — shaw.oats • agridat Multi-environment trial of oats in India — shaw.oats • agridat Skip to contents
diff --git a/reference/siao.cotton.uniformity.html b/reference/siao.cotton.uniformity.html
index 34540ee..22b5388 100644
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Uniformity trials of cotton in China — siao.cotton.uniformity • agridat
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diff --git a/reference/silva.cotton.html b/reference/silva.cotton.html
index 0653202..fe255a1 100644
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Number of cotton bolls for different levels of defoliation. — silva.cotton • agridat
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Number of cotton bolls for different levels of defoliation. — silva.cotton • agridat Skip to contents
diff --git a/reference/sinclair.clover.html b/reference/sinclair.clover.html
index 2f627ec..c00480d 100644
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Clover yields in a factorial fertilizer experiment — sinclair.clover • agridat
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diff --git a/reference/smith.beans.uniformity.html b/reference/smith.beans.uniformity.html
index e2f4166..cfbb6ed 100644
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Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity • agridat
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diff --git a/reference/smith.corn.uniformity.html b/reference/smith.corn.uniformity.html
index 0b73aa5..97a1267 100644
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Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity • agridat
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diff --git a/reference/smith.wheat.uniformity.html b/reference/smith.wheat.uniformity.html
index cd29551..a6deedf 100644
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Uniformity trial of wheat — smith.wheat.uniformity • agridat
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diff --git a/reference/snedecor.asparagus.html b/reference/snedecor.asparagus.html
index 9075553..9e47cb0 100644
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Asparagus yields for different cutting treatments — snedecor.asparagus • agridat
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diff --git a/reference/snijders.fusarium.html b/reference/snijders.fusarium.html
index d7d0c5b..6211d80 100644
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Fusarium infection in wheat varieties — snijders.fusarium • agridat
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diff --git a/reference/stephens.sorghum.uniformity.html b/reference/stephens.sorghum.uniformity.html
index 3d3dc8a..024bebd 100644
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Uniformity trial of sorghum silage — stephens.sorghum.uniformity • agridat
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diff --git a/reference/steptoe.morex.pheno.html b/reference/steptoe.morex.pheno.html
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Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno • agridat Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno • agridat Uniformity trial of sorghum — stickler.sorghum.uniformity • agridat
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diff --git a/reference/stirret.borers.html b/reference/stirret.borers.html
index 5f07f20..4676de2 100644
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Corn borer control by application of fungal spores. — stirret.borers • agridat
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diff --git a/reference/streibig.competition.html b/reference/streibig.competition.html
index 42bb5b1..192ec88 100644
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Competition experiment between barley and sinapis. — streibig.competition • agridat Competition experiment between barley and sinapis. — streibig.competition • agridat Skip to contents
diff --git a/reference/strickland.apple.uniformity.html b/reference/strickland.apple.uniformity.html
index e48065a..2b5d51c 100644
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Uniformity trial in apple — strickland.apple.uniformity • agridat
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diff --git a/reference/strickland.grape.uniformity.html b/reference/strickland.grape.uniformity.html
index c8e753b..e91c212 100644
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Uniformity trial of grape — strickland.grape.uniformity • agridat
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diff --git a/reference/strickland.peach.uniformity.html b/reference/strickland.peach.uniformity.html
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Uniformity trial of peach — strickland.peach.uniformity • agridat
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diff --git a/reference/strickland.tomato.uniformity.html b/reference/strickland.tomato.uniformity.html
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Uniformity trial of tomato — strickland.tomato.uniformity • agridat
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diff --git a/reference/stroup.nin.html b/reference/stroup.nin.html
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RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin • agridat RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin • agridat Skip to contents
diff --git a/reference/stroup.splitplot.html b/reference/stroup.splitplot.html
index 069934b..0774212 100644
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Split-plot experiment of simulated data — stroup.splitplot • agridat Split-plot experiment of simulated data — stroup.splitplot • agridat Multi-environment trial of barley — student.barley • agridat Multi-environment trial of barley — student.barley • agridat Skip to contents
diff --git a/reference/summerby.multi.uniformity.html b/reference/summerby.multi.uniformity.html
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Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity • agridat
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diff --git a/reference/tai.potato.html b/reference/tai.potato.html
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Multi-environment trial of potato — tai.potato • agridat
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diff --git a/reference/talbot.potato.html b/reference/talbot.potato.html
index 74e6daf..eb9b889 100644
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Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato • agridat Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato • agridat Skip to contents
diff --git a/reference/tesfaye.millet.html b/reference/tesfaye.millet.html
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Multi-environment trial of millet — tesfaye.millet • agridat
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diff --git a/reference/theobald.barley.html b/reference/theobald.barley.html
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Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley • agridat
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diff --git a/reference/theobald.covariate.html b/reference/theobald.covariate.html
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Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate • agridat
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diff --git a/reference/thompson.cornsoy.html b/reference/thompson.cornsoy.html
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Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy • agridat Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy • agridat Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity • agridat
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diff --git a/reference/turner.herbicide.html b/reference/turner.herbicide.html
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Herbicide control of larkspur — turner.herbicide • agridat
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diff --git a/reference/urquhart.feedlot.html b/reference/urquhart.feedlot.html
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Weight gain calves in a feedlot — urquhart.feedlot • agridat
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Weight gain calves in a feedlot — urquhart.feedlot • agridat Skip to contents
diff --git a/reference/usgs.herbicides.html b/reference/usgs.herbicides.html
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Concentrations of herbicides in streams in the United States — usgs.herbicides • agridat Concentrations of herbicides in streams in the United States — usgs.herbicides • agridat Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter • agridat
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Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter • agridat Skip to contents
diff --git a/reference/vaneeuwijk.fusarium.html b/reference/vaneeuwijk.fusarium.html
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Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium • agridat
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Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium • agridat Skip to contents
diff --git a/reference/vaneeuwijk.nematodes.html b/reference/vaneeuwijk.nematodes.html
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Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes • agridat Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes • agridat Skip to contents
diff --git a/reference/vargas.txe.html b/reference/vargas.txe.html
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Treatment x environment interaction in agronomy trials — vargas.txe • agridat
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Treatment x environment interaction in agronomy trials — vargas.txe • agridat Skip to contents
diff --git a/reference/vargas.wheat1.html b/reference/vargas.wheat1.html
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Wheat yields in 7 years with genetic and environment covariates — vargas.wheat1 • agridat Wheat yields in 7 years with genetic and environment covariates — vargas.wheat1 • agridat Skip to contents
diff --git a/reference/vargas.wheat2.html b/reference/vargas.wheat2.html
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Multi-environment trial of wheat with environmental covariates — vargas.wheat2 • agridat Multi-environment trial of wheat with environmental covariates — vargas.wheat2 • agridat Skip to contents
diff --git a/reference/verbyla.lupin.html b/reference/verbyla.lupin.html
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Multi-environment trial of lupin, multiple varieties and densities — verbyla.lupin • agridat Multi-environment trial of lupin, multiple varieties and densities — verbyla.lupin • agridat Skip to contents
diff --git a/reference/vishnaadevi.rice.uniformity.html b/reference/vishnaadevi.rice.uniformity.html
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Uniformity trial of rice — vishnaadevi.rice.uniformity • agridat
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Uniformity trial of rice — vishnaadevi.rice.uniformity • agridat Skip to contents
diff --git a/reference/vold.longterm.html b/reference/vold.longterm.html
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Long-term barley yields at different fertilizer levels — vold.longterm • agridat
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Long-term barley yields at different fertilizer levels — vold.longterm • agridat Skip to contents
diff --git a/reference/vsn.lupin3.html b/reference/vsn.lupin3.html
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Multi-environment trial of lupin, early generation trial — vsn.lupin3 • agridat
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Multi-environment trial of lupin, early generation trial — vsn.lupin3 • agridat Skip to contents
diff --git a/reference/wallace.iowaland.html b/reference/wallace.iowaland.html
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Iowa farmland values by county in 1925 — wallace.iowaland • agridat
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Iowa farmland values by county in 1925 — wallace.iowaland • agridat Skip to contents
diff --git a/reference/walsh.cottonprice.html b/reference/walsh.cottonprice.html
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Acres and price of cotton 1910-1943 — walsh.cottonprice • agridat
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Acres and price of cotton 1910-1943 — walsh.cottonprice • agridat Skip to contents
diff --git a/reference/wassom.brome.uniformity.html b/reference/wassom.brome.uniformity.html
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Uniformity trials of bromegrass — wassom.brome.uniformity • agridat
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Uniformity trials of bromegrass — wassom.brome.uniformity • agridat Skip to contents
diff --git a/reference/waynick.soil.html b/reference/waynick.soil.html
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Soil nitrogen and carbon in two fields — waynick.soil • agridat
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Soil nitrogen and carbon in two fields — waynick.soil • agridat Skip to contents
diff --git a/reference/wedderburn.barley.html b/reference/wedderburn.barley.html
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Multi-environment trial of barley, percent of leaves affected by leaf blotch — wedderburn.barley • agridat Multi-environment trial of barley, percent of leaves affected by leaf blotch — wedderburn.barley • agridat Skip to contents
diff --git a/reference/weiss.incblock.html b/reference/weiss.incblock.html
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Soybean balanced incomplete block experiment — weiss.incblock • agridat
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Soybean balanced incomplete block experiment — weiss.incblock • agridat Skip to contents
diff --git a/reference/weiss.lattice.html b/reference/weiss.lattice.html
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Lattice experiment in soybeans. — weiss.lattice • agridat
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Lattice experiment in soybeans. — weiss.lattice • agridat Skip to contents
diff --git a/reference/welch.bermudagrass.html b/reference/welch.bermudagrass.html
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Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers — welch.bermudagrass • agridat
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Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers — welch.bermudagrass • agridat Skip to contents
diff --git a/reference/wheatley.carrot.html b/reference/wheatley.carrot.html
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Insecticide treatments for carrot fly larvae — wheatley.carrot • agridat Insecticide treatments for carrot fly larvae — wheatley.carrot • agridat Skip to contents
diff --git a/reference/wiebe.wheat.uniformity.html b/reference/wiebe.wheat.uniformity.html
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Uniformity trial of wheat — wiebe.wheat.uniformity • agridat
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Uniformity trial of wheat — wiebe.wheat.uniformity • agridat Skip to contents
diff --git a/reference/wiedemann.safflower.uniformity.html b/reference/wiedemann.safflower.uniformity.html
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Uniformity trial of safflower — wiedemann.safflower.uniformity • agridat
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Uniformity trial of safflower — wiedemann.safflower.uniformity • agridat Skip to contents
diff --git a/reference/williams.barley.uniformity.html b/reference/williams.barley.uniformity.html
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Uniformity trial of barley — williams.barley.uniformity • agridat
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Uniformity trial of barley — williams.barley.uniformity • agridat Skip to contents
diff --git a/reference/williams.cotton.uniformity.html b/reference/williams.cotton.uniformity.html
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Uniformity trial of cotton — williams.cotton.uniformity • agridat
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Uniformity trial of cotton — williams.cotton.uniformity • agridat Skip to contents
diff --git a/reference/williams.trees.html b/reference/williams.trees.html
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Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand — williams.trees • agridat
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Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand — williams.trees • agridat Skip to contents
diff --git a/reference/woodman.pig.html b/reference/woodman.pig.html
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Weight gain in pigs for different treatments — woodman.pig • agridat Weight gain in pigs for different treatments — woodman.pig • agridat Skip to contents
diff --git a/reference/wyatt.multi.uniformity.html b/reference/wyatt.multi.uniformity.html
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Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity • agridat
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Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity • agridat Skip to contents
diff --git a/reference/yan.winterwheat.html b/reference/yan.winterwheat.html
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Multi-environment trial of winter wheat in Ontario — yan.winterwheat • agridat Multi-environment trial of winter wheat in Ontario — yan.winterwheat • agridat Skip to contents
diff --git a/reference/yang.barley.html b/reference/yang.barley.html
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Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley • agridat
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Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley • agridat Skip to contents
diff --git a/reference/yates.missing.html b/reference/yates.missing.html
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Factorial experiment of potato, 3x3 with missing values — yates.missing • agridat
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Factorial experiment of potato, 3x3 with missing values — yates.missing • agridat Skip to contents
diff --git a/reference/yates.oats.html b/reference/yates.oats.html
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Split-plot experiment of oats — yates.oats • agridat Split-plot experiment of oats — yates.oats • agridat Daily weight, feed, egg measurements for a broiler chicken — zuidhof.broiler • agridat
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Daily weight, feed, egg measurements for a broiler chicken — zuidhof.broiler • agridat Skip to contents
diff --git a/search.json b/search.json
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-[{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 agridat authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"rothamsted-library","dir":"Articles","previous_headings":"Other","what":"Rothamsted Library","title":"Additional sources of agricultural data","text":"https://www.rothamsted.ac.uk/library--information-services now scanned PDFs put GitHub repository agridat package. Box uniformity trial data","code":"STATS17 WG Cochran 1. Uniformity trial data. 2. Genstat data. Data received since publication of the catalogue. 1935-1943. 3. Uniformity trial data. 1930-1936. 4. Uniformity trials. 1936-1938. 5. Uniformity trials. R data. 1936-1937. 6. O. V. S. Heath. Cotton uniformity trial data. 1934-1935. 7. Data. Yields of grain per foot length. 1934. 8. Catalogue of field uniformity trial data. N. d. 9. Demandt. 1931. 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Heath (1970). “Investigation by experiment”.","title":"Additional sources of agricultural data","text":"https://archive.org/details/investigationbye0000heat","code":"23 uniformity trial of radish - agridat::heath.raddish.uniformity 50 uniformity trial of cabbage - agridat::heath.cabbage.uniformity"},{"path":"/articles/agridat_data.html","id":"kwanchai-a--gomez-gomez-1984--statistical-procedures-for-agricultural-research-","dir":"Articles","previous_headings":"Books","what":"Kwanchai A. Gomez & Gomez (1984). “Statistical Procedures for Agricultural Research”.","title":"Additional sources of agricultural data","text":"Extensive collection datasets rice experiments. Many added agridat.","code":""},{"path":"/articles/agridat_data.html","id":"cyril-h--goulden-1939-methods-of-statistical-analysis-","dir":"Articles","previous_headings":"Books","what":"Cyril H. Goulden (1939), “Methods of Statistical Analysis”.","title":"Additional sources of agricultural data","text":"First edition: https://archive.org/details/methodsofstatist031744mbp Second edition: http://krishikosh.egranth.ac./handle/1/2034118 (broken)","code":"18 Uniformity trial - agridat::goulden.barley.uniformity 153 Split-split plot with factorial sub-plot treatment - agridat::goulden.splitsplit 194 Incomplete block 197 Inc block 205 Latin square 208 Inc block 255 Covariates in feeding trial - agridat::crampton.pig 216 Latin square - agridat::goulden.latin 423 Control chart with egg weights - agridat::goulden.eggs"},{"path":"/articles/agridat_data.html","id":"harry-love-1936--applications-of-statistical-methods-to-agricultural-research-","dir":"Articles","previous_headings":"Books","what":"Harry Love (1936). “Applications of Statistical Methods to Agricultural Research”.","title":"Additional sources of agricultural data","text":"","code":"379 MET 4 year, 2 field, 5 block, 5 gen"},{"path":[]},{"path":"/articles/agridat_data.html","id":"kuehl-robert--design-of-experiments-2nd-ed-","dir":"Articles","previous_headings":"Books","what":"Kuehl, Robert. “Design of Experiments”, 2nd ed.","title":"Additional sources of agricultural data","text":"","code":"357 alfalfa quadruple lattice 358 alpha design 488 split-plot sorghum hybrid,density 516 alfalfa rcb, two-year 521 crossover design cattle feedstuff"},{"path":"/articles/agridat_data.html","id":"erwin-leclerg-warren-leonard-andrew-clark-1962--field-plot-technique","dir":"Articles","previous_headings":"Books","what":"Erwin LeClerg, Warren Leonard, Andrew Clark (1962). “Field Plot Technique”","title":"Additional sources of agricultural data","text":"https://archive.org/details/fieldplottechniq00leon Many small datasets.","code":"27 uniformity - agridat::goulden.barley.uniformity 213 split-plot 234 immer multi-environment 260 lattice pinto-bean 276 triple lattice cotton 280 lattice sugar beet 289 balanced lattice 336 repeated wheat"},{"path":"/articles/agridat_data.html","id":"thomas-m-little-f--jackson-hills-1978--agricultural-experimentation-","dir":"Articles","previous_headings":"Books","what":"Thomas M Little & F. Jackson Hills (1978). “Agricultural Experimentation”.","title":"Additional sources of agricultural data","text":"","code":"79 Latin square 89 Split-plot 103 Split-split 117 Split-block - agridat::little.splitblock 126 Repeated harvests. In data-unused. 144 Non-IID errors 155 Square root transform 158 Germination, 3 reps, 24 treatments 261 Response surface, nitrogen, harvest 277 Count data"},{"path":"/articles/agridat_data.html","id":"harald-martens-magni-martens--multivariate-analysis-of-quality","dir":"Articles","previous_headings":"Books","what":"Harald Martens & Magni Martens. “Multivariate Analysis of Quality”","title":"Additional sources of agricultural data","text":"https://www.wiley.com/legacy/wileychi/chemometrics/datasets.html ‘NIR’ data NIR spectra measurements wheat purpose understanding protein quality.","code":""},{"path":"/articles/agridat_data.html","id":"roger-mead-robert-n--curnow-anne-m--hasted-2002--statistical-methods-in-agriculture-and-experimental-biology-3rd-ed-","dir":"Articles","previous_headings":"Books","what":"Roger Mead, Robert N. Curnow, Anne M. Hasted (2002). “Statistical Methods in Agriculture and Experimental Biology”, 3rd ed.","title":"Additional sources of agricultural data","text":"","code":"10 weekly milk yields 24 carrot weight 96 cabbage fertilizer 143 intercropping cowpea maize 177 honeybee repellent non-normal 251 cauliflower poisson - agridat::mead.cauliflower 273 rhubarb RCB covariate 296 onion density 316 lambs 341 germination 350 germination factorial - agridat::mead.germination 352 poppy 359 lamb loglinear - agridat::mead.lambs 375 rats 386 intercrop 390 intercrop cowpea maize - agridat::mead.cowpeamaize 404 apple characteristics (incomplete)"},{"path":"/articles/agridat_data.html","id":"roger-mead-1988--the-design-of-experiments","dir":"Articles","previous_headings":"Books","what":"Roger Mead (1988). “The Design of Experiments”","title":"Additional sources of agricultural data","text":"https://books.google.com/books?id=CaFZPbCllrMC&pg=PA323","code":"323 Turnip spacing data - agridat::mead.turnip"},{"path":"/articles/agridat_data.html","id":"leonard-c--onyiah-2008-","dir":"Articles","previous_headings":"Books","what":"Leonard C. Onyiah (2008).","title":"Additional sources of agricultural data","text":"“Design Analysis Experiments: Classical Regression Approaches SAS”. https://books.google.com/books?id=_P3LBQAAQBAJ&pg=PA334","code":"334 Two examples of 5x5 Graeco-Latin squares in cassava and maize"},{"path":"/articles/agridat_data.html","id":"bernard-ostle-1963--statistics-in-research-2nd-ed-","dir":"Articles","previous_headings":"Books","what":"Bernard Ostle (1963). “Statistics in Research”, 2nd ed.","title":"Additional sources of agricultural data","text":"https://archive.org/details/secondeditionsta001000mbp","code":"455 2 factors, 1 covariate - agridat::woodman.pig 458 1 factor, 2 covariates - agridat::crampton.pig"},{"path":"/articles/agridat_data.html","id":"v--g--panse-and-p--v--sukhatme-1957--statistical-methods-for-agricultural-workers-","dir":"Articles","previous_headings":"Books","what":"V. G. Panse and P. V. Sukhatme (1957). “Statistical Methods for Agricultural Workers”.","title":"Additional sources of agricultural data","text":"Note: 1954 edition can found https://archive.org/details/dli.scoerat.949statisticalmethodsforagriculturalworkers/page/138/mode/2up","code":"3 Length and number of grains per ear of wheat 138 Uniformity trial - agridat::panse.cotton.uniformity 154 RCB 8 blocks 167 two factorial, 6 rep trial 178 2^4 factorial, 8 blocks, partial confounding 192 3^3 factorial, 3 reps/9 blocks, partial confounding 200 split-plot, 6 rep 212 strip-plot, 6 rep 219 cotton variety trial, yield & stand counts 256 8x8 simpple lattice, 4 reps 282 5 varieties at 6 locations 295 5 N levels at 5 locations 332 4 regions, 9-11 villages in each region, 3 fertilizer treatments"},{"path":"/articles/agridat_data.html","id":"d--d--paterson-1939--statistical-technique-in-agricultural-research-","dir":"Articles","previous_headings":"Books","what":"D. D. Paterson (1939). “Statistical Technique in Agricultural Research”.","title":"Additional sources of agricultural data","text":"https://archive.org/details/statisticaltechn031729mbp","code":"84 Distribution of purple/white starchy/sweet seeds from 11 ears 190 Sugar cane MET: 2 year, 5 block, 5 variety 199 Tea MET: 3 year, 2^2 factorial fertilizer 206 Grass: 4 rep, 2 gen, 4 cutting treatments 211 Cotton: 4 dates, 3 spacings, 3 irrigation, 2 nitro - agridat::gregory.cotton"},{"path":"/articles/agridat_data.html","id":"roger-petersen-agricultural-field-experiments","dir":"Articles","previous_headings":"Books","what":"Roger Petersen, “Agricultural Field Experiments”","title":"Additional sources of agricultural data","text":"","code":"8 Uniformity trial 18 * 6 plots 56 RCB 4 rep, 5 trt 71 Latin square 5x5 86 Factorial 4x2, 3 rep 97 Factorial 2x3x2, 3 rep 125 Fertilizer trial, 3 rep, 5 levels 136 Split plot variety x planting date, 3 rep 148 Strip plot 2 potash x 3 potassium, 3 rep 170 Augmented breeding trial with 3 checks, 6 inc blocks 174 Inc Block 182 Lattice 5x5, 2 rep 192 GxE 10 gen, 12 env. Stability analysis. 208 Factorial 2x3 at 8 locs, homogeneous variance, early lentils 217 GxE 8 gen, 5 loc, heterogeneous variance 232 Factorial 2x3 at 8 locs, late lentils (see also page 208) 249 On-farm trial, 24 entries, 3 rep RCB 257 Demonstration trials, 5 locs 272 Covariance example, RCB 6 rep, 4rt 278 Multi-year 2x2 factorial, 4 rep 309 Pasture trial 323 On-farm trial, 2 variety 8 loc 327 On-farm trial 6 trt, 5 loc 334 On-farm trial 4 trt, 6 loc 343 On-farm trial 2x3 factorial, 3 loc 351 Feeding trial, 2 trt, 2 periods 357 Intercrop, 2 crops 372 Intercrop, 2 crop, 4 mixtures, 4 rep. agridat::petersen.sorghum.cowpea"},{"path":"/articles/agridat_data.html","id":"richard-plant-spatial-data-analysis-in-ecology-and-agriculture-using-r","dir":"Articles","previous_headings":"Books","what":"Richard Plant, “Spatial Data Analysis in Ecology and Agriculture using R”","title":"Additional sources of agricultural data","text":"https://psfaculty.plantsciences.ucdavis.edu/plant/","code":""},{"path":"/articles/agridat_data.html","id":"arthur-asquith-rayner-1969--a-first-course-in-biometry-for-agriculture-students-","dir":"Articles","previous_headings":"Books","what":"Arthur Asquith Rayner (1969). “A First Course In Biometry For Agriculture Students”.","title":"Additional sources of agricultural data","text":"","code":"19 456 2x2x4 Factorial, 2 rep 19 466 2x4 factorial, layout, plot size, kale (from Rothamsted) 19 466 3x5 factorial, 3 rep, potato 20 494 3x4 Split-plot with layout 21 505 2x2x2 Factorial, 5 rep 21 515 2x2x2x2 Factorial, 3 rep, with layout. (Evaluated, rejected as too variable) 22 537 2x2x2 factorial, 6 rep, potato 22 537 2x2x2x2 factorial, 2 rep, wheat, layout"},{"path":"/articles/agridat_data.html","id":"f-s-f--shaw-1936--a-handbook-of-statistics-for-use-in-plant-breeding-and-agricultural-problems","dir":"Articles","previous_headings":"Books","what":"F.S.F. Shaw (1936). “A Handbook of Statistics For Use in Plant Breeding and Agricultural Problems”","title":"Additional sources of agricultural data","text":"https://archive.org/details/.ernet.dli.2015.176662","code":"5 Length of ear head and number of grains per ear, 400 ears. 95 variety RCB, 5 gen, 25 rep, diagonal layout 107 Latin square, 8 entries. 117 Factorial: 8 blocks, 3 varieties, 5 treatments, 2 infections 126 Multi-environment trial, 3 year, 13 varieties, 2 loc, 5 blocks agridat::shaw.oats"},{"path":"/articles/agridat_data.html","id":"g--w--snedecor-w--g--cochran--statistical-methods-","dir":"Articles","previous_headings":"Books","what":"G. W. Snedecor & W. G. Cochran. “Statistical Methods”.","title":"Additional sources of agricultural data","text":"","code":"168 regression 352 3x3 factorial, 4 blocks 359 2x2x2 factorial, 8 blocks, daily pig gain 362 2x3x4 factorial, 2 blocks, daily pig gain 371 3x4 split-plot, 3 var, 4 date, 6 blocks 374 2x3x3 split-split-plot, irrig, stand, fert, block 378 4x4 split-plot, 4 block, 4 year, 4 cuttings asparagus 384 regression with 2 predictors 428 covariates, 6 varieties, 4 blocks, yield vs stand 440 pig gain vs initial weight, 4 treatments, 40 pigs 454 protein vs yield for wheat, 91 plots, quadratic regression"},{"path":"/articles/agridat_data.html","id":"robert-g--d--steel-james-hiram-torrie--principles-and-procedures-of-statistics-2nd-ed-","dir":"Articles","previous_headings":"Books","what":"Robert G. D. Steel & James Hiram Torrie. “Principles and Procedures of Statistics”, 2nd ed.","title":"Additional sources of agricultural data","text":"","code":"154 Mint plant growth, 2-way + pot + plant 244 Trivariate data 319 Regression with three predictors 384 Split-plot yield 387 Split-plot row spacing 400 Soybean 3 loc 423 Pig weight gain 429 Guinea pig weight gain 434 Soybean lodging"},{"path":"/articles/agridat_data.html","id":"oliver-schabenberger-and-francis-j--pierce--contemporary-statistical-models-for-the-plant-and-soil-sciences-","dir":"Articles","previous_headings":"Books","what":"Oliver Schabenberger and Francis J. Pierce. “Contemporary Statistical Models for the Plant and Soil Sciences”.","title":"Additional sources of agricultural data","text":"Many datasets. added agridat.","code":""},{"path":"/articles/agridat_data.html","id":"s--j--welham-et-al--2015--statistical-methods-in-biology-","dir":"Articles","previous_headings":"Books","what":"S. J. Welham et al. (2015). “Statistical Methods In Biology”.","title":"Additional sources of agricultural data","text":"online-supplements contain many small datasets examples exercises.","code":""},{"path":"/articles/agridat_data.html","id":"pesticides-in-the-nations-streams-and-ground-water-1992-2001","dir":"Articles","previous_headings":"Books","what":"Pesticides in the Nation’s Streams and Ground Water, 1992-2001","title":"Additional sources of agricultural data","text":"Extensive data detection pesticides water samples. See Appendix 5 Appendix 6 supporting info. https://water.usgs.gov/nawqa/pnsp/pubs/circ1291/supporting_info.php","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"ag-data-commons","dir":"Articles","previous_headings":"Data Repositories","what":"Ag Data Commons","title":"Additional sources of agricultural data","text":"https://data.nal.usda.gov/-ag-data-commons https://data.nal.usda.gov/search/type/dataset","code":""},{"path":"/articles/agridat_data.html","id":"cyverse-data-commons","dir":"Articles","previous_headings":"Data Repositories","what":"CyVerse Data Commons","title":"Additional sources of agricultural data","text":"https://datacommons.cyverse.org/ https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"harvard-dataverse","dir":"Articles","previous_headings":"Data Repositories","what":"Harvard Dataverse","title":"Additional sources of agricultural data","text":"https://dataverse.harvard.edu/ IRRI Rice Research includes plot-level data long term rice experiments. https://dataverse.harvard.edu/dataverse/RiceResearch","code":""},{"path":"/articles/agridat_data.html","id":"kellogg-biological-station-long-term-research","dir":"Articles","previous_headings":"Data Repositories","what":"Kellogg Biological Station Long-Term Research","title":"Additional sources of agricultural data","text":"KBS037:Precision Agriculture Yield Monitoring Row Crop Agriculture https://lter.kbs.msu.edu/datasets/40 https://doi.org/10.6073/pasta/423c07d6ea3317c545beabb4b8e502c8 Yield monitor data across several years crops. Un-friendly license.","code":""},{"path":"/articles/agridat_data.html","id":"nature-scientific-data","dir":"Articles","previous_headings":"Data Repositories","what":"Nature Scientific Data","title":"Additional sources of agricultural data","text":"https://www.nature.com/sdata/","code":""},{"path":"/articles/agridat_data.html","id":"open-data-journal-for-agricultural-research","dir":"Articles","previous_headings":"Data Repositories","what":"Open Data Journal for Agricultural Research","title":"Additional sources of agricultural data","text":"https://library.wur.nl/ojs/index.php/odjar/","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"wolfram-data-repository","dir":"Articles","previous_headings":"Data Repositories","what":"Wolfram Data Repository","title":"Additional sources of agricultural data","text":"https://datarepository.wolframcloud.com/","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"iowa-state-agricultural-research-bulletins","dir":"Articles","previous_headings":"Journals - Bulletins","what":"Iowa State Agricultural Research Bulletins","title":"Additional sources of agricultural data","text":"https://lib.dr.iastate.edu/ag_researchbulletins/","code":"Vol 26/ 281. Cox: Analysis of Lattice and Triple Lattice. Page 11: Lattice, 81 hybs, 4 reps Page 24: Triple lattice, 81 hybs, 6 reps Vol 29/347. Homeyer. Punched Card and Calculating Machine Methods for Analyzing Lattice Experiments Including Lattice Squares and the Cubic Lattice. Page 37: Triple lattice (9 blocks * 9 hybrids) with 6 reps. Page 60: Simple lattice, 8 blocks * 8 hybrids, 4 reps. Page 76: Balanced lattice, 25 hybrids Page 87: Lattice square with (k+1)/2 reps, 121 hybrids, 6 rep Page 109: Lattice square with k+1 reps, 7 blocks * 7 hyb, 8 reps Page 126: Cubic lattice, 16 blocks * 4 plots = 64 varieties, 9 reps, cotton Vol 32/396. Wassom. Bromegrass Uniformity Trial: agridat::wassom.bromegrass.uniformity Vol 33/424. Heady. Crop Response Surfaces and Economic Optima in Fertilizer - agridat::heady.fertilizer Vol 34/358. Schwab. Research on Irrigation of Corn and Soybeans At Conesville. Page 257. 2 year, 2 loc, 4 rep, 2 nitro. Stand & yield. Nice graph of soil moisture deficit (fig 9) Vol. 34/463. Doll. Fertilizer Production Functions for Corn and Oats. Table 1, 1954 Clarion Loam. N,P,K. Table 14, 1955 McPaul Silt Loam. N,P. Table 25, 1955 corn. K,P,N. Table 31, 1956 oats, K,P,N. Trends difficult to establish. Vol 34/472. Pesek. Production Surfaces and Economic Optima For Corn Yields. Same data published in SSA journal? Vol 34/488. Walker. Application of Game Theory Models to Decisions. Vol 35/494. North Central Regional Potassium Studies with Alfalfa. Page 176. Two years, several locs per state, multiple states, multiple fertilizer levels, multiple cuttings. Soil test attributes. Page 183. Yield and %K. Vol 35/503. North Central Regional Potassium Studies with Corn."},{"path":[]},{"path":"/articles/agridat_data.html","id":"bakare-et-al","dir":"Articles","previous_headings":"Papers","what":"Bakare et al","title":"Additional sources of agricultural data","text":"Exploring genotype environment interaction cassava yield yield related traits using classical statistical methods https://doi.org/10.1371/journal.pone.0268189 36 gen, 20 env, 3 rep. Analysis data : https://github.com/mab658/classical_analysis_GxE","code":""},{"path":"/articles/agridat_data.html","id":"chaves-2023-et-al","dir":"Articles","previous_headings":"Papers","what":"Chaves 2023 et al","title":"Additional sources of agricultural data","text":"Analysis multi-harvest data mixed models: application Theobroma grandiflorum breeding https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.20995 Nice. Complete data R code. found FA3 best genetic covariances, AR1H best residual structure. Used FAST OP (Cullis) selection.","code":""},{"path":"/articles/agridat_data.html","id":"cleveland-m-a--and-john-m--hickey-selma-forni-2012-","dir":"Articles","previous_headings":"Papers","what":"Cleveland, M.A. and John M. Hickey, Selma Forni (2012).","title":"Additional sources of agricultural data","text":"Common Dataset Genomic Analysis Livestock Populations. G3, 2, 429-435. https://doi.org/10.1534/g3.111.001453 supplemental information paper contains data 3534 pigs high-density genotypes (50000 SNPs), pedigree including parents grandparents animals.","code":""},{"path":"/articles/agridat_data.html","id":"coelho-2021-et-al","dir":"Articles","previous_headings":"Papers","what":"Coelho 2021 et al","title":"Additional sources of agricultural data","text":"Accounting spatial trends multi-environment diallel analysis maize breeding https://doi.org/10.1371/journal.pone.0258473 78 hybrids diallel, 4 environments, 3 reps. Compared spatial non-spatial analyses.","code":""},{"path":"/articles/agridat_data.html","id":"daillant-spinnler-1996--relationships-between-perceived-sensory-properties-and-major-preference-directions-of-12-variaties-of-apples-from-the-southern-hemisphere-","dir":"Articles","previous_headings":"Papers","what":"Daillant-Spinnler (1996). Relationships between perceived sensory properties and major preference directions of 12 variaties of apples from the southern hemisphere.","title":"Additional sources of agricultural data","text":"Food Quality Preference, 7(2), 113-126. https://doi.org/10.1016/0950-3293(95)00043-7 data ClustVarLV::apples_sh$pref ClustVarLV::apples_sh$senso 12 apple varieties, 43 traits, 60 consumers","code":""},{"path":"/articles/agridat_data.html","id":"gregory-crowther-lambert-1932--the-interrelation-of-factors-controlling-the-production-of-cotton-under-irrigation-in-the-sudan-","dir":"Articles","previous_headings":"Papers","what":"Gregory, Crowther & Lambert (1932). The interrelation of factors controlling the production of cotton under irrigation in the Sudan.","title":"Additional sources of agricultural data","text":"Jour Agric Sci, 22, p. 617.","code":""},{"path":"/articles/agridat_data.html","id":"hedrick-1920-","dir":"Articles","previous_headings":"Papers","what":"Hedrick (1920).","title":"Additional sources of agricultural data","text":"Twenty years fertilizers apple orchard. https://books.google.com/books?hl=en&lr=&id=SqlJAAAAMAAJ&oi=fnd&pg=PA446 authors found significant differences fertilizer treatments.","code":""},{"path":"/articles/agridat_data.html","id":"meehan-gratton-2016-","dir":"Articles","previous_headings":"Papers","what":"Meehan & Gratton (2016).","title":"Additional sources of agricultural data","text":"Landscape View Agricultural Insecticide Use across Conterminous US 1997 2012. PLOS ONE, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166724 Supplemental material contains county-level data 4 years. Complete R-INLA code analysis.","code":""},{"path":"/articles/agridat_data.html","id":"monteverde-et-al","dir":"Articles","previous_headings":"Papers","what":"Monteverde et al","title":"Additional sources of agricultural data","text":"Integrating Molecular Markers Environmental Covariates Interpret Genotype Environment Interaction Rice (Oryza sativa L.) Grown Subtropical Areas https://doi.org/10.1534/g3.119.400064 https://gsajournals.figshare.com/articles/dataset/Supplemental_Material_for_Monteverde_et_al_2019/7685636 Supplemental information contains phenotypic data markers environmental covariates PLS analysis.","code":""},{"path":"/articles/agridat_data.html","id":"kenward-michael-g--1987-","dir":"Articles","previous_headings":"Papers","what":"Kenward, Michael G. (1987).","title":"Additional sources of agricultural data","text":"Method Comparing Profiles Repeated Measurements. Applied Statistics, 36, 296-308. ante-dependence model fit repeated measures cattle weight.","code":""},{"path":"/articles/agridat_data.html","id":"klumper-qaim-2015-","dir":"Articles","previous_headings":"Papers","what":"Klumper & Qaim (2015).","title":"Additional sources of agricultural data","text":"Meta-Analysis Impacts Genetically Modified Crops. https://doi.org/10.1371/journal.pone.0111629 Nice meta-analysis dataset. Published data include differences, standard-errors. See comments PLOS article peculiarities data.","code":""},{"path":"/articles/agridat_data.html","id":"lado-b--et-al--2013-","dir":"Articles","previous_headings":"Papers","what":"Lado, B. et al. (2013).","title":"Additional sources of agricultural data","text":"“Increased Genomic Prediction Accuracy Wheat Breeding Spatial Adjustment Field Trial Data”. G3, 3, 2105-2114. https://doi.org/10.1534/g3.113.007807 large haplotype dataset (83 MB) two-year phenotype data multiple traits.","code":""},{"path":"/articles/agridat_data.html","id":"oakey-cullis-thompson-2016","dir":"Articles","previous_headings":"Papers","what":"Oakey, Cullis, Thompson 2016","title":"Additional sources of agricultural data","text":"Genomic Selection Multi-environment Crop Trials https://www.g3journal.org/content/6/5/1313 http://www.g3journal.org/content/6/5/1313/suppl/DC1 648 genotypes planted pots yr 1, 856 lines yr 2, 639 common years. 7864 SNP markerks","code":""},{"path":"/articles/agridat_data.html","id":"peixoto-marco-antonio-et-al-2020","dir":"Articles","previous_headings":"Papers","what":"Peixoto, Marco Antonio et al (2020)","title":"Additional sources of agricultural data","text":"Random regression modeling yield genetic trajectories Jatropha curcas breeding. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244021 Repeated measurements six years. Data supplemental Word doc.","code":""},{"path":"/articles/agridat_data.html","id":"perez-valencia-2022-","dir":"Articles","previous_headings":"Papers","what":"Perez-Valencia (2022).","title":"Additional sources of agricultural data","text":"two‑stage approach spatio‑temporal analysis high‑throughput phenotyping data. https://doi.org/10.1038/s41598-022-06935-9 Time-series data individual plots field many genotypes.","code":""},{"path":"/articles/agridat_data.html","id":"roger-w--hexem-earl-o-heady-metin-caglar-1974","dir":"Articles","previous_headings":"Papers","what":"Roger W. Hexem, Earl O.Heady, Metin Caglar (1974)","title":"Additional sources of agricultural data","text":"compendium experimental data corn, wheat, cotton sugar beets grown selected sites western United States alternative production functions fitted data. Technical report: Center Agricultural Rural Development, Iowa State University. https://babel.hathitrust.org/cgi/pt?id=wu.89031116783;view=1up;seq=3 technical report provides data experiments corn, wheat, cotton & sugar beets, crop tested several locations two years, factorial structure irrigation nitrogen treatments, replications. Three polynomial functions fit data location (quadratic, square root, three-halves).","code":""},{"path":"/articles/agridat_data.html","id":"snedecor-george-and-e--s--haber-1946-","dir":"Articles","previous_headings":"Papers","what":"Snedecor, George and E. S. Haber (1946).","title":"Additional sources of agricultural data","text":"Statistical Methods Incomplete Experiment Perennial Crop. Biometrics Bulletin, 2, 61-67. https://www.jstor.org/stable/3001959 Harvest asparagus 10 years, three cutting dates per year, 6 blocks.","code":""},{"path":"/articles/agridat_data.html","id":"tanaka-takashi-x--t-","dir":"Articles","previous_headings":"Papers","what":"Tanaka, Takashi X. T.","title":"Additional sources of agricultural data","text":"Assessment design analysis frameworks -farm experimentation simulation study wheat yield Japan. https://github.com/takashit754/geostat Yield-monitor data 3 fields.","code":""},{"path":"/articles/agridat_data.html","id":"technow-frank-et-al--2014-","dir":"Articles","previous_headings":"Papers","what":"Technow, Frank, et al. (2014).","title":"Additional sources of agricultural data","text":"Genome Properties Prospects Genomic Prediction Hybrid Performance Breeding Program Maize. August 1, 2014 vol. 197 . 4 1343-1355. https://doi.org/10.1534/genetics.114.165860 Genotype phenotype data appears sommer package.","code":""},{"path":"/articles/agridat_data.html","id":"tian-ting-2015-","dir":"Articles","previous_headings":"Papers","what":"Tian, Ting (2015).","title":"Additional sources of agricultural data","text":"Application Multiple Imputation Missing Values Three-Way Three-Mode Multi-Environment Trial Data. https://doi.org/10.1371/journal.pone.0144370 Uses agridat::australia.soybean data one real dataset 4 traits identified. data code available.","code":""},{"path":"/articles/agridat_data.html","id":"randall-j--wisser-et-al--2011-","dir":"Articles","previous_headings":"Papers","what":"Randall J. Wisser et al. (2011).","title":"Additional sources of agricultural data","text":"Multivariate analysis maize disease resistances suggests pleiotropic genetic basis implicates GST gene. PNAS. https://doi.org/10.1073/pnas.1011739108 supplement contains genotype data, phenotype data.","code":""},{"path":"/articles/agridat_data.html","id":"rife-et-al--2018","dir":"Articles","previous_headings":"Papers","what":"Rife et al. (2018)","title":"Additional sources of agricultural data","text":"Genomic analysis prediction within US public collaborative winter wheat regional testing nursery. https://doi.org/10.5061/dryad.q968v83 Large phenotypic dataset 691 wheat lines, 33 years, 670 environments, 3-4 reps, 120000 datapoints. genotypic data included.","code":""},{"path":"/articles/agridat_data.html","id":"schmitz-carley-et-al-2018","dir":"Articles","previous_headings":"Papers","what":"Schmitz Carley et al (2018)","title":"Additional sources of agricultural data","text":"Genetic Covariance Environments Potato National Chip Processing Trial https://dl.sciencesocieties.org/publications/cs/articles/59/1/107 Supp 2 contains genomic data, easy way find phenotypic data.","code":""},{"path":"/articles/agridat_data.html","id":"van-der-voet-et-al--2017-","dir":"Articles","previous_headings":"Papers","what":"van der Voet et al. (2017).","title":"Additional sources of agricultural data","text":"Equivalence testing using existing reference data: example genetically modified conventional crops animal feeding studies. https://doi.org/10.1016/j.fct.2017.09.044 full datasets GRACE studies -E available : https://www.cadima.info/index.php/area/publicAnimalFeedingTrials CC license.","code":""},{"path":"/articles/agridat_data.html","id":"volpato-et-al-2024","dir":"Articles","previous_headings":"Papers","what":"Volpato et al (2024)","title":"Additional sources of agricultural data","text":"retrospective analysis historical data multi-environment trials dry bean ( Phaseolus vulgaris L.) Michigan. https://github.com/msudrybeanbreeding/DryBean_MultiEnvTrials Full dataset R code.","code":""},{"path":"/articles/agridat_data.html","id":"xavier-alencar-et-al--","dir":"Articles","previous_headings":"Papers","what":"Xavier, Alencar et al..","title":"Additional sources of agricultural data","text":"Genome-Wide Analysis Grain Yield Stability Environmental Interactions Multiparental Soybean Population. https://doi.org/10.1534/g3.117.300300 Data SoyNAM NAM packages.","code":""},{"path":"/articles/agridat_data.html","id":"yan-weikei-2002-","dir":"Articles","previous_headings":"Papers","what":"Yan, Weikei (2002).","title":"Additional sources of agricultural data","text":"Singular value partitioning biplots. Agron Journal. Winter wheat, 31 gen 8 loc. data different Yan’s earlier papers. Unfortunately, data given paper missing two rows.","code":""},{"path":"/articles/agridat_data.html","id":"r-packages-on-cran-github-etc-","dir":"Articles","previous_headings":"","what":"R packages on CRAN, Github, etc.","title":"Additional sources of agricultural data","text":"See also: https://cran.r-project.org/web/views/Agriculture.html","code":""},{"path":"/articles/agridat_data.html","id":"agml","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"AgML","title":"Additional sources of agricultural data","text":"https://github.com/Project-AgML/AgML Datasets agricultural machine learning image classification, semantic segmentation, object detection, etc.","code":""},{"path":"/articles/agridat_data.html","id":"agricensdata","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"agriCensData","title":"Additional sources of agricultural data","text":"https://github.com/OnofriAndreaPG/agriCensData Three datasets censored observations paper “Analyzing interval-censored data agricultural research: review examples software tips”.","code":""},{"path":"/articles/agridat_data.html","id":"agritutorial","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"agriTutorial","title":"Additional sources of agricultural data","text":"https://myaseen208.github.io/agriTutorial/ Five datasets used illustrate analyses.","code":""},{"path":"/articles/agridat_data.html","id":"agricolae","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"agricolae","title":"Additional sources of agricultural data","text":"assorted data functions analysis agricultural data.","code":""},{"path":"/articles/agridat_data.html","id":"agrobiodata","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"agroBioData","title":"Additional sources of agricultural data","text":"https://github.com/OnofriAndreaPG/agroBioData Datasets agriculture applied biology. Referenced blog: https://www.statforbiology.com/","code":""},{"path":"/articles/agridat_data.html","id":"aml---adaptive-mixed-lasso","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"aml - Adaptive Mixed LASSO","title":"Additional sources of agricultural data","text":"Data aml::wheat genetic phenotypic data wheat. Modest size.","code":""},{"path":"/articles/agridat_data.html","id":"bglr---bayesian-generalized-linear-regression-","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"BGLR - Bayesian Generalized Linear Regression.","title":"Additional sources of agricultural data","text":"matrix (pedigree) 499 genotypes 4 locations.","code":""},{"path":"/articles/agridat_data.html","id":"blr---bayesian-linear-regression-","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"BLR - Bayesian Linear Regression.","title":"Additional sources of agricultural data","text":"matrix (pedigree) 499 genotypes 4 locations.","code":""},{"path":"/articles/agridat_data.html","id":"bsagri","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"BSagri","title":"Additional sources of agricultural data","text":"Safety assessment agriculture trials","code":""},{"path":"/articles/agridat_data.html","id":"clustvarlv","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"ClustVarLV","title":"Additional sources of agricultural data","text":"Data apples_sh sensory attributes preference scores 12 apple varieties.","code":""},{"path":"/articles/agridat_data.html","id":"cropcc---climate-change-on-crops","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"cropcc - Climate change on crops","title":"Additional sources of agricultural data","text":"https://r-forge.r-project.org/projects/cropcc/","code":""},{"path":"/articles/agridat_data.html","id":"drc---dose-response-curves","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"drc - Dose response curves","title":"Additional sources of agricultural data","text":"nice herbicide dose response curves germination data mungbean, rice, wheat.","code":""},{"path":"/articles/agridat_data.html","id":"epiphy","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"epiphy","title":"Additional sources of agricultural data","text":"https://github.com/chgigot/epiphy Contains 10 historical datasets plant disease epidemics.","code":""},{"path":"/articles/agridat_data.html","id":"fw---finlay-wilkinson-regression","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"FW - Finlay-Wilkinson regression","title":"Additional sources of agricultural data","text":"https://github.com/lian0090/FW/ phenotype data marker data 599 wheat lines 4 environments.","code":""},{"path":"/articles/agridat_data.html","id":"ggenealogy","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"ggenealogy","title":"Additional sources of agricultural data","text":"https://doi.org/10.18637/jss.v089.i13 Data sbGeneal contains soybean pedigree 230 varieties.","code":""},{"path":"/articles/agridat_data.html","id":"grbase","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"gRbase","title":"Additional sources of agricultural data","text":"Data gRbase::carcass: thickness meat fat slaughter pigs","code":""},{"path":"/articles/agridat_data.html","id":"lmdiallel","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"lmDiallel","title":"Additional sources of agricultural data","text":"https://github.com/OnofriAndreaPG/lmDiallel/tree/master/data","code":""},{"path":"/articles/agridat_data.html","id":"lmtest","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"lmtest","title":"Additional sources of agricultural data","text":"Data lmtest::ChickEgg time series annual chicken egg production United States 1930-1983.","code":""},{"path":"/articles/agridat_data.html","id":"nada","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"NADA","title":"Additional sources of agricultural data","text":"Data Atra Recon contain measurements Atrazine water samples.","code":""},{"path":"/articles/agridat_data.html","id":"nlraa","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"nlraa","title":"Additional sources of agricultural data","text":"Miguez. Non-linear models agriculture. nlraa::sm = agridat::miguez.biomass Vignettes functions working (non)linear mixed models","code":""},{"path":"/articles/agridat_data.html","id":"nlme","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"nlme","title":"Additional sources of agricultural data","text":"nlme::Orange: Growth orange trees nlme::Soybean: Growth soybean plants. book “Nonlinear Models Repeated Measurement Data”.","code":""},{"path":"/articles/agridat_data.html","id":"ofpe---on-farm-precision-experiments","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"OFPE - On-Farm Precision Experiments","title":"Additional sources of agricultural data","text":"https://paulhegedus.github.io/OFPE-Website/ https://github.com/paulhegedus/OFPEDATA/","code":""},{"path":"/articles/agridat_data.html","id":"onfant-dataset","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"onfant.dataset","title":"Additional sources of agricultural data","text":"https://github.com/AnabelleLaurent/onfant.dataset","code":""},{"path":"/articles/agridat_data.html","id":"pbkrtest","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"pbkrtest","title":"Additional sources of agricultural data","text":"pbkrtest::beets Yield percent sugar split-plot experiment.","code":""},{"path":"/articles/agridat_data.html","id":"plantbreeding","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"plantbreeding","title":"Additional sources of agricultural data","text":"https://r-forge.r-project.org/projects/plantbreeding/","code":"Data: fulldial Data: linetester Data: peanut - same as agridat::kang.peanut"},{"path":"/articles/agridat_data.html","id":"sdaa---survey-data-and-analysis","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"SDaA - Survey Data and Analysis","title":"Additional sources of agricultural data","text":"package county-level data United States Census Agriculture, along vignette illustrate survey sampling analyses.","code":""},{"path":"/articles/agridat_data.html","id":"semipar","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"SemiPar","title":"Additional sources of agricultural data","text":"Data: SemiPar::onions agridat::ratkowski.onions","code":""},{"path":"/articles/agridat_data.html","id":"soildb","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"soilDB","title":"Additional sources of agricultural data","text":"https://ncss-tech.github.io/AQP/soilDB/soilDB-Intro.html Soil database interface.","code":""},{"path":"/articles/agridat_data.html","id":"sommer---solving-mixed-model-equations-in-r","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"sommer - Solving mixed model equations in R","title":"Additional sources of agricultural data","text":"Data: h2. Modest-sized GxE experiment potato Data: cornHybrid. Yield/PLTHT 100 hybrids 20 inbred * 20 inbred, 4 locs. Phenotype relationship matrix. Data: Data: RICE Data: FDdata taken agridat::bond.diallel Data:","code":"data(DT_wheat) # CIMMYT wheat data DT_wheat # 599 varieties, yield in 4 envts GT_wheat # 599 varieties, 1279 markers coded -1,1 data(DT_technow) # From http://www.genetics.org/content/197/4/1343.supplemental DT <- DT_technow # 1254 hybs, parents, GY=yield, GM=moisture Md <- Md_technow # 123 dent parents, 35478 markers Mf <- Mf_technow # 86 flint parents, 37478 markers Ad <- Ad_technow # 123 x 123 A matrix Af <- Af_technow # 86 x 85 A matrix"},{"path":"/articles/agridat_data.html","id":"soynam---soybean-nested-association-mapping","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"SoyNAM - Soybean nested association mapping","title":"Additional sources of agricultural data","text":"Dataset phenotype data 3 yr, 9 locations, 18 environments, 60 thousand observations height, maturity, lodging, moisture, protein, oil, fiber, seed size. 5000+ strains, 40 families. Data formatted analysis NAM package available following command: SoyNAM::ENV().","code":""},{"path":"/articles/agridat_data.html","id":"soyurt","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"SoyURT","title":"Additional sources of agricultural data","text":"https://github.com/mdkrause/SoyURT Large historical data yield trials Uniform Soybean Tests Northern States. Years 1989-2019, 63 locations, 4257 genotypes. package also contains soils weather data trial locations. Note: USDA published papers results : National Cotton Variety Tests, Uniform Soybean Tests Northern States, Uniform Soybean Tests Southern States : https://www.ars.usda.gov/southeast-area/stoneville-ms/crop-genetics-research/docs/","code":""},{"path":"/articles/agridat_data.html","id":"spdep","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"spdep","title":"Additional sources of agricultural data","text":"vignette ‘Problem Spatial Autocorrelation: forty years ’ examines agriculture Irish counties. See also data ade4::irishdata.","code":""},{"path":"/articles/agridat_data.html","id":"spurs","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"spuRs","title":"Additional sources of agricultural data","text":"Data: spuRs::trees data 107 trees cut cross sections volume calculated roughly 10-year increments. subset much-larger original data Guttenberg: https://archive.org/stream/wachstumundertra00gutt","code":""},{"path":"/articles/agridat_data.html","id":"statforbiology","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"StatForBiology","title":"Additional sources of agricultural data","text":"https://www.statforbiology.com/ Blog posts example analyses.","code":""},{"path":"/articles/agridat_data.html","id":"biometris---statgengxe","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"Biometris - statgenGxE","title":"Additional sources of agricultural data","text":"https://github.com/Biometris/statgenGxE https://biometris.github.io/statgenGxE/ AMMI, FW, GGE stability analyses.","code":""},{"path":"/articles/agridat_data.html","id":"biometris---statgengwas","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"Biometris - statgenGWAS","title":"Additional sources of agricultural data","text":"https://github.com/Biometris/statgenGWAS/ https://CRAN.R-project.org/package=statgenGWAS nice package full GxE data marker data 41722 loci 246 lines. 256 hybrids, 29 envts across 2 years, multi-trait (yield, silking, pltht, earht, etc). Includes worked example data : https://data.inra.fr/dataset.xhtml?persistentId=doi:10.15454/IASSTN publication: Millet 2016, Genome-Wide Analysis Yield Europe: Allelic Effects Vary Drought Heat Scenarios, https://academic.oup.com/plphys/article/172/2/749/6115953","code":""},{"path":"/articles/agridat_data.html","id":"biometris---statgensta","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"Biometris - statgenSTA","title":"Additional sources of agricultural data","text":"https://github.com/Biometris/statgenSTA/ https://CRAN.R-project.org/package=statgenSTA Analysis phenotypic data field experiments using SpATS, lme4, asreml.","code":""},{"path":"/articles/agridat_data.html","id":"st4gi---stat-for-genetic-improvement","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"st4gi - Stat for genetic improvement","title":"Additional sources of agricultural data","text":"https://github.com/reyzaguirre/st4gi","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"ars-oat-trials","dir":"Articles","previous_headings":"Web sites","what":"ARS oat trials","title":"Additional sources of agricultural data","text":"https://www.ars.usda.gov/Main/docs.htm?docid=8419&page=4","code":""},{"path":"/articles/agridat_data.html","id":"cimmyt-research-data","dir":"Articles","previous_headings":"Web sites","what":"CIMMYT Research Data","title":"Additional sources of agricultural data","text":"https://data.cimmyt.org/dataverse/cimmytdatadvn","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"grain-genes","dir":"Articles","previous_headings":"Web sites","what":"Grain genes","title":"Additional sources of agricultural data","text":"https://wheat.pw.usda.gov/ggpages/HxT/ Harrington x TR306 Barley Mapping Population. genotype phenotype data comes Mapmaker, seems slightly non-standard format; 145 DH lines, 217 markers, 25 env, 1 rep. https://wheat.pw.usda.gov/ggpages/SxM/ . data agridat::steptoe.morex.","code":""},{"path":"/articles/agridat_data.html","id":"glten---a-network-of-long-term-trials-around-the-world","dir":"Articles","previous_headings":"Web sites","what":"GLTEN - A network of Long-Term trials around the world","title":"Additional sources of agricultural data","text":"https://glten.org/","code":""},{"path":"/articles/agridat_data.html","id":"ideals","dir":"Articles","previous_headings":"Web sites","what":"Ideals","title":"Additional sources of agricultural data","text":"https://www.ideals.illinois.edu/handle/2142/3528 Data File : Raw data ear analyzed year Illinois long-term selection experiment oil protein corn (1896-2004)","code":""},{"path":"/articles/agridat_data.html","id":"international-potato-center","dir":"Articles","previous_headings":"Web sites","what":"International Potato Center","title":"Additional sources of agricultural data","text":"https://data.cipotato.org/dataverse.xhtml","code":""},{"path":"/articles/agridat_data.html","id":"ilri-international-livestock-research-institute","dir":"Articles","previous_headings":"Web sites","what":"ILRI International Livestock Research Institute","title":"Additional sources of agricultural data","text":"Case study 4 nice diallel example sheep data. Available agridat::ilri.sheep","code":""},{"path":"/articles/agridat_data.html","id":"irri-biometrics-and-breeding-informatics","dir":"Articles","previous_headings":"Web sites","what":"IRRI Biometrics and Breeding Informatics","title":"Additional sources of agricultural data","text":"http://bbi.irri.org/products STAR, PBTools, CropStat. STAR user guide well-documented data (even using 2 agridat), PBTools user guide document data.","code":""},{"path":"/articles/agridat_data.html","id":"miappe-minimum-information-about-plant-phenotyping-experiments","dir":"Articles","previous_headings":"Web sites","what":"MIAPPE Minimum Information About Plant Phenotyping Experiments","title":"Additional sources of agricultural data","text":"https://www.miappe.org/ limited data.","code":""},{"path":"/articles/agridat_data.html","id":"rothamsted-electronic-archive","dir":"Articles","previous_headings":"Web sites","what":"Rothamsted Electronic Archive","title":"Additional sources of agricultural data","text":"http://www.era.rothamsted.ac.uk/index.php Data Broadbalk long-term experiments. Github draft data: https://github.com/Rothamsted-Ecoinformatics/YieldbookDatasetDrafts","code":""},{"path":"/articles/agridat_data.html","id":"rothamsted-documents-archive","dir":"Articles","previous_headings":"Web sites","what":"Rothamsted Documents Archive","title":"Additional sources of agricultural data","text":"http://www.era.rothamsted.ac.uk/eradoc/collections.php Annual reports Rothamsted 1908-1987. Many data, especially early years (WWII) data given ‘Classical Experiments’.","code":"Year, page 1908-1926 1926-1927 agridat::sawyer.multi.uniformity 1927-1928 agridat::sawyer.multi.uniformity 1929-1930 1931,143 agridat::yates.oats 1932 1933 1934,215-222 Sugar beet multi-environment trial with 3^3 fertilizer treatments at each site Roots, SugarPercent, SugarWeight, PlantNumber, Tops, Purity. 1935 1936,241 Similar to the 1934 experiment, but only gives the main effects, not the actual data. 1937-1939 1946-1955 1986"},{"path":"/articles/agridat_data.html","id":"yates-1937-the-design-and-analysis-of-factorial-experiments","dir":"Articles","previous_headings":"Web sites","what":"Yates (1937), The Design and analysis of factorial experiments","title":"Additional sources of agricultural data","text":"","code":"9 2x2x2, 4 rep 27 2x2x2x2x2 factorial 33 2x2x2 factorial in two 4x4 Latin Squares 42 3x3x3 factorial 59 3x2x2 factorial in 3 reps. See also page 39. 74 Split-plot agridat::yates.oats"},{"path":"/articles/agridat_data.html","id":"statistical-analysis-of-agricultural-experiments-with-r","dir":"Articles","previous_headings":"Web sites","what":"Statistical Analysis of Agricultural Experiments with R","title":"Additional sources of agricultural data","text":"rstats4ag.org (http included firewall problems). Datasets mixed models, ancova, dose response curves, competition.","code":""},{"path":"/articles/agridat_data.html","id":"syngenta-crop-challenge","dir":"Articles","previous_headings":"Web sites","what":"Syngenta Crop Challenge","title":"Additional sources of agricultural data","text":"https://www.ideaconnection.com/syngenta-crop-challenge/ Annual Kaggle-style competition sponsored Syngenta.","code":""},{"path":"/articles/agridat_data.html","id":"terra-ref","dir":"Articles","previous_headings":"Web sites","what":"Terra-Ref","title":"Additional sources of agricultural data","text":"https://terraref.org/ Sensor observations, plant phenotypes, derived traits, genetic genomic data. Beta version Nov 2018.","code":""},{"path":"/articles/agridat_data.html","id":"usda-national-agricultural-statistics-service","dir":"Articles","previous_headings":"Web sites","what":"USDA National Agricultural Statistics Service","title":"Additional sources of agricultural data","text":"https://www.nass.usda.gov https://quickstats.nass.usda.gov/ Group: Field Crops Commodity: Corn Category: Area Harvested, Yield Data Item: Corn grain Acres Harvested, Yield Bu/Ac Domain: Total Geography: State See agridat::nass.corn, nass.wheat, etc.","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Graphical Gems in the agridat Package","text":"exhibit agricultural data uses following packages: agridat, desplot, gge, HH, lattice, latticeExtra, mapproj, maps, reshape2.","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"potato-blight-incidence-over-space-and-time","dir":"Articles","previous_headings":"","what":"Potato blight incidence over space and time","title":"Graphical Gems in the agridat Package","text":"@lee2009random analyzed large dataset evaluate resistance potato varieties blight. data contains evaluations changing set varieties every two years, evaluated 5 blocks, repeatedly throughout growing season track progress disease. panel shows field map given date, separate row panels year. include field spatial trends model data? 1983, 20 varieties evaluated 5 blocks (shown colored numbers) throughout growing season disease resistance. Resistance scores start 9 varieties (shown panels). growing season progresses, ‘.HARDY’ variety succumbs quickly blight, ‘IWA’ succumbs steadily, ‘064.1’ resists blight near end season. view show differences blocks?","code":"## Loading required package: desplot ## Loading required package: latticeExtra ## Loading required package: lattice"},{"path":"/articles/agridat_graphical_gems.html","id":"an-informative-prior","dir":"Articles","previous_headings":"","what":"An informative prior","title":"Graphical Gems in the agridat Package","text":"@harrison2012bayesian used Bayesian approach model daidzein levels soybean samples. 18 previous publications, extracted published minimum maximum daidzein levels, number samples tested. line dotplot shows large, dark dots one published minimum maximum. small dots imputed using lognormal distribution. observed/imputed data used fit common lognormal distribution can used informative prior. common prior shown density top dotplot. think better use non-informative prior, informative prior?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"data-densities-for-a-binomial-glm","dir":"Articles","previous_headings":"","what":"Data densities for a binomial GLM","title":"Graphical Gems in the agridat Package","text":"@mead2002statistical present data germination seeds four temperatures (T1-T4) four chemical concentrations. 4*4=16 treatments, 50 seeds tested four reps. graphic, point one rep. blue line fitted curve GLM Temperature factor log concentration covariate. gray lines show central 95 percent binomial density position. display help understand logit link changing shape binomial density?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"verification-of-experiment-layout","dir":"Articles","previous_headings":"","what":"Verification of experiment layout","title":"Graphical Gems in the agridat Package","text":"@gomez1984statistical provide data experiment 3 reps, 6 genotypes, 3 levels nitrogen 2 planting dates. experiment layout putatively ‘’split strip-plot’’. verify design, desplot package used plotting design field experiments. design different ‘’split-split-plot’’ design?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"visualizing-main-effects-two-way-interactions","dir":"Articles","previous_headings":"","what":"Visualizing main effects, two-way interactions","title":"Graphical Gems in the agridat Package","text":"@heiberger2004statistical provide interesting way use lattice graphics visualize main effects (using boxplots) interactions (using interaction plots) data. Rice yield plotted versus replication, nitrogen, management type, genotype variety. Box plots show minor differences reps, increaing yield due nitrogen, high yield intensive management, large differences varieties. think interaction plots show interaction (lack parallelism)?","code":"## Loading required package: HH ## Loading required package: grid ## Loading required package: multcomp ## Loading required package: mvtnorm ## Loading required package: survival ## Loading required package: TH.data ## Loading required package: MASS ## ## Attaching package: 'TH.data' ## The following object is masked from 'package:MASS': ## ## geyser ## Loading required package: gridExtra ## ## Attaching package: 'HH' ## The following object is masked from 'package:base': ## ## is.R"},{"path":"/articles/agridat_graphical_gems.html","id":"d-yield-response-to-fertilizers","dir":"Articles","previous_headings":"","what":"3D yield response to fertilizers","title":"Graphical Gems in the agridat Package","text":"Note: image created manual manipulation rgl device. manual manipulation makes non-reproducible Rmd file. See example sinclair.clover data code. @sinclair1994sulphur examined clover yields function sulfur phosphorous fertilizer factorial-treatment experiment. @dodds1996bivariate modeled yield response using Mitzerlisch-like equation allows interacting curvature two dimensions xx yy: yield=α*(1+β*(σ+τ*xx+1)y)*(1+δ*(θ+ρ*yy+1)x) yield = \\alpha * \\left( 1 + \\beta *\\left(\\frac{\\sigma + \\tau*x}{x+1}\\right)^y\\right) * \\left( 1+\\delta*\\left(\\frac{\\theta + \\rho *y}{y+1}\\right)^x \\right) blue dots observed data, tan surface fitted surface drawn rgl package). decide optimal fertilizer levels?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"mosaic-plot-of-potato-damage-from-harvesting","dir":"Articles","previous_headings":"","what":"Mosaic plot of potato damage from harvesting","title":"Graphical Gems in the agridat Package","text":"@keen1997analysis looked damage potatoes caused lifting rods harvest. experiment, eight types lifting rods compared. Two energy levels, six genotypes three weight classes used. combinations treatments, 20 potato tubers rated undamaged (D1, yellow) severely damaged (D4, red). Counts per treatment shown mosaic plot. style lifting rods cause least/damage potatoes?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"yield-vs-covariate-for-latticebarley","dir":"Articles","previous_headings":"","what":"Yield vs covariate for lattice::barley","title":"Graphical Gems in the agridat Package","text":"@wright2013revisiting investigated lattice::barley data. original two years data extended 10 years (original source documents), supplemented weather covariates 6 locations 10 years. panel shows scatterplot regression average location yield verses weather covariate. Horizontal strips locations, vertical strips covariates: cdd = Cooling Degree Days, hdd = Heating Degree Days, precip = Precipitation). Higher values heating imply cooler weather. plotting symbol last digit year (1927-1936) location. barley yield better cooler warmer weather?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"gge-biplot","dir":"Articles","previous_headings":"","what":"GGE biplot","title":"Graphical Gems in the agridat Package","text":"@laffont2013genotype developed variation GGE (genotype plus genotype--environment) biplot include auxiliary information block/group environments. location classified one two mega-environments (colored). mosaic plots partition variation simultaneously principal component axis source (genotype, genotype--block, residual). genotypes best mega-environment?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"nebraska-farming-income-choropleth","dir":"Articles","previous_headings":"","what":"Nebraska farming income choropleth","title":"Graphical Gems in the agridat Package","text":"Red-Blue palette RColorBrewer package divergent palette light colors near middle scale. can cause problems missing values, appear white (technically, background). order increase visibility missing values, agridat package uses Red-Gray-Blue palette, gray color dark enough clearly distinguish missing values. outlier county (Butler) northeast Nebraska limit interpration spatial patterns data? counties different sizes, second graphic uses income rate per square mile. outlier, might smart use percentile break points, hides outlier. Instead, break points calculated using method called Fisher-Jenks. break points show outlier spatial patterns. now easy see northwest (Sandhills) Nebraska low farming income, especially crops. Counties missing data white, easily distinguished gray. farm incomes highest? ?","code":"## Loading required package: maps ## Loading required package: mapproj"},{"path":"/articles/agridat_graphical_gems.html","id":"las-rosas-yield-monitor","dir":"Articles","previous_headings":"","what":"Las Rosas yield monitor","title":"Graphical Gems in the agridat Package","text":"@anselin2004spatial @lambert2004comparison looked yield monitor data collected corn field Argentina 1999 2001, see yield affected field topography nitrogen fertilizer. figures show heatmaps yield year, also experiment design (colors reps, shades color nitrogen level, plotting character topography). year showed greater spatial variation yield?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"time-series-of-corn-yields-by-state","dir":"Articles","previous_headings":"","what":"Time series of corn yields by state","title":"Graphical Gems in the agridat Package","text":"National Agricultural Statistics Service tracks total number acres planted corn (crops) state U.S. large changes past century corn acreage selected states. states corn belt 1925? states corn belt 2000?","code":""},{"path":[]},{"path":"/articles/agridat_intro.html","id":"comments-on-the-package-purpose","dir":"Articles","previous_headings":"","what":"Comments on the package purpose","title":"Introduction to agridat","text":"project first begun early 2000s, electronic versions agricultural datasets hard find. Since , revolution availability datasets related agriculture. See vignette describes data sources. Box (1957) said, “hoped seen end obscene tribal habit practiced statisticians continually exhuming massaging dead data sets purpose life long since forgotten possibility anything useful result treatment.” Massaging dead data sets lead genetics released commercial use. value package : 1. Validating published analyses. 2. Providing data testing new analysis methods. 3. Illustrating (validating) use R packages. White van Evert (2008) present guidelines publication data. examples use asreml package since R tool fitting mixed models complex variance structures large datasets, best option modelling AR1xAR1 residual variance structures. Commercial use asreml requires license VSN. (Use search engine find latest version).","code":""},{"path":"/articles/agridat_intro.html","id":"comments-on-the-package-structure","dir":"Articles","previous_headings":"","what":"Comments on the package structure","title":"Introduction to agridat","text":"Many datasets appear electronic form first time. tremendous amount effort given curating process identifying datasets, extracting data source materials, checking data values, documenting data. effect, make data somewhat ‘computable’ (Wolfram 2017). original sources data use different words refer genotypes including accession, breed, cultivar, genotype, hybrid, line, progeny, stock, type, variety. consistency, datasets mostly use gen (genotype). Also consistency, row col usually used field coordinates. dataframes, block, rep, similar terms almost always coded like B1, B2, B3 instead 1, 2, 3. causes R treat data factor instead numeric covariate (good thing). Almost data presented ‘tidy’ dataframes observations rows variables columns. Although using data() necessary access data files, example sections include use data() devtools::run_examples() needs .","code":""},{"path":"/articles/agridat_intro.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Introduction to agridat","text":"G. E. P. Box (1957). Integration Techniques Process Development, Transactions American Society Quality Control. J. White Frits van Evert. (2008). Publishing Agronomic Data. Agronomy Journal, 100, 1396-1400. https://doi.org/10.2134/agronj2008.0080F Stephen Wolfram (2017). Launching Wolfram Data Repository: Data Publishing Really Works. https://writings.stephenwolfram.com/2017/04/launching--wolfram-data-repository-data-publishing--really-works/","code":""},{"path":[]},{"path":"/articles/agridat_mixed_model_example.html","id":"brms","dir":"Articles","previous_headings":"GLM (not mixed–no random plate)","what":"brms","title":"Example generalized linear mixed model analysis with different packages","text":"takes minute compile Stan program… Note, Emacs brms ends R process reason!","code":"if(require(brms)){ m1.brms <- brms::brm( germ|trials(n)~ gen*extract, data = dat, family = binomial, chains=3, iter=3000, warmup=1000) summary(m1.brms) # round( summary(m1.brms)$fixed[,1:4] , 2) # Estimate Est.Error l-95% CI u-95% CI # Intercept -0.42 0.18 -0.77 -0.06 # genO75 -0.14 0.22 -0.56 0.29 # extractcucumber 0.55 0.25 0.07 1.05 # genO75:extractcucumber 0.77 0.30 0.18 1.36 }"},{"path":"/articles/agridat_mixed_model_example.html","id":"glm","dir":"Articles","previous_headings":"GLM (not mixed–no random plate)","what":"glm","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"# ----- GLM. # family=binomial() fixes dispersion at 1 # family=quasibinomial() estimates dispersion, had larger std errors m1.glm <- glm(cbind(germ,n-germ) ~ gen*extract, data=dat, #family=\"binomial\", family=quasibinomial() ) summary(m1.glm) ## round(summary(m1.glm)$coef,2) ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -0.41 0.25 -1.64 0.12 ## genO75 -0.15 0.30 -0.48 0.64 ## extractcucumber 0.54 0.34 1.58 0.13 ## genO75:extractcucumber 0.78 0.42 1.86 0.08"},{"path":"/articles/agridat_mixed_model_example.html","id":"rstan","dir":"Articles","previous_headings":"GLM (not mixed–no random plate)","what":"rstan","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"# ----- Stan using pre-built models from rstanarm libs(tidyverse, rstan, rstanarm,bayesplot) set.seed(42) m1.stan <- stan_glm( cbind(germ,n-germ) ~ gen*extract, data=dat, family = binomial(link=\"logit\") ) summary(m1.stan) ## round(posterior_interval(m1.stan, prob=.90),3) # 5% 95% # (Intercept) -0.728 -0.115 # genO75 -0.506 0.243 # extractcucumber 0.133 0.947 # genO75:extractcucumber 0.255 1.267 libs(bayesplot) mcmc_areas(m1.stan, prob = 0.9) + ggtitle(\"Posterior distributions\", \"with medians and 95 pct intervals\")"},{"path":[]},{"path":"/articles/agridat_mixed_model_example.html","id":"asreml","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"asreml","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"if(require(asreml)){ m1.asreml <- asreml(germ ~ gen*extract, data=dat, random= ~ plate, family=asr_binomial(dispersion=1, total=n)) summary(m1.asreml) ## ## effect ## (Intercept) -0.47 ## gen_O73 0.00 ## gen_O75 -0.08 ## extract_bean 0.00 ## extract_cucumber 0.51 ## gen_O73:extract_bean 0.00 ## gen_O73:extract_cucumber 0.00 ## gen_O75:extract_bean 0.00 ## gen_O75:extract_cucumber 0.83 }"},{"path":"/articles/agridat_mixed_model_example.html","id":"massglmmpql","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"MASS::glmmPQL","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"# --- GLMM. Assumes Gaussian random effects libs(MASS) m1.glmm <- glmmPQL(cbind(germ, n-germ) ~ gen*extract, random= ~1|plate, family=binomial(), data=dat) summary(m1.glmm) ## round(summary(m1.glmm)$tTable,2) ## Value Std.Error DF t-value p-value ## (Intercept) -0.44 0.25 17 -1.80 0.09 ## genO75 -0.10 0.31 17 -0.34 0.74 ## extractcucumber 0.52 0.34 17 1.56 0.14 ## genO75:extractcucumber 0.80 0.42 17 1.88 0.08"},{"path":"/articles/agridat_mixed_model_example.html","id":"glmmtmb","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"glmmTMB","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"libs(glmmTMB) m1.glmmtmb <- glmmTMB(cbind(germ, n-germ) ~ gen*extract + (1|plate), data=dat, family=binomial) round(summary(m1.glmmtmb)$coefficients$cond , 2) ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -0.45 0.22 -2.03 0.04 ## genO75 -0.10 0.28 -0.35 0.73 ## extractcucumber 0.53 0.30 1.74 0.08 ## genO75:extractcucumber 0.81 0.38 2.11 0.04"},{"path":"/articles/agridat_mixed_model_example.html","id":"hglm","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"hglm","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"# ----- HGML package. Beta-binomial with beta-distributed random effects if(require(hglm)){ m1.hglm <- hglm(fixed= germ/n ~ I(gen==\"O75\")*extract, weights=n, data=dat, random=~1|plate, family=binomial(), rand.family=Beta(), fix.disp=1) summary(m1.hglm) # round(summary(m1.hglm)$FixCoefMat,2) ## Estimate Std. Error t-value Pr(>|t|) ## (Intercept) -0.47 0.24 -1.92 0.08 ## I(gen == \"O75\")TRUE -0.08 0.31 -0.25 0.81 ## extractcucumber 0.51 0.33 1.53 0.16 ## I(gen == \"O75\")TRUE:extractcucumber 0.83 0.43 1.92 0.08 }"},{"path":"/articles/agridat_mixed_model_example.html","id":"inla","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"INLA","title":"Example generalized linear mixed model analysis with different packages","text":"See: https://haakonbakka.bitbucket.io/btopic102.html","code":"if(require(INLA)){ #gen,extract are fixed. plate is a random effect #Priors for hyper parameters. See: inla.doc(\"pc.prec\") hyper1 = list(theta = list(prior=\"pc.prec\", param=c(1,0.01))) m1.inla = inla(germ ~ gen*extract + f(plate, model=\"iid\", hyper=hyper1), data=crowder.seeds, family=\"binomial\", Ntrials=n, control.family=list(control.link=list(model=\"logit\"))) round( summary(m1.inla)$fixed, 2) ## mean sd 0.025quant 0.5quant 0.975quant mode kld ## (Intercept) -0.47 0.24 -0.96 -0.46 0.00 -0.46 0 ## genO75 -0.08 0.31 -0.68 -0.09 0.54 -0.09 0 ## extractcucumber 0.53 0.33 -0.13 0.53 1.18 0.53 0 ## genO75:extractcucumber 0.82 0.43 -0.01 0.82 1.69 0.82 0 }"},{"path":"/articles/agridat_mixed_model_example.html","id":"rjags","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"rjags","title":"Example generalized linear mixed model analysis with different packages","text":"Requires JAGS installed.","code":"# JAGS/BUGS. See https://mathstat.helsinki.fi/openbugs/Examples/Seeds.html # Germination rate depends on p, which is a logit of a linear predictor # based on genotype and extract, plus random deviation to intercept # To match the output on the BUGS web page, use: dat$gen==\"O73\". # We use dat$gen==\"O75\" to compare with the parameterization above. jdat =list(germ = dat$germ, n = dat$n, root = as.numeric(dat$extract==\"cucumber\"), gen = as.numeric(dat$gen==\"O75\"), nobs = nrow(dat)) jinit = list(int = 0, genO75 = 0, extcuke = 0, g75ecuke = 0, tau = 10) # Use logical names (unlike BUGS documentation) mod.bug = \"model { for(i in 1:nobs) { germ[i] ~ dbin(p[i], n[i]) b[i] ~ dnorm(0.0, tau) logit(p[i]) <- int + genO75 * gen[i] + extcuke * root[i] + g75ecuke * gen[i] * root[i] + b[i] } int ~ dnorm(0.0, 1.0E-6) genO75 ~ dnorm(0.0, 1.0E-6) extcuke ~ dnorm(0.0, 1.0E-6) g75ecuke ~ dnorm(0.0, 1.0E-6) tau ~ dgamma(0.001, 0.001) sigma <- 1 / sqrt(tau) }\" libs(rjags) oo <- textConnection(mod.bug) j1 <- jags.model(oo, data=jdat, inits=jinit, n.chains=1) close(oo) c1 <- coda.samples(j1, c(\"int\",\"genO75\",\"g75ecuke\",\"extcuke\",\"sigma\"), n.iter=20000) summary(c1) # Medians are very similar to estimates from hglm # libs(lucid) # print(vc(c1),3) ## Mean SD 2.5% Median 97.5% ## extcuke 0.543 0.331 -0.118 0.542 1.2 ## g75ecuke 0.807 0.436 -0.0586 0.802 1.7 ## genO75 -0.0715 0.309 -0.665 -0.0806 0.581 ## int -0.479 0.241 -0.984 -0.473 -0.0299 ## sigma 0.289 0.142 0.0505 0.279 0.596 # Plot observed data with HPD intervals for germination probability c2 <- coda.samples(j1, c(\"p\"), n.iter=20000) hpd <- HPDinterval(c2)[[1]] med <- summary(c2, quantiles=.5)$quantiles fit <- data.frame(med, hpd) libs(latticeExtra) obs <- dotplot(1:21 ~ germ/n, dat, main=\"crowder.seeds\", ylab=\"plate\", col=as.numeric(dat$gen), pch=substring(dat$extract,1)) obs + segplot(1:21 ~ lower + upper, data=fit, centers=med)"},{"path":"/articles/agridat_mixed_model_example.html","id":"r2jags","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"R2jags","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"libs(\"agridat\") libs(\"R2jags\") dat <- crowder.seeds # To match the output on the BUGS web page, use: dat$gen==\"O73\". # We use dat$gen==\"O75\" to compare with the parameterization above. jdat =list(germ = dat$germ, n = dat$n, root = as.numeric(dat$extract==\"cucumber\"), gen = as.numeric(dat$gen==\"O75\"), nobs = nrow(dat)) jinit = list(list(int = 0, genO75 = 0, extcuke = 0, g75ecuke = 0, tau = 10)) mod.bug = function() { for(i in 1:nobs) { germ[i] ~ dbin(p[i], n[i]) b[i] ~ dnorm(0.0, tau) logit(p[i]) <- int + genO75 * gen[i] + extcuke * root[i] + g75ecuke * gen[i] * root[i] + b[i] } int ~ dnorm(0.0, 1.0E-6) genO75 ~ dnorm(0.0, 1.0E-6) extcuke ~ dnorm(0.0, 1.0E-6) g75ecuke ~ dnorm(0.0, 1.0E-6) tau ~ dgamma(0.001, 0.001) sigma <- 1 / sqrt(tau) } parms <- c(\"int\",\"genO75\",\"g75ecuke\",\"extcuke\",\"sigma\") j1 <- jags(data=jdat, inits=jinit, parms, model.file=mod.bug, n.iter=20000, n.chains=1) print(j1) ## mu.vect sd.vect 2.5% 25% 50% 75% 97.5% ## extcuke 0.519 0.325 -0.140 0.325 0.531 0.728 1.158 ## g75ecuke 0.834 0.429 -0.019 0.552 0.821 1.101 1.710 ## genO75 -0.096 0.305 -0.670 -0.295 -0.115 0.089 0.552 ## int -0.461 0.236 -0.965 -0.603 -0.455 -0.312 0.016 ## sigma 0.255 0.148 0.033 0.140 0.240 0.352 0.572 ## deviance 103.319 7.489 90.019 98.010 102.770 108.689 117.288 traceplot(as.mcmc(j1)) densityplot(as.mcmc(j1)) HPDinterval(as.mcmc(j1)) }"},{"path":[]},{"path":"/articles/agridat_uniformity_data.html","id":"archive-org---2023-04-07","dir":"Articles","previous_headings":"Searches","what":"archive.org - 2023.04.07","title":"Notes on uniformity data","text":"“uniformity trial” “optimum size plots” “Optimum Size Shape Plots”","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"google","dir":"Articles","previous_headings":"Searches","what":"Google","title":"Notes on uniformity data","text":"“uniformity trial data” “optimum size plots” “Optimum Size Shape Plots” “plot shape size” “field plot technique”","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"hathitrust","dir":"Articles","previous_headings":"Searches","what":"HathiTrust","title":"Notes on uniformity data","text":"“blank experiment” “plot technic” “plot technique” “uniformity trial”","code":""},{"path":[]},{"path":"/articles/agridat_uniformity_data.html","id":"u--nebr","dir":"Articles","previous_headings":"ToDo","what":"U. Nebr","title":"Notes on uniformity data","text":"Love, “Experimental methods agricultural research”. storage.","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"iowa-state","dir":"Articles","previous_headings":"ToDo","what":"Iowa State","title":"Notes on uniformity data","text":"Parks Library Indian J Ag Sciences, 1931-1968, S1 In2 Storage. Tropical Agriculture. Empire Journal Experimental Agriculture","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"paper-not-found","dir":"Articles","previous_headings":"","what":"Paper not found","title":"Notes on uniformity data","text":"Abraham, T.P. & Vachhani, M.V. 1964. Investigations field experimental techniques rice crop. Indian J. Agric. Sci. 34, 152-165. Agarwal, K.N. (1973). Uniformity trial Apple. Indian Journal horticulture. 30:525-528. https://www.indianjournals.com/ijor.aspx?target=ijor:ijh&volume=30&issue=3and4&article=013 Paywall. available. Agarwal, K.N. & Deshpande, M.R. 1967. 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Influence shape size plots accuracy field experiments rice, Chinsurah, Bengal Procedings Indian Science Congress (Bombay) Sec Agricultural Statistics 21, 71. http://library.isical.ac.:8080/xmlui/handle/10263/1793 found; pdf contains abstract. Mangat, N. S. (1984). Optimum size shape plot block cotton field experiments. Journal Research-Punjab Agricultural University (India). McHatton, T.H. 1947. comparison plot size peach experiment Proceedings American Society Hort Science 49: 18-20 https://eurekamag.com/research/013/864/013864510.php access. found. Medina Urrutia, V. M. S. Becerra Rodriguez,J. L. Macias Gonzalez (1980). Optimum size shape experimental plots research studies Mexican lemon (Citrus aurantifolia). Agricultura tec. Mex. 1980 Vol.6 .2 pp.93-105 ref.11 Menon, T.C.M. Tyagi, B.N. (1971). Optimum size shape plots experiments mandarin orange. Indian Journal agricultural science, 41, 857-861. https://eurekamag.com/research/000/153/000153659.php available. 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M., Prajapati, B.H., Raja, K.R.V. Darji, V.B. (1994). Optimum plot size summer paddy Navsari zone Gujarat. Gujarat Agricultural University Research Journal, 19, 92-97. found. Vaidyanathan, M. (1934). method covariance applicable utilization previous crop records judging improved precision experiments. Ind. J. Agric. Sci., 4, 327-42. found. data 24 plots. TODO Weber, Charles Robert. 1941. Statistical analysis uniformity trial soybeans reference plot size shape comparison efficiency various experimental designs. Univ Illinois. Thesis. https://-share-uiu.primo.exlibrisgroup.com/permalink/01CARLI_UIU/gpjosq/alma99265257912205899 found. Physical copy Univ Illinois. TODO Wray, William Dean (1941). Applications Uniformity Trials. PhD, Cornell University. https://newcatalog.library.cornell.edu/catalog/302914 found. Physical copy Cornell. Yates, F., Vernon, . J. Nelson, S. W. (1964). example analysis uniformity trial data electronic computer. Empire Journal Experimental Agriculture. 32 (125). https://eurekamag.com/research/014/342/014342010.php found. Physical copy UNL Storage. TODO Yun, K.H.; Park, S.H.; Lee, Y.M. (1968). estimation optimum plot size, shape number replications yield performance trials rice. Res. Rep. Office rur. Dev, Suwon, 11: 1, 53-7. Bibl. 21 https://eurekamag.com/research/014/341/014341511.php Zuhlke, Thomas Albert (1968). Uniformity Trial Pisum Sativum Estimating Optimum Plot Size Shape, Replication Number, Relative Efficiency Lattice Designs. https://search.library.wisc.edu/catalog/999876004102121 found. Physical copy Univ Wisconsin Madison. TODO","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"reviewed-papers","dir":"Articles","previous_headings":"","what":"Reviewed papers","title":"Notes on uniformity data","text":"Afonja, Biyi (1968). Analysis Uniformity Trial Cassava. Experimental Agriculture, 4(2), 135-141. https://doi.org/:10.1017/S0014479700022468 Reviewed. data. Aftab--Islam, M. Ashfaq, M. Idrees Ahmed. (1984) Sizes shapes plots field plot experiments wheat uniformity trial data. Pakistan Journal Agricultural Sciences, Volume 21, Issue 3,4. https://www.tehqeeqat.com/english/articleDetails/36510 Reviewed. data. Ahring, R. M., Morrison, R. D., & Wilhite, M. L. (1959). Uniformity Trials Germination Switchgrass Seed 1. Agronomy Journal, 51(12), 734-737. Aly, .E.; Salem, S..; Shalaan, M.. (1978). Optimum plot size shape relative efficiency different designs yield trials rice Oryza sativa L. Alexandria Journal Agricultural Research 26(2): 317-326 https://eurekamag.com/research/000/711/000711675.php found. Ansari, M. . ., G. K. Sant (1943). Study Soil Heterogeneity Relation Size Shape Plots Wheat Field Raya (Muhra District). Ind. J. Agr. Sci, 13, 652-658. https://archive.org/details/.ernet.dli.2015.271748 agridat::ansari.wheat.uniformity Arny, . C. H. K. Hayes. (1918) Experiments field technic plot tests. J. Agric. Res., 15, 251-262. Reviewed. data. Assis, Janilson & Sousa, Roberto & Linhares, Paulo & Cardoso, Eudes & Rodrigues, Walter & Pereira, Joaquim & Sousa, Robson & Medeiros, Aline & Silva, Neurivan & Andrade, Anderson & Gomes, Geovanna & Santos, Mateus & Alves, Lunara. (2020). Optimum plot size field experiments sesame. Australian Journal Crop Science. 1957-1960. https://doi.org/10.21475/ajcs.20.14.12.2828. Reviewed. data. Awake, Girma Taye; Amsal Tarekegne; D. G. Tanner. (2000). “Estimation optimum plot dimensions replication number wheat experimentation Ethiopia.” African crop science journal 8.1 (2000): 11-23. https://repository.cimmyt.org/handle/10883/2321 Reviewed. data. Bailey, M. ., Trought, T. (1926). account experiments carried determine experimental error field trials cotton Egypt. Egypt Ministry Agriculture, Technical Science Service Bulletin 63, Min. Agriculture Egypt Technical Science Bulletin 63. https://www.google.com/books/edition/Bulletin/xBQlAQAAIAAJ?pg=PA46-IA205 agridat::bailey.cotton.uniformity Baker, G. . Huberty, MR Veihmeyer, FJ. (1952) uniformity trial unirrigated barley ten years’ duration. Agronomy Journal, 44, 267-270. https://doi.org/10.2134/agronj1952.00021962004400050011x agridat::baker.barley.uniformity Baker, G. .; R. E. Baker (1953). Strawberry Uniformity Yield Trials. Biometrics, 9, 412-421. https://doi.org/10.2307/3001713 agridat::baker.strawberry.uniformity Baker, G. . E. B. Roessler (1957). Implications uniformity trial small plots wheat. Hilgardia, 27, 183-188. https://hilgardia.ucanr.edu/Abstract/?=hilg.v27n05p183 agridat::baker.wheat.uniformity Bakke, Olaf Andreas. (1988). Tamanho e forma ótimos de parcelas em delineamentos experimentais. Dissertation. Escola Superior de Agricultura Luiz de Queiroz. https://teses.usp.br/teses/disponiveis/11/11134/tde-20181127-160559/pt-br.php Used Gomez rice uniformity. Bancroft, T. . et a1., (1948). Size Shape Plots Distribution Plot Yield Field Experiments Peanuts. Alabama Agricultural Experiment Station Progress Report, sec. 39. Table 4, page 6. http://hdl.handle.net/11200/1345 agridat::bancroft.peanut.uniformity Barber, Clarence W. (1914). Note Influence Shape Size Plot Tests Varieties Grain. Maine Agr. Expt. Sta. Bul. 226:76-84. 1914. https://www.google.com/books/edition/Annual_Report/QF84AQAAMAAJ Reviewed. data. Batchelor, L. D.; H. S. Reed. (1918). Relation variability yields fruit trees accuracy field trials. J. Agric. Res, 12, 245–283. https://books.google.com/books?id=Lil6AAAAMAAJ&lr&pg=PA245 agridat::batchelor.uniformity Bayoumi, Tarek Youssef; S El-Demardash. Effect water deficit soil variability, plot size, shape number replications chickpea. https://www.academia.edu/21549629/Effect_of_Water_Deficit_on_Soil_Variability_Plot_size_Shape_and_Number_of_Replications_for_Chickpea Reviewed. data. Beard, J.S. (1954). Investigations experimental plot technique black wattle. Empire Forestry Review, 33, 158-171. https://www.jstor.org/stable/42602653 Reviewed. data. Beattie, J. H. Victor R. Boswell E. T. Batten. (1936). Pot plant variation Virginia Peanuts. Proceedings American Society Horticultural Science, 34. https://archive.org/details/dli.ernet.26569/page/585/mode/2up?q=beattie Reviewed. data. Bhatt, Hitesh M. (1998). Plot technique Potato. AAU, Anand. https://krishikosh.egranth.ac./handle/1/5810043200 Reviewed. data. Bose, R. D. (1935). soil heterogeneity trials Pusa size shape experimental plots. Ind. J. Agric. Sci., 5, 579-608. Table 1 (p. 585), Table 4 (p. 589), Table 5 (p. 590). https://archive.org/details/.ernet.dli.2015.271739 agridat::bose.multi.uniformity Bose, S. S.; Ganguli, P. M., Mahalanobis, P. C. frequency distribution plot yields optimum size plots uniformity trial rice Assam. Indian J. Agric. Sci., 1936, 6 part 5, pp. 1107-22. https://archive.org/details/.ernet.dli.2015.271737/page/n1263/mode Reviewed. data. Bose, S. S.; Khanna, K L; Mahalanobis, P C Note optimum shape size plots sugarcane experiments Bihar. India Journal Agricultural Science 9, 807-816 https://hdl.handle.net/10263/1896 http://library.isical.ac.:8080/jspui/handle/10263/1896 Reviewed. data. Boyhan, George E.; David B. Langston; Albert C. Purvis; C. Randell Hill. (2003). Research determine suitable plot size number replications field trials sugarbeet. J. Amer. Soc. Hort. Sci., 128, 409-424. Reviewed. data. Compared Hatheway’s method Cochran Cox’s method, latter gave smaller number plots. Boyhan, George E., David B. Langston, Albert C. Purvis, C. Randell Hill. (2003). Optimum plot size number replications short-day onions yield, seedstem formation, number doubles, incidence foliar diseases. J. Amer. Soc. Hort. Sci., 128, 409-424. https://doi.org/10.21273/JASHS.128.3.0409 Reviewed. data. Boyhan, George E. (2013). Optimum Plot Size Number Replications Determining Watermelon Yield, Fruit Size, Fruit Firmness, Soluble Solids. HortScience, 48, 1200-1208. https://doi.org/10.21273/HORTSCI.48.9.1200 Reviewed. data. Bradley, P. L. (1941). study variation productivity number fixed plots field 2. Dissertation: University West Indies. Appendix 1a, 1b, 1c, 1d. https://uwispace.sta.uwi.edu/dspace/handle/2139/41259 agridat::bardley.multi.uniformity Brewer, . C. R. Mead (1986). Continuous Second Order Models Spatial Variation Application Efficiency Field Crop Experiments. Journal Royal Statistical Society. Series (General), 149(4), 314–348. See page 325. http://doi.org/10.2307/2981720 Reviewed. data. Used StVincent cotton data found Rothamsted. agridat::hutchinson.cotton.uniformity Brim, C. . D. D. Mason. (1959). Estimates Optimum Plot Size soybean Yield Trials. Agron. Journal, 51: pp. 331-335. https://doi.org/10.2134/agronj1959.00021962005100060008x Reviewed. data. Brown, .R. Morris, H.D. (1967), Estimation Optimum Plot Size Shape Grain Sorghum Yield Trials. Agron. J., 59: 576-577. https://doi.org/10.2134/agronj1967.00021962005900060026x Reviewed. data. Chaves, L. J.; J. B. Miranda Filho (1992). Plot size progeny selection maize (Zea maysL.). Theoretical Applied Genetics, 84, 963–970. https://doi.org/10.1007/bf00227411 Reviewed. data. Cheesman, E. E., Pound, F. J. Uniformity trials Cacao. Trop. Agric., 9, 277-88. Reviweed. data. Christidis, Basil G (1931). importance shape plots field experimentation. Journal Agricultural Science, 21, 14-37. Table VI, p. 28. https://doi.org/10.1017/S0021859600007942 agridat::christidis.wheat.uniformity Christidis, B. G. (1939). Variability Plots Various Shapes Affected Plot Orientation. Empire Journal Experimental Agriculture 7: 330-342. Table 1. agridat::christidis.cotton.uniformity G. Peter Y. Clarke Katia T. Steanova. (2011). Optimal design early-generation plant-breeding trials unreplicated partially replicated test lines. Aust. N. Z. Jour. Stat., 53, 461-480. Reviewed. data. Collison, R. C. J. D. Harlan. Technical bulletin 194. relationships soil properties performance Baldwin Greening Apple Trees. New York State Agricultural Experiment Station https://babel.hathitrust.org/cgi/pt?id=uiug.30112019767000 Reviewed. data trees. Collison, R. C. J. D. Harlan. Technical bulletin 126. Annual variation apple yields - possible cause. New York State Agricultural Experiment Station https://babel.hathitrust.org/cgi/pt?id=uiug.30112019766267 Reviewed. data. Conners, Helen Elizabeth (1951). Field plot techniques sweet potatoes obtained uniformity trial data. Master’s Thesis, Iowa State University. digital copy available. Reviewed. data. Coombs, G. E. J. Grantham (1916). Field Experiments Interpretation results. Agriculture Bulletin Federated Malay States, 7. https://www.google.com/books/edition/The_Agricultural_Bulletin_of_the_Federat/M2E4AQAAMAAJ agridat::coombs.rice.uniformity Cordeiro, Célia Maria Torres, João Eustáquio Cabral de Miranda, Jarbas Campos. “Tamanho de parcelas e número de repetições em experimento de batatas.” Pesquisa Agropecuária Brasileira 17.9 (1982): 1341-1348. Reviewed. data. Crews, Julian W., Jones, G.L. Mason, D.D. (1963). Field Plot Technique Studies Flue-Cured Tobacco. . Optimum Plot Size Shape. Agron. J., 55: 197-199. https://doi.org/10.2134/agronj1963.00021962005500020033x Reviewed. data. Damor, Bhavika. (2019). Comparison uniformity trial data experimental data plot technique. Department Agricultural Statistics, B. . College Agriculture, Anand Agricultural University. https://krishikosh.egranth.ac./handle/1/5810169594 Reviewed. used. data 3 uniformity trials, data just looks strange. Appendix , 500 numbers 9 “4” hundredths place. expect see 50. data collected non-kg weights converted kg. Appendix III, plots along left edge measured 3 decimals (0.993, 0.631), field measured 2 decimals. Weird. da Silva, Enedino Correa. (1974). Estudo tamanho e forma de parcelas para experimentos de soja (Plot size shape soybean yield trials). Pesquisa Agropecuaria Brasileira, Serie Agronomia, 9, 49-59. Table 3, page 52-53. agridat::dasilva.soybean.uniformity da Silva, Willerson Custódio; Mário Puiatti; Paulo Roberto Cecon; Leandro Roberto de Macedo; Tocio Sediyama. (2019) Estimation optimum experimental plot size taro culture. Ciência Rural, Santa Maria, v.49 https://doi.org/10.1590/0103-8478cr20180440 Reviewed. data. da Silva, Luiz Fernando de Oliveira; Katia Alves Campos; Augusto Ramalho de Morais; Franciane Diniz Cogo; Carolina Ruiz Zambon. Optimal plot size experiments radish. Revista Ceres 59(5):624-629 https://doi.org/10.1590/S0034-737X2012000500007 Reviewed. data. de Assis, J. P., de Sousa, R. P., Rodrigues, W. M., Linhares, P. C. F., Cardoso, E. de ., Soares Pereira, M. F., Araújo Paula, J. . de, & Oliveira, . da M. (2019). Optimum Size Shape Experimental Units Cassava Cropping. Journal Experimental Agriculture International, 31(6), 1–10. https://doi.org/10.9734/jeai/2019/v31i630090 Reviewed. data. de Sousa, Roberto Pequeno Assis, Janilson Pinheiro de Rodrigues, Walter Martins Linhares, Paulo César Ferreira Cardoso, Eudes de Almeida Pereira, Maria Francisca Soares Paula, José Aluisio de Araújo (2018). Optimum Plot Size Experimental Cassava Production. Journal Agricultural Science, 10, 231-237. Reviewed. data. https://doi.org/10.5539/jas.v10n10p231 de Sousa, Roberto Pequeno; Paulo Sérgio Lima e Silva; Janilson Pinheiro de Assis; Paulo Igor Barbosa e Silva; Júlio César DoVale. 2015. Optimum plot size experiments sunflower. Revista Ciencia Agronomica, 46, 170-175. https://doi.org/10.1590/S1806-66902015000100020 Reviewed. data. Precision increased plot sizes 5 m^2. Davies, J. Griffiths (1931). Experimental Error Yield Small Plots Natural Pasture. Council Scientific Industrial Research (Aust.) Bulletin 48. Table 1. agridat::davies.pasture.uniformity Day, James Westbay (1916). relation size, shape, number replications plats probable error field experimentation. Dissertation, University Missouri. Table 1, page 22. https://hdl.handle.net/10355/56391 agridat::day.wheat.uniformity Dorph-Petersen, K. 1949. Parcelfordeling markforsog. Tidsskrift Planteavl. 52, 111-175 https://dca.au.dk/publikationer/historiske/planteavl/ Reviewed. data. see anything looked like field 12x43 plots. Mentioned Kristensen (2003). Draper, Arlen D. (1959). Optimum plot size shape safflower yield tests. Dissertation. University Arizona. https://hdl.handle.net/10150/319371 agridat::draper.safflower.uniformity Eden, T. E. J. Maskell. (1928). influence soil heterogeneity growth yield successive crops. Jour Agricultural Science, 18, 163-185. https://archive.org/stream/.ernet.dli.2015.25895/2015.25895.Journal--Agricultural-Science-Vol-xviii-1928#page/n175 agridat::sawyer.multi.uniformity Eden, T. (1931). Studies yield tea. 1. experimental errors field experiments tea. Agricultural Science, 21, 547-573. https://doi.org/10.1017/S0021859600088511 agridat::eden.tea.uniformity Elliott, F. C., Darroch, J.G. Wang, H.L. (1952). Uniformity trials spring wheat. Agronomy Journal, 44, 524-528. https://doi.org/10.2134/agronj1952.00021962004400100005x Reviewed. data. England, F. (1968). Non-sward densities assessment yield Italian ryegrass: II. Convenient plot block size shape. Journal Agricultural Science, 70(02), 105. https://doi.org/10.1017/s0021859600010923 Reviewed. data. Ersboll, Annette. Spatial Temporal Variations Applications Agriculture. “Models action”, Proceedings seminar series 1995/1996. Reviewed. data. Facco, Giovani; Cargnelutti Filho Alberto; Mendonça Alves Bruna; et. al. (2017). Basic experimental unit plot sizes method maximum curvature coefficient variation sunn hemp. African journal agricultural research 12(6):415-423 https://doi.org/10.5897/AJAR2016.11814 Reviewed. data. Faria, Glaucia Amorim Lopes, Beatriz Garcia Peixoto, Ana Patrícia Bastos Ferreira, Antonio Flávio Arruda Maltoni, Kátia Luciene Pigari, Lucas Bernardo (2020). Experimental plot size passion fruit. Revista Brasileira de Fruticultura, 42. http://dx.doi.org/10.1590/0100-29452020125. Reviewed. data. Filho, Alberto; Cargnelutti; Marcos Toebe; Cláudia Burin; André Luis Fick; Gabriele Casarotto. (2011). Plot sizes uniformity assays turnip. Ciência Rural 41.9 (2011): 1517-1525. https://doi.org/10.1590/S0103-84782011005000119 Reviewed. data. Filho, Alberto Cargnelutti; Marcos Vinícius Loregian, Gabriel Elias Dumke, Felipe Manfio Somavilla, Samanta Luiza da Costa, Lucas Fillipin Osmari, Bruno Fillipin Osmari (2021). Optimal plot size buckwheat. https://doi.org/10.5433/1679-0359.2021v42n2p501 https://www.uel.br/revistas/uel/index.php/semagrarias/article/view/39448/29330 Reviewed. data Filho, Alberto Cargnelutti, Ismael Mario Marcio Neu, Valeria Escaio Bubans, Felipe Manfio Somafilla, Bruno Fillipin Osmari. (2022). Method estimating optimal plot size Black Oat, Common Vetch, Forage Turnip intercropping. http://dx.doi.org/10.1590/1983-21252022v35n425rc Reviewed. data. Fleming, .., Roger, T.H. Bancroft, T.. (1957). Field plot technique hybrid corn Alabama conditions. Agronomy Journal, 49, 1-4. https://doi.org/10.2134/agronj1957.00021962004900010001x Reviwed. data. Forster, Howard Carlyle Vasey, . J. (1928). Experimental error field trials Australia. Proceedings Royal Society Victoria. New series, 40, 70–80. https://www.biodiversitylibrary.org/page/54367272 Reviewed. agridat::forster.wheat.uniformity Frey, K.J. Baten, W.D. (1953). Optimum Plot Size Oat Yield Tests. Agron. J., 45: 502-504. https://doi.org/10.2134/agronj1953.00021962004500100012x Reviewed. data. Garber, RJ McIlvaine, TC Hoover, MM. (1926). study soil heterogeneity experiment plots. Jour Agr Res, 33, 255-268. Tables 3, 5. https://naldc.nal.usda.gov/download/IND43967148/PDF agridat::garber.multi.uniformity Garber, R. J. T. C. McIlvaine M. M. Hoover (1931). Method Laying Experimental Plats. Journal American Society Agronomy, 23, 286-298. https://archive.org/details/.ernet.dli.2015.229753/page/n299 agridat::garber.multi.uniformity Gardenhire, James H. (1949). Field Plot Technique Plant Characteristic Studies Varieties Castor Plants. Thesis, Okla. Agr Mech. Coll., Stillwater, Okla. https://shareok.org/handle/11244/43284 Reviewed. data. Garner, F. H.; Grantham, J.; Sanders, H. G. (1934). value covariance analysing field experimental data. Journal Agricultural Science, 24(2), 250–. https://doi:10.1017/s0021859600006626 George, M. V.; M. Sannamarappa (1984). Uniformity trial: Size, Shape, Direction Experimental Plots Turmeric. Proceedings Sixth Symposium Plantation Crops, p. 429. https://archive.org/details/.ernet.dli.2015.502074/page/n453 Reviewed. data. Gomez, K.. R. C. Alicbusan (1969). Estimation optimum plot size rice uniformity data. Philippine Agriculturist. 52, 586-601 https://www.google.com/books/edition/The_Philippine_Agriculturist/2irOAAAAMAAJ found, probably data used Gomez & Gomez (1984). Gomez, K.. Gomez, .. (1984). Statistical Procedures Agricultural Research. Wiley-Interscience. Page 481. agridat::gomez.rice.uniformity Gopalakrishna, S. (1992). Optimum plot size shape, block size shape relative efficiency designs field experiments navane (Setaria italica). Thesis. https://krishikosh.egranth.ac./handle/1/5810120871 Reviewed. data. Goulden, C. H. (1937). Efficiency field trials pseudo-factorial incomplete randomized block methods. Canadian Journal Research, 15. https://doi.org/10.1139/cjr37c-020 Reviewed. paper used uniformity trials papers (Batchelor, Wiebe, etc). Goulden, C. H. (1939). Methods statistical analysis, 1st ed. Page 18. https://archive.org/stream/methodsofstatist031744mbp version 20x20. full 48x48 data obtained Rothamsted. agridat::goulden.barley.uniformity Goyal, Manoj Kumar. (1998). Study uniformity trial wheat (Triticum aestivum L.). Thesis, Haryana Agricultural University. https://krishikosh.egranth.ac./handle/1/5810078304 . Reviewed. data. Guimarães, B. V. C., de Carvalho, . J., Aspiazú, ., da Silva, L. S., da Silva, R. R. P., Pimenta, . M. L., & Moura, M. M. . (2021). Optimal plot size experimentation common beans (Phaseolus vulgaris L.) northern region Minas Gerais, Brazil: Experimental plots. Revista de la Facultad de Ciencias Agrarias UNCuyo, 55-63, 1853-8665. https://doi.org/10.48162/rev.39.039 Reviewed. data. Guimarães, Bruno Vinícius Castro, et al. (2020). Optimal plot size experimental trials Opuntia cactus pear. Acta Scientiarum. Technology 42 (2020): e42579-e42579. https://doi.org/10.5539/jas.v11n18p206 Reviewed. data. Gupton, C.L. (1972). Estimates Optimum Plot Size Shape Uniformity Data Burley Tobacco (Nicotiana tabacum L.). Agron. J., 64, 678-682. https://doi.org/10.2134/agronj1972.00021962006400050038x Reviewed. data. Haapanen, Matti (1992). Effet plot size shape efficiency progeny tests. Silva Fennica, 26, 201-209. Reviewed. data. Handa, D. P.; Sreenath, P. R., & Rajpali, S. K. (1995). Uniformity trial lucerne grown fodder. Grass Forage Science, 50(3), 209–216. https://doi.org/10.1111/j.1365-2494.1995.tb02316.x Reviewed. data. Hansen, Niels Anton (1914). Prøvedyrkning paa Forsøgsstationen ved Aarslev. Tidsskrift landbrugets planteavl, Bind 21, page 553. Danish. https://dca.au.dk/publikationer/historiske/planteavl/ Reviewed. agridat::hansen.multi.uniformity Haque, H. N. Azad, N. K. Jha, R. N. Sing, S. N. (1988). Optimum Size Shape Plots Wheat. Annals Agricultural Research, 9, 165-170. https://eurekamag.com/research/001/901/001901913.php Reviewed. data. criterion selection optimum plot size CV point maximum curvature. Hariharan, V., Jacob Thomas, M. George, K.C. (1986). Optimum size shape plots field experiments brinjal. Agricultural Research Journal Kerala, 24(2), 189-194. https://krishikosh.egranth.ac./handle/1/5810100010 Reviewed. data. Haritonenko, Pavlo. Neue Präzisionsmethoden auf den Versuchsfeldern. Arbeiten der landw. Versuchsstation Iwanowskoje 1904-06, S. 159. Russian German summary. Original found. Data appears Roemer (1920). Harris, J Arthur Scofield, CS. (1920). Permanence differences plats experimental field. Jour. Agr. Res., 20, 335-356. https://naldc.nal.usda.gov/catalog/IND43966236 agridat::harris.multi.uniformity Harris, J. Arthur Scofield, CS. (1928). studies permanence differences plots experimental field. Jour. Agr. Res. 36, 15–40. https://naldc.nal.usda.gov/catalog/IND43967538 agridat::harris.multi.uniformity Harris, J.. 1920. Practical universality field heterogeneity factor influencing plot yields. Journal Agricultural Research, 19, 279–314. Page 296-297. https://books.google.com/books?id=jyEXAAAAYAAJ&pg=PA279 agridat::harris.multi.uniformity Hartman, J. D.; E. C. Stair (1942). Field plot technique studies tomatoes. Proceedings American Society Horticultural Science, 41, 315-320. https://archive.org/details/.ernet.dli.2015.240678 agridat::hartman.tomato.uniformity Hatheway, W. H., E. J. Williams. (1958). Efficient estimation relationship plot size variability crop yields. Biometrics, 14, 207-222. https://doi.org/10.2307/2527785 Reviewed. data. Hatton, R. G.; N. H. Grubb & R. C. Knight (1928) Black Currant Variety Trials: Reliability Results. Journal Pomology Horticultural Science, 4:4, 200-220. https://doi.org/10.1080/03683621.1925.11513282 used. data 21 bushes * 4 years 52 bushes * 4 years. Hayes, H. K. . C. Arny (1917). Experiments field technic rod row tests. J. Agr. Res. 11, 399-419. Reviewed. data. Heath, O.V.S. (1970). Investigation Experiment. Table 1. https://archive.org/details/investigationbye0000heat agridat::heath.raddish.uniformity Heidmann, T. (1988). Startkarakterisering af arealer til systemforskning . Forsøgsarealer, måleprogram og metoder. Tidsskrift Planteavls Specialserie. S 1958, 89 pp. https://dca.au.dk/publikationer/historiske/planteavlspecial/ data. obvious data found field 26x20 plots. Mentioned Kristensen (2003). Heidmann, T. (1989). Startkarakterisering af arealer til systemforskning: IV Resultater fra arealet ved Jyndevad. Tidsskrift Planteavls Specialserie. S 2021, 163 pp. https://dca.au.dk/publikationer/historiske/planteavlspecial/ data. obvious data found field 26x20 plots. Mentioned Kristensen (2003). Hidalgo, Eduardo Calero Calero (1965). Estudio Del Tamano Y Forma De La Parcela Experimental Para Ensayos De Campo En Frijol (Phaseolus vulgaris L.). Instituto Interamericano de Ciencias Agricolas de la OEA. https://www.google.com/books/edition/Estudio_Del_Tamano_Y_Forma_De_La_Parcela/rNIOAQAAIAAJ data. Spanish. Hodnett, G.E. Uniformity Trial Groundnuts. Journal Agricultural Science, 43, 323-328. https://doi.org/10.1017/S002185960005749X Reviewed. data. Holle, Michael. 1960. Plot technique field evaluation three characters lima bean. Master’s Thesis, Iowa State University. digital copy available. Reviweed. data. Holtsmark, G Larsen, BR (1905). Om Muligheder indskraenke de Fejl, som ved Markforsog betinges af Jordens Uensartethed. Tidsskrift Landbrugets Planteavl. 12, 330-351. (Danish) https://books.google.com/books?id=MdM0AQAAMAAJ&pg=PA330 https://dca.au.dk/publikationer/historiske/planteavl/ agridat::holtsmark.timothy.uniformity Hudson, H. G. (1939). Population studies wheat: 1. Sampling. J. Agric. Sci, 29, 76-109. https://doi.org/10.1017/s0021859600051571 Reviewed. data. researchers collected 7200 plots * 13 traits = 93,000 data points. Evaluation possible use “Hollerith equipment”. Humada-Gonzalez, G.G. (2013). Estimação tamanho otimo de parcela experimental em experimento com soja. Dissertation, Universidade Federal de Lavras. http://repositorio.ufla.br/jspui/handle/1/744 Reviewed. data published data agridat::dasilva.soybean.uniformity. Humada-Gonzalez, G.G.; Barbuio, R.; Cardozo, N.; MOREIRA, J.M.; Llanes Oviedo, L. Estimação tamanho otimo de parcela experimental em experimento com soja transgenica. : semana de iniciação cientifica e seminario integrado da Pos-graduação, 2018 Goias Brasil semana de iniciação cientifica e seminario integrado da Pos-graduação. 2018. Humada González G. G., Ramalho de Morais , Caballero Mendoza CA, Bortolini J, Rodrigues Liska G. (2018). Estimation Optimum Plot Size Experimentation Sweet Potato. Agrociencia Uruguay, 22(2):e13. https://agrocienciauruguay.uy/index.php/agrociencia/article/view/13 Reviewed. data. Idrees, Nadia; Muhammad Inayat Khan (2009). Design improvement using uniformity trials experimental data. Pak. J. Agri. Sci., Vol. 46(4), 2009. https://pakjas.com.pk Reviewed. data. Igue, Toshio; Ademar Espironelo, Heitor Cantarella, Erseni Joao Nelli. (1991). Tamanho e forma de parcela experimental para cana-de-acucar (Plot size shape sugar cane experiments). Bragantia, 50, 163-180. Appendix, page 169-170. https://doi.org/10.1590/S0006-87051991000100016 agridat::igue.sugarcane.uniformity Immer, F. R. (1932). Size shape plot relation field experiments sugar beets. Jour. Agr. Research, 44, 649–668. https://naldc.nal.usda.gov/download/IND43968078/PDF agridat::immer.sugarbeet.uniformity Immer, F. R. S. M. Raleigh (1933). studies size shape plot relation field experiments sugar beets. Journal Agricultural Research, 47, 591-598. https://naldc.nal.usda.gov/download/IND43968370/PDF agridat::immer.sugarbeet.uniformity Iyer, P. V. Krishna (1942). Studies wheat uniformity trial data. . Size shape experimental plots relative efficiency different layouts. Indian Journal Agricultural Science, 12, 240-262. Page 259-262. https://archive.org/stream/.ernet.dli.2015.7638/2015.7638.-Indian-Journal--Agricultural-Science-Vol-xii-1942#page/n267/mode/2up agridat::iyer.wheat.uniformity Jaggard, K. W. (1975). size shape plots sugar-beet experiments. Annals Applied Biology, 80(3), 351–357. https://doi.org/10.1111/j.1744-7348.1975.tb01641.x Reviewed. data. Jain, M. B. Studies techniques field trials range lands . Size, Shape arrangement plots. Jain, M. B. R. K. Bohra. Size Shape Plots Blocks field Experiments Lasiurus Sindicus. Reviewed. data. James, W. C., & Shih, C. S. (1973). Size Shape Plots Estimating Yield Losses Cereal Foliage Diseases. Experimental Agriculture, 9(01), 63. https://doi.org/10.1017/s0014479700023693 Reviewed. data. Jegorow, M. (1909). Zur Methodik des feldversuches. Russian Journ Expt Agric, 10, 502-520. https://www.google.com/books/edition/Journal_de_l_agriculture_experimentale/510jAQAAIAAJ?hl=en uniformity trial oats. agridat::jegorow.oats.uniformity Joachim, .W.R. 1935. uniformity trial coconuts. Trop. Agric. Mag. Ceylon. Agric. Soc. 85:198-207. https://eurekamag.com/research/013/298/013298524.php https://southasiacommons.net/artifacts/2343967/-tropical-agriculturist/3172871/ Reviewed. data, blocks different sizes. used. Johnson, .J. & Murphy, H.C. 1943. Lattice lattice square designs oat uniformity data varietal trials. J. Agric. Sci., 22, 366-372. https://doi.org/10.2134/agronj1943.00021962003500040004x data. contour map fertility. Jones, W.W.; T.W. Embleton; C.B. Cree. (1957). Number Replications Plot Sizes Required Reliable Evaluation Nutritional Studies Yield Relationships Citrus Avocado. American Society Horticultural Science, 69:208-216. Reviewed. data. Fruit yields much variable quality factors. Jones, Marcus; Marin Harbur, Ken J. Moore (2021). Automating Uniformity Trials Optimize Precision Agronomic Field Trials. Agronomy 2021, 11, 1254. https://doi.org/10.3390/agronomy11061254 agridat::jones.corn.uniformity Data 12x12 corn expt. Jua, Junagadh. Optimum plot size field experiments tomato – statistical investigation. Thesis, Junagadh Agricultural University. https://krishikosh.egranth.ac./handle/1/5810006490. Reviewed. data. Justesen, S. (1932). Influence Size Shape Plots Precision Field Experiments Potatoes. Journal Agricultural Science, 22(2), 366-372. doi:10.1017/S0021859600053685 https://doi.org/10.1017/S0021859600053685 Reviewed. data. Conclusion: Long, narrow plots efficient (unless competition rows). Kadam, B. S. Kadam; S. M. Patel. (1937). Studies Field-Plot Technique P. Typhoideum Rich. Empire Journal Experimental Agriculture, 5, 219-230. https://archive.org/details/.ernet.dli.2015.25282 agridat::kadam.millet.uniformity Kalamkar, R.J. (1932). Experimental Error Field-Plot Technique Potatoes. Journal Agricultural Science, 22, 373-385. https://doi.org/10.1017/S0021859600053697 agridat::kalamkar.potato.uniformity Kalamkar, R. J (1932). Study Sampling Technique Wheat. Journal Agricultural Science, Vol.22(4), pp.783-796. https://doi.org/10.1017/S0021859600054599 agridat::kalamkar.wheat.uniformity Katyal, Visay (1994). Relative efficiency neighbouring techniques block design field experiments wheat Sodic soils. J. Ind. Soc. Ag. Stat. 46, 231-234. Reviewed. data. Kaushik, L. S., R. P. Singh, T. P. Yadava. (1977). uniformity trial mustard [India]. Indian Journal Agricultural Sciences. Reviewed. data. Kavitha, B. (2010). Study optimum plot size optimum plot shape soybean crop. https://krishikosh.egranth.ac./handle/1/84302 Reviewed. data. Keller, Kenneth R. (1949). Uniformity Trial Hops, Humulus lupulus L., Increasing Precision Field Experiments. https://doi.org/10.2134/agronj1949.00021962004100080011x Reviewed. data. Keller, K.R. (1951). Relative Efficiency Rectangular Triple Rectangular Lattice Designs Using Hop Uniformity Trial Data. Agron. J., 43: 93-96. https://doi.org/10.2134/agronj1951.00021962004300020009x Reviewed. data. Kempton, R. . C. W. Howes (1981). use neighbouring plot values analysis variety trials. Applied Statistics, 30, 59–70. https://doi.org/10.2307/2346657 agridat::kempton.barley.uniformity Kerr, H. W. (1939). Notes plot technique. Proc. Internat. Soc. Sugarcane Technol. 6, 764-778. agridat::kerr.sugarcane.uniformity Khan, Abdur Rashid Jage Ram Dalal (1943). Optimum Size Shape Plots Brassica Experiments Punjab. Sankhyā: Indian Journal Statistics ,6, 3. Proceedings Indian Statistical Conference 1942 (1943), pp. 317-320 (4 pages). https://www.jstor.org/stable/25047782 agridat::khan.brassica.uniformity Khan, Mujahid; Ramesh Chander Hasija; Des Raj Aneja; Manish Kumar Sharma (2016). uniformity trial Indian mustard determination optimum size shape blocks. Journal Applied Natural Science 8 (3): 1589 - 1593. Reviewed. data. Khan, Mujahid & R. C. HASIJA NITIN TANWAR (2017). Optimum size shape plots based data uniformity trial Indian Mustard Haryana. MAUSAM, 68, 67-74. https://doi.org/10.54302/mausam.v68i1.434 Reviewed. data. Khin, San. 1950. Investigation relative costs rice experiments based efficiency designs. Dissertation: Imperial College Tropical Agriculture (ICTA). Appendix XV. https://uwispace.sta.uwi.edu/dspace/handle/2139/42396 agridat::khin.rice.uniformity Khurana, Alka. (1991). Study Uniformity Trial Soyabean. Thesis, Haryana Agricultural University. https://krishikosh.egranth.ac./handle/1/5810076525 Reviewed. data. Smith’s Law. Kiesselbach, Theodore . (1917). Studies Concerning Elimination Experimental Error Comparative Crop Tests. University Nebraska Agricultural Experiment Station Research Bulletin . 13. Pages 51-72. https://archive.org/details/StudiesConcerningTheEliminationOfExperimentalErrorInComparativeCrop https://digitalcommons.unl.edu/extensionhist/430/ agridat::kiesselbach.oats.uniformity Koch, E.J. Rigney, J.. (1951). Method Estimating Optimum Plot Size Experimental Data. Agron. J., 43: 17-21. https://doi.org/10.2134/agronj1951.00021962004300010005x Reviewed. data. Idea: footnote paper says paper part MS Thesis North Carolina State. unable find online. Kulkarni, R. K., Bose, S. S., Mahalanobis, P. C. (1936). influence shape size plots effective precision field experiments sorghum. Indian J. Agric. Sci., 6, 460-474. Appendix 1, page 172. https://archive.org/details/.ernet.dli.2015.271737 agridat::kulkarni.sorghum.uniformity Kristensen, R. K. (1925). Anlaeg og Opgoerelse af Markforsoeg. Tidsskrift landbrugets planteavl, Vol 31, 464-494. Fig 1, pg. 467. https://dca.au.dk/publikationer/historiske/planteavl/ agridat::kristensen.barley.uniformity Kristensen, K. (2003). Incomplete split-plots variety trials - based -designs. : Biuletyn Oceny Odmian, 31, pp. 7-17. Reviewed. data. Refers papers uniformity trials. Krysczun, Dionatan Ketzer; Lúcio, Alessandro Dal’Col; Sari, Bruno Giacomini; Diel, Maria Inês; Olivoto, Tiago; Santana, Cinthya Souza; Ubessi, Cassiane; Schabarum, Denison Esequiel (2018). Sample size, plot size number replications trials Solanum melongena L.. Scientia Horticulturae, 233(), 220–224. https://doi.org/10.1016/j.scienta.2018.01.044 Reviewed. data. Krysczun, Dionatan, Alessandro D. Lúcio, Bruno G. Sari, Maria . Diel, Tiago Olivoto, José . G. da Silva, Cinthya S. Santana, Patrícia J. Melo, & Sabrina M. Gomes. (2018). Size Uniformity Trial Affects Accuracy Plot Size Estimation Eggplant. Journal Agricultural Science; Vol. 10, . 11. https://www.researchgate.net/publication/328291020 Reviewed. data. Kuehl, R.O. Kittock, D.L. (1969). Estimate Optimum Plot Size Cotton Yield Trials. Agron. Journal, 61: 584-586. https://doi.org/10.2134/agronj1969.00021962006100040031x data. Kumar, Ajay. 1999. study uniformity trial sesame (Til) Sesamum indicum. https://krishikosh.egranth.ac./handle/1/5810078711. Reviewed. data. Kumar, Ajay; Kiran Kapoor; Gupta, S. C.; Hasija, R. C. Uniformity trial sesame Sesamum indicum. https://eurekamag.com/research/017/592/017592269.php found. find Kumar’s thesis, data. Lakhera, M.L.; M.. Ali (1996). Optimum plot size shape estimates sunflower yield trials. Journal Maharashtra Agricultural Universities 21(3): 350-353 Reviewed. data. Optimum plot size 20 units, 10 rows 2 m long. Lamb, J.., Dowdy, R.H., Anderson, J.L. Rehm, G.W. (1997). Spatial Temporal Stability Corn Grain Yields. Journal Production Agriculture, 10: 410-414. https://doi.org/10.2134/jpa1997.0410 Reviewed. data plots 5 years, scaled relative yield year, harvested area 20-foot long 60-foot long plot. Decided use. Lambert, Edmund B. (1934). Size arrangement plots yield tests cultivated mushrooms. Journal Agricultural Research, Vol 48, 971-980. https://naldc.nal.usda.gov/naldc/download.xhtml?id=IND43968493 Reviewed. used. Uniformity trial three locations mushrooms growth houses. Lander, P. E. et al. (1938). Soil Uniformity Trials Punjab . Ind. J. Agr. Sci. 8:271-307. agridat::lander.multi.uniformity Lavezo, André; Alberto Cargnelutti Filho, Cláudia Marques de Bem, Cláudia Burin, Jéssica Andiara Kleinpaul, Rafael Vieira Pezzini. Plot size number replications evaluate grain yield oat cultivars. Bragantia, Campinas, v. 76, n. 4, p.512-520, 2017. https://doi.org/10.1590/1678-4499.2016.410 Reviewed. data. Laycock, D. H. (1955). effect plot shape reducing errors tea experiments. Tropical Agriculture, 32, 107-114. agridat::laycock.tea.uniformity Lehmann, . Ninth Annual Report Agricultural Chemist Year 1907-08. Department Agriculture, Mysore State. [2nd-9th] Annual Report Agricultural Chemist. https://books.google.com/books?id=u_dHAAAAYAAJ agridat::lehmann.millet.uniformity Lessman, Koert James (1962). Comparisons methods testing grain yield sorghum. Iowa State University. Retrospective Theses Dissertations. Paper 2063. Appendix Table 17. https://dr.lib.iastate.edu/handle/20.500.12876/73575 agridat::lessman.sorghum.uniformity Lessman, K.J. Atkins, R.E. (1963). Optimum Plot Size Relative Efficiency Lattice Designs Grain Sorghum Yield Tests. Crop Science, 3: 477-481 https://doi.org/10.2135/cropsci1963.0011183X000300060006x Reviewed. See Lessman thesis data. agridat::lessman.sorghum.uniformity Li, HW Meng, CJ Liu, TN. 1936. Field Results Millet Breeding Experiment. Agronomy Journal, 28, 1-15. Table 1. DOI: 10.2134/agronj1936.00021962002800010001x agridat::li.millet.uniformity Ligon, L. L. (1930). Size Plat Number Replications Field Experiments Cotton. Agronomy Journal, 22, 689-699. https://doi.org/10.2134/agronj1930.00021962002200080003x Reviewed. uniformity test, rather ‘cultural’ test ‘varietal’ test. Liji, Kumari (1997). Optimum size plots coconut using multivariate techniques. Thesis, Kerala Agriculture Univ. Reviewed. data. Lizy, M.J. (1986). Uniformity trials colocasia. https://krishikosh.egranth.ac./handle/1/5810113181 Reviewed. data. Lizy, M. J.; K. C. George Jacob Thomas (1988). Optimum Size Shape Plots Colocasia. Agric. Res. J. Kerala, 25, 241-248. http://14.139.185.57:8080/jspui/bitstream/123456789/4103/1/25_2_241-248_0002-1628.pdf Reviewed. data. Lizy, M.J.; George, K.C.; Thomas, M.J. (1988). Optimum size shape plots colocasia (Colocasia esculenta L.) Agricultural Research Journal Kerala 25(2): 241-248 https://eurekamag.com/research/001/901/001901914.php Paper found, Lizy thesis found, data. Loesell, Clarence (1936). Size plot & number replications necessary varietal trials white pea beans. Thesis, Michigan State. https://d.lib.msu.edu/etd/5271 agridat::loesell.bean.uniformity Lohmor, Nishu (2015). Estimation optimum plot size, shape number replications sunflower (Helianthus annuus). Thesis. https://krishikosh.egranth.ac./handle/1/81261 Reviewed. data. Lohmor, Nishu; Mujahid Khan; Kiran Kapoor; Nitin Tanwar (2017). Study Optimum Block Size Shape Uniformity Trial Sunflower (Helianthus annuus). Advances Research, 9, 1-8. 2017. Reviewed. data. Lord, L. (1931). Uniformity Trial Irrigated Broadcast Rice. Journal Agricultural Science, 21(1), 178-188. https://doi.org/10.1017/S0021859600008029 agridat::lord.rice.uniformity Love, Harry (1937). Application Statistical Methods Agricultural Research. Commercial Press, Shanghai. Page 411. https://archive.org/details/.ernet.dli.2015.233346/page/n421 agridat::love.cotton.uniformity Love, H.H. W.T. Craig (1938). Investigations plot technic small grains. Cornell University, Memoir 214. https://catalog.hathitrust.org/Record/011481484 Reviewed. data. Described 3 trials 600 plots. Lorentz, Leandro Homrich; Alexandra Augusti Boligon; Lindolfo Storck; Alessandro Dal’Col Lúcio. (2010). Plot size experimental precision sunflower production. Sci. Agric. (Piracicaba, Braz.), v.67, n.4, p.408-413. Reviewed. data. Love, H. H. (1936). Uniformity Trials Useful?. Agronomy Journal, 28(3), 234. https://10.2134/agronj1936.00021962002800030007x Reviewed. data. Lúcio, . D. C., Nunes, L. F., Rego, F., & Pasini, M. P. (2016). Relations zero-inflated variables trials horticultural crops. Spanish Journal Agricultural Research, 14(2), 17. https://dialnet.unirioja.es/servlet/articulo?codigo=6802883 Reviewed. data. Lúcio, . D. C., & Benz, V. (2016). Accuracy estimates zucchini production related plot size number harvests. Ciência Rural, 47. https://doi.org/10.1590/0103-8478cr20160078 Reviewed. data. Lucyamma, Mathew. (1986). 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Res., Vol. 25 . 4, 2012 agridat::masood.rice.uniformity McClelland, Chalmer Kirk (1926). determinations plat variability. Agronomy Journal, 18, 819-823. https://doi.org/10.2134/agronj1926.00021962001800090009x agridat::mcclelland.corn.uniformity McGuire, Judson U. (1954). Uniformity Data European Corn Borer, Pyrausta Nubilalis Hbn., Populations. [Ames, Ia.: s.n.], 1954. https://hdl.handle.net/2027/umn.31951000404095s Reviewed. publication data relating corn borer infestation thousands individual corn plants. used. Meier, V.D. Lessman, K.J. (1971). Estimation Optimum Field Plot Shape Size Testing Yield Crambe abyssinica Hochst. Crop Science, 11: 648-650. https://doi.org/10.2135/cropsci1971.0011183X001100050013x Reviewed. data. Idea: thesis Meier Perdue University. looked online found anything. Mercer, WB Hall, AD, (1911). experimental error field trials Journal Agricultural Science, 4, 107-132. 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Gross, J. O. Rawlings. (1975). Optimum Size Sampling Unit Estimate Coastal Bermudagrass Yield”. Agronomy Journal 67.4 (1975): 533-537. https://doi.org/10.2134/agronj1975.00021962006700040020x Reviewed. data. Stadler, Lewis John. (1921). Experiments field plot technic preliminary determination comparative yields small grains. University Missouri. https://hdl.handle.net/2027/umn.319510008017444 Reviewed. data. Uniformity trial discussed page 43. Stadler, L. J. (1926). Experimental Error Field Plot Tests. Proceedings International Congress Plant Sciences, Vol 1, 107-127. https://babel.hathitrust.org/cgi/pt?id=mdp.39015069524372&seq=197 Reviewed. original data. Used datasets similar Roemer (1920). Page 110 summarizes blank experiments 100 plots. Stephens, Joseph C. (1928). Experimental methods probable error field experiments sorghum. Journal Agricultural Research, 37, 629–646. https://naldc.nal.usda.gov/catalog/IND43967516 agridat::stephens.sorghum.uniformity Stickler, F. C. (1960). Estimates Optimum Plot Size Grain Sorghum Uniformity Trial Data. Technical bulletin, Kansas Agricultural Experiment Station. Page 17-20. https://babel.hathitrust.org/cgi/pt?id=uiug.30112019584322&view=1up&seq=21 agridat::stickler.sorghum.uniformity Stockberger, W. W. (1912). Study Individual Performance Hops. Journal Heredity, 1, 452-457. https://doi.org/10.1093/jhered/os-8.1.452 Reviewed. data. Stockem, J.E., Korontzis, G., Wilson, S.E. et al. (2021) Optimal Plot Dimensions Performance Testing Hybrid Potato Field. Potato Research, 65, 417–434. https://doi.org/10.1007/s11540-021-09526-9 Reviewed. data. Storck, Lindolfo (2010). Partial collection data potato yield experimental planning. Field Crops Research, 121, 286-290. https://doi.org/10.1016/j.fcr.2010.12.018. Reviewed. data. Storck, Lindolfo; Sidinei Lopes; Alessandro Dal’Col Lúcio; Alberto Cargnelutti Filho (2011). Optimum plot size number replications related selective precision. Ciência Rural 41(3):390-396. https://doi.org/10.1590/S0103-84782011000300005 https://www.researchgate.net/publication/270761907_Optimum_plot_size_and_number_of_replications_related_to_selective_precision data Strickland, . G. (1932). vine uniformity trial. Journal Agriculture, Victoria, 30, 584-593. Strickland, . G. (1935). Error horticultural experiments. Journal Agriculture, Victoria, 33, 408-416. agridat::strickland.apple.uniformity agridat::strickland.grape.uniformity agridat::strickland.peach.uniformity agridat::strickland.tomato.uniformity Strydom, G. J. (1966). Studies planning field experiments vegetable crops. S. Afr. J. Agric. Science 9, 183-194. https://hdl.handle.net/10520/AJA05858860_582 Reviewed. data. Summerby, R. (1923). Replication relation accuracy comparative crop tests. J. Soc Agron, 15. https://www.google.com/books/edition/Proceedings_of_the_American_Society_of_A/MWU4AQAAMAAJ Also: https://doi.org/10.2134/agronj1923.00021962001500050004x Reviewed. data. Data sent Rothamsted 1938. Two areas land, 100 links 505 links. One area Alaska Oats. Another area Huron wheat. fixme check can find Rothamsted papers. Summerby, R. (1925). Study Sizes Plats, Number Replications, Frequency Methods Using Check Plots Relation Accuracy Field Experiments. Jour. Amer. Soc, Agron. 17: 140-149. Reviewed. Data uniform. data-unused/summerby.oat.uniformity Summerby, R. (1934). value preliminary uniformity trials increasing precision field experiments. Macdonald College. https://books.google.com/books?id=6zlMAAAAYAAJ&pg=RA14-PA47 Reviewed. data 5x35 1x35 grid layouts. agridat::summerby.multi.uniformity. Swallow, William H.; Todd C. Wehner (1986). Optimum Plot Size Determination Application Cucumber Yield Trials. Euphytica, 35, 421-432. https://doi.org/10.1007/BF00021850 Reviewed. data. multiple-harvest yield trials, optimum plot sizes estimated 6.4 10.3 m^2. Swanson, .F. (1930). Variability Grain Sorghum Yields Influenced Size, Shape, Number Plats. Agron. J., 22: 833-838. https://doi.org/10.2134/agronj1930.00021962002200100002x Reviewed. data. Swearingin, M.L. Holt, D.. (1976). Using “Blank” trial teaching tool. Journal Agronomic Education, 5: 3-8. https://doi.org/10.2134/jae.1976.0003 Data 9 trials. trial RCBD 4 ‘varieties’ 4 reps. clear layout rows columns randomized. Taha, R.S. M.M. Shafik (2000). Relative precision incomplete block designs soybean uniformity trials optimum sample size. J. Agric. Sci. Mansoura Univ, 25, 5601-5610. Reviewed. data. Tartaglia, FdL, Lúcio, , Diel, MI, et al. Experimental Plan Tests Pea. Agronomy Journal. 2021; 113: 1394–1406. https://doi.org/10.1002/agj2.20575 Reviewed. data. Taylor, F.W. (1907-1909). size experimental plot field crops. Proceedings annual Society agronomy 1, 56-58. https://acsess.onlinelibrary.wiley.com/doi/10.2134/agronj1907-1909.00021962000100010014x Reviewed. data. Taylor, Howard Lewis (1951). effect plot shape experimental error. Master’s Thesis, Iowa State University. Reviewed. digital copy available. data given. Used data corn uniformity trial, oats uniformity, data Fairfield Smith. Smaller experimental errors found long narrow plots. Tedin, Olof (1931). Influence Systematic Plot Arrangement upon Estimate Error field Experiments. J. Agric. Science, 21, 191-208. https://doi.org/10.1017/S0021859600008613 Reviewed. data. Uses uniformity trials papers. Thomas, H.L. Abou-El-Fittouh, H.. (1968) Optimum Plot Size Number Replications Estimating Forage Yield Moisture Percentage. Agron. J., 60: 549-550. https://doi.org/10.2134/agronj1968.00021962006000050031x Reviewed. data. Thompson, Ross C. (1934). Size, Shape, Orientation Plots Number Replications Required Sweetpotato Field-Plot Experiments. J. Agric. Rsch., 5, 379-399. https://naldc.nal.usda.gov/catalog/IND43968506 Reviewed. data. Interesting paper experiment conducted 4 plots year/loc combinations. Toebe, Marcos, et al. (2020). Plot size number replications ryegrass experiments. Ciência Rural 50.1. https://doi.org/10.1590/0103-8478cr20190195 Reviewed. data. Toebe, Marcos, et. al. (2022). Plot size number replicates ryegrass experiments sowed rows. Pesquisa Agropecuária Brasileira, v.57, e02976, 2022. DOI: https://doi.org/10.1590/S1678-3921.pab2022.v57.02976. Reviewed. data. Torrie, J.H., Schmidt, D.R. Tenpas, G.H. (1963). Estimates Optimum Plot Size Shape Replicate Number Forage Yield Alfalfa-Bromegrass Mixtures. Agron. Journal, 55: 258-260. https://doi.org/10.2134/agronj1963.00021962005500030015x Tulaikow, N. (1913) Resultate einer mathematischen Bearbeitung von Ernteergebnissen. Russian Journal fur Exp Landw., 14, 88-113. Russian German summary. Two uniformity trials winter summer wheat. agridat::tulaikow.wheat.uniformity Vagholkar, B. P. ; Apte, V. N. ; Iyer, S. S. (1940). study plot size shape technique field experiments sugarcane. Indian Journal Agricultural Science 1940 Vol.10 pp.388-403 https://archive.org/details/.ernet.dli.2015.25316/page/n437/mode/2up Reviewed. data. Vallejo, R.L. & H. . Mendoza (1988). Determination optimum plot size adequate number replications evaluate potato seedling populations. VIIth Symposium International Society Tropical Root Crops, Gosier (Guadeloupe), 1-6 July 1985, Ed. INRA, Paris, 1988. http://www.istrc.org/images/Documents/Symposiums/Seventh/7th_symposium_proceedings_0081.pdf Reviewed. data. Vallejo, Roger L.; VHumberto . Mendoza. (1992). Plot Technique Studies Sweetpotato Yield Trials. J. AMER. Soc. HORT. SCI. 117(3):508-511. https://doi.org/10.21273/JASHS.117.3.508 Reviewed. data. Vargas-Rojas, Jorge Claudio. (2020). Size shape experimental unit yield trials Brachiaria , hybrid CIAT 3608. https://www.semanticscholar.org/paper/Size--shape---experimental-unit--yield--Vargas-Rojas/e9964eca6ac42471aa44bc45e84bc376b02daf09 Vishnaadevi, S.; K. Prabakaran, E. Subramanian, P. Arunachalam. (2019). Determination fertility gradient direction optimum plot shape paddy crop Madurai District. Green Farming, 10, 155-159. https://www.researchgate.net/publication/333892867 agridat::vishnaadevi.rice.uniformity Warren, J.. Mendez, . (1981). Block Size Orientation, Allowance Positional Effects, Field Experiments. Experimental Agriculture, 17, 17 - 24. https://doi.org/10.1017/S0014479700011182 Reviewed. Used dozen existing datasets. Wassom R.R. Kalton. (1953). Estimations Optimum Plot Size Using Data Bromegrass Uniformity Trials. Agricultural Experiment Station, Iowa State College, Bulletin 396, page 314-319. https://lib.dr.iastate.edu/ag_researchbulletins/32/ https://babel.hathitrust.org/cgi/pt?id=uiug.30112019570701&view=1up&seq=26&skin=2021 agridat::wassom.brome.uniformity Webster, C. 1939. note uniformity trial oil palms. Tropical Agriculture, 16, 15-19. data according Mendez. Westover, Kyle C. (1924). influence plot size replication experimental error field trials potatoes. Agricultural Experiment Station, West Virginia University, . 189. https://babel.hathitrust.org/cgi/pt?id=uiug.30112019921359&view=1up&seq=1 https://www.google.com/books/edition/The_Influence_of_Plot_Size_and_Replicati/Px6K44dYd1QC Reviewed. data. Wiebe, G.. 1935. Variation Correlation Grain Yield among 1,500 Wheat Nursery Plots. Journal Agricultural Research, 50, 331-357. https://naldc.nal.usda.gov/download/IND43968632/PDF agridat::wiebe.wheat.uniformity Wiedemann, Alfred Max. 1962. Estimation Optimum Plot Size Shape Use Safflower Yield Trails. Table 5. Graduate Theses Dissertations. Paper 3600. Table 5. https://digitalcommons.usu.edu/etd/3600/ agridat::wiedemann.safflower.uniformity Wiedemann, .M. Leininger, L.N. (1963), Estimation Optimum Plot Size Shape Safflower Yield Trials. Agron. J., 55: 222-225. https://doi.org/10.2134/agronj1963.00021962005500030004x Reviewed. See Wiedemann thesis data. agridat::wiedemann.safflower.uniformity Williams, ER Luckett, DJ. (1988). use uniformity data design analysis cotton barley variety trials. Australian Journal Agricultural Research, 39, 339-350. https://doi.org/10.1071/AR9880339 agridat::williams.barley.uniformity agridat::williams.cotton.uniformity Wilson, C. E. Study plots laid field II view obtaining plot-fertility data use future experiments plots, season 1940-41. Dissertation: University West Indies. Page 36-39. https://uwispace.sta.uwi.edu/dspace/handle/2139/43658 See also dissertation Bradley. agridat::bardley.multi.uniformity Wilson, Wendell W. (1970). Texas Vegetable Remote Sensing Study, 1969 Determination Optimum Plot Size Shape Estimation Carrot Yield. Research Development Branch Standards Research Division Statistical Reporting Service. https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/Yield_Reports/Texas%20Vegetable%20Remote%20Sensing%20Study%201969.pdf data found. Wood, T. B. F. J. M. Stratton. (1910). interpretation experimental results. J. Agric Sci 3, 417-440. Reviewed. data. Several plots distribution data. Wood, Ronald . (1972). Optimum Plot size winter wheat. Research & Development Branch, Standards Research Division Statistical Reporting Service. https://naldc.nal.usda.gov/catalog/27961 Reviewed. data. Wyatt, F. . (1927). Variation plot yields due soil heterogeneity. Scientific Agriculture, 7, 248-256. Table 1. https://doi.org/10.4141/sa-1927-0020 agridat::wyatt.multi.uniformity Yadav, Raj Pal. 1991. Spatial Correlation Analysis Uniformity Trial Data. College Basic Sciences Humanities Chaudhary Charan Singh Haryana Agricultural University Hisar. https://krishikosh.egranth.ac./handle/1/5810076755 Reviewed. data. Zandonadi, Cecília Uliana Viçosi, David Brunelli Fornazier, Maurício Lorenção Botacim, Luciana Aparecida Sousa, Douglas Gonzaga de Martinuzzo, Marx Bussular Alixandre, Fabiano Tristão Favarato, Luiz Fernando Krohling, Cesar Abel Guarçoni, Rogério Carvalho Alixandre, Ricardo Dias Fornazier, Maurício José (2022). Determining minimum size experimental plot evaluating field parameters Arabica coffee Research, Society Development, 11, 1-9. http://dx.doi.org/10.33448/rsd-v11i6.29384 Reviewed. data. Zhang, R . W. Warrick, D. E. Myers (1994). Heterogeneity, plot shape effect optimum plot size. Geoderma, 62, 183-197. https://doi.org/10.1016/0016-7061(94)90035-3 Reviewed. data. Oddly, appear use data Kuehl & Kittock, data paper. Zuber (1942). Relative Efficiency Incomplete Block Designs Using Corn Uniformity Trial Data. Agron Journal, 34, 30-47. https://doi.org/10.2134/agronj1942.00021962003400010004x Reviewed. Limited data, 3 reps 4x5. Zuhlke, Thomas . Gritton, E.T. (1969). Optimum Plot Size Shape Estimates Pea Yield Trials. Agron. J., 61: 905-908. https://doi.org/10.2134/agronj1969.00021962006100060023x Reviewed. data. paper based thesis W Wisconsin. Zuhlke, Thomas . Gritton, E.T. (1970). Relative Precision Different Experimental Designs Number Replications Pea Yield Trials. Agron J., 62, 61-64. https://doi.org/10.2134/agronj1970.00021962006200010020x Reviewed. data.","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section","dir":"Articles","previous_headings":"","what":"—————————————————————————","title":"Notes on uniformity data","text":"Roemer Note: Neyman (1935) says, “used data uniformity trials published T. Roemer (”Der Feldversuch, Berlin, 1920), suitable purpose, many rows columns. Cochran says Roemer: Sugarbeet: 96 plots 1916 1918 - table 1 & 3 Sugarbeet: 416 plots - table 4 Millet 105 plots (also Lehman) - table 13 Oats: 240 plots 8x30 - table 10 Ragi: 34 plots <—- fixme Timothy: 240 plots (also Holtsmark) - table 11 Summer wheat: 16x15=240 plots. Tulaikow Russian Journ Exp Agric , 14. (1913) - table 9 Winter wheat: 10x25=230 plots Tulaikow Russian Journ Exp Agric , 14. (1913) - table 8 Roemer (1920). Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ Page34. P 37 mean. | Tab | Orig qm | Mu | Wt | unit | note | |—–|———|——|—-|————-|————————————————————————-| | 1 | 6.8 | 21.7 | kg | | agridat::roemer.sugarbeet.uniformity (1916) | | 2 | 6.8 | | kg | | 1917 2x48=96 plots | | 3 | 6.8 | 23.5 | kg | | agridat::roemer.sugarbeet.uniformity (1918) | | 4 | 136.5 | 617 | pf | 1.33 x 22.5 | agridat::haritonenko.sugarbeet.uniformity | | 5 | 20.2 | 329 | lb | 1/10 acre | agridat::mercer.mangold.uniformity | | 6 | 8.1 | 395 | lb | 1/10 acre | agridat::mercer.wheat.uniformity | | 7 | 2.8 | 681 | g | | agridat::montgomery.wheat.uniformity 1909, 14x16 | | 8 | 4.6 | 700 | g | qm | agridat::tulaikow.wheat.uniformity (winter) | | 9 | 4.6 | 532 | g | qm | agridat::tulaikow.wheat.uniformity (summer) | | 10 | 4.6 | 203 | kg | qm | agridat::jegorow.oats.uniformity | | 11 | 6.3 | 17.8 | kg | 25 qm | agridat::holtsmark.timothy.uniformity (orig 5m x 5m) | | 12 | 404.7 | 286 | lb | 20.2 qm | todo 17x2 4 years Lehmann rice | | 13 | 404.7 | 167 | lb | 20.2 qm | agridat::lehmann.millet.uniformity (20 rows instead 22) | Roemer (1925) Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. https://www.google.com/books/edition/Arbeiten/6LJGAAAAYAAJ","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-1","dir":"Articles","previous_headings":"","what":"—————————————————————————","title":"Notes on uniformity data","text":"Roemer (1920). Note, based note Larsen (240 plot * 25 sq m = 6000 sq m / 60 = 100 sq m per “” appears “” “= 100m^2”) Page 27 1-3. Eigene Versuche mit Zuckerrüben, ausgeführt auf dem Neßthaler Zuchtfeld des Kaiser-Wilhelm-Institutes, Bromberg, den Jahren 1916, 1917 und 1918. 1916 und 1918 war die Versuchsfläche ein und dieselbe, 6,80 groß und den beiden Jahren mit Original Klein-Wanzlebener Zuckerrüben auf 30 X 40 cm bebaut. Vorfrucht für 1916 war Hafer, für 1918 Roggen; 1917 war eine andere Fläche, ebenfalls 6,80 groß, für den Versuch benußt; gesät wurden zwei verschiedene Zuchten von Strube, Schlanstedt. Beide Flächen sind von sehr gleichmäßiger Bodenbeschaffenheit. Bei der Fläche 1916 und 1918 machte sich im ersten Jahre bei den Reihen 31-33 eine geringe Stelle bemerkbar, die 1918 weit weniger Erscheinung trat. Die Bodenunterschiede sind allen drei Jahren geringer als die durch die Versuchstechnik bedingten Fehler. Translated: (Roemer) experiments sugar beets, carried Neßthal breeding field Kaiser Wilhelm Institute, Bromberg, years 1916, 1917 1918. 1916 1918 test area one , 6.80 large original years Klein-Wanzleben sugar beets cultivated 30 x 40 cm. previous crop 1916 oats, 1918 rye; 1917 another area, also 6.80 large, used experiment; Two different varieties Strube, Schlanstedt sown. areas uniform soil conditions. 1916 1918 area, small spot noticeable rows 31-33 first year, much less noticeable 1918. three years soil differences smaller errors caused experimental technology.","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-2","dir":"Articles","previous_headings":"—————————————————————————","what":"—","title":"Notes on uniformity data","text":"Haritonenko (36), Versuch der landwirtschaftlichen Versuchsstation Iwanowskoje, Gouvernement Nowgorod. Versuchsfläche 5 ha 68 mit 416 Teilstücken zu 136,5 qm. Die Reihe weist erheblich geringeren Boden auf als die drei anderen Reihen. Translated: Haritonenko (36), experiment Ivanovskoye Agricultural Experimental Station, Novgorod Governorate. Test area 5 ha 68 416 sections (plots) 136.5 square meters. Row significantly less soil three rows. Haritonenko, Pavlo. Neue Präzisionsmethoden auf den Versuchsfeldern. Arbeiten der landw. Versuchsstation Iwanowskoje 1904-06, S. 159. Note: P. Haritonenko sometimes translated Pavlo/Pavel Kharitonenko. English: Pavel Kharitonenko Russian: Павел Иванович Харитоненко Russian name Journal: Журнал опытной агрономии Paper volume? Zhurnal opytnoĭ agronomii. Russisches journal fūr experimentelle landwirtschaft. Journal de l’agriculture experimentale v.5 (1904) https://babel.hathitrust.org/cgi/pt?id=uc1.b2907722&seq=770 1905 vol 6. Paper volume? https://babel.hathitrust.org/cgi/pt?id=uc1.b2907723&seq=8 List volumes . Missing 1906 issue 1-2 https://catalog.hathitrust.org/Record/007918356","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-3","dir":"Articles","previous_headings":"—————————————————————————","what":"—-","title":"Notes on uniformity data","text":"Mercer und Hall (71), Mangold, Versuchsfeld Rothamsted. Versuchsfläche 40,40 mit 200 Teilstücken zu 20,2 qm (9,2×2,2 m). Das Feld ist weitgehend gleichmäßig, obwohl seine Qualität von Nord nach Süd abnimmt, wie aus den Mittelwerten zu ersehen ist. Trans: 5. Mercer Hall (71), Mangold, Rothamsted experimental field. Test area 40.40 200 sections 20.2 square meters (9.2 × 2.2 m). field largely uniform, although quality decreases north south, can seen averages. Mercer und Hall (71), Winterweizen, Versuchsfeld Rothamsted. Versuchsfläche 40,45 mit 500 Teilstücken zu 8,09 qm. Die Fläche ist weniger aus geglichen als jene von Versuch 5. Trans: 6. Mercer Hall (71), winter wheat, Rothamsted experimental field. Test area 40.45 500 sections 8.09 square meters. area less uniform experiment 5. Montgomery (86), Winterweizen, Versuchsfeld der Versuchsstation Nebraska, U. S. . Versuchsfläche 6 30 qm mit 224 Teilstücken zu 2,81 qm. Das Feld wird von unten nach oben hin besser, ist auch der rechten Hälfte ertragreicher als der linken, entspricht aber einem sehr ausgeglichenen Versuchsfelde der Praxis. Trans: 7. Montgomery (86), winter wheat, experimental field Nebraska Experimental Station, USA. Experimental area 6 30 square meters 224 sections 2.81 square meters. field gets better bottom top, also productive right half left, corresponds uniform experimental field practice. Tulaikow (131), Winterweizen, Versuchsfeld der landwirtschaftlichen Versuchsstation Besentschuk. Versuchsfläche 10 92 qm mit 240 Teilstücken zu 4,55 qm. Diese Fläche ist mäßig ausgeglichen. Translated: Tulaikow (131), Winter wheat, experimental field Besenchuk Agricultural Experimental Station. Test area 10 x 92 sqm 240 plots 4.55 sqm. area moderately uniform. Neyman cites: Tulaikow, Russian Journ Exp Agric 14 (1913). Number 2, p. 113 Tulaikow (131), Sommerweizen, genau wie bei Winterweizen, jedoch erscheint das Bild gleichmäßiger als jenes von Versuch 8. Trans: 9. Tulaikow (131), spring wheat, winter wheat, picture appears uniform experiment 8. Jegerow (52), Hafer, Versuchsfeld der landwirtschaftlichen Versuchsstation Saumy (Gouvernement Charkow), Versuchsfläche 10 92 qm mit 240 Teilstücken zu 4,55 qm. Sehr gleichmäßiges Versuchsfeld. Translated: 10. Jegerow (52), oats, experimental field Saumy agricultural experimental station (Kharkov Governorate), experimental area 10 x 92 sqm 240 sections 4.55 sqm. even test field. Larsen (49), Timothyheu, Versuchsfläche 60 mit 240 Teilstücken zu 25 qm. Die Versuchsfläche wird von Ost nach West langsam etwas besser, und von Süd nach Nord nimmt die Ertragsfähigkeit ziemlich stark ab (-Streifen 1496 kg, B-Streifen 1449 kg, C-Streifen 1326 kg). Trans: Timothy hay, test area 60 240 sections 25 square meters. test area slowly improves slightly east west, south north yield decreases quite sharply (strip 1496 kg, B strip 1449 kg, C strip 1326 kg). Lehmann (61), Reis, Versuchsfeld Hebbal bei Benjalore, Indien. Versuchsfläche 68,68 mit 34 Teilstücken zu 20,2 qm, durch vier Jahre (1905-1908) fortgeführt. Dieser Versuch ist seines geringen Umfanges wegen nicht für alle zu handelnden Fragen brauchbar. Trans: 12. Lehmann (61), Reis, Hebbal experimental field near Benjalore, India. Experimental area 68.68 34 sections 20.2 square meters, continued four years (1905-1908). small scope, experiment used questions addressed. Lehmann (61), Hirse, Versuchsfeld Hebbal bei Benjalore. Versuchsfläche 21 21 qm mit 105 Teilstüden zu 20,2 qm, durch drei Jahre (1905-1907) fortgesezt, jedoch sind die Teilstücke durch Gebäude getrennt. Dieses Versuchsfeld ist aus kleinbäuerlichem Besit zusammengekauft, also früheren Jahren verschieden arbeitet und bestellt worden, es ist die ungleichmäßigste Fläche von allen 13 Versuchen. Trans: 13. Lehmann (61), millet, Hebbal test field near Benjalore. Experimental area 21 x 21 sqm 105 sections 20.2 sqm, continued three years (1905-1907), sections separated buildings. experimental field purchased small farmers property therefore worked cultivated differently previous years. uneven area 13 experiments. Die Originalerntezahlen finden sich den Tabellen des Anhanges, die die Lage der Teilstücke erkennen lassen. Es ließ sich infolge verschiedener Anlage der Versuche nicht ermöglichen, daß alle Versuche einheitlicher Anordnung verrechnet wurden. Die Ergebnisse sind übereinstimmend, daß die gezogenen Schlußfolgerungen große Sicherheit besigen. Trans: original harvest figures can found tables appendix, show location cuts. Due different design experiments, possible experiments calculated uniform manner. results consistent conclusions drawn certain. Untersuchungen über Beeinflussung der Genauigkeit. Trans: Studies influence accuracy. Hand der bezeichneten 13 Versuche ich nun im einzelnen nachweisen, welche Umstände und welchem Maße diese auf die Genauigkeit der Versuchsergeb nisse einwirken. Es können jeweils nur die Endzahlen der mühsamen und sehr umfangreichen Berechnungen gegeben werden, die ich selbst ausgeführt habe. Trans: Using 13 experiments mentioned, now want demonstrate detail circumstances extent influence accuracy test results. final numbers laborious extensive calculations carried can given.","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-4","dir":"Articles","previous_headings":"","what":"—————————————————————————","title":"Notes on uniformity data","text":"Ehrenberg, P. 1920. Versuch eines Beweises für die Anwendbarkeit der Wahrscheinlichkeitsrechnung bei Feldversuchen. Die Landwirtschaflichen versuchs-stationen, 95, 157-294 https://archive.org/details/dielandwirtschaf9519reun/page/156/ https://www.google.com/books/edition/Die_Landwirtschaftlichen_Versuchs_Statio/h9FGAAAAYAAJ?hl=en&gbpv=1&dq=bolatitz+jegorow&pg=RA1-PA160&printsec=frontcover Extensive datasets given 1 row text per plot! , field layout given??? Ehrenberg, Mitteilungen der Landwirtschaflichen Institute der Kgl. Universitat Breslau, 6, 21 (1910). Jegorow Tulaikow Lehmann ragi Lehmann Lehmann 1907 Montgomery Larsen Page 288-290 summarizes many studies refers paper: Landw. Versuchs-Stationen Bd. 87 (1915), S. 34. https://www.google.com/books/edition/Die_Landwirthschaftlichen_Versuchs_Stati/qFPbIBaHZKUC?hl=en&gbpv=1 # ————————————————————————— Vageler, H. (1919) Beziehung zwischen Parzellengrösse und Fehler der Einzelbeobachtung bei Felderversuchen (Relation size plats error detached observations field experiments). Journal für Landwirtschaft, 67, 97-108. https://www.google.com/books/edition/Journal_f%C3%BCr_Landwirtschaft/JHAZAQAAIAAJ?hl=en&gbpv=1&dq=Vageler+Beziehung+zwischen+parzellengr%C3%B6sse&pg=PA107&printsec=frontcover 128 plots roggen (rye) hafer (oats) kartoffeln (potatoes) wruken (rape/rutabega/kohlrabi?) layout? : https://archive.org/details/journalfurlandwi6719unse/page/96/mode/2up?q=ehrenberg+%22versuch+eines+beweises%22","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-5","dir":"Articles","previous_headings":"","what":"—————————————————————————","title":"Notes on uniformity data","text":"Gorski Mentioned Neyman (1935). Gorski, M. Stefaniow, M. (1917). Die Anwendbarkeit der Wahrscheinlichkeits-rechnung bei Feldversuchen. Die Landwirtschaflichen Versuchsstationen, 90, 225-240. https://babel.hathitrust.org/cgi/pt?id=coo.31924078248048&seq=241&q1=gorski Two experiments given, 200 300 plots, layout? Gorski & Stefaniow. (1917) Zastosowanie rachunku prawdopodobienstwa doswiadczen polowych Roczniki Nauk Rolniczych. searched google books, hathi, archive Roczniki journal https://catalog.hathitrust.org/Record/007913696 11-12 1924 17-18 1927 1917 vol 11 1923 vol 9 1930 vol 24 See also Cochran, # 52","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-6","dir":"Articles","previous_headings":"","what":"—————————————————————————","title":"Notes on uniformity data","text":"Geisler 1958. Downloaded. Uniformity trial (?) page 269.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kevin Wright. Author, maintainer, copyright holder.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Wright K (2024). agridat: Agricultural Datasets. R package version 1.24, https://kwstat.github.io/agridat/.","code":"@Manual{, title = {agridat: Agricultural Datasets}, author = {Kevin Wright}, year = {2024}, note = {R package version 1.24}, url = {https://kwstat.github.io/agridat/}, }"},{"path":"/index.html","id":"agridat-","dir":"","previous_headings":"","what":"Agricultural Datasets","title":"Agricultural Datasets","text":"Homepage: https://kwstat.github.io/agridat Repository: https://github.com/kwstat/agridat agridat package provides extensive collection datasets agricultural experiments. datasets come books, papers, websites related agriculture. Example graphics analyses included. Data come small-plot trials, multi-environment trials, uniformity trials, yield monitors, . package tries make data FAIR: Findable, Accessible, Interoperable, Reusable.","code":""},{"path":"/index.html","id":"key-features","dir":"","previous_headings":"","what":"Key features","title":"Agricultural Datasets","text":"Thorough documentation. Examples (almost) every dataset.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Agricultural Datasets","text":"","code":"# Install the released version from CRAN: install.packages(\"agridat\") # Install the development version from GitHub: install.packages(\"devtools\") devtools::install_github(\"kwstat/agridat\")"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Agricultural Datasets","text":"","code":"library(agridat) ?agridat # list all datasets with keywords"},{"path":"/notes_agridat.html","id":null,"dir":"","previous_headings":"","what":"License note","title":"License note","text":"Substantial effort made contact authors papers published within past decades secure permission use data package. U.S., raw data generally subject copyright. See discussion. Data produced work United States government (including U.S. Department Agriculture) subject copyright. Creative Commons licenses can apply database, factual data.","code":""},{"path":"/notes_agridat.html","id":"cochran-uniformity-done","dir":"","previous_headings":"","what":"Cochran uniformity done","title":"License note","text":"evans.sugarcane.uniformity goulden.barley.uniformity ducker.groundnut.uniformity immer.sugarbeet.uniformity (1931) mckinstry.cotton.uniformity saunders.maize.uniformity smith.wheat.uniformity data made available special help staff Rothamsted Research Library. Murray, E. K. S. (1934). value uniformity trial field experimentation rubber. Journal Agricultural Science, 24(2), 177-184. https://doi.org/10.1017/s0021859600006572 Reviewed. used. 2 years 5x5 grid rubber trees. Based data archived Rothamsted, used time.","code":""},{"path":"/notes_agridat.html","id":"folder-1-genstat-data","dir":"","previous_headings":"","what":"Folder 1 Genstat data","title":"License note","text":"101 # 1925-1934 single rows, series E/F 102 # 1925-1934 single rows, series G 103_goulden_barley # done Grown Dominion, 1931 104_beckett_coconut # 1919-1928, 22 plots, nuts per plot. Cochran paper #17 105_panse_cotton # done (see also 509) 106_mckinstry_cotton # done Gatooma 480 plots 107_saunders_maize # done Maize, Potchefstroom 1929-30 108 # Field B2a, B5b 109_saunders_maize # done Maize, Potchefstroom 1928-1929 110_immer_sugarbeet # done U Minn 1931 111_evans_sugarcane # done Squared values 112_sayer_sugarcane # done Harpr Jhili 113_christidis_wheat # done 114_smith_wheat # done Fairfield Smith data","code":""},{"path":"/notes_agridat.html","id":"folder-2-data-received-since-publication","dir":"","previous_headings":"","what":"Folder 2 Data received since publication","title":"License note","text":"201 # cotton Wad Medani, Sudan 202 # Wad Medani, Cotton 24x8=192 203 # 3x20, 1935-1936, weight rotl 204 # Letter Parish Fisher, 205 # Oats 1923, Sudan Grass 1923, Wheat 1922 (10x26=260 plots) 206 # done hutchinson.cotton 207 # done Letter Finney Kilby 1943 208_ducker_groundnut # done Letter Killby Fisher, Ducker groundnut data 209 # 1938 Summerby Cochran, Oats, 1921 3x33, Wheat todo. Data. pdf. 210 # 1937 Letter Summerby Cochran, Cochran Summerby 211 # 1943 Letter Finney Wadley. Thanks data. 212 # 1934 Letter Wadley Finney. Ribes data. 213 # ignore China, 1x16 214 # omit - clove trees 600, 5 years, coordinates, East Africa 215 # omit - clove tree part 2 216 # omit - wireworm traps, plant trial 217 # LeClerg field expt 1937-1938 sugar beet U Minn 218 # LeClerg greenhouse 1933 219 # 1939 letter LeClerg Cochran, Narain Cochran, Cochran Westover, Eden Cochran","code":""},{"path":"/notes_agridat.html","id":"folder-3","dir":"","previous_headings":"","what":"Folder 3","title":"License note","text":"301_day_table # done data.","code":""},{"path":"/notes_agridat.html","id":"folder-4-uniformity-trials-1936-1938","dir":"","previous_headings":"","what":"Folder 4 Uniformity trials 1936-1938","title":"License note","text":"folder contains correspondence 401 402_pound 403 cheesman christidis collinson collison 404 405_day # Done. demandt vandyk 406 407 408 posthumus 409_konigsberger demandt trials 410_demandt $ done 411 hutchinson immer look parnell 412 413 kirk parnell macdonald 414 metzger parker pound 415 pound reynolds(cotton) richardson 416_saunders # done 417_saunders sayer swanson 418_swanson thompson 419 thompson notes 420 day westover wilcox","code":""},{"path":"/notes_agridat.html","id":"folder-5-uniformity-data","dir":"","previous_headings":"","what":"Folder 5 Uniformity data","title":"License note","text":"501_metzger # multi-year series 502_kansas 503 504_goulden_barley # done goulden.barley.uniformity 505_beckett_coconut # multi-year, Cochran #17 506 507_coffee # coffee 1934-1939, Cochran #18 508_mckinstry_cotton # done mckinstry.cotton.uniformity (correspondence, hand-written) 509_panse_cotton # done panse.cotton.uniformity (see also 109) 510_christidis_wheat # done christidis.wheat.uniformity 511_sayer_sugarcane # done sayer.sugarcane.uniformity 1932, 48 rows, 20 columns 513_hastings_oats_1911 # 514_immer_sugarbeet # done. 60 row, 10 col, 2nd year data 515_sugarcane # barbados sugarcane 516_macdonald_cotton # field B2a B5b. See folder 1, file 8 517_rothamsted 518_saunders_maize_28_29 # done potchefstroom 519_saunders_maize_29_30 # done potchefstroom","code":""},{"path":"/notes_agridat.html","id":"folder-6-ovs-heath-cotton-uniformity-1934-1935","dir":"","previous_headings":"","what":"Folder 6 OVS Heath cotton uniformity 1934-1935","title":"License note","text":"Decided use data. 2 cut dates. data dry matter highly variable. field notes bit cryptic suggest lack uniformity plants handled (cut morning, afternoon, etc). 601 7/1 pt 2, copy 1, row 1-84, col k-u 602 7/1 pt 2, copy 2, row 1-84, col k-u 603 4/2 pt 1, copy2, row 1-84, col -k 604 4/2 pt 1, copy1, row 1-84, col -k 605 4/2 pt 2, copy2, row 1-84, col l-v 606 7/1 pt 1, copy 2, row 1-84, col za-j 607 experiment details 608 7/1 pt 1, copy 1, row 1-84, col za-j 609 4/2 pt 2, copy1, row 1-84, col l-v","code":""},{"path":"/notes_agridat.html","id":"folder-7-yield-of-grain-per-foot-fairfield-smith","dir":"","previous_headings":"","what":"Folder 7 Yield of grain per foot, Fairfield smith","title":"License note","text":"701_smith_correspondence # done 702_smith_reference # done 703_smith_ears_copy_B # done 704_smith_grain_copy_B # done 705_smith_grain_copy_A # done 707_smith_ears_copy_A # done","code":""},{"path":"/notes_agridat.html","id":"folder-8-catalog-of-uniformity-data","dir":"","previous_headings":"","what":"Folder 8 Catalog of uniformity data","title":"License note","text":"801_cochran_notes_1 802_cochran_notes_2 803_cochran_notes_3 804_cochran_notes_4 800_cochran_notes_0 805_evans_sugarcane_letter # done evans.sugarcane.uniformity 806_evans_sugarcane_data # done evans.sugarcane.uniformity","code":""},{"path":"/notes_agridat.html","id":"folder-9-demandt-1931","dir":"","previous_headings":"","what":"Folder 9 Demandt 1931","title":"License note","text":"Decided use data. little contextual information data. 901_demandt_diagram 902_demandt_data","code":""},{"path":"/notes_agridat.html","id":"to-do","dir":"","previous_headings":"","what":"To do","title":"License note","text":"change theobald.covariate JAGS brms? Figure best way use jags JAGS code edwards.oats JAGS code lee.potatoblight JAGS code theobald.barley JAGS code besag.elbatan Note: R_MAX_NUM_DLLS=150 Rcmd check –run-dontrun release devtools::run_examples(run=FALSE, start=“butron.maize”) build_site(lazy=TRUE, run=TRUE) use Roxygen document data, complain data/*.txt files error messages like: Error: ‘uscrime’ exported object ‘namespace:agridat’ document lists notes data sources searched additional sources agricultural data. Although .md file, formatting best viewed plain text mode. Henry Wallace archive Univ Iowa Library http://www.lib.uiowa.edu/sc/location--hours/ Henry Wallace research papers http://aspace.lib.uiowa.edu/repositories/2/archival_objects/400608 500 Ear experiment. Might data “corn judge’s mind” paper http://aspace.lib.uiowa.edu/repositories/2/archival_objects/400615","code":""},{"path":"/notes_agridat.html","id":"wanted","dir":"","previous_headings":"","what":"wanted","title":"License note","text":"Yield-monitor data split-planter field Yield-monitor data strip trial. Jose Crossa papers http://repository.cimmyt.org/xmlui/handle/10883/1/browse?value=Crossa,%20J.&type=author Meta-r http://repository.cimmyt.org/xmlui/handle/10883/4130 Data http://repository.cimmyt.org/xmlui/handle/10883/4036 http://repository.cimmyt.org/xmlui/handle/10883/2976 http://repository.cimmyt.org/xmlui/handle/10883/1380 http://repository.cimmyt.org/xmlui/handle/10883/4128 http://repository.cimmyt.org/xmlui/handle/10883/4290 Review meta-analyses agronomy http://www6.versailles-grignon.inra.fr/agronomie/Meta-analysis--agronomy/References","code":""},{"path":"/notes_agridat.html","id":"papers","dir":"","previous_headings":"","what":"Papers","title":"License note","text":"Biplot/StdErr/ VanEeuwijk 1993 - Incorporating env info Biplot Bartkowiak GxE table GE folder: Crossa 1997","code":""},{"path":"/notes_agridat.html","id":"malosetti-2013","dir":"","previous_headings":"","what":"Malosetti 2013","title":"License note","text":"F2 data. Folder: GE http://www.frontiersin.org/Plant_Physiology/10.3389/fphys.2013.00044/abstract CC license:","code":""},{"path":"/notes_agridat.html","id":"perez","dir":"","previous_headings":"","what":"Perez","title":"License note","text":"Comparison Linear Non-parametric Regression Models Genome-Enabled Prediction Wheat https://www.scienceopen.com/document/vid/4017fb51-381c-4374-93aa-608423df4004;jsessionid=0TLjjSbaooSUk1y3JKd4nUeb.master:-app1-prd Data: http://www.g3journal.org/content/suppl/2012/12/05/2.12.1595.DC1 content CC license","code":""},{"path":"/notes_agridat.html","id":"extension","dir":"","previous_headings":"","what":"Extension","title":"License note","text":"https://plant-breeding-genomics.extension.org/plant-breeding--genomics-learning-lessons https://plant-breeding-genomics.extension.org/estimating-heritability--blups--traits-using-tomato-phenotypic-data/ https://plant-breeding-genomics.extension.org/genomic-relationships--gblup <— todo review https://plant-breeding-genomics.extension.org/rrblup-package--r--genomewide-selection/","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"/notes_agridat.html","id":"agronomy-journal","dir":"","previous_headings":"","what":"Agronomy Journal","title":"License note","text":"Skimmed Vol 1","code":""},{"path":"/notes_agridat.html","id":"the-american-statistician","dir":"","previous_headings":"","what":"The American Statistician","title":"License note","text":"Vol 1-13","code":""},{"path":"/notes_agridat.html","id":"biometrics-skimmed-1947-2006","dir":"","previous_headings":"","what":"Biometrics. Skimmed 1947-2006","title":"License note","text":"http://www.jstor.org/action/showPublication?journalCode=biometrics","code":""},{"path":"/notes_agridat.html","id":"the-empire-journal-of-experimental-agriculture","dir":"","previous_headings":"","what":"The Empire Journal of Experimental Agriculture","title":"License note","text":"http://archive.org Vol 3-5, 23-24 26","code":""},{"path":"/notes_agridat.html","id":"field-crops-research","dir":"","previous_headings":"","what":"Field Crops Research.","title":"License note","text":"http://www.sciencedirect.com/science/journal/03784290/157 Vol 1-40","code":""},{"path":"/notes_agridat.html","id":"iasri-newsletters","dir":"","previous_headings":"","what":"IASRI newsletters","title":"License note","text":"http://www.iasri.res./NewsLetters/nl.HTM","code":""},{"path":"/notes_agridat.html","id":"indian-journal-of-agricultural-science","dir":"","previous_headings":"","what":"Indian Journal of Agricultural Science","title":"License note","text":"","code":"Vol 1. Vol 2. Vol 3. https://archive.org/details/in.ernet.dli.2015.271738/page/n653/mode/2up 544 5 varieties, 2 blocks, 4 reps/block Vol 4. Vol 5. 579. agridat::bose.multi.uniformity Vol 6. https://archive.org/details/in.ernet.dli.2015.271737 34. 4-way factorial (3 gen, 5 date, 3 spacing, 3 pop) non-contiguous sub-plots. agridat::chakravertti.factorial 460. agridat::kulkarni.sorghum.uniformity 917. agridat::sayer.sugarcane.uniformity Vol 9. Vol 10. Vol 11. Vol 12. 240. Wheat uniformity trial. agridat::iyer.wheat.uniformity Vol 14. Vol 16. Vol 17. Vol 19."},{"path":"/notes_agridat.html","id":"jabes","dir":"","previous_headings":"","what":"JABES","title":"License note","text":"Vol 6.","code":""},{"path":"/notes_agridat.html","id":"journal-of-the-american-society-of-agronomy","dir":"","previous_headings":"","what":"Journal of the American Society of Agronomy","title":"License note","text":"Vol 23.","code":""},{"path":"/notes_agridat.html","id":"journal-of-the-indian-society-of-agricultural-statistics","dir":"","previous_headings":"","what":"Journal of the Indian Society of Agricultural Statistics","title":"License note","text":"http://www.isas.org./jsp/onlinejournal.jsp Skimmed: Vol 50-56","code":""},{"path":[]},{"path":"/notes_agridat.html","id":"jrssb-1940-1997","dir":"","previous_headings":"","what":"JRSSB 1940-1997","title":"License note","text":"http://www.jstor.org/action/showPublication?journalCode=jroyastatsocise4 Datasets 1998-2015 http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9868/homepage/seriesb_datasets.htm http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-985X/homepage/datasets_all_series.htm","code":""},{"path":"/notes_agridat.html","id":"jrssc-applied-statistics-datasets","dir":"","previous_headings":"","what":"JRSSC Applied Statistics datasets","title":"License note","text":"http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-985X/homepage/datasets_all_series.htm 1998-2015","code":""},{"path":"/notes_agridat.html","id":"tidsskrift-for-planteavl-1895-1992","dir":"","previous_headings":"","what":"Tidsskrift for Planteavl 1895-1992","title":"License note","text":"http://dca.au.dk/publikationer/historiske/planteavl/","code":""},{"path":[]},{"path":"/notes_agridat.html","id":"nebraska-agricultural-experiment-station-annual-report","dir":"","previous_headings":"","what":"Nebraska Agricultural Experiment Station Annual Report","title":"License note","text":"Vol 19-24, 1906-1911 https://books.google.com/books?id=HBlJAAAAMAAJ Vol 25, 1912 https://books.google.com/books?id=M-5BAQAAMAAJ","code":""},{"path":"/notes_agridat.html","id":"iowa-state-university-library-special-collections","dir":"","previous_headings":"","what":"Iowa State University Library Special Collections","title":"License note","text":"Helen Elizabeth Conners (1951). Field plot techniques sweet potatoes obtained uniformity trial data. Master’s Thesis. data given. Robert LeRoy Plaisted (1954). Field plot techniques estimating onion yields. Master’s Thesis. data given. Michael Holle. 1960. Plot technique field evaluation three characters lima bean. Master’s Thesis. data given. Howard Lewis Taylor (1951). effect plot shape experimental error. Master’s Thesis. data given. Used data corn uniformity trial, oats uniformity, data Fairfield Smith. Smaller experimental errors found long narrow plots.","code":""},{"path":[]},{"path":"/notes_agridat.html","id":"jack-weiss","dir":"","previous_headings":"","what":"Jack Weiss","title":"License note","text":"Ecol 563 Stat Meth Ecology http://www.unc.edu/courses/2010fall/ecol/563/001/ Env Studies 562 Stat Envt Science http://www.unc.edu/courses/2010spring/ecol/562/001/ Ecol 145 http://www.unc.edu/courses/2006spring/ecol/145/001/docs/lectures.htm","code":""},{"path":[]},{"path":"/notes_agridat.html","id":"applied-statistics-in-agriculture-conference","dir":"","previous_headings":"","what":"Applied Statistics in Agriculture Conference","title":"License note","text":"http://newprairiepress.org/agstatconference/ 1989-2014","code":""},{"path":"/notes_agridat.html","id":"computers-and-electronics-in-agriculture","dir":"","previous_headings":"","what":"Computers and Electronics in Agriculture.","title":"License note","text":"http://www.sciencedirect.com/science/journal/01681699/103 Vol 1-110 180-191","code":""},{"path":"/notes_agridat.html","id":"journal-of-agricultural-science","dir":"","previous_headings":"","what":"Journal of Agricultural Science","title":"License note","text":"https://www.cambridge.org/core/journals/journal--agricultural-science/-issues 1900-2016","code":""},{"path":"/notes_agridat.html","id":"experimental-agriculture","dir":"","previous_headings":"","what":"Experimental Agriculture","title":"License note","text":"https://www.cambridge.org/core/journals/experimental-agriculture 1965-2016","code":""},{"path":"/reference/aastveit.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Barley heights and environmental covariates in Norway — aastveit.barley","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"Average height 15 genotypes barley 9 years. Also 19 covariates 9 years.","code":""},{"path":"/reference/aastveit.barley.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"","code":"data(\"aastveit.barley.covs\") data(\"aastveit.barley.height\")"},{"path":"/reference/aastveit.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"'aastveit.barley.covs' dataframe 9 observations following 20 variables. year year R1 avg rainfall (mm/day) period 1 R2 avg rainfall (mm/day) period 2 R3 avg rainfall (mm/day) period 3 R4 avg rainfall (mm/day) period 4 R5 avg rainfall (mm/day) period 5 R6 avg rainfall (mm/day) period 6 S1 daily solar radiation (ca/cm^2) period 1 S2 daily solar radiation (ca/cm^2) period 2 S3 daily solar radiation (ca/cm^2) period 3 S4 daily solar radiation (ca/cm^2) period 4 S5 daily solar radiation (ca/cm^2) period 5 S6 daily solar radiation (ca/cm^2) period 6 ST sowing time, measured days April 1 T1 avg temp (deg Celsius) period 1 T2 avg temp (deg Celsius) period 2 T3 avg temp (deg Celsius) period 3 T4 avg temp (deg Celsius) period 4 T5 avg temp (deg Celsius) period 5 T6 avg temp (deg Celsius) period 6 'aastveit.barley.height' dataframe 135 observations following 3 variables. year year, 9 years spanning 1974 1982 gen genotype, 15 levels height height (cm)","code":""},{"path":"/reference/aastveit.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"Experiments conducted , Norway. height dataframe contains average plant height (cm) 15 varieties barley 9 years. growth season year divided eight periods sowing harvest. plant stop growing 20 days ear emergence, first 6 periods included . Used permission Harald Martens.","code":""},{"path":"/reference/aastveit.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"Aastveit, . H. Martens, H. (1986). ANOVA interactions interpreted partial least squares regression. Biometrics, 42, 829–844. https://doi.org/10.2307/2530697","code":""},{"path":"/reference/aastveit.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"J. Chadoeuf J. B. Denis (1991). Asymptotic variances multiplicative interaction model. J. App. Stat., 18, 331-353. https://doi.org/10.1080/02664769100000032","code":""},{"path":"/reference/aastveit.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(\"aastveit.barley.covs\") data(\"aastveit.barley.height\") libs(reshape2, pls) # First, PCA of each matrix separately Z <- acast(aastveit.barley.height, year ~ gen, value.var=\"height\") Z <- sweep(Z, 1, rowMeans(Z)) Z <- sweep(Z, 2, colMeans(Z)) # Double-centered sum(Z^2)*4 # Total SS = 10165 sv <- svd(Z)$d round(100 * sv^2/sum(sv^2),1) # Prop of variance each axis # Aastveit Figure 1. PCA of height biplot(prcomp(Z), main=\"aastveit.barley - height\", cex=0.5) U <- aastveit.barley.covs rownames(U) <- U$year U$year <- NULL U <- scale(U) # Standardized covariates sv <- svd(U)$d # Proportion of variance on each axis round(100 * sv^2/sum(sv^2),1) # Now, PLS relating the two matrices m1 <- plsr(Z~U) loadings(m1) # Aastveit Fig 2a (genotypes), but rotated differently biplot(m1, which=\"y\", var.axes=TRUE) # Fig 2b, 2c (not rotated) biplot(m1, which=\"x\", var.axes=TRUE) # Adapted from section 7.4 of Turner & Firth, # \"Generalized nonlinear models in R: An overview of the gnm package\" # who in turn reproduce the analysis of Chadoeuf & Denis (1991), # \"Asymptotic variances for the multiplicative interaction model\" libs(gnm) dath <- aastveit.barley.height dath$year = factor(dath$year) set.seed(42) m2 <- gnm(height ~ year + gen + Mult(year, gen), data = dath) # Turner: \"To obtain parameterization of equation 1, in which sig_k is the # singular value for component k, the row and column scores must be constrained # so that the scores sum to zero and the squared scores sum to one. # These contrasts can be obtained using getContrasts\" gamma <- getContrasts(m2, pickCoef(m2, \"[.]y\"), ref = \"mean\", scaleWeights = \"unit\") delta <- getContrasts(m2, pickCoef(m2, \"[.]g\"), ref = \"mean\", scaleWeights = \"unit\") # estimate & std err gamma <- gamma$qvframe delta <- delta$qvframe # change sign of estimate gamma[,1] <- -1 * gamma[,1] delta[,1] <- -1 * delta[,1] # conf limits based on asymptotic normality, Chadoeuf table 8, p. 350, round(cbind(gamma[,1], gamma[, 1] + outer(gamma[, 2], c(-1.96, 1.96))) ,3) round(cbind(delta[,1], delta[, 1] + outer(delta[, 2], c(-1.96, 1.96))) ,3) } # }"},{"path":"/reference/acorsi.grayleafspot.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"Multi-environment trial evaluating 36 maize genotypes 9 locations","code":""},{"path":"/reference/acorsi.grayleafspot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"","code":"data(\"acorsi.grayleafspot\")"},{"path":"/reference/acorsi.grayleafspot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"data frame 324 observations following 3 variables. gen genotype, 36 levels env environment, 9 levels rep replicate, 2 levels y grey leaf spot severity","code":""},{"path":"/reference/acorsi.grayleafspot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"Experiments conducted 9 environments Brazil 2010-11. location RCB 2 reps. response variable percentage leaf area affected gray leaf spot within experimental unit (plot). Acorsi et al. use data illustrate fitting generalized AMMI model non-normal data.","code":""},{"path":"/reference/acorsi.grayleafspot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"C. R. L. Acorsi, T. . Guedes, M. M. D. Coan, R. J. B. Pinto, C. . Scapim, C. . P. Pacheco, P. E. O. Guimaraes, C. R. Casela. (2016). Applying generalized additive main effects multiplicative interaction model analysis maize genotypes resistant grey leaf spot. Journal Agricultural Science. https://doi.org/10.1017/S0021859616001015 Electronic data R code kindly provided Marlon Coan.","code":""},{"path":"/reference/acorsi.grayleafspot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"None","code":""},{"path":"/reference/acorsi.grayleafspot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(acorsi.grayleafspot) dat <- acorsi.grayleafspot # Acorsi figure 2. Note: Acorsi used cell means op <- par(mfrow=c(2,1), mar=c(5,4,3,2)) libs(lattice) boxplot(y ~ env, dat, las=2, xlab=\"environment\", ylab=\"GLS severity\") title(\"acorsi.grayleafspot\") boxplot(y ~ gen, dat, las=2, xlab=\"genotype\", ylab=\"GLS severity\") par(op) # GLM models # glm main-effects model with logit u(1-u) and wedderburn u^2(1-u)^2 # variance functions # glm1 <- glm(y~ env/rep + gen + env, data=dat, family=quasibinomial) # glm2 <- glm(y~ env/rep + gen + env, data=dat, family=wedderburn) # plot(glm2, which=1); plot(glm2, which=2) # GAMMI models of Acorsi. See also section 7.4 of Turner # \"Generalized nonlinear models in R: An overview of the gnm package\" # full gnm model with wedderburn, seems to work libs(gnm) set.seed(1) gnm1 <- gnm(y ~ env/rep + env + gen + instances(Mult(env,gen),2), data=dat, family=wedderburn, iterMax =800) deviance(gnm1) # 433.8548 # summary(gnm1) # anova(gnm1, test =\"F\") # anodev, Acorsi table 4 ## Df Deviance Resid. Df Resid. Dev F Pr(>F) ## NULL 647 3355.5 ## env 8 1045.09 639 2310.4 68.4696 < 2.2e-16 *** ## env:rep 9 12.33 630 2298.1 0.7183 0.6923 ## gen 35 1176.23 595 1121.9 17.6142 < 2.2e-16 *** ## Mult(env, gen, inst = 1) 42 375.94 553 745.9 4.6915 < 2.2e-16 *** ## Mult(env, gen, inst = 2) 40 312.06 513 433.9 4.0889 3.712e-14 *** # maybe better, start simple and build up the model gnm2a <- gnm(y ~ env/rep + env + gen, data=dat, family=wedderburn, iterMax =800) # add first interaction term res2a <- residSVD(gnm2a, env, gen, 2) gnm2b <- update(gnm2a, . ~ . + Mult(env,gen,inst=1), start = c(coef(gnm2a), res2a[, 1])) deviance(gnm2b) # 692.19 # add second interaction term res2b <- residSVD(gnm2b, env, gen, 2) gnm2c <- update(gnm2b, . ~ . + Mult(env,gen,inst=1) + Mult(env,gen,inst=2), start = c(coef(gnm2a), res2a[, 1], res2b[,1])) deviance(gnm2c) # 433.8548 # anova(gnm2c) # weird error message # note, to build the ammi biplot, use the first column of res2a to get # axis 1, and the FIRST column of res2b to get axis 2. Slightly confusing emat <- cbind(res2a[1:9, 1], res2b[1:9, 1]) rownames(emat) <- gsub(\"fac1.\", \"\", rownames(emat)) gmat <- cbind(res2a[10:45, 1], res2b[10:45, 1]) rownames(gmat) <- gsub(\"fac2.\", \"\", rownames(gmat)) # match Acorsi figure 4 biplot(gmat, emat, xlim=c(-2.2, 2.2), ylim=c(-2.2, 2.2), expand=2, cex=0.5, xlab=\"Axis 1\", ylab=\"Axis 2\", main=\"acorsi.grayleafspot - GAMMI biplot\") } # }"},{"path":"/reference/adugna.sorghum.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","title":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","text":"Multi-environment trial sorghum 3 locations across 5 years","code":""},{"path":"/reference/adugna.sorghum.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","text":"data frame 289 observations following 6 variables. gen genotype, 28 levels trial trial, 2 levels env environment, 13 levels yield yield kg/ha year year, 2001-2005 loc location, 3 levels","code":""},{"path":"/reference/adugna.sorghum.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","text":"Sorghum yields 3 locations across 5 years. trials carried three locations dry, hot lowlands Ethiopia: Melkassa (39 deg 21 min E, 8 deg 24 min N) Mieso (39 deg 22 min E, 8 deg 41 min N) Kobo (39 deg 37 min E, 12 deg 09 min N) Trial 1 14 hybrids one open-pollinated variety. Trial 2 12 experimental lines. Used permission Asfaw Adugna.","code":""},{"path":"/reference/adugna.sorghum.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","text":"Asfaw Adugna (2008). Assessment yield stability sorghum using univariate multivariate statistical approaches. Hereditas, 145, 28–37. https://doi.org/10.1111/j.0018-0661.2008.2023.x","code":""},{"path":"/reference/adugna.sorghum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(adugna.sorghum) dat <- adugna.sorghum libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(yield ~ env*gen, data=dat, main=\"adugna.sorghum gxe heatmap\", col.regions=redblue) # Genotype means match Adugna tapply(dat$yield, dat$gen, mean) # CV for each genotype. G1..G15 match, except for G2. # The table in Adugna scrambles the means for G16..G28 libs(reshape2) mat <- acast(dat, gen~env, value.var='yield') round(sqrt(apply(mat, 1, var, na.rm=TRUE)) / apply(mat, 1, mean, na.rm=TRUE) * 100,2) # Shukla stability. G1..G15 match Adugna. Can't match G16..G28. dat1 <- droplevels(subset(dat, trial==\"T1\")) mat1 <- acast(dat1, gen~env, value.var='yield') w <- mat1; k=15; n=8 # k=p gen, n=q env w <- sweep(w, 1, rowMeans(mat1, na.rm=TRUE)) w <- sweep(w, 2, colMeans(mat1, na.rm=TRUE)) w <- w + mean(mat1, na.rm=TRUE) w <- rowSums(w^2, na.rm=TRUE) sig2 <- k*w/((k-2)*(n-1)) - sum(w)/((k-1)*(k-2)*(n-1)) round(sig2/10000,1) # Genotypes in T1 are divided by 10000 } # }"},{"path":"/reference/agridat.html","id":null,"dir":"Reference","previous_headings":"","what":"Datasets from agricultural experiments — agridat","title":"Datasets from agricultural experiments — agridat","text":"package contains datasets publications relating agriculture, including field crops, tree crops, animal studies, others.","code":""},{"path":"/reference/agridat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Datasets from agricultural experiments — agridat","text":"use data, please cite agridat package original source data. Abbreviations '' column include: xy = coordinates, pls = partial least squares, rsm = response surface methodology, row-col = row-column design, ts = time series, Uniformity trials single genotype Yield monitor Animals Trees Field horticulture crops Time series Summaries: Diallel experiments: Factorial experiments: Multi-environment trials multi-genotype,loc,rep,year: Data markers: hadasch.lettuce.markers, steptoe.morex.geno Data pedigree: butron.maize","code":""},{"path":"/reference/agridat.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Datasets from agricultural experiments — agridat","text":"Kevin Wright, support many people granted permission include data package.","code":""},{"path":"/reference/agridat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Datasets from agricultural experiments — agridat","text":"J. White Frits van Evert. (2008). Publishing Agronomic Data. Agron J. 100, 1396-1400. https://doi.org/10.2134/agronj2008.0080F","code":""},{"path":"/reference/allcroft.lodging.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of cereal with lodging data — allcroft.lodging","title":"Multi-environment trial of cereal with lodging data — allcroft.lodging","text":"Percent lodging given 32 genotypes 7 environments.","code":""},{"path":"/reference/allcroft.lodging.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of cereal with lodging data — allcroft.lodging","text":"data frame 224 observations following 3 variables. env environment, 1-7 gen genotype, 1-32 y percent lodged","code":""},{"path":"/reference/allcroft.lodging.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of cereal with lodging data — allcroft.lodging","text":"data first year three-year study. Used permission Chris Glasbey.","code":""},{"path":"/reference/allcroft.lodging.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of cereal with lodging data — allcroft.lodging","text":"D. J. Allcroft C. . Glasbey, 2003. Analysis crop lodging using latent variable model. Journal Agricultural Science, 140, 383–393. https://doi.org/10.1017/S0021859603003332","code":""},{"path":"/reference/allcroft.lodging.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of cereal with lodging data — allcroft.lodging","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(allcroft.lodging) dat <- allcroft.lodging # Transformation dat$sy <- sqrt(dat$y) # Variety 4 has no lodging anywhere, so add a small amount dat[dat$env=='E5' & dat$gen=='G04',]$sy <- .01 libs(lattice) dotplot(env~y|gen, dat, as.table=TRUE, xlab=\"Percent lodged (by genotype)\", ylab=\"Variety\", main=\"allcroft.lodging\") # Tobit model libs(AER) m3 <- tobit(sy ~ 1 + gen + env, left=0, right=100, data=dat) # Table 2 trial/variety means preds <- expand.grid(gen=levels(dat$gen), env=levels(dat$env)) preds$pred <- predict(m3, newdata=preds) round(tapply(preds$pred, preds$gen, mean),2) round(tapply(preds$pred, preds$env, mean),2) } # }"},{"path":"/reference/alwan.lamb.html","id":null,"dir":"Reference","previous_headings":"","what":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"34 sheep sires, number lambs 5 foot shape classes.","code":""},{"path":"/reference/alwan.lamb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"","code":"data(\"alwan.lamb\")"},{"path":"/reference/alwan.lamb.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"data frame 340 observations following 11 variables. year numeric 1980/1981 breed breed PP, BRP, BR sex sex lamb M/F sire0 sire ID according Alwan shape sire ID according Gilmour count number lambs sire shape foot yr numeric contrast year b1 numeric contrast breeds b2 numeric contrast breeds b3 numeric contrast breeds","code":""},{"path":"/reference/alwan.lamb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"2513 lambs classified presence deformities feet. lambs represent offspring 34 sires, 5 strains, 2 years. variables yr, b1, b2, b3 numeric contrasts fixed effects defined paper Gilmour (1987) used SAS example. Gilmour explain reason particular contrasts. counts classes LF1, LF2, LF3 combined.","code":""},{"path":"/reference/alwan.lamb.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"Mohammed Alwan (1983). Studies flock mating performance Booroola merino crossbred ram lambs, foot conditions Booroola merino crossbreds Perendale sheep grazed hill country. Thesis, Massey University. https://hdl.handle.net/10179/5900 Appendix , II.","code":""},{"path":"/reference/alwan.lamb.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"Gilmour, Anderson, Rae (1987). Variance components underlying scale ordered multiple threshold categorical data using generalized linear mixed model. Journal Animal Breeding Genetics, 104, 149-155. https://doi.org/10.1111/j.1439-0388.1987.tb00117.x SAS/STAT(R) 9.2 Users Guide, Second Edition Example 38.11 Maximum Likelihood Proportional Odds Model Random Effects https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm","code":""},{"path":"/reference/alwan.lamb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(alwan.lamb) dat <- alwan.lamb # merge LF1 LF2 LF3 class counts, and combine M/F dat$shape <- as.character(dat$shape) dat$shape <- ifelse(dat$shape==\"LF2\", \"LF3\", dat$shape) dat$shape <- ifelse(dat$shape==\"LF1\", \"LF3\", dat$shape) dat <- aggregate(count ~ year+breed+sire0+sire+shape+yr+b1+b2+b3, dat, FUN=sum) dat <- transform(dat, year=factor(year), breed=factor(breed), sire0=factor(sire0), sire=factor(sire)) # LF5 or LF3 first is a bit arbitary...affects the sign of the coefficients dat <- transform(dat, shape=ordered(shape, levels=c(\"LF5\",\"LF4\",\"LF3\"))) # View counts by year and breed libs(latticeExtra) dat2 <- aggregate(count ~ year+breed+shape, dat, FUN=sum) useOuterStrips(barchart(count ~ shape|year*breed, data=dat2, main=\"alwan.lamb\")) # Model used by Gilmour and SAS dat <- subset(dat, count > 0) libs(ordinal) m1 <- clmm(shape ~ yr + b1 + b2 + b3 + (1|sire), data=dat, weights=count, link=\"probit\", Hess=TRUE) summary(m1) # Very similar to Gilmour results ordinal::ranef(m1) # sign is opposite of SAS ## SAS var of sires .04849 ## Effect Shape Estimate Standard Error DF t Value Pr > |t| ## Intercept 1 0.3781 0.04907 29 7.71 <.0001 ## Intercept 2 1.6435 0.05930 29 27.72 <.0001 ## yr 0.1422 0.04834 2478 2.94 0.0033 ## b1 0.3781 0.07154 2478 5.28 <.0001 ## b2 0.3157 0.09709 2478 3.25 0.0012 ## b3 -0.09887 0.06508 2478 -1.52 0.1289 ## Gilmour results for probit analysis ## Int1 .370 +/- .052 ## Int2 1.603 +/- .061 ## Year -.139 +/- .052 ## B1 -.370 +/- .076 ## B2 -.304 +/- .103 ## B3 .098 +/- .070 # Plot random sire effects with intervals, similar to SAS example plot.random <- function(model, random.effect, ylim=NULL, xlab=\"\", main=\"\") { tab <- ordinal::ranef(model)[[random.effect]] tab <- data.frame(lab=rownames(tab), est=tab$\"(Intercept)\") tab <- transform(tab, lo = est - 1.96 * sqrt(model$condVar), hi = est + 1.96 * sqrt(model$condVar)) # sort by est, and return index ix <- order(tab$est) tab <- tab[ix,] if(is.null(ylim)) ylim <- range(c(tab$lo, tab$hi)) n <- nrow(tab) plot(1:n, tab$est, axes=FALSE, ylim=ylim, xlab=xlab, ylab=\"effect\", main=main, type=\"n\") text(1:n, tab$est, labels=substring(tab$lab,2) , cex=.75) axis(1) axis(2) segments(1:n, tab$lo, 1:n, tab$hi, col=\"gray30\") abline(h=c(-.5, -.25, 0, .25, .5), col=\"gray\") return(ix) } ix <- plot.random(m1, \"sire\") # foot-shape proportions for each sire, sorted by estimated sire effects # positive sire effects tend to have lower proportion of lambs in LF4 and LF5 tab <- prop.table(xtabs(count ~ sire+shape, dat), margin=1) tab <- tab[ix,] tab <- tab[nrow(tab):1,] # reverse the order lattice::barchart(tab, horizontal=FALSE, auto.key=TRUE, main=\"alwan.lamb\", xlab=\"Sire\", ylab=\"Proportion of lambs\", scales=list(x=list(rot=70)), par.settings = simpleTheme(col=c(\"yellow\",\"orange\",\"red\")) ) detach(\"package:ordinal\") # to avoid VarCorr clash with lme4 } # }"},{"path":"/reference/ansari.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — ansari.wheat.uniformity","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"Uniformity trial wheat India 1940.","code":""},{"path":"/reference/ansari.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"","code":"data(\"ansari.wheat.uniformity\")"},{"path":"/reference/ansari.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"data frame 768 observations following 3 variables. row row col column yield yield grain per plot, half-ounces","code":""},{"path":"/reference/ansari.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"experiment conducted Government Research Farm, Raya (Muttra District), rainy season 1939-40. \"Wheat sown area 180 ft. x 243 ft. 324 rows field average fertility. wheat 1938-39 rabi fallow 1939-40 kharif. seed sown behind desi plough rows 9 inches apart, length row 180 feet\". \"time harvest, 18 rows sides 10 feet end field discarded eliminate border effects area 160 feet x 216 feet 288 rows harvested small units, 2 feet 3 inches broad three rows 20 feet long. 96 units across rows eight units along rows. total number unit plots thus obtained 768. yield grain unit plot weighed recorded separately given appendix.\" Field width: 96 plots * 2.25 feet = 216 feet. Field length: 8 plots * 20 feet = 160 feet. Comment: seems strong cyclical patern fertility gradient. \"History field reveals explanation phenomenon, average field usually found farm selected trial.\"","code":""},{"path":"/reference/ansari.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"Ansari, M. . ., G. K. Sant (1943). Study Soil Heterogeneity Relation Size Shape Plots Wheat Field Raya (Muhra District). Ind. J. Agr. Sci, 13, 652-658. https://archive.org/details/.ernet.dli.2015.271748","code":""},{"path":"/reference/ansari.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"None","code":""},{"path":"/reference/ansari.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ansari.wheat.uniformity) dat <- ansari.wheat.uniformity # match Ansari figure 3 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=216/160, # true aspect main=\"ansari.wheat.uniformity\") } # }"},{"path":"/reference/arankacami.groundnut.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"Uniformity trial groundnut","code":""},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"","code":"data(\"arankacami.groundnut.uniformity\")"},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"data frame 96 observations following 3 variables. row row col column yield yield, kg/plot","code":""},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"year experiment unknown, 1995. Basic plot size 0.75 m (rows) x 4 m (columns).","code":""},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"Ira Arankacami, R. Rangaswamy. (1995). Text Book Agricultural Statistics. New Age International Publishers. Table 19.1. Page 237. https://www.google.com/books/edition/A_Text_Book_of_Agricultural_Statistics/QDLWE4oakSgC","code":""},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"None","code":""},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(arankacami.groundnut.uniformity) dat <- arankacami.groundnut.uniformity require(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(12*.75)/(8*4), main=\"arankacami.groundnut.uniformity\") } # }"},{"path":"/reference/archbold.apple.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-split plot experiment of apple trees — archbold.apple","title":"Split-split plot experiment of apple trees — archbold.apple","text":"Split-split plot experiment apple trees different spacing, root stock, cultivars.","code":""},{"path":"/reference/archbold.apple.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-split plot experiment of apple trees — archbold.apple","text":"data frame 120 observations following 10 variables. rep block, 5 levels row row pos position within row spacing spacing trees, 6,10,14 feet stock rootstock, 4 levels gen genotype, 2 levels yield yield total, kg/tree 1975-1979 trt treatment code","code":""},{"path":"/reference/archbold.apple.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-split plot experiment of apple trees — archbold.apple","text":"rep 1, 10-foot-spacing main plot split two non-contiguous pieces. also happened rep 4. analysis Cornelius Archbold, consider row x within-row-spacing distinct main plot. (Also true 14-foot row-spacing, even though 14-foot spacing plots contiguous.) treatment code defined 100 * spacing + 10 * stock + gen, stock=0,1,6,7 Seedling,MM111,MM106,M0007 gen=1,2 Redspur,Golden, respectively.","code":""},{"path":"/reference/archbold.apple.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-split plot experiment of apple trees — archbold.apple","text":"D Archbold G. R. Brown P. L. Cornelius. (1987). Rootstock -row spacing effects growth yield spur-type delicious Golden delicious apple. Journal American Society Horticultural Science, 112, 219-222.","code":""},{"path":"/reference/archbold.apple.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-split plot experiment of apple trees — archbold.apple","text":"Cornelius, PL Archbold, DD, 1989. Analysis split-split plot experiment missing data using mixed model equations. Applications Mixed Models Agriculture Related Disciplines. Pages 55-79.","code":""},{"path":"/reference/archbold.apple.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-split plot experiment of apple trees — archbold.apple","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(archbold.apple) dat <- archbold.apple # Define main plot and subplot dat <- transform(dat, rep=factor(rep), spacing=factor(spacing), trt=factor(trt), mp = factor(paste(row,spacing,sep=\"\")), sp = factor(paste(row,spacing,stock,sep=\"\"))) # Due to 'spacing', the plots are different sizes, but the following layout # shows the relative position of the plots and treatments. Note that the # 'spacing' treatments are not contiguous in some reps. libs(desplot) desplot(dat, spacing~row*pos, col=stock, cex=1, num=gen, # aspect unknown main=\"archbold.apple\") libs(lme4, lucid) m1 <- lmer(yield ~ -1 + trt + (1|rep/mp/sp), dat) vc(m1) # Variances/means on Cornelius, page 59 ## grp var1 var2 vcov sdcor ## sp:(mp:rep) (Intercept)
193.3 13.9 ## mp:rep (Intercept) 203.8 14.28 ## rep (Intercept) 197.3 14.05 ## Residual 1015 31.86 } # }"},{"path":"/reference/ars.earlywhitecorn96.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","title":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","text":"Multi-environment trial early white food corn 60 white hybrids.","code":""},{"path":"/reference/ars.earlywhitecorn96.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","text":"data frame 540 observations following 9 variables. loc location, 9 levels gen gen, 60 levels yield yield, bu/ac stand stand, percent rootlodge root lodging, percent stalklodge stalk lodging, percent earht ear height, inches flower days flower moisture moisture, percent","code":""},{"path":"/reference/ars.earlywhitecorn96.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","text":"Data average 3 replications. Yields measured plot converted bushels / acre adjusted 15.5 percent moisture. Stand expressed percentage optimum plant stand. Lodging expressed percentage total plants hybrid. Ear height measured soil level top ear leaf collar. Heights expressed inches. Days flowering number days planting mid-tassel mid-silk. Moisture grain measured harvest.","code":""},{"path":"/reference/ars.earlywhitecorn96.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","text":"L. Darrah, R. Lundquist, D. West, C. Poneleit, B. Barry, B. Zehr, . Bockholt, L. Maddux, K. Ziegler, P. Martin. (1996). White Food Corn 1996 Performance Tests. Agricultural Research Service Special Report 502.","code":""},{"path":"/reference/ars.earlywhitecorn96.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ars.earlywhitecorn96) dat <- ars.earlywhitecorn96 libs(lattice) # These views emphasize differences between locations dotplot(gen~yield, dat, group=loc, auto.key=list(columns=3), main=\"ars.earlywhitecorn96\") ## dotplot(gen~stalklodge, dat, group=loc, auto.key=list(columns=3), ## main=\"ars.earlywhitecorn96\") splom(~dat[,3:9], group=dat$loc, auto.key=list(columns=3), main=\"ars.earlywhitecorn96\") # MANOVA m1 <- manova(cbind(yield,earht,moisture) ~ gen + loc, dat) m1 summary(m1) } # }"},{"path":"/reference/australia.soybean.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of soybean in Australia — australia.soybean","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"Yield traits 58 varieties soybeans, grown four locations across two years Australia. four-way data Year x Loc x Gen x Trait.","code":""},{"path":"/reference/australia.soybean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"data frame 464 observations following 10 variables. env environment, 8 levels, first character location last two characters year loc location year year gen genotype soybeans, 1-58 yield yield, metric tons / hectare height height (meters) lodging lodging size seed size, (millimeters) protein protein (percentage) oil oil (percentage)","code":""},{"path":"/reference/australia.soybean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"Measurement available four locations Queensland, Australia two consecutive years 1970, 1971. 58 different genotypes soybeans consisted 43 lines (40 local Australian selections cross, two parents, one used parent earlier trials) 15 lines 12 US. Lines 1-40 local Australian selections Mamloxi (CPI 172) Avoyelles (CPI 15939). Note data Basford Tukey book. values line 58 Nambour 1970 Redland Bay 1971 incorrectly listed page 477 20.490 15.070. 17.350 13.000, respectively. data set made available , values corrected. Used permission Kaye Basford, Pieter Kroonenberg.","code":""},{"path":"/reference/australia.soybean.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"Basford, K. E., Tukey, J. W. (1999). Graphical analysis multiresponse data illustrated plant breeding trial. Chapman Hall/CRC. Retrieved : https://three-mode.leidenuniv.nl/data/soybeaninf.htm","code":""},{"path":"/reference/australia.soybean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"K E Basford (1982). Use Multidimensional Scaling Analysing Multi-Attribute Genotype Response Across Environments, Aust J Agric Res, 33, 473–480. Kroonenberg, P. M., & Basford, K. E. B. (1989). investigation multi-attribute genotype response across environments using three-mode principal component analysis. Euphytica, 44, 109–123. Marcin Kozak (2010). Use parallel coordinate plots multi-response selection interesting genotypes. Communications Biometry Crop Science, 5, 83-95.","code":""},{"path":"/reference/australia.soybean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(australia.soybean) dat <- australia.soybean libs(reshape2) dm <- melt(dat, id.var=c('env', 'year','loc','gen')) # Joint plot of genotypes & traits. Similar to Figure 1 of Kroonenberg 1989 dmat <- acast(dm, gen~variable, fun=mean) dmat <- scale(dmat) biplot(princomp(dmat), main=\"australia.soybean trait x gen biplot\", cex=.75) # Figure 1 of Kozak 2010, lines 44-58 libs(reshape2, lattice, latticeExtra) data(australia.soybean) dat <- australia.soybean dat <- melt(dat, id.var=c('env', 'year','loc','gen')) dat <- acast(dat, gen~variable, fun=mean) dat <- scale(dat) dat <- as.data.frame(dat)[,c(2:6,1)] dat$gen <- rownames(dat) # data for the graphic by Kozak dat2 <- dat[44:58,] dat3 <- subset(dat2, is.element(gen, c(\"G48\",\"G49\",\"G50\",\"G51\"))) parallelplot( ~ dat3[,1:6]|dat3$gen, main=\"australia.soybean\", as.table=TRUE, horiz=FALSE) + parallelplot( ~ dat2[,1:6], horiz=FALSE, col=\"gray80\") + parallelplot( ~ dat3[,1:6]|dat3$gen, as.table=TRUE, horiz=FALSE, lwd=2) } # }"},{"path":"/reference/bachmaier.nitrogen.html","id":null,"dir":"Reference","previous_headings":"","what":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"Trial wheat nitrogen fertilizer two fertility zones","code":""},{"path":"/reference/bachmaier.nitrogen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"","code":"data(\"bachmaier.nitrogen\")"},{"path":"/reference/bachmaier.nitrogen.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"data frame 88 observations following 3 variables. nitro nitrogen fertilizer, kg/ha yield wheat yield, Mg/ha zone fertility zone","code":""},{"path":"/reference/bachmaier.nitrogen.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"Data wheat fertilizer experiment Germany two yield zones. zone, design RCB 4 blocks 11 nitrogen levels. yield plot measured. Electronic data originally downloaded http://www.tec.wzw.tum.de/bachmaier/vino.zip (longer available).","code":""},{"path":"/reference/bachmaier.nitrogen.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"Bachmaier, Martin. 2009. Confidence Set X-Coordinate Quadratic Regression Model Given Gradient. Statistical Papers 50: 649–60. https://doi.org/10.1007/s00362-007-0104-1.","code":""},{"path":"/reference/bachmaier.nitrogen.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"Bachmaier, Martin. Test confidence set difference x-coordinates vertices two quadratic regression models. Stat Papers (2010) 51:285–296, https://doi.org/10.1007/s00362-008-0159-7","code":""},{"path":"/reference/bachmaier.nitrogen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"","code":"library(agridat) data(bachmaier.nitrogen) dat <- bachmaier.nitrogen # Fit a quadratic model for the low-fertility zone dlow <- subset(dat, zone==\"low\") m1 <- lm(yield ~ nitro + I(nitro^2), dlow) # Slope of tangent line for economic optimum m <- .005454 # = (N 0.60 euro/kg) / (wheat 110 euro/Mg) # x-value of tangent point b1 <- coef(m1)[2] b2 <- coef(m1)[3] opt.bach <- (m-b1)/(2*b2) round(opt.bach, 0) #> nitro #> 199 # conf int for x value of tangent point round(vcovs <- vcov(m1), 7) #> (Intercept) nitro I(nitro^2) #> (Intercept) 0.1346512 -0.0016680 4.7e-06 #> nitro -0.0016680 0.0000295 -1.0e-07 #> I(nitro^2) 0.0000047 -0.0000001 0.0e+00 b1b1 <- vcovs[2,2] # estimated var of b1 b1b2 <- vcovs[2,3] # estimated cov of b1,b2 b2b2 <- vcovs[3,3] tval <- qt(1 - 0.05/2, nrow(dlow)-3) A <- b2^2 - b2b2 * tval^2 B <- (b1-m)*b2 - b1b2 * tval^2 C <- ((b1-m)^2 - b1b1 * tval^2)/4 D <- B^2 - 4*A*C x.lo <- -2*C / (B-sqrt(B^2-4*A*C)) x.hi <- (-B + sqrt(B^2-4*A*C))/(2*A) ci.bach <- c(x.lo, x.hi) round(ci.bach,0) # 95% CI 173,260 Matches Bachmaier #> nitro nitro #> 173 260 # Plot raw data, fitted quadratic, optimum, conf int plot(yield~nitro, dlow) p1 <- data.frame(nitro=seq(0,260, by=1)) p1$pred <- predict(m1, new=p1) lines(pred~nitro, p1) abline(v=opt.bach, col=\"blue\") abline(v=ci.bach, col=\"skyblue\") title(\"Economic optimum with 95 pct confidence interval\")"},{"path":"/reference/bailey.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"Uniformity trial cotton Egypt 1921-1923.","code":""},{"path":"/reference/bailey.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"","code":"data(\"bailey.cotton.uniformity\")"},{"path":"/reference/bailey.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"data frame 794 observations following 5 variables. row row ordinate col column ordinate yield yield, rotls year year loc location","code":""},{"path":"/reference/bailey.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"Two pickings taken. weights seeds cotton first second pickings totaled. Yields measured \"rotl\", \"order pound\". Layout Sakha Gemmeiza (page 9): Total area 4.86 feddans. bed 20 ridges 7 m , total dimension 15 m x 7 m. Add 1.5m irrigation channel. Center--center distances 15m x 8.5m. Charts 3 & 5 show yield \"Selected Average Plants\". data used . Chart 1: Sakha 1921, 8 x 20. Bed yield rotls. Length 20 ridges * .75 m = 15m. Width = 7m. Chart 2: Gemmeiza 1921, 8 x 20. Chart 3: Total S..P. yield grams. (used ) Chart 4: Gemmeiza 1922, 8 x 20. Chart 5: Total S..P. yield grams. (used ) Layout Giza (page 10) Beds 8 ridges 7 m , total dimension 6m x 7m. Add 1.5m irrigation channel. Center--center distance 6m x 8.5m Chart 6 - Giza 1921, 14 x 11 = 154 plots Chart 7 - Giza 1923, 20 x 8 = 160 plots Bailey said results Giza 1921 suitable reliability experiments. Data typed proofread KW 2023.01.11","code":""},{"path":"/reference/bailey.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"Bailey, M. ., Trought, T. (1926). account experiments carried determine experimental error field trials cotton Egypt. Egypt Ministry Agriculture, Technical Science Service Bulletin 63, Min. Agriculture Egypt Technical Science Bulletin 63. https://www.google.com/books/edition/Bulletin/xBQlAQAAIAAJ?pg=PA46-IA205","code":""},{"path":"/reference/bailey.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"None","code":""},{"path":"/reference/bailey.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bailey.cotton.uniformity) dat <- bailey.cotton.uniformity dat <- transform(dat, env=paste(year,loc)) # Data check. Matches Bailey 1926 Table 1. 28.13, , 46.02, 31.74, 13.52 libs(dplyr) # dat libs(desplot) desplot(dat, yield ~ col*row|env, main=\"bailey.cotton.uniformity\") # The yield scales are quite different at each loc, and the dimensions # are different, so plot each location separately. # Note: Bailey does not say if plots are 7x15 meters, or 15x7 meters. # The choices here seem most likely in our opinion. desplot(dat, yield ~ col*row, subset= env==\"1921 Sakha\", main=\"1921 Sakha\", aspect=(20*8.5)/(8*15)) desplot(dat, yield ~ col*row, subset= env==\"1921 Gemmeiza\", main=\"1921 Gemmeiza\", aspect=(20*8.5)/(8*15)) desplot(dat, yield ~ col*row, subset= env==\"1922 Gemmeiza\", main=\"1922 Gemmeiza\", aspect=(20*8.5)/(8*15)) desplot(dat, yield ~ col*row, subset= env==\"1921 Giza\", main=\"1921 Giza\", aspect=(11*6)/(14*8.5)) # 1923 Giza has alternately hi/lo yield rows. Not noticed by Bailey. desplot(dat, yield ~ col*row, subset= env==\"1923 Giza\", main=\"1923 Giza\", aspect=(20*6)/(8*8.5)) } # }"},{"path":"/reference/baker.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","title":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","text":"Uniformity trials barley Davis, California, 1925-1935, 10 years ground.","code":""},{"path":"/reference/baker.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","text":"data frame 570 observations following 4 variables. row row col column year year yield yield, pounds/acre","code":""},{"path":"/reference/baker.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","text":"Ten years uniformity trials sown ground. Baker (1952) shows map field, gravel subsoil extended upper right corner diagonally lower-center. part field lower yields 10-year average map. Plot 41 1928 missing. Field width: 19 plots = 827 ft Field length: 3 plots * 161 ft + 2 alleys * 15 feet = 513 ft","code":""},{"path":"/reference/baker.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","text":"Baker, GA Huberty, MR Veihmeyer, FJ. (1952) uniformity trial unirrigated barley ten years' duration. Agronomy Journal, 44, 267-270. https://doi.org/10.2134/agronj1952.00021962004400050011x","code":""},{"path":"/reference/baker.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(baker.barley.uniformity) dat <- baker.barley.uniformity # Ten-year average dat2 <- aggregate(yield ~ row*col, data=dat, FUN=mean, na.rm=TRUE) libs(desplot) desplot(dat, yield~col*row|year, aspect = 513/827, # true aspect main=\"baker.barley.uniformity - heatmaps by year\") desplot(dat2, yield~col*row, aspect = 513/827, # true aspect main=\"baker.barley.uniformity - heatmap of 10-year average\") # Note low yield in upper right, slanting to left a bit due to sandy soil # as shown in Baker figure 1. # Baker fig 2, stdev vs mean dat3 <- aggregate(yield ~ row*col, data=dat, FUN=sd, na.rm=TRUE) plot(dat2$yield, dat3$yield, xlab=\"Mean yield\", ylab=\"Std Dev yield\", main=\"baker.barley.uniformity\") # Baker table 4, correlation of plots across years # libs(reshape2) # mat <- acast(dat, row+col~year) # round(cor(mat, use='pair'),2) } # }"},{"path":"/reference/baker.strawberry.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of strawberry — baker.strawberry.uniformity","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"Uniformity trial strawberry","code":""},{"path":"/reference/baker.strawberry.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"","code":"data(\"baker.strawberry.uniformity\")"},{"path":"/reference/baker.strawberry.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"data frame 700 observations following 4 variables. trial Factor trial row row ordinate col column ordinate yield yield per plant/plot grams","code":""},{"path":"/reference/baker.strawberry.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"Trial T1: 200 plants grown two double-row beds Davis, California, 1946. rows 1 foot apart. beds 42 inches apart. plants 10 inches apart within row, row consisting 50 plants. Field length: 50 plants * 10 inches = 500 inches. Field width: 12 + 42 + 12 = 66 inches. layout experiment Table 1 shows 4 columns. 12 inches column 1 column 2, 42 inches, 12 inches column 3 column 4. data R package, added 3 right two columns index values indicate layout. (3.5, want integer). Trial T2: 500 plants grown single beds. beds 30 inches apart. bed 50 plants long 10 inches plants. Field length: 50 plants * 10 = 500 . Field width: 10 beds * 30 = 300 .","code":""},{"path":"/reference/baker.strawberry.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"G. . Baker R. E. Baker (1953). Strawberry Uniformity Yield Trials. Biometrics, 9, 412-421. https://doi.org/10.2307/3001713","code":""},{"path":"/reference/baker.strawberry.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"None","code":""},{"path":"/reference/baker.strawberry.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(baker.strawberry.uniformity) dat <- baker.strawberry.uniformity # Match mean and cv of Baker p 414. libs(dplyr) dat <- group_by(dat, trial) summarize(dat, mn=mean(yield), cv=sd(yield)/mean(yield)) libs(desplot) desplot(dat, yield ~ col*row, subset=trial==\"T1\", flip=TRUE, aspect=500/66, tick=TRUE, main=\"baker.strawberry.uniformity - trial T1\") desplot(dat, yield ~ col*row, subset=trial==\"T2\", flip=TRUE, aspect=500/300, tick=TRUE, main=\"baker.strawberry.uniformity - trial T2\") } # }"},{"path":"/reference/baker.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — baker.wheat.uniformity","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"Uniformity trial wheat","code":""},{"path":"/reference/baker.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"","code":"data(\"baker.wheat.uniformity\")"},{"path":"/reference/baker.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"data frame 225 observations following 3 variables. row row col col yield yield (grams)","code":""},{"path":"/reference/baker.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"Data collected 1939-1940. trial consists sixteen 40 ft. x 40 ft. blocks subdivided nine plots . data secured 1939-1940 White Federation wheat. design experiment square alleys 20 feet wide blocks. plots 10 feet long two guard rows side. Morning glories infested middle two columns blocks, uniformly blocks affected. data include missing values alleys field map approximately correct shape size. Field width: 4 blocks 40 feet + 3 alleys 20 feet = 220 feet. Field length: 4 blocks 40 feet + 3 alleys 20 feet = 220 feet.","code":""},{"path":"/reference/baker.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"G. . Baker, E. B. Roessler (1957). Implications uniformity trial small plots wheat. Hilgardia, 27, 183-188. https://hilgardia.ucanr.edu/Abstract/?=hilg.v27n05p183 https://doi.org/10.3733/hilg.v27n05p183","code":""},{"path":"/reference/baker.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"None","code":""},{"path":"/reference/baker.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(baker.wheat.uniformity) dat <- baker.wheat.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=1, main=\"baker.wheat.uniformity\") } # }"},{"path":"/reference/bancroft.peanut.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of peanuts — bancroft.peanut.uniformity","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"Uniformity trial peanuts Alabama, 1946.","code":""},{"path":"/reference/bancroft.peanut.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"","code":"data(\"bancroft.peanut.uniformity\")"},{"path":"/reference/bancroft.peanut.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"data frame 216 observations following 5 variables. row row col column yield yield, pounds per plot block block","code":""},{"path":"/reference/bancroft.peanut.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"data obtained two parts field, located Wiregrass Substation, Headland, Alabama, USA. part 18 rows, 3 feet wide, 100 feet long. Plots harvested 1946. Green weights pounds recorded. plot 16.66 linear feet row 3 feet width, 50 sq feet. Field width: 6 plots * 16.66 feet = 100 feet Field length: 18 plots * 3 feet = 54 feet Conclusions: Based relative efficiencies, increasing size plot along row better across row. Narrow, rectangular plots efficient.","code":""},{"path":"/reference/bancroft.peanut.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"Bancroft, T. . et a1., (1948). Size Shape Plots Distribution Plot Yield Field Experiments Peanuts. Alabama Agricultural Experiment Station Progress Report, sec. 39. Table 4, page 6. https://aurora.auburn.edu/bitstream/handle/11200/1345/0477PROG.pdf;sequence=1","code":""},{"path":"/reference/bancroft.peanut.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"None","code":""},{"path":"/reference/bancroft.peanut.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bancroft.peanut.uniformity) dat <- bancroft.peanut.uniformity # match means Bancroft page 3 ## dat ## # A tibble: 2 x 2 ## block mn ## ## 1 B1 2.46 ## 2 B2 2.05 libs(desplot) desplot(dat, yield ~ col*row|block, flip=TRUE, aspect=(18*3)/(6*16.66), # true aspect main=\"bancroft.peanut.uniformity\") } # }"},{"path":"/reference/barrero.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize in Texas. — barrero.maize","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"Multi-environment trial maize Texas.","code":""},{"path":"/reference/barrero.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"","code":"data(\"barrero.maize\")"},{"path":"/reference/barrero.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"data frame 14568 observations following 15 variables. year year testing, 2000-2010 yor year release, 2000-2010 loc location, 16 places Texas env environment (year+loc), 107 levels rep replicate, 1-4 gen genotype, 847 levels daystoflower numeric plantheight plant height, cm earheight ear height, cm population plants per hectare lodged percent plants lodged moisture moisture percent testweight test weight kg/ha yield yield, Mt/ha","code":""},{"path":"/reference/barrero.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"large (14500 records), multi-year, multi-location, 10-trait dataset Texas AgriLife Corn Performance Trials. data 2-row plots approximately 36in wide 25 feet long. Barrero et al. used data estimate genetic gain maize hybrids 10-year period time. Used permission Seth Murray.","code":""},{"path":"/reference/barrero.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"Barrero, Ivan D. et al. (2013). multi-environment trial analysis shows slight grain yield improvement Texas commercial maize. Field Crops Research, 149, Pages 167-176. https://doi.org/10.1016/j.fcr.2013.04.017","code":""},{"path":"/reference/barrero.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"None.","code":""},{"path":"/reference/barrero.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(barrero.maize) dat <- barrero.maize library(lattice) bwplot(yield ~ factor(year)|loc, dat, main=\"barrero.maize - Yield trends by loc\", scales=list(x=list(rot=90))) # Table 6 of Barrero. Model equation 1. if(require(\"asreml\", quietly=TRUE)){ libs(dplyr,lucid) dat <- arrange(dat, env) dat <- mutate(dat, yearf=factor(year), env=factor(env), loc=factor(loc), gen=factor(gen), rep=factor(rep)) m1 <- asreml(yield ~ loc + yearf + loc:yearf, data=dat, random = ~ gen + rep:loc:yearf + gen:yearf + gen:loc + gen:loc:yearf, residual = ~ dsum( ~ units|env), workspace=\"500mb\") # Variance components for yield match Barrero table 6. lucid::vc(m1)[1:5,] ## effect component std.error z.ratio bound ## rep:loc:yearf 0.111 0.01092 10 P 0 ## gen 0.505 0.03988 13 P 0 ## gen:yearf 0.05157 0.01472 3.5 P 0 ## gen:loc 0.02283 0.0152 1.5 P 0.2 ## gen:loc:yearf 0.2068 0.01806 11 P 0 summary(vc(m1)[6:112,\"component\"]) # Means match last row of table 6 ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.1286 0.3577 0.5571 0.8330 1.0322 2.9867 } } # }"},{"path":"/reference/batchelor.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"Uniformity trials apples, lemons, oranges, walnuts, California & Utah, 1915-1918.","code":""},{"path":"/reference/batchelor.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"dataset following format row row col column yield yield per tree pounds","code":""},{"path":"/reference/batchelor.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"trees affected disease eliminated yield replaced average eight surrounding trees. following details Batchelor (1918). Jonathan Apples \"apple records obtained 10-year old Jonathan apple orchard located Providence, Utah. surface soil orchard uniform appearances except extreme eastern edge, percentage gravel increases slightly. trees planted 16 feet apart, east west, 30 feet apart north south.\" Note: orientation field given paper, fields paper north top, assumed true field well. Yields may 1916. Field width: 8 trees * 16 feet = 128 feet Field length: 28 rows * 30 feet = 840 feet Eureka Lemon lemon (Citrus limonia) tree yields obtained grove 364 23-year-old trees, located Upland, California. records extend October 1, 1915, October 1, 1916. grove consists 14 rows 23-year-old trees, extending north south, 26 trees row, planted 24 24 feet apart. grove presents uniform appearance consideration [paper]. land practically level, soil apparently uniform texture. records show grouping several low-yielding trees; yet field observation gives one impression grove whole remarkably uniform. Field width: 14 trees * 24 feet = 336 feet Field length: 26 trees * 24 feet = 624 feet Navel 1 Arlington records 1915-16 yields one thousand 24-year-old navel-orange trees near Arlington station, Riverside, California. grove consists 20 rows trees north south, 50 trees row, planted 22 22 feet. study records shows certain distinct high- low-yielding areas. northeast corner south end contain notably high-yielding trees. north two-thirds west side contains large number low-yielding trees. areas apparently correlated soil variation. Variations tree tree also occur, cause evident. variations, present every orchard, bring uncertainty results offield experiments. Field width: 20 trees * 22 feet = 440 feet Field length: 50 trees * 22 feet = 1100 feet Navel 2 Antelope navel-orange grove later referred Antelope Heights navels plantation 480 ten-yearold trees planted 22 22 feet, located Naranjo, California. yields 1916. general appearance trees gives visual impression uniformity greater comparison individual tree production substantiates. Field width: 15 trees * 22 feet = 330 feet Field length: 33 trees * 22 feet = 726 feet Valencia Orange Valencia orange grove composed 240 15-year-old trees, planted 21 feet 6 inches 22 feet 6 inches, located Villa Park, California. yields obtained 1916. Field width: 12 rows * 22 feet = 264 feet Field length: 20 rows * 22 feet = 440 feet Walnut walnut (Juglans regia) yields obtained seasons 1915 1916 24-year-old Santa Barbara softshell seedling grove, located Whittier, California. [Note, yields appear 1915 yields.] planting laid 10 trees wide 32 trees long, entirely surrounded additional walnut plantings, except part one side adjacent orange grove. trees planted square system, 50 feet apart. Field width: 10 trees * 50 feet = 500 feet Field length: 32 trees * 50 feet = 1600 feet","code":""},{"path":"/reference/batchelor.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"L. D. Batchelor H. S. Reed. (1918). Relation variability yields fruit trees accuracy field trials. J. Agric. Res, 12, 245–283. https://books.google.com/books?id=Lil6AAAAMAAJ&lr&pg=PA245","code":""},{"path":"/reference/batchelor.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667","code":""},{"path":"/reference/batchelor.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(desplot) # Apple data(batchelor.apple.uniformity) desplot(batchelor.apple.uniformity, yield~col*row, aspect=840/128, tick=TRUE, # true aspect main=\"batchelor.apple.uniformity\") # Lemon data(batchelor.lemon.uniformity) desplot(batchelor.lemon.uniformity, yield~col*row, aspect=624/336, # true aspect main=\"batchelor.lemon.uniformity\") # Navel1 (Arlington) data(batchelor.navel1.uniformity) desplot(batchelor.navel1.uniformity, yield~col*row, aspect=1100/440, # true aspect main=\"batchelor.navel1.uniformity - Arlington\") # Navel2 (Antelope) data(batchelor.navel2.uniformity) desplot(batchelor.navel2.uniformity, yield~col*row, aspect=726/330, # true aspect main=\"batchelor.navel2.uniformity - Antelope\") # Valencia data(batchelor.valencia.uniformity) desplot(batchelor.valencia.uniformity, yield~col*row, aspect=440/264, # true aspect main=\"batchelor.valencia.uniformity\") # Walnut data(batchelor.walnut.uniformity) desplot(batchelor.walnut.uniformity, yield~col*row, aspect=1600/500, # true aspect main=\"batchelor.walnut.uniformity\") } # }"},{"path":"/reference/battese.survey.html","id":null,"dir":"Reference","previous_headings":"","what":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"Survey satellite data corn soy areas Iowa","code":""},{"path":"/reference/battese.survey.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"","code":"data(\"battese.survey\")"},{"path":"/reference/battese.survey.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"data frame 37 observations following 9 variables. county county name segment sample segment number (within county) countysegs number segments county cornhect hectares corn segment soyhect hectares soy cornpix pixels corn segment soypix pixels soy cornmean county mean corn pixels per segment soymean county mean soy pixels per segment","code":""},{"path":"/reference/battese.survey.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"data 12 counties north-central Iowa 1978. USDA determined area soybeans 37 area sampling units (called 'segments'). segment one square mile (259 hectares). number pixels classified corn soybeans came Landsat images obtained Aug/Sep 1978. pixel represents approximately 0.45 hectares. Data originally compiled USDA. data also available R packages: 'rsae::landsat' 'JoSAE::landsat'.","code":""},{"path":"/reference/battese.survey.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"Battese, George E Harter, Rachel M Fuller, Wayne . (1988). error-components model prediction county crop areas using survey satellite data. Journal American Statistical Association, 83, 28-36. https://doi.org/10.2307/2288915 Battese (1982) preprint version. https://www.une.edu.au/__data/assets/pdf_file/0017/15542/emetwp15.pdf","code":""},{"path":"/reference/battese.survey.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"Pushpal K Mukhopadhyay Allen McDowell. (2011). Small Area Estimation Survey Data Analysis Using SAS Software SAS Global Forum 2011.","code":""},{"path":"/reference/battese.survey.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(battese.survey) dat <- battese.survey # Battese fig 1 & 2. Corn plot shows outlier in Hardin county libs(lattice) dat <- dat[order(dat$cornpix),] xyplot(cornhect ~ cornpix, data=dat, group=county, type=c('p','l'), main=\"battese.survey\", xlab=\"Pixels of corn\", ylab=\"Hectares of corn\", auto.key=list(columns=3)) dat <- dat[order(dat$soypix),] xyplot(soyhect ~ soypix, data=dat, group=county, type=c('p','l'), main=\"battese.survey\", xlab=\"Pixels of soy\", ylab=\"Hectares of soy\", auto.key=list(columns=3)) libs(lme4, lucid) # Fit the models of Battese 1982, p.18. Results match m1 <- lmer(cornhect ~ 1 + cornpix + (1|county), data=dat) fixef(m1) ## (Intercept) cornpix ## 5.4661899 0.3878358 vc(m1) ## grp var1 var2 vcov sdcor ## county (Intercept) 62.83 7.926 ## Residual 290.4 17.04 m2 <- lmer(soyhect ~ 1 + soypix + (1|county), data=dat) fixef(m2) ## (Intercept) soypix ## -3.8223566 0.4756781 vc(m2) ## grp var1 var2 vcov sdcor ## county (Intercept) 239.2 15.47 ## Residual 180 13.42 # Predict for Humboldt county as in Battese 1982 table 2 5.4662+.3878*290.74 # 118.2152 # mu_i^0 5.4662+.3878*290.74+ -2.8744 # 115.3408 # mu_i^gamma (185.35+116.43)/2 # 150.89 # y_i bar # Survey regression estimator of Battese 1988 # Delete the outlier dat2 <- subset(dat, !(county==\"Hardin\" & soyhect < 30)) # Results match top-right of Battese 1988, p. 33 m3 <- lmer(cornhect ~ cornpix + soypix + (1|county), data=dat2) fixef(m3) ## (Intercept) cornpix soypix ## 51.0703979 0.3287217 -0.1345684 vc(m3) ## grp var1 var2 vcov sdcor ## county (Intercept) 140 11.83 ## Residual 147.3 12.14 m4 <- lmer(soyhect ~ cornpix + soypix + (1|county), data=dat2) fixef(m4) ## (Intercept) cornpix soypix ## -15.59027098 0.02717639 0.49439320 vc(m4) ## grp var1 var2 vcov sdcor ## county (Intercept) 247.5 15.73 ## Residual 190.5 13.8 } # }"},{"path":"/reference/beall.webworms.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"Counts webworms beet field, insecticide treatments.","code":""},{"path":"/reference/beall.webworms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"","code":"data(\"beall.webworms\")"},{"path":"/reference/beall.webworms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"data frame 1300 observations following 7 variables. row row col column y count webworms block block trt treatment spray spray treatment yes/lead lead treatment yes/","code":""},{"path":"/reference/beall.webworms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"beet webworm lays egg masses small 1 egg, seldom exceeding 5 eggs. larvae can move freely, usually mature plant hatch. plot contained 25 unit areas, 1 row 3 feet long. row width 22 inches. arrangement plots within blocks seems certain, arrangement blocks/treatments certain, since authors say \"since plots 5 units long 5 wide practicable combine groups 5 one direction \". Treatment 1 = None. Treatment 2 = Contact spray. Treatment 3 = Lead arsenate. Treatment 4 = spray, lead arsenate.","code":""},{"path":"/reference/beall.webworms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"Beall, Geoffrey (1940). fit significance contagious distributions applied observations larval insects. Ecology, 21, 460-474. Table 6. https://doi.org/10.2307/1930285","code":""},{"path":"/reference/beall.webworms.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"Michal Kosma et al. (2019). -dispersed count data crop agronomy research. Journal Agronomy Crop Science. https://doi.org/10.1111/jac.12333","code":""},{"path":"/reference/beall.webworms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(beall.webworms) dat <- beall.webworms # Match Beall table 1 # with(dat, table(y,trt)) libs(lattice) histogram(~y|trt, data=dat, layout=c(1,4), as.table=TRUE, main=\"beall.webworms\") # Visualize Beall table 6. Block effects may exist, but barely. libs(desplot) grays <- colorRampPalette(c(\"white\",\"#252525\")) desplot(dat, y ~ col*row, col.regions=grays(10), at=0:10-0.5, out1=block, out2=trt, num=trt, flip=TRUE, # aspect unknown main=\"beall.webworms (count of worms)\") # Following plot suggests interaction is needed # with(dat, interaction.plot(spray, lead, y)) # Try the models of Kosma et al, Table 1. # Poisson model m1 <- glm(y ~ block + spray*lead, data=dat, family=\"poisson\") logLik(m1) # -1497.719 (df=16) # Negative binomial model # libs(MASS) # m2 <- glm.nb(y ~ block + spray*lead, data=dat) # logLik(m2) # -1478.341 (df=17) # # Conway=Maxwell-Poisson model (takes several minutes) # libs(spaMM) # # estimate nu parameter # m3 <- fitme(y ~ block + spray*lead, data=dat, family = COMPoisson()) # logLik(m3) # -1475.999 # # Kosma logLik(m3)=-1717 seems too big. Typo? Different model? } # }"},{"path":"/reference/beaven.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"Yields 8 barley varieties 1913.","code":""},{"path":"/reference/beaven.barley.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"","code":"data(\"beaven.barley\")"},{"path":"/reference/beaven.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"data frame 160 observations following 4 variables. row row col column gen genotype yield yield (grams)","code":""},{"path":"/reference/beaven.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"Eight races barley grown regular pattern plots. data prepared Richey (1926) text cleaner. plot planted 40 inches side, middle square 36 inches side harvested. Field width: 32 plots * 3 feet = 96 feet Field length: 5 plots * 3 feet = 15 feet","code":""},{"path":"/reference/beaven.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"Student. (1923). testing varieties cereals. Biometrika, 271-293. https://doi.org/10.1093/biomet/15.3-4.271","code":""},{"path":"/reference/beaven.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"Frederick D. Richey (1926). moving average basis measuring correlated variation agronomic experiments. Jour. Agr. Research, 32, 1161-1175.","code":""},{"path":"/reference/beaven.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(beaven.barley) dat <- beaven.barley # Match the means shown in Richey table IV tapply(dat$yield, dat$gen, mean) ## a b c d e f g h ## 298.080 300.710 318.685 295.260 306.410 276.475 304.605 271.820 # Compare to Student 1923, diagram I,II libs(desplot) desplot(dat, yield ~ col*row, aspect=15/96, # true aspect main=\"beaven.barley - variety trial\", text=gen) } # }"},{"path":"/reference/becker.chicken.html","id":null,"dir":"Reference","previous_headings":"","what":"Mating crosses of chickens — becker.chicken","title":"Mating crosses of chickens — becker.chicken","text":"Mating crosses chickens","code":""},{"path":"/reference/becker.chicken.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mating crosses of chickens — becker.chicken","text":"","code":"data(\"becker.chicken\")"},{"path":"/reference/becker.chicken.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mating crosses of chickens — becker.chicken","text":"data frame 45 observations following 3 variables. male male parent female female parent weight weight (g) 8 weeks","code":""},{"path":"/reference/becker.chicken.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mating crosses of chickens — becker.chicken","text":"large flock White Rock chickens, five male sires chosen mated three female dams, producing 3 female progeny. data body weights eight weeks age. Becker (1984) used data demonstrate calculation heritability.","code":""},{"path":"/reference/becker.chicken.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mating crosses of chickens — becker.chicken","text":"Walter . Becker (1984). Manual Quantitative Genetics, 4th ed. Page 83.","code":""},{"path":"/reference/becker.chicken.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mating crosses of chickens — becker.chicken","text":"None","code":""},{"path":"/reference/becker.chicken.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mating crosses of chickens — becker.chicken","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(becker.chicken) dat <- becker.chicken libs(lattice) dotplot(weight ~ female, data=dat, group=male, main=\"becker.chicken - progeny weight by M*F\", xlab=\"female parent\",ylab=\"progeny weight\", auto.key=list(columns=5)) # Sums match Becker # sum(dat$weight) # aggregate(weight ~ male + female, dat, FUN=sum) # Variance components libs(lme4,lucid) m1 <- lmer(weight ~ (1|male) + (1|female), data=dat) # vc(m1) ## grp var1 var2 vcov sdcor ## 1 female (Intercept) 1096 33.1 ## 2 male (Intercept) 776.8 27.87 ## 3 Residual 5524 74.32 # Calculate heritabilities # s2m <- 776 # variability for males # s2f <- 1095 # variability for females # s2w <- 5524 # variability within crosses # vp <- s2m + s2f + s2w # 7395 # 4*s2m/vp # .42 male heritability #4*s2f/vp # .59 female heritability } # }"},{"path":"/reference/belamkar.augmented.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"Multi-environment trial wheat Nebraska Augmented design","code":""},{"path":"/reference/belamkar.augmented.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"","code":"data(\"belamkar.augmented\")"},{"path":"/reference/belamkar.augmented.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"data frame 2700 observations following 9 variables. loc location rep replicate iblock incomplete block gen_new new genotype (1=yes, 0=) gen_check check genotype (0=) gen genotype name col column ordinate row row ordinate yield yield, bu/ac","code":""},{"path":"/reference/belamkar.augmented.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"experiment 8 locations 270 new, experimental lines (genotypes) 3 check lines. 10 incomplete blocks location. 2 replicate blocks Alliance 1 block locations. plot 3 m long 1.2 m wide. electronic data found supplement S4 downloaded https://doi.org/10.25387/g3.6249410 license data CC-4.0.","code":""},{"path":"/reference/belamkar.augmented.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"Vikas Belamkar, Mary J. Guttieri, Waseem Hussain, Diego Jarquín, Ibrahim El-basyoni, Jesse Poland, Aaron J. Lorenz, P. Stephen Baenziger (2018). Genomic Selection Preliminary Yield Trials Winter Wheat Breeding Program. G3 Genes|Genomes|Genetics, 8, Pages 2735–2747. https://doi.org/10.1534/g3.118.200415","code":""},{"path":"/reference/belamkar.augmented.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"data appear ASRtriala package: https://vsni.co.uk/free-software/asrtriala","code":""},{"path":"/reference/belamkar.augmented.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(belamkar.augmented) dat <- belamkar.augmented libs(desplot) desplot(dat, yield ~ col*row|loc, out1=rep, out2=iblock) # Experiment design showing check placement dat$gen_check <- factor(dat$gen_check) desplot(dat, gen_check ~ col*row|loc, out1=rep, out2=iblock, main=\"belamkar.augmented\") # Belamkar supplement S3 has R code for analysis if(require(\"asreml\", quietly=TRUE)){ library(asreml) # AR1xAR1 model to calculate BLUEs for a single loc d1 <- droplevels(subset(dat, loc==\"Lincoln\")) d1$colf <- factor(d1$col) d1$rowf <- factor(d1$row) d1$gen <- factor(d1$gen) d1$gen_check <- factor(d1$gen_check) d1 <- d1[order(d1$col),] d1 <- as.data.frame(d1) m1 <- asreml(fixed=yield ~ gen_check, data=d1, random = ~ gen_new:gen, residual = ~ar1(colf):ar1v(rowf) ) p1 <- predict(m1, classify=\"gen\") head(p1$pvals) } } # }"},{"path":"/reference/besag.bayesian.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of spring barley in United Kingdom — besag.bayesian","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"RCB experiment spring barley United Kingdom","code":""},{"path":"/reference/besag.bayesian.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"data frame 225 observations following 4 variables. col column (also blocking factor) row row yield yield gen variety/genotype","code":""},{"path":"/reference/besag.bayesian.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"RCB design, column one rep. Used permission David Higdon.","code":""},{"path":"/reference/besag.bayesian.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"Besag, J. E., Green, P. J., Higdon, D. Mengersen, K. (1995). Bayesian computation stochastic systems. Statistical Science, 10, 3-66. https://www.jstor.org/stable/2246224","code":""},{"path":"/reference/besag.bayesian.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"Davison, . C. 2003. Statistical Models. Cambridge University Press. Pages 534-535.","code":""},{"path":"/reference/besag.bayesian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.bayesian) dat <- besag.bayesian # Yield values were scaled to unit variance # var(dat$yield, na.rm=TRUE) # .999 # Besag Fig 2. Reverse row numbers to match Besag, Davison dat$rrow <- 76 - dat$row libs(lattice) xyplot(yield ~ rrow|col, dat, layout=c(1,3), type='s', xlab=\"row\", ylab=\"yield\", main=\"besag.bayesian\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml, lucid) # Use asreml to fit a model with AR1 gradient in rows dat <- transform(dat, cf=factor(col), rf=factor(rrow)) m1 <- asreml(yield ~ -1 + gen, data=dat, random= ~ rf) m1 <- update(m1, random= ~ ar1v(rf)) m1 <- update(m1) m1 <- update(m1) m1 <- update(m1) lucid::vc(m1) # Visualize trends, similar to Besag figure 2. # Need 'as.vector' because asreml uses a named vector dat$res <- unname(m1$resid) dat$geneff <- coef(m1)$fixed[as.numeric(dat$gen)] dat <- transform(dat, fert=yield-geneff-res) libs(lattice) xyplot(geneff ~ rrow|col, dat, layout=c(1,3), type='s', main=\"besag.bayesian - Variety effects\", ylim=c(5,15 )) xyplot(fert ~ rrow|col, dat, layout=c(1,3), type='s', main=\"besag.bayesian - Fertility\", ylim=c(-2,2)) xyplot(res ~ rrow|col, dat, layout=c(1,3), type='s', main=\"besag.bayesian - Residuals\", ylim=c(-4,4)) } } # }"},{"path":"/reference/besag.beans.html","id":null,"dir":"Reference","previous_headings":"","what":"Competition experiment in beans with height measurements — besag.beans","title":"Competition experiment in beans with height measurements — besag.beans","text":"Competition experiment beans height measurements","code":""},{"path":"/reference/besag.beans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Competition experiment in beans with height measurements — besag.beans","text":"","code":"data(\"besag.beans\")"},{"path":"/reference/besag.beans.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Competition experiment in beans with height measurements — besag.beans","text":"data frame 152 observations following 6 variables. gen genotype / variety height plot height, cm yield plot yield, g row row / block rep replicate factor col column","code":""},{"path":"/reference/besag.beans.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Competition experiment in beans with height measurements — besag.beans","text":"Field beans regular height grown beside shorter varieties. block, variety occurred left-side neighbor right-side neighbor every variety (including ). Border plots placed ends block. block 38 adjacent plots. plot one row, 3 meters long 50 cm spacing rows. gaps plots. Spacing plants 6.7 cm. Four blocks (rows) used, six replicates. Plot yield height recorded. Kempton Lockwood used models adjusted yield according difference height neighboring plots. Field length: 4 plots * 3m = 12m Field width: 38 plots * 0.5 m = 19m","code":""},{"path":"/reference/besag.beans.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Competition experiment in beans with height measurements — besag.beans","text":"Julian Besag Rob Kempton (1986). Statistical Analysis Field Experiments Using Neighbouring Plots. Biometrics, 42, 231-251. Table 6. https://doi.org/10.2307/2531047","code":""},{"path":"/reference/besag.beans.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Competition experiment in beans with height measurements — besag.beans","text":"Kempton, RA Lockwood, G. (1984). Inter-plot competition variety trials field beans (Vicia faba L.). Journal Agricultural Science, 103, 293–302.","code":""},{"path":"/reference/besag.beans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Competition experiment in beans with height measurements — besag.beans","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.beans) dat = besag.beans libs(desplot) desplot(dat, yield ~ col*row, aspect=12/19, out1=row, out2=rep, num=gen, cex=1, # true aspect main=\"besag.beans\") libs(reshape2) # Add a covariate = excess height of neighbors mat <- acast(dat, row~col, value.var='height') mat2 <- matrix(NA, nrow=4, ncol=38) mat2[,2:37] <- (mat[,1:36] + mat[,3:38] - 2*mat[,2:37]) dat2 <- melt(mat2) colnames(dat2) <- c('row','col','cov') dat <- merge(dat, dat2) # Drop border plots dat <- subset(dat, rep != 'R0') libs(lattice) # Plot yield vs neighbors height advantage xyplot(yield~cov, data=dat, group=gen, main=\"besag.beans\", xlab=\"Mean excess heights of neighbor plots\", auto.key=list(columns=3)) # Trial mean. mean(dat$yield) # 391 matches Kempton table 3 # Mean excess height of neighbors for each genotype # tapply(dat$cov, dat$gen, mean)/2 # Matches Kempton table 4 # Variety means, matches Kempton table 4 mean yield m1 <- lm(yield ~ -1 + gen, dat) coef(m1) # Full model used by Kempton, eqn 5. Not perfectly clear. # Appears to include rep term, perhaps within block dat$blk <- factor(dat$row) dat$blkrep <- factor(paste(dat$blk, dat$rep)) m2 <- lm(yield ~ -1 + gen + blkrep + cov, data=dat) coef(m2) # slope 'cov' = -.72, while Kempton says -.79 } # }"},{"path":"/reference/besag.checks.html","id":null,"dir":"Reference","previous_headings":"","what":"Check variety yields in winter wheat. — besag.checks","title":"Check variety yields in winter wheat. — besag.checks","text":"Check variety yields winter wheat.","code":""},{"path":"/reference/besag.checks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check variety yields in winter wheat. — besag.checks","text":"","code":"data(\"besag.checks\")"},{"path":"/reference/besag.checks.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Check variety yields in winter wheat. — besag.checks","text":"data frame 364 observations following 4 variables. yield yield, units 10g row row col column gen genotype/variety","code":""},{"path":"/reference/besag.checks.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check variety yields in winter wheat. — besag.checks","text":"data used Besag show spatial variation field experiment, Besag use data analysis. Yields winter wheat varieties (Bounty Huntsman) Plant Breeding Institute, Cambridge, 1980. data 'checks' genotypes larger variety trial. column checks, five columns new varieties. Repeat. Plot dimensions approx 1.5 4.5 metres Field length: 52 rows * 4.5 m = 234 m Field width: 31 columns * 1.5 m = 46.5 Electronic version data supplied David Clifford.","code":""},{"path":"/reference/besag.checks.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Check variety yields in winter wheat. — besag.checks","text":"Besag, J.E. & Kempton R.. (1986). Statistical analysis field experiments using neighbouring plots. Biometrics, 42, 231-251. https://doi.org/10.2307/2531047","code":""},{"path":"/reference/besag.checks.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Check variety yields in winter wheat. — besag.checks","text":"Kempton, Statistical Methods Plant Variety Evaluation, page 91–92","code":""},{"path":"/reference/besag.checks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check variety yields in winter wheat. — besag.checks","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.checks) dat <- besag.checks libs(desplot) desplot(dat, yield~col*row, num=gen, aspect=234/46.5, # true aspect main=\"besag.checks\") } # }"},{"path":"/reference/besag.elbatan.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"RCB experiment wheat, 50 varieties 3 blocks strong spatial trend.","code":""},{"path":"/reference/besag.elbatan.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"data frame 150 observations following 4 variables. yield yield wheat gen genotype, factor 50 levels col column/block row row","code":""},{"path":"/reference/besag.elbatan.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"RCB experiment wheat El Batan, Mexico. three single-column replicates 50 varieties replicate. Plot dimensions given Besag. Data retrieved https://web.archive.org/web/19991008143232/www.stat.duke.edu/~higdon/trials/elbatan.dat Used permission David Higdon.","code":""},{"path":"/reference/besag.elbatan.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"Julian Besag D Higdon, 1999. Bayesian Analysis Agricultural Field Experiments, Journal Royal Statistical Society: Series B,61, 691–746. Table 1. https://doi.org/10.1111/1467-9868.00201","code":""},{"path":"/reference/besag.elbatan.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"Wilkinson 1984. Besag & Seheult 1989.","code":""},{"path":"/reference/besag.elbatan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.elbatan) dat <- besag.elbatan libs(desplot) desplot(dat, yield~col*row, num=gen, # aspect unknown main=\"besag.elbatan - wheat yields\") # Besag figure 1 library(lattice) xyplot(yield~row|col, dat, type=c('l'), layout=c(1,3), main=\"besag.elbatan wheat yields\") # RCB m1 <- lm(yield ~ 0 + gen + factor(col), dat) p1 <- coef(m1)[1:50] # Formerly used gam package, but as of R 3.1, Rcmd check --as-cran # is complaining # Calls: plot.gam ... model.matrix.gam -> predict -> predict.gam -> array # but it works perfectly in interactive mode !!! # Remove the FALSE to run the code below if(is.element(\"gam\", search())) detach(package:gam) libs(mgcv) m2 <- mgcv::gam(yield ~ -1 + gen + factor(col) + s(row), data=dat) plot(m2, residuals=TRUE, main=\"besag.elbatan\") pred <- cbind(dat, predict(m2, dat, type=\"terms\")) # Need to correct for the average loess effect, which is like # an overall intercept term. adjlo <- mean(pred$\"s(row)\") p2 <- coef(m2)[1:50] + adjlo # Compare estimates lims <- range(c(p1,p2)) plot(p1, p2, xlab=\"RCB prediction\", ylab=\"RCB with smooth trend (predicted)\", type='n', xlim=lims, ylim=lims, main=\"besag.elbatan\") text(p1, p2, 1:50, cex=.5) abline(0,1,col=\"gray\") } # }"},{"path":"/reference/besag.endive.html","id":null,"dir":"Reference","previous_headings":"","what":"Presence of footroot disease in an endive field — besag.endive","title":"Presence of footroot disease in an endive field — besag.endive","text":"Presence footroot disease endive field","code":""},{"path":"/reference/besag.endive.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Presence of footroot disease in an endive field — besag.endive","text":"data frame 2506 observations following 3 variables. col column row row disease plant diseased, Y=yes,N=","code":""},{"path":"/reference/besag.endive.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Presence of footroot disease in an endive field — besag.endive","text":"field endives, plant footrot, ? Data binary lattice 14 x 179 plants. Modeled autologistic distribution. assume endives single genotype. Besag (1978) may data taken 4 time points. data extracted Friel Pettitt. clear , , time point used. Friel give dimensions. Besag available.","code":""},{"path":"/reference/besag.endive.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Presence of footroot disease in an endive field — besag.endive","text":"J Besag (1978). Methods Statistical Analysis Spatial Data. Bulletin International Statistical Institute, 47, 77-92.","code":""},{"path":"/reference/besag.endive.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Presence of footroot disease in an endive field — besag.endive","text":"N Friel & . N Pettitt (2004). Likelihood Estimation Inference Autologistic Model. Journal Computational Graphical Statistics, 13:1, 232-246. https://doi.org/10.1198/1061860043029","code":""},{"path":"/reference/besag.endive.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Presence of footroot disease in an endive field — besag.endive","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.endive) dat <- besag.endive # Incidence map. Figure 2 of Friel and Pettitt libs(desplot) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) desplot(dat, disease~col*row, col.regions=grays(2), aspect = 0.5, # aspect unknown main=\"besag.endive - Disease incidence\") # Besag (2000) \"An Introduction to Markov Chain Monte Carlo\" suggested # that the autologistic model is not a very good fit for this data. # We try it anyway. No idea if this is correct or how to interpret... libs(ngspatial) A = adjacency.matrix(179,14) X = cbind(x=dat$col, y=dat$row) Z = as.numeric(dat$disease==\"Y\") m1 <- autologistic(Z ~ 0+X, A=A, control=list(confint=\"none\")) summary(m1) ## Coefficients: ## Estimate Lower Upper MCSE ## Xx -0.007824 NA NA NA ## Xy -0.144800 NA NA NA ## eta 0.806200 NA NA NA if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # Now try an AR1xAR1 model. dat2 <- transform(dat, xf=factor(col), yf=factor(row), pres=as.numeric(disease==\"Y\")) m2 <- asreml(pres ~ 1, data=dat2, resid = ~ar1(xf):ar1(yf)) # The 0/1 response is arbitrary, but there is some suggestion # of auto-correlation in the x (.17) and y (.10) directions, # suggesting the pattern is more 'patchy' than just random noise, # but is it meaningful? lucid::vc(m2) ## effect component std.error z.ratio bound ## xf:yf(R) 0.1301 0.003798 34 P 0 ## xf:yf!xf!cor 0.1699 0.01942 8.7 U 0 ## xf:yf!yf!cor 0.09842 0.02038 4.8 U 0 } } # }"},{"path":"/reference/besag.met.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn, incomplete-block design — besag.met","title":"Multi-environment trial of corn, incomplete-block design — besag.met","text":"Multi-environment trial corn, incomplete-block designlocation.","code":""},{"path":"/reference/besag.met.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn, incomplete-block design — besag.met","text":"data frame 1152 observations following 7 variables. county county row row col column rep rep block incomplete block yield yield gen genotype, 1-64","code":""},{"path":"/reference/besag.met.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn, incomplete-block design — besag.met","text":"Multi-environment trial 64 corn hybrids six counties North Carolina. location 3 replicates incomplete-block design 18x11 lattice plots whose length--width ratio 2:1. Note: original data, county 6 missing plots. data rows missing plot uses county/block/rep fill-row, sets genotype G01, sets yield missing. missing values added data asreml easily AR1xAR1 analysis using rectangular regions. location/panel : Field length: 18 rows * 2 units = 36 units. Field width: 11 plots * 1 unit = 11 units. Retrieved https://web.archive.org/web/19990505223413/www.stat.duke.edu/~higdon/trials/nc.dat Used permission David Higdon.","code":""},{"path":"/reference/besag.met.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn, incomplete-block design — besag.met","text":"Julian Besag D Higdon, 1999. Bayesian Analysis Agricultural Field Experiments, Journal Royal Statistical Society: Series B, 61, 691–746. Table 1. https://doi.org/10.1111/1467-9868.00201","code":""},{"path":"/reference/besag.met.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn, incomplete-block design — besag.met","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.met) dat <- besag.met libs(desplot) desplot(dat, yield ~ col*row|county, aspect=36/11, # true aspect out1=rep, out2=block, main=\"besag.met\") # Average reps datm <- aggregate(yield ~ county + gen, data=dat, FUN=mean) # Sections below fit heteroskedastic variance models (variance for each variety) # asreml takes 1 second, lme 73 seconds, SAS PROC MIXED 30 minutes # lme # libs(nlme) # m1l <- lme(yield ~ -1 + gen, data=datm, random=~1|county, # weights = varIdent(form=~ 1|gen)) # m1l$sigma^2 * c(1, coef(m1l$modelStruct$varStruct, unc = FALSE))^2 ## G02 G03 G04 G05 G06 G07 G08 ## 91.90 210.75 63.03 112.05 28.39 237.36 72.72 42.97 ## ... etc ... if(require(\"asreml\", quietly=TRUE)) { libs(asreml, lucid) # Average reps datm <- aggregate(yield ~ county + gen, data=dat, FUN=mean) # asreml Using 'rcov' ALWAYS requires sorting the data datm <- datm[order(datm$gen),] m1 <- asreml(yield ~ gen, data=datm, random = ~ county, residual = ~ dsum( ~ units|gen)) vc(m1)[1:7,] ## effect component std.error z.ratio bound ## county 1324 836.1 1.6 P 0.2 ## gen_G01!R 91.98 58.91 1.6 P 0.1 ## gen_G02!R 210.6 133.6 1.6 P 0.1 ## gen_G03!R 63.06 40.58 1.6 P 0.1 ## gen_G04!R 112.1 71.59 1.6 P 0.1 ## gen_G05!R 28.35 18.57 1.5 P 0.2 ## gen_G06!R 237.4 150.8 1.6 P 0 # We get the same results from asreml & lme # plot(m1$vparameters[-1], # m1l$sigma^2 * c(1, coef(m1l$modelStruct$varStruct, unc = FALSE))^2) # The following example shows how to construct a GxE biplot # from the FA2 model. dat <- besag.met dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$county, dat$xf, dat$yf), ] # First, AR1xAR1 m1 <- asreml(yield ~ county, data=dat, random = ~ gen:county, residual = ~ dsum( ~ ar1(xf):ar1(yf)|county)) # Add FA1 m2 <- update(m1, random=~gen:fa(county,1)) # rotate.FA=FALSE # FA2 m3 <- update(m2, random=~gen:fa(county,2)) asreml.options(extra=50) m3 <- update(m3, maxit=50) asreml.options(extra=0) # Use the loadings to make a biplot vars <- vc(m3) psi <- vars[grepl(\"!var$\", vars$effect), \"component\"] la1 <- vars[grepl(\"!fa1$\", vars$effect), \"component\"] la2 <- vars[grepl(\"!fa2$\", vars$effect), \"component\"] mat <- as.matrix(data.frame(psi, la1, la2)) # I tried using rotate.fa=FALSE, but it did not seem to # give orthogonal vectors. Rotate by hand. rot <- svd(mat[,-1])$v # rotation matrix lam <- mat[,-1] colnames(lam) <- c(\"load1\", \"load2\") co3 <- coef(m3)$random # Scores are the GxE coefficients ix1 <- grepl(\"_Comp1$\", rownames(co3)) ix2 <- grepl(\"_Comp2$\", rownames(co3)) sco <- matrix(c(co3[ix1], co3[ix2]), ncol=2, byrow=FALSE) sco <- sco dimnames(sco) <- list(levels(dat$gen) , c('load1','load2')) rownames(lam) <- levels(dat$county) sco[,1:2] <- -1 * sco[,1:2] lam[,1:2] <- -1 * lam[,1:2] biplot(sco, lam, cex=.5, main=\"FA2 coefficient biplot (asreml)\") # G variance matrix gvar <- lam # Now get predictions and make an ordinary biplot p3 <- predict(m3, data=dat, classify=\"county:gen\") p3 <- p3$pvals libs(\"gge\") bi3 <- gge(p3, predicted.value ~ gen*county, scale=FALSE) if(interactive()) dev.new() # Very similar to the coefficient biplot biplot(bi3, stand=FALSE, main=\"SVD biplot of FA2 predictions\") } } # }"},{"path":"/reference/besag.triticale.html","id":null,"dir":"Reference","previous_headings":"","what":"Four-way factorial agronomic experiment in triticale — besag.triticale","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"Four-way factorial agronomic experiment triticale","code":""},{"path":"/reference/besag.triticale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"","code":"data(\"besag.triticale\")"},{"path":"/reference/besag.triticale.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"data frame 54 observations following 7 variables. yield yield, g/m^2 row row col column gen genotype / variety, 3 levels rate seeding rate, kg/ha nitro nitrogen rate, kw/ha regulator growth regulator, 3 levels","code":""},{"path":"/reference/besag.triticale.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"Experiment conducted factorial yields triticale. Fully randomized. Plots 1.5m x 5.5m, orientation clear. Besag Kempton show accounting neighbors changes non-significant genotype differences significant differences.","code":""},{"path":"/reference/besag.triticale.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"Julian Besag Rob Kempton (1986). Statistical Analysis Field Experiments Using Neighbouring Plots. Biometrics, 42, 231-251. Table 2. https://doi.org/10.2307/2531047","code":""},{"path":"/reference/besag.triticale.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"None.","code":""},{"path":"/reference/besag.triticale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.triticale) dat <- besag.triticale dat <- transform(dat, rate=factor(rate), nitro=factor(nitro)) dat <- transform(dat, xf=factor(col), yf=factor(row)) libs(desplot) desplot(dat, yield ~ col*row, # aspect unknown main=\"besag.triticale\") # Besag & Kempton are not perfectly clear on the model, but # indicate that there was no evidence of any two-way interactions. # A reduced, main-effect model had genotype effects that were # \"close to significant\" at the five percent level. # The model below has p-value of gen at .04, so must be slightly # different than their model. m2 <- lm(yield ~ gen + rate + nitro + regulator + yf, data=dat) anova(m2) # Similar, but not exact, to Besag figure 5 dat$res <- resid(m2) libs(lattice) xyplot(res ~ col|as.character(row), data=dat, as.table=TRUE, type=\"s\", layout=c(1,3), main=\"besag.triticale\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml) # Besag uses an adjustment based on neighboring plots. # This analysis fits the standard AR1xAR1 residual model dat <- dat[order(dat$xf, dat$yf), ] m3 <- asreml(yield ~ gen + rate + nitro + regulator + gen:rate + gen:nitro + gen:regulator + rate:nitro + rate:regulator + nitro:regulator + yf, data=dat, resid = ~ ar1(xf):ar1(yf)) wald(m3) # Strongly significant gen, rate, regulator ## Df Sum of Sq Wald statistic Pr(Chisq) ## (Intercept) 1 1288255 103.971 < 2.2e-16 *** ## gen 2 903262 72.899 < 2.2e-16 *** ## rate 1 104774 8.456 0.003638 ** ## nitro 1 282 0.023 0.880139 ## regulator 2 231403 18.676 8.802e-05 *** ## yf 2 3788 0.306 0.858263 ## gen:rate 2 1364 0.110 0.946461 ## gen:nitro 2 30822 2.488 0.288289 ## gen:regulator 4 37269 3.008 0.556507 ## rate:nitro 1 1488 0.120 0.728954 ## rate:regulator 2 49296 3.979 0.136795 ## nitro:regulator 2 41019 3.311 0.191042 ## residual (MS) 12391 } } # }"},{"path":"/reference/blackman.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"Multi-environment trial wheat, conventional semi-dwarf varieties, 7 locs low/high fertilizer levels.","code":""},{"path":"/reference/blackman.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"data frame 168 observations following 5 variables. gen genotype loc location nitro nitrogen fertilizer, low/high yield yield (g/m^2) type type factor, conventional/semi-dwarf","code":""},{"path":"/reference/blackman.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"Conducted U.K. 1975. loc three reps, two nitrogen treatments. Locations Begbroke, Boxworth, Crafts Hill, Earith, Edinburgh, Fowlmere, Trumpington. two highest-yielding locations, Earith Edinburgh, yield _lower_ high-nitrogen treatment. Blackman et al. say \" seems probable effects development structure crop responsible reductions yield high nitrogen\".","code":""},{"path":"/reference/blackman.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"Blackman, JA Bingham, J. Davidson, JL (1978). Response semi-dwarf conventional winter wheat varieties application nitrogen fertilizer. Journal Agricultural Science, 90, 543–550. https://doi.org/10.1017/S0021859600056070","code":""},{"path":"/reference/blackman.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"Gower, J. Lubbe, S.G. Gardner, S. Le Roux, N. (2011). Understanding Biplots, Wiley.","code":""},{"path":"/reference/blackman.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(blackman.wheat) dat <- blackman.wheat libs(lattice) # Semi-dwarf generally higher yielding than conventional # bwplot(yield~type|loc,dat, main=\"blackman.wheat\") # Peculiar interaction--Ear/Edn locs have reverse nitro response dotplot(gen~yield|loc, dat, group=nitro, auto.key=TRUE, main=\"blackman.wheat: yield for low/high nitrogen\") # Height data from table 6 of Blackman. Height at Trumpington loc. # Shorter varieties have higher yields, greater response to nitro. heights <- data.frame(gen=c(\"Cap\", \"Dur\", \"Fun\", \"Hob\", \"Hun\", \"Kin\", \"Ran\", \"Spo\", \"T64\", \"T68\",\"T95\", \"Tem\"), ht=c(101,76,76,80,98,88,98,81,86,73,78,93)) dat$height <- heights$ht[match(dat$gen, heights$gen)] xyplot(yield~height|loc,dat,group=nitro,type=c('p','r'), main=\"blackman.wheat\", subset=loc==\"Tru\", auto.key=TRUE) libs(reshape2) # AMMI-style biplot Fig 6.4 of Gower 2011 dat$env <- factor(paste(dat$loc,dat$nitro,sep=\"-\")) datm <- acast(dat, gen~env, value.var='yield') datm <- sweep(datm, 1, rowMeans(datm)) datm <- sweep(datm, 2, colMeans(datm)) biplot(prcomp(datm), main=\"blackman.wheat AMMI-style biplot\") } # }"},{"path":"/reference/bliss.borers.html","id":null,"dir":"Reference","previous_headings":"","what":"Corn borer infestation under four treatments — bliss.borers","title":"Corn borer infestation under four treatments — bliss.borers","text":"Corn borer infestation four treatments","code":""},{"path":"/reference/bliss.borers.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Corn borer infestation under four treatments — bliss.borers","text":"data frame 48 observations following 3 variables. borers number borers per hill treat treatment factor freq frequency borer count","code":""},{"path":"/reference/bliss.borers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Corn borer infestation under four treatments — bliss.borers","text":"Four treatments control corn borers. Treatment 1 control. 15 blocks, treatment, 8 hills plants examined, number corn borers present recorded. data aggregated across blocks. Bliss mentions level infestation varied significantly blocks.","code":""},{"path":"/reference/bliss.borers.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Corn borer infestation under four treatments — bliss.borers","text":"C. Bliss R. . Fisher. (1953). Fitting Negative Binomial Distribution Biological Data. Biometrics, 9, 176–200. Table 3. https://doi.org/10.2307/3001850 Geoffrey Beall. 1940. Fit Significance Contagious Distributions Applied Observations Larval Insects. Ecology, 21, 460-474. Page 463. https://doi.org/10.2307/1930285","code":""},{"path":"/reference/bliss.borers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Corn borer infestation under four treatments — bliss.borers","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bliss.borers) dat <- bliss.borers # Add 0 frequencies dat0 <- expand.grid(borers=0:26, treat=c('T1','T2','T3','T4')) dat0 <- merge(dat0,dat, all=TRUE) dat0$freq[is.na(dat0$freq)] <- 0 # Expand to individual (non-aggregated) counts for each hill dd <- data.frame(borers = rep(dat0$borers, times=dat0$freq), treat = rep(dat0$treat, times=dat0$freq)) libs(lattice) histogram(~borers|treat, dd, type='count', breaks=0:27-.5, layout=c(1,4), main=\"bliss.borers\", xlab=\"Borers per hill\") libs(MASS) m1 <- glm.nb(borers~0+treat, data=dd) # Bliss, table 3, presents treatment means, which are matched by: exp(coef(m1)) # 4.033333 3.166667 1.483333 1.508333 # Bliss gives treatment values k = c(1.532,1.764,1.333,1.190). # The mean of these is 1.45, similar to this across-treatment estimate m1$theta # 1.47 # Plot observed and expected distributions for treatment 2 libs(latticeExtra) xx <- 0:26 yy <- dnbinom(0:26, mu=3.17, size=1.47)*120 # estimates are from glm.nb histogram(~borers, dd, type='count', subset=treat=='T2', main=\"bliss.borers - trt T2 observed and expected\", breaks=0:27-.5) + xyplot(yy~xx, col='navy', type='b') # \"Poissonness\"-type plot libs(vcd) dat2 <- droplevels(subset(dat, treat=='T2')) vcd::distplot(dat2$borers, type = \"nbinomial\", main=\"bliss.borers neg binomialness plot\") # Better way is a rootogram g1 <- vcd::goodfit(dat2$borers, \"nbinomial\") plot(g1, main=\"bliss.borers - Treatment 2\") } # }"},{"path":"/reference/bond.diallel.html","id":null,"dir":"Reference","previous_headings":"","what":"Diallel cross of winter beans — bond.diallel","title":"Diallel cross of winter beans — bond.diallel","text":"Diallel cross winter beans","code":""},{"path":"/reference/bond.diallel.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diallel cross of winter beans — bond.diallel","text":"data frame 36 observations following 3 variables. female female parent male male parent yield yield, grams/plot stems stems per plot nodes podded nodes per stem pods pods per podded node seeds seeds per pod weight weight (g) per 100 seeds height height (cm) April width width (cm) April flower mean flowering date May","code":""},{"path":"/reference/bond.diallel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diallel cross of winter beans — bond.diallel","text":"Yield grams/plot full diallel cross 6 inbred lines winter beans. Values means two years.","code":""},{"path":"/reference/bond.diallel.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Diallel cross of winter beans — bond.diallel","text":"D. . Bond (1966). Yield components yield diallel crosses inbred lines winter beans (Viciafaba). Journal Agricultural Science, 67, 325–336. https://doi.org/10.1017/S0021859600017329","code":""},{"path":"/reference/bond.diallel.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diallel cross of winter beans — bond.diallel","text":"Peter John, Statistical Design Analysis Experiments, p. 85.","code":""},{"path":"/reference/bond.diallel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diallel cross of winter beans — bond.diallel","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bond.diallel) dat <- bond.diallel # Because these data are means, we will not be able to reproduce # the anova table in Bond. More useful as a multivariate example. libs(corrgram) corrgram(dat[ , 3:11], main=\"bond.diallel\", lower=panel.pts) # Multivariate example from sommer package corrgram(dat[,c(\"stems\",\"pods\",\"seeds\")], lower=panel.pts, upper=panel.conf, main=\"bond.diallel\") libs(sommer) m1 <- mmer(cbind(stems,pods,seeds) ~ 1, random= ~ vs(female)+vs(male), rcov= ~ vs(units), dat) #### genetic variance covariance cov2cor(m1$sigma$`u:female`) cov2cor(m1$sigma$`u:male`) cov2cor(m1$sigma$`u:units`) } # }"},{"path":"/reference/bose.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"Uniformity trials barley, wheat, lentils India 1930-1932.","code":""},{"path":"/reference/bose.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"","code":"data(\"bose.multi.uniformity\")"},{"path":"/reference/bose.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"data frame 1170 observations following 5 variables. year year crop crop row row ordinate col column ordinate yield yield per plot grams","code":""},{"path":"/reference/bose.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"field 1/4 acre sown three consecutive years (beginning 1929-1930) barley, wheat, lentil. harvest, borders 3 feet east west 6 feet north south removed. field divided plots four feet square, harvested separately, measured grams. Fertility contours field somewhat similar across years, correlation values across years 0.45, 0.48, 0.21. Field width: 15 plots * 4 feet = 60 feet. Field length: 26 plots * 4 feet = 104 feet. Conclusions: \"experimental field may sensibly uniform one crop one season may another crop different season\" p. 592.","code":""},{"path":"/reference/bose.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"Bose, R. D. (1935). soil heterogeneity trials Pusa size shape experimental plots. Ind. J. Agric. Sci., 5, 579-608. Table 1 (p. 585), Table 4 (p. 589), Table 5 (p. 590). https://archive.org/details/.ernet.dli.2015.271739","code":""},{"path":"/reference/bose.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"Shaw (1935). Handbook Statistics Use Plant-Breeding Agricultural Problems, p. 149-170. https://krishikosh.egranth.ac./handle/1/21153","code":""},{"path":"/reference/bose.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bose.multi.uniformity) dat <- bose.multi.uniformity # match sum at bottom of Bose tables 1, 4, 5 # library(dplyr) # dat libs(desplot, dplyr) # Calculate percent of mean yield for each year dat <- group_by(dat, year) dat <- mutate(dat, pctyld = (yield-mean(yield))/mean(yield)) dat <- ungroup(dat) dat <- mutate(dat, year=as.character(year)) # Bose smoothed the data by averaging 2x3 plots together before drawing # contour maps. Heatmaps of raw data have similar structure to Bose Fig 1. desplot(dat, pctyld ~ col*row|year, tick=TRUE, flip=TRUE, aspect=(26)/(15), main=\"bose.multi.* - Percent of mean yield\") # contourplot() results need to be mentally flipped upside down # contourplot(pctyld ~ col*row|year, dat, # region=TRUE, as.table=TRUE, aspect=26/15) } # }"},{"path":"/reference/box.cork.html","id":null,"dir":"Reference","previous_headings":"","what":"Weight of cork samples on four sides of trees — box.cork","title":"Weight of cork samples on four sides of trees — box.cork","text":"cork data gives weights cork borings trunk 28 trees north (N), east (E), south (S) west (W) directions.","code":""},{"path":"/reference/box.cork.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weight of cork samples on four sides of trees — box.cork","text":"Data frame 28 observations following 5 variables. tree tree number dir direction N,E,S,W y weight cork deposit (centigrams), north direction","code":""},{"path":"/reference/box.cork.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Weight of cork samples on four sides of trees — box.cork","text":"C.R. Rao (1948). Tests significance multivariate analysis. Biometrika, 35, 58-79. https://doi.org/10.2307/2332629","code":""},{"path":"/reference/box.cork.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Weight of cork samples on four sides of trees — box.cork","text":"K.V. Mardia, J.T. Kent J.M. Bibby (1979) Multivariate Analysis, Academic Press. Russell D Wolfinger, (1996). Heterogeneous Variance: Covariance Structures Repeated Measures. Journal Agricultural, Biological, Environmental Statistics, 1, 205-230.","code":""},{"path":"/reference/box.cork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weight of cork samples on four sides of trees — box.cork","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(box.cork) dat <- box.cork libs(reshape2, lattice) dat2 <- acast(dat, tree ~ dir, value.var='y') splom(dat2, pscales=3, prepanel.limits = function(x) c(25,100), main=\"box.cork\", xlab=\"Cork yield on side of tree\", panel=function(x,y,...){ panel.splom(x,y,...) panel.abline(0,1,col=\"gray80\") }) ## Radial star plot, each tree is one line libs(plotrix) libs(reshape2) dat2 <- acast(dat, tree ~ dir, value.var='y') radial.plot(dat2, start=pi/2, rp.type='p', clockwise=TRUE, radial.lim=c(0,100), main=\"box.cork\", lwd=2, labels=c('North','East','South','West'), line.col=rep(c(\"royalblue\",\"red\",\"#009900\",\"dark orange\", \"#999999\",\"#a6761d\",\"deep pink\"), length=nrow(dat2))) if(require(\"asreml\", quietly=TRUE)) { libs(asreml, lucid) # Unstructured covariance dat$dir <- factor(dat$dir) dat$tree <- factor(dat$tree) dat <- dat[order(dat$tree, dat$dir), ] # Unstructured covariance matrix m1 <- asreml(y~dir, data=dat, residual = ~ tree:us(dir)) lucid::vc(m1) # Note: 'rcor' is a personal function to extract the correlations # into a matrix format # round(kw::rcor(m1)$dir, 2) # E N S W # E 219.93 223.75 229.06 171.37 # N 223.75 290.41 288.44 226.27 # S 229.06 288.44 350.00 259.54 # W 171.37 226.27 259.54 226.00 # Note: Wolfinger used a common diagonal variance # Factor Analytic with different specific variances # fixme: does not work with asreml4 # m2 <- update(m1, residual = ~tree:facv(dir,1)) # round(kw::rcor(m2)$dir, 2) # E N S W # E 219.94 209.46 232.85 182.27 # N 209.46 290.41 291.82 228.43 # S 232.85 291.82 349.99 253.94 # W 182.27 228.43 253.94 225.99 } } # }"},{"path":"/reference/bradley.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"Uniformity trial 4 crops land Trinidad.","code":""},{"path":"/reference/bradley.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"","code":"data(\"bradley.multi.uniformity\")"},{"path":"/reference/bradley.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"data frame 440 observations following 5 variables. row row col column yield yield, pounds per plot season season crop crop","code":""},{"path":"/reference/bradley.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"Experiments conducted Trinidad. Plots marked May 1939 Fields 1, 2, 3. Prior 1939 difficult obtain significant results land. Plots 1/40 acre , 33 feet square. Discard blocks ( rows) 7 feet plots (columns) 4 feet. roadways, gap 14 feet blocks 10 11 gap 10 feet plots E/F (call columns 5/6). Data collected 4 crops. Two crops poor germination omitted. Field width: 10 plots * 33 feet + 8 gaps * 4 feet + 1 gap * 10 = 372 feet Field length: 11 blocks (plots) * 33 feet + 9 gaps * 7 feet + 1 gap * 14 feet = 440 feet Crop 1. Woolly Pyrol. Crop cut flowering weighed pounds. Note, woolly pyrol appears bean also called black gram, phaseolus mungo. Crop 2. Woolly Pyrol. Crop cut flowering weighed pounds. Crop 3. Maize. Net weight cobs pounds. Source document also number cobs. Crop 4. Yams. Weights pounds. Source document weight 1/4 pound, rounded nearest pound. (Half pounds rounded nearest even pound.) Source document also number yams. Notes Bradley. edges field tended slightly higher yielding. Thought due heavier cultivation edges recieve (p. 18). plot row 9, col 7 (9G Bradley) higher yielding neighbors, thought site saman tree dug burned field plotted. Bits charcoal still soil. Bradley also examined soil samples selected plots looked nutrients, moisture, texture, etc. selected plots 4 high-yielding plots 4 low-yielding plots. Little difference observed. Unexpectedly, yams gave higher yield plots compaction.","code":""},{"path":"/reference/bradley.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"P. L. Bradley (1941). study variation productivity number fixed plots field 2. Dissertation: University West Indies. Appendix 1a, 1b, 1c, 1d. https://uwispace.sta.uwi.edu/items/e874561d-52e5-4e39-8416-ff8c1756049c https://hdl.handle.net/2139/41259 data repeated : C. E. Wilson. Study plots laid field II view obtaining plot-fertility data use future experiments plots, season 1940-41. Dissertation: University West Indies. Page 36-39. https://uwispace.sta.uwi.edu/dspace/handle/2139/43658","code":""},{"path":"/reference/bradley.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"None","code":""},{"path":"/reference/bradley.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bradley.multi.uniformity) dat <- bradley.multi.uniformity # figures similar to Bradley, pages 11-15 libs(desplot) desplot(dat, yield ~ col*row, subset=season==1, flip=TRUE, aspect=433/366, # true aspect (omits roadways) main=\"bradley.multi.uniformity - season 1, woolly pyrol\") desplot(dat, yield ~ col*row, subset=season==2, flip=TRUE, aspect=433/366, # true aspect (omits roadways) main=\"bradley.multi.uniformity - season 2, woolly pyrol\") desplot(dat, yield ~ col*row, subset=season==3, flip=TRUE, aspect=433/366, # true aspect (omits roadways) main=\"bradley.multi.uniformity - season 3, maize\") desplot(dat, yield ~ col*row, subset=season==4, flip=TRUE, aspect=433/366, # true aspect (omits roadways) main=\"bradley.multi.uniformity - season 4, yams\") dat1 <- subset(bradley.multi.uniformity, season==1) dat2 <- subset(bradley.multi.uniformity, season==2) dat3 <- subset(bradley.multi.uniformity, season==3) dat4 <- subset(bradley.multi.uniformity, season==4) # to combine plots across seasons, each yield value was converted to percent # of maximum yield in that season. Same as Bradley, page 17. dat1$percent <- dat1$yield / max(dat1$yield) * 100 dat2$percent <- dat2$yield / max(dat2$yield) * 100 dat3$percent <- dat3$yield / max(dat3$yield) * 100 dat4$percent <- dat4$yield / max(dat4$yield) * 100 # make sure data is in same order, then combine dat1 <- dat1[order(dat1$col, dat1$row),] dat2 <- dat2[order(dat2$col, dat2$row),] dat3 <- dat3[order(dat3$col, dat3$row),] dat4 <- dat4[order(dat4$col, dat4$row),] dat14 <- dat1[,c('row','col')] dat14$fertility <- dat1$percent + dat2$percent + dat3$percent + dat4$percent libs(desplot) desplot(dat14, fertility ~ col*row, tick=TRUE, flip=TRUE, aspect=433/366, # true aspect (omits roadways) main=\"bradley.multi.uniformity - fertility\") } # }"},{"path":"/reference/brandle.rape.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of rape in Manitoba — brandle.rape","title":"Multi-environment trial of rape in Manitoba — brandle.rape","text":"Rape seed yields 5 genotypes, 3 years, 9 locations.","code":""},{"path":"/reference/brandle.rape.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of rape in Manitoba — brandle.rape","text":"data frame 135 observations following 4 variables. gen genotype year year, numeric loc location, 9 levels yield yield, kg/ha","code":""},{"path":"/reference/brandle.rape.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of rape in Manitoba — brandle.rape","text":"yields mean 4 reps. Note, table 2 Brandle, value Triton 1985 Bagot shown 2355, 2555 match means reported paper. Used permission P. McVetty.","code":""},{"path":"/reference/brandle.rape.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of rape in Manitoba — brandle.rape","text":"Brandle, JE McVetty, PBE. (1988). Genotype x environment interaction stability analysis seed yield oilseed rape grown Manitoba. Canadian Journal Plant Science, 68, 381–388.","code":""},{"path":"/reference/brandle.rape.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of rape in Manitoba — brandle.rape","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(brandle.rape) dat <- brandle.rape libs(lattice) dotplot(gen~yield|loc, dat, group=year, auto.key=list(columns=3), main=\"brandle.rape, yields per location\", ylab=\"Genotype\") # Matches table 4 of Brandle # round(tapply(dat$yield, dat$gen, mean),0) # Brandle reports variance components: # sigma^2_gl: 9369 gy: 14027 g: 72632 resid: 150000 # Brandle analyzed rep-level data, so the residual variance is different. # The other components are matched by the following analysis. libs(lme4) libs(lucid) dat$year <- factor(dat$year) m1 <- lmer(yield ~ year + loc + year:loc + (1|gen) + (1|gen:loc) + (1|gen:year), data=dat) vc(m1) ## grp var1 var2 vcov sdcor ## gen:loc (Intercept) 9363 96.76 ## gen:year (Intercept) 14030 118.4 ## gen (Intercept) 72630 269.5 ## Residual 75010 273.9 } # }"},{"path":"/reference/brandt.switchback.html","id":null,"dir":"Reference","previous_headings":"","what":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":"Switchback experiment dairy cattle, milk yield two treatments","code":""},{"path":"/reference/brandt.switchback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":"","code":"data(\"brandt.switchback\")"},{"path":"/reference/brandt.switchback.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":"data frame 30 observations following 5 variables. group group: ,B cow cow, 10 levels trt treatment, 2 levels period period, 3 levels yield milk yield, pounds","code":""},{"path":"/reference/brandt.switchback.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":"experiment, 10 cows selected Iowa State College Holstein-Friesian herd divided two equal groups. Care taken groups nearly equal possible regard milk production, stage gestation, body weight, condition age. cows given 10 pounds timothy hay 30 pounds corn silage daily fed different grain mixtures. Treatment T1, , consisted feeding grain mixture 1 part corn cob meal 1 part ground oats, treatment T2 consisted feeding grain mixture 4 parts corn cob meal, 4 parts ground oats 3 parts gluten feed. three treatment periods covered 105 days – three periods 35 days . yields first 7 days period considered possible effect transition one treatment . data, together sums differences aid calculations incidental testing, given table 2. seems safe conclude inclusion gluten feed grain mixture fed timothy hay ration Holstein-Friesian cows increased production milk. average increase 21.7 pounds per cow 28-day period.","code":""},{"path":"/reference/brandt.switchback.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":".E. Brandt (1938). Tests Significance Reversal Switchback Trials Iowa State College, Agricultural Research Bulletins. Bulletin 234. Book 22. https://lib.dr.iastate.edu/ag_researchbulletins/22/","code":""},{"path":"/reference/brandt.switchback.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(brandt.switchback) dat <- brandt.switchback # In each period, treatment 2 is slightly higher # bwplot(yield~trt|period,dat, layout=c(3,1), main=\"brandt.switchback\", # xlab=\"Treatment\", ylab=\"Milk yield\") # Yield at period 2 (trt T2) is above the trend in group A, # below the trend (trt T1) in group B. # Equivalently, treatment T2 is above the trend line libs(lattice) xyplot(yield~period|group, data=dat, group=cow, type=c('l','r'), auto.key=list(columns=5), main=\"brandt.switchback\", xlab=\"Period. Group A: T1,T2,T1. Group B: T2,T1,T2\", ylab=\"Milk yield (observed and trend) per cow\") # Similar to Brandt Table 10 m1 <- aov(yield~period+group+cow:group+period:group, data=dat) anova(m1) } # }"},{"path":"/reference/bridges.cucumber.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","title":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","text":"Cucumber yields latin square design two locs.","code":""},{"path":"/reference/bridges.cucumber.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","text":"data frame 32 observations following 5 variables. loc location gen genotype/cultivar row row col column yield weight marketable fruit per plot","code":""},{"path":"/reference/bridges.cucumber.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","text":"Conducted Clemson University 1985. four cucumber cultivars grown latin square design Clemson, SC, Tifton, GA. Separate variances modeled location. Plot dimensions given. Bridges (1989) used data illustrate fitting heterogeneous mixed model. Used permission William Bridges.","code":""},{"path":"/reference/bridges.cucumber.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","text":"William Bridges (1989). Analysis plant breeding experiment heterogeneous variances using mixed model equations. Applications mixed models agriculture related disciplines, S. Coop. Ser. Bull, 45–51.","code":""},{"path":"/reference/bridges.cucumber.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bridges.cucumber) dat <- bridges.cucumber dat <- transform(dat, rowf=factor(row), colf=factor(col)) libs(desplot) desplot(dat, yield~col*row|loc, # aspect unknown text=gen, cex=1, main=\"bridges.cucumber\") # Graphical inference test for heterogenous variances libs(nullabor) # Create a lineup of datasets fun <- null_permute(\"loc\") dat20 <- lineup(fun, dat, n=20, pos=9) # Now plot libs(lattice) bwplot(yield ~ loc|factor(.sample), dat20, main=\"bridges.cucumber - graphical inference\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) ## Random row/col/resid. Same as Bridges 1989, p. 147 m1 <- asreml(yield ~ 1 + gen + loc + loc:gen, random = ~ rowf:loc + colf:loc, data=dat) lucid::vc(m1) ## effect component std.error z.ratio bound ## rowf:loc 31.62 23.02 1.4 P 0 ## colf:loc 18.08 15.32 1.2 P 0 ## units(R) 31.48 12.85 2.4 P 0 ## Random row/col/resid at each loc. Matches p. 147 m2 <- asreml(yield ~ 1 + gen + loc + loc:gen, random = ~ at(loc):rowf + at(loc):colf, data=dat, resid = ~ dsum( ~ units|loc)) lucid::vc(m2) ## effect component std.error z.ratio bound ## at(loc, Clemson):rowf 32.32 36.58 0.88 P 0 ## at(loc, Tifton):rowf 30.92 28.63 1.1 P 0 ## at(loc, Clemson):colf 22.55 28.78 0.78 P 0 ## at(loc, Tifton):colf 13.62 14.59 0.93 P 0 ## loc_Clemson(R) 46.85 27.05 1.7 P 0 ## loc_Tifton(R) 16.11 9.299 1.7 P 0 predict(m2, data=dat, classify='loc:gen')$pvals ## loc gen predicted.value std.error status ## 1 Clemson Dasher 45.6 5.04 Estimable ## 2 Clemson Guardian 31.6 5.04 Estimable ## 3 Clemson Poinsett 21.4 5.04 Estimable ## 4 Clemson Sprint 26 5.04 Estimable ## 5 Tifton Dasher 50.5 3.89 Estimable ## 6 Tifton Guardian 38.7 3.89 Estimable ## 7 Tifton Poinsett 33 3.89 Estimable ## 8 Tifton Sprint 39.2 3.89 Estimable # Is a heterogeneous model justified? Maybe not. # m1$loglik ## -67.35585 # m2$loglik ## -66.35621 } } # }"},{"path":"/reference/broadbalk.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"Long term wheat yields Broadbalk fields Rothamsted.","code":""},{"path":"/reference/broadbalk.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"data frame 1258 observations following 4 variables. year year plot plot grain grain yield, tonnes straw straw yield, tonnes","code":""},{"path":"/reference/broadbalk.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"Note: data 1852-1925. can find recent data experiments Electronic Rothamsted Archive: https://www.era.rothamsted.ac.uk/ Rothamsted Experiment station conducted wheat experiments Broadbalk Fields beginning 1844 data yields grain straw collected 1852 1925. Ronald Fisher hired analyze data agricultural trials. Organic manures inorganic fertilizer treatments applied various combinations plots. N1 48kg, N1.5 72kg, N2 96kg, N4 192kg nitrogen. Electronic version data retrieved http://lib.stat.cmu.edu/datasets/Andrews/","code":""},{"path":"/reference/broadbalk.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"D.F. Andrews .M. Herzberg. 1985. \tData: Collection Problems Many Fields Student Research Worker. Springer.","code":""},{"path":"/reference/broadbalk.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"Broadbalk Winter Wheat Experiment. https://www.era.rothamsted.ac.uk/index.php?area=home&page=index&dataset=4","code":""},{"path":"/reference/broadbalk.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(broadbalk.wheat) dat <- broadbalk.wheat libs(lattice) ## xyplot(grain~straw|plot, dat, type=c('p','smooth'), as.table=TRUE, ## main=\"broadbalk.wheat\") xyplot(grain~year|plot, dat, type=c('p','smooth'), as.table=TRUE, main=\"broadbalk.wheat\") # yields are decreasing # See the treatment descriptions to understand the patterns redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(grain~year*plot, dat, main=\"broadbalk.wheat: Grain\", col.regions=redblue) } # }"},{"path":"/reference/bryan.corn.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"Uniformity trial corn 3 locations Iowa.","code":""},{"path":"/reference/bryan.corn.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"","code":"data(\"bryan.corn.uniformity\")"},{"path":"/reference/bryan.corn.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"data frame 1728 observations following 4 variables. expt experiment (variety/orientation) row row col column yield yield, pounds per plot","code":""},{"path":"/reference/bryan.corn.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"Three varieties corn planted. experiment 48 rows, row 48 hills long, .65 acres. \"hill\" single hole possibly multiple seeds. Spacing hills sqrt(43560 sq ft * .64) / 48 = 3.5 feet. experiment code, K=Krug, =Iodent, M=McCulloch (varieties corn), 23=1923, 25=1925, E=East/West, N=North/South. experiment aggregated experimental units combining 8 hills, East/West direction also North/South direction. Thus, field represented twice data, \"E\" field name \"N\".","code":""},{"path":"/reference/bryan.corn.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"Arthur Bryan (1933). Factors Affecting Experimental Error Field Plot Tests Corn. Agricultural Experiment Station, Iowa State College. Tables 22-27. https://hdl.handle.net/2027/uiug.30112019568168","code":""},{"path":"/reference/bryan.corn.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"None","code":""},{"path":"/reference/bryan.corn.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bryan.corn.uniformity) dat <- bryan.corn.uniformity libs(desplot) desplot(dat, yield ~ col*row|expt, main=\"bryan.corn.uniformity\", aspect=(48*3.5/(6*8*3.5)), # true aspect flip=TRUE, tick=TRUE) # CVs in Table 5, column 8 hills # libs(dplyr) # dat # summarize(cv=sd(yield)/mean(yield)*100) ## expt cv ## 1 K23E 10.9 ## 2 K23N 10.9 ## 3 I25E 16.3 ## 4 I25N 17.0 ## 5 M25E 16.2 ## 6 M25N 17.2 } # }"},{"path":"/reference/buntaran.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"Multi-environment trial wheat Sweden 2016.","code":""},{"path":"/reference/buntaran.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"","code":"data(\"buntaran.wheat\")"},{"path":"/reference/buntaran.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"data frame 1069 observations following 7 variables. zone Geographic zone: south, middle, north loc Location rep Block replicate (4) alpha Incomplete-block alpha design gen Genotype (cultivar) yield Dry matter yield, kg/ha","code":""},{"path":"/reference/buntaran.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"Dry matter yield wheat trials Sweden 2016. experiments location multi-rep incomplete blocks alpha design. Electronic data online supplement Buntaran (2020) also \"init\" package https://github.com/Flavjack/inti.","code":""},{"path":"/reference/buntaran.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"Buntaran, Harimurti et al. (2020). Cross-validation stagewise mixed-model analysis Swedish variety trials winter wheat spring barley. Crop Science, 60, 2221-2240. http://doi.org/10.1002/csc2.20177","code":""},{"path":"/reference/buntaran.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"None.","code":""},{"path":"/reference/buntaran.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"","code":"if (FALSE) { # \\dontrun{ data(buntaran.wheat) library(agridat) dat <- buntaran.wheat library(lattice) bwplot(yield~loc|zone, dat, layout=c(1,3), scales=list(x=list(rot=90)), main=\"buntaran.wheat\") } # }"},{"path":"/reference/burgueno.alpha.html","id":null,"dir":"Reference","previous_headings":"","what":"Incomplete block alpha design — burgueno.alpha","title":"Incomplete block alpha design — burgueno.alpha","text":"Incomplete block alpha design","code":""},{"path":"/reference/burgueno.alpha.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incomplete block alpha design — burgueno.alpha","text":"","code":"data(\"burgueno.alpha\")"},{"path":"/reference/burgueno.alpha.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Incomplete block alpha design — burgueno.alpha","text":"data frame 48 observations following 6 variables. rep rep, 3 levels block block, 12 levels row row col column gen genotype, 16 levels yield yield","code":""},{"path":"/reference/burgueno.alpha.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Incomplete block alpha design — burgueno.alpha","text":"field experiment 3 reps, 4 blocks per rep, laid alpha design. plot size given. Electronic version data obtained CropStat software. Used permission Juan Burgueno.","code":""},{"path":"/reference/burgueno.alpha.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Incomplete block alpha design — burgueno.alpha","text":"J Burgueno, Cadena, J Crossa, M Banziger, Gilmour, B Cullis. 2000. User's guide spatial analysis field variety trials using ASREML. CIMMYT. https://books.google.com/books?id=PR_tYCFyLCYC&pg=PA1","code":""},{"path":"/reference/burgueno.alpha.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incomplete block alpha design — burgueno.alpha","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(burgueno.alpha) dat <- burgueno.alpha libs(desplot) desplot(dat, yield~col*row, out1=rep, out2=block, # aspect unknown text=gen, cex=1,shorten=\"none\", main='burgueno.alpha') libs(lme4,lucid) # Inc block model m0 <- lmer(yield ~ gen + (1|rep/block), data=dat) vc(m0) # Matches Burgueno p. 26 ## grp var1 var2 vcov sdcor ## block:rep (Intercept) 86900 294.8 ## rep (Intercept) 200900 448.2 ## Residual 133200 365 if(require(\"asreml\", quietly=TRUE)) { libs(asreml) dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf, dat$yf),] # Sequence of models on page 36 of Burgueno m1 <- asreml(yield ~ gen, data=dat) m1$loglik # -232.13 m2 <- asreml(yield ~ gen, data=dat, random = ~ rep) m2$loglik # -223.48 # Inc Block model m3 <- asreml(yield ~ gen, data=dat, random = ~ rep/block) m3$loglik # -221.42 m3$coef$fixed # Matches solution on p. 27 # AR1xAR1 model m4 <- asreml(yield ~ 1 + gen, data=dat, resid = ~ar1(xf):ar1(yf)) m4$loglik # -221.47 plot(varioGram(m4), main=\"burgueno.alpha\") # Figure 1 m5 <- asreml(yield ~ 1 + gen, data=dat, random= ~ yf, resid = ~ar1(xf):ar1(yf)) m5$loglik # -220.07 m6 <- asreml(yield ~ 1 + gen + pol(yf,-2), data=dat, resid = ~ar1(xf):ar1(yf)) m6$loglik # -204.64 m7 <- asreml(yield ~ 1 + gen + lin(yf), data=dat, random= ~ spl(yf), resid = ~ar1(xf):ar1(yf)) m7$loglik # -212.51 m8 <- asreml(yield ~ 1 + gen + lin(yf), data=dat, random= ~ spl(yf)) m8$loglik # -213.91 # Polynomial model with predictions m9 <- asreml(yield ~ 1 + gen + pol(yf,-2) + pol(xf,-2), data=dat, random= ~ spl(yf), resid = ~ar1(xf):ar1(yf)) m9 <- update(m9) m9$loglik # -191.44 vs -189.61 m10 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat, resid = ~ar1(xf):ar1(yf)) m10$loglik # -211.56 m11 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat, random= ~ spl(yf), resid = ~ar1(xf):ar1(yf)) m11$loglik # -208.90 m12 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat, random= ~ spl(yf)+spl(xf), resid = ~ar1(xf):ar1(yf)) m12$loglik # -206.82 m13 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat, random= ~ spl(yf)+spl(xf)) m13$loglik # -207.52 } } # }"},{"path":"/reference/burgueno.rowcol.html","id":null,"dir":"Reference","previous_headings":"","what":"Row-column design — burgueno.rowcol","title":"Row-column design — burgueno.rowcol","text":"Row-column design","code":""},{"path":"/reference/burgueno.rowcol.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row-column design — burgueno.rowcol","text":"","code":"data(\"burgueno.rowcol\")"},{"path":"/reference/burgueno.rowcol.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Row-column design — burgueno.rowcol","text":"data frame 128 observations following 5 variables. rep rep, 2 levels row row col column gen genotype, 64 levels yield yield, tons/ha","code":""},{"path":"/reference/burgueno.rowcol.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Row-column design — burgueno.rowcol","text":"field experiment two contiguous replicates 8 rows, 16 columns. plot size given. Electronic version data obtained CropStat software. Used permission Juan Burgueno.","code":""},{"path":"/reference/burgueno.rowcol.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Row-column design — burgueno.rowcol","text":"J Burgueno, Cadena, J Crossa, M Banziger, Gilmour, B Cullis (2000). User's guide spatial analysis field variety trials using ASREML. CIMMYT.","code":""},{"path":"/reference/burgueno.rowcol.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row-column design — burgueno.rowcol","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(burgueno.rowcol) dat <- burgueno.rowcol # Two contiguous reps in 8 rows, 16 columns libs(desplot) desplot(dat, yield ~ col*row, out1=rep, # aspect unknown text=gen, shorten=\"none\", cex=.75, main=\"burgueno.rowcol\") libs(lme4,lucid) # Random rep, row and col within rep # m1 <- lmer(yield ~ gen + (1|rep) + (1|rep:row) + (1|rep:col), data=dat) # vc(m1) # Match components of Burgueno p. 40 ## grp var1 var2 vcov sdcor ## rep:col (Intercept) 0.2189 0.4679 ## rep:row (Intercept) 0.1646 0.4057 ## rep (Intercept) 0.1916 0.4378 ## Residual 0.1796 0.4238 if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # AR1 x AR1 with linear row/col effects, random spline row/col dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf,dat$yf),] m2 <- asreml(yield ~ gen + lin(yf) + lin(xf), data=dat, random = ~ spl(yf) + spl(xf), resid = ~ ar1(xf):ar1(yf)) m2 <- update(m2) # More iterations # Scaling of spl components has changed in asreml from old versions lucid::vc(m2) # Match Burgueno p. 42 ## effect component std.error z.ratio bound ## spl(yf) 0.09077 0.08252 1.1 P 0 ## spl(xf) 0.08107 0.08209 0.99 P 0 ## xf:yf(R) 0.1482 0.03119 4.8 P 0 ## xf:yf!xf!cor 0.1152 0.2269 0.51 U 0.1 ## xf:yf!yf!cor 0.009467 0.2414 0.039 U 0.9 plot(varioGram(m2), main=\"burgueno.rowcol\") } } # }"},{"path":"/reference/burgueno.unreplicated.html","id":null,"dir":"Reference","previous_headings":"","what":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"Field experiment unreplicated genotypes plus one repeated check.","code":""},{"path":"/reference/burgueno.unreplicated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"","code":"data(\"burgueno.unreplicated\")"},{"path":"/reference/burgueno.unreplicated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"data frame 434 observations following 4 variables. gen genotype, 281 levels col column row row yield yield, tons/ha","code":""},{"path":"/reference/burgueno.unreplicated.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"field experiment 280 new genotypes. check genotype planted every 4th column. plot size given. Electronic version data obtained CropStat software. Used permission Juan Burgueno.","code":""},{"path":"/reference/burgueno.unreplicated.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"J Burgueno, Cadena, J Crossa, M Banziger, Gilmour, B Cullis (2000). User's guide spatial analysis field variety trials using ASREML. CIMMYT.","code":""},{"path":"/reference/burgueno.unreplicated.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(burgueno.unreplicated) dat <- burgueno.unreplicated # Define a 'check' variable for colors dat$check <- ifelse(dat$gen==\"G000\", 2, 1) # Every fourth column is the 'check' genotype libs(desplot) desplot(dat, yield ~ col*row, col=check, num=gen, #text=gen, cex=.3, # aspect unknown main=\"burgueno.unreplicated\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # AR1 x AR1 with random genotypes dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf,dat$yf),] m2 <- asreml(yield ~ 1, data=dat, random = ~ gen, resid = ~ ar1(xf):ar1(yf)) lucid::vc(m2) ## effect component std.error z.ratio bound ## gen 0.9122 0.127 7.2 P 0 ## xf:yf(R) 0.4993 0.05601 8.9 P 0 ## xf:yf!xf!cor -0.2431 0.09156 -2.7 U 0 ## xf:yf!yf!cor 0.1255 0.07057 1.8 U 0.1 # Note the strong saw-tooth pattern in the variogram. Seems to # be column effects. plot(varioGram(m2), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5), main=\"burgueno.unreplicated - AR1xAR1\") # libs(lattice) # Show how odd columns are high # bwplot(resid(m2) ~ col, data=dat, horizontal=FALSE) # Define an even/odd column factor as fixed effect # dat$oddcol <- factor(dat$col # The modulus operator throws a bug, so do it the hard way. dat$oddcol <- factor(dat$col - floor(dat$col / 2) *2 ) m3 <- update(m2, yield ~ 1 + oddcol) m3$loglik # Matches Burgueno table 3, line 3 plot(varioGram(m3), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5), main=\"burgueno.unreplicated - AR1xAR1 + Even/Odd\") # Much better-looking variogram } } # }"},{"path":"/reference/butron.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize with pedigrees — butron.maize","title":"Multi-environment trial of maize with pedigrees — butron.maize","text":"Maize yields multi-environment trial. Pedigree included.","code":""},{"path":"/reference/butron.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize with pedigrees — butron.maize","text":"data frame 245 observations following 5 variables. gen genotype male male parent female female parent env environment yield yield, Mg/ha","code":""},{"path":"/reference/butron.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize with pedigrees — butron.maize","text":"Ten inbreds crossed produce diallel without reciprocals. 45 F1 crosses evaluated along 4 checks triple-lattice 7x7 design. Pink stem borer infestation natural. Experiments performed 1995 1996 three sites northwestern Spain: Pontevedra (42 deg 24 min N, 8 deg 38 min W, 20 m sea), Pontecaldelas (42 deg 23 N, 8 min 32 W, 300 m sea), Ribadumia (42 deg 30 N, 8 min 46 W, 50 m sea). two-letter location code year concatenated define environment. average number larvae per plant environment: Used permission Ana Butron.","code":""},{"path":"/reference/butron.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize with pedigrees — butron.maize","text":"Butron, Velasco, P Ordas, Malvar, RA (2004). Yield evaluation maize cultivars across environments different levels pink stem borer infestation. Crop Science, 44, 741-747. https://doi.org/10.2135/cropsci2004.7410","code":""},{"path":"/reference/butron.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize with pedigrees — butron.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(butron.maize) dat <- butron.maize libs(reshape2) mat <- acast(dat, gen~env, value.var='yield') mat <- sweep(mat, 2, colMeans(mat)) mat.svd <- svd(mat) # Calculate PC1 and PC2 scores as in Table 4 of Butron # Comment out to keep Rcmd check from choking on ' # round(mat.svd$u[,1:2] biplot(princomp(mat), main=\"butron.maize\", cex=.7) # Figure 1 of Butron if(require(\"asreml\", quietly=TRUE)) { # Here we see if including pedigree information is helpful for a # multi-environment model # Including the pedigree provided little benefit # Create the pedigree ped <- dat[, c('gen','male','female')] ped <- ped[!duplicated(ped),] # remove duplicates unip <- unique(c(ped$male, ped$female)) # Unique parents unip <- unip[!is.na(unip)] # We have to define parents at the TOP of the pedigree ped <- rbind(data.frame(gen=c(\"Dent\",\"Flint\"), # genetic groups male=c(0,0), female=c(0,0)), data.frame(gen=c(\"A509\",\"A637\",\"A661\",\"CM105\",\"EP28\", \"EP31\",\"EP42\",\"F7\",\"PB60\",\"Z77016\"), male=rep(c('Dent','Flint'),each=5), female=rep(c('Dent','Flint'),each=5)), ped) ped[is.na(ped$male),'male'] <- 0 ped[is.na(ped$female),'female'] <- 0 libs(asreml) ped.ainv <- ainverse(ped) m0 <- asreml(yield ~ 1+env, data=dat, random = ~ gen) m1 <- asreml(yield ~ 1+env, random = ~ vm(gen, ped.ainv), data=dat) m2 <- update(m1, random = ~ idv(env):vm(gen, ped.ainv)) m3 <- update(m2, random = ~ diag(env):vm(gen, ped.ainv)) m4 <- update(m3, random = ~ fa(env,1):vm(gen, ped.ainv)) #summary(m0)$aic #summary(m4)$aic ## df AIC ## m0 2 229.4037 ## m1 2 213.2487 ## m2 2 290.6156 ## m3 6 296.8061 ## m4 11 218.1568 p0 <- predict(m0, data=dat, classify=\"gen\")$pvals p1 <- predict(m1, data=dat, classify=\"gen\")$pvals p1par <- p1[1:12,] # parents p1 <- p1[-c(1:12),] # remove parents # Careful! Need to manually sort the predictions p0 <- p0[order(as.character(p0$gen)),] p1 <- p1[order(as.character(p1$gen)),] # lims <- range(c(p0$pred, p1$pred)) * c(.95,1.05) lims <- c(6,8.25) # zoom in on the higher-yielding hybrids plot(p0$predicted.value, p1$predicted.value, pch=\"\", xlim=lims, ylim=lims, main=\"butron.maize\", xlab=\"BLUP w/o pedigree\", ylab=\"BLUP with pedigree\") abline(0,1,col=\"lightgray\") text(x=p0$predicted.value, y=p1$predicted.value, p0$gen, cex=.5, srt=-45) text(x=min(lims), y=p1par$predicted.value, p1par$gen, cex=.5, col=\"red\") round( cor(p0$predicted.value, p1$predicted.value), 3) # 0.994 # Including the pedigree provided very little change } } # }"},{"path":"/reference/byers.apple.html","id":null,"dir":"Reference","previous_headings":"","what":"Diameters of apples — byers.apple","title":"Diameters of apples — byers.apple","text":"Measurements diameters apples","code":""},{"path":"/reference/byers.apple.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diameters of apples — byers.apple","text":"data frame 480 observations following 6 variables. tree tree, 10 levels apple apple, 24 levels size size apple appleid unique id number apple time time period, 1-6 = (week/2) diameter diameter, inches","code":""},{"path":"/reference/byers.apple.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diameters of apples — byers.apple","text":"Experiment conducted Winchester Agricultural Experiment Station Virginia Polytechnic Institute State University. Twentyfive apples chosen ten apple trees. , 80 apples largest size class, 2.75 inches diameter greater. diameters apples recorded every two weeks 12-week period.","code":""},{"path":"/reference/byers.apple.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Diameters of apples — byers.apple","text":"Schabenberger, Oliver Francis J. Pierce. 2002. Contemporary Statistical Models Plant Soil Sciences. CRC Press, Boca Raton, FL.","code":""},{"path":"/reference/byers.apple.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diameters of apples — byers.apple","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(byers.apple) dat <- byers.apple libs(lattice) xyplot(diameter ~ time | factor(appleid), data=dat, type=c('p','l'), strip=strip.custom(par.strip.text=list(cex=.7)), main=\"byers.apple\") # Overall fixed linear trend, plus random intercept/slope deviations # for each apple. Observations within each apple are correlated. libs(nlme) libs(lucid) m1 <- lme(diameter ~ 1 + time, data=dat, random = ~ time|appleid, method='ML', cor = corAR1(0, form=~ time|appleid), na.action=na.omit) vc(m1) ## effect variance stddev corr ## (Intercept) 0.007354 0.08575 NA ## time 0.00003632 0.006027 0.83 ## Residual 0.0004555 0.02134 NA } # }"},{"path":"/reference/caribbean.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize with fertilization — caribbean.maize","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"Maize fertilization trial Antigua St. Vincent.","code":""},{"path":"/reference/caribbean.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"data frame 612 observations following 7 variables. isle island, 2 levels site site block block plot plot, numeric trt treatment factor combining N,P,K ears number ears harvested yield yield kilograms N nitrogen fertilizer level P phosphorous fertilizer level K potassium fertilizer level","code":""},{"path":"/reference/caribbean.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"Antigua coral island Caribbean sufficient level land experiments semi-arid climate, St. Vincent volcanic level areas uncommon, rainfall can seasonally heavy. 8-9 sites island. Plots 16 feet 18 feet. central area 12 feet 12 feet harvested recorded. number ears harvested recorded isle Antigua. actual amounts N, P, K given. 0, 1, 2, 3. digits treatment represent levels nitrogen, phosphorus, potassium fertilizer, respectively. TEAN site suffered damage goats plot 27, 35 36. LFAN site suffered damage cattle one boundary–plots 9, 18, 27, 36. Electronic version data retrieved http://lib.stat.cmu.edu/datasets/Andrews/ https://www2.stat.duke.edu/courses/Spring01/sta114/data/andrews.html","code":""},{"path":"/reference/caribbean.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"D.F. Andrews .M. Herzberg. 1985. \tData: Collection Problems Many Fields Student \tResearch Worker. Springer. Table 58.1 58.2.","code":""},{"path":"/reference/caribbean.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"Also DAAG package data sets antigua stVincent.","code":""},{"path":"/reference/caribbean.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"","code":"library(agridat) data(caribbean.maize) dat <- caribbean.maize # Yield and ears are correlated libs(lattice) xyplot(yield~ears|site, dat, ylim=c(0,10), subset=isle==\"Antigua\", main=\"caribbean.maize - Antiqua\") # Some locs show large response to nitrogen (as expected), e.g. UISV, OOSV dotplot(trt~yield|site, data=dat, main=\"caribbean.maize treatment response\") # Show the strong N*site interaction with little benefit on Antiqua, but # a strong response on St.Vincent. dat <- transform(dat, env=paste(substring(isle,1,1),site,sep=\"-\")) bwplot(yield~N|env, dat, main=\"caribbean.maize\", xlab=\"nitrogen\")"},{"path":"/reference/carlson.germination.html","id":null,"dir":"Reference","previous_headings":"","what":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"Germination alfalfa seeds various salt concentrations","code":""},{"path":"/reference/carlson.germination.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"","code":"data(\"carlson.germination\")"},{"path":"/reference/carlson.germination.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"data frame 120 observations following 3 variables. gen genotype factor, 15 levels germ germination percent, 0-100 nacl salt concentration percent, 0-2","code":""},{"path":"/reference/carlson.germination.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"Data means averaged 5, 10, 15, 20 day counts. Germination expressed percent -salt control account differences germination among cultivars.","code":""},{"path":"/reference/carlson.germination.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"Carlson, JR Ditterline, RL Martin, JM Sands, DC Lund, RE. (1983). Alfalfa Seed Germination Antibiotic Agar Containing NaCl. Crop science, 23, 882-885. https://doi.org/10.2135/cropsci1983.0011183X002300050016x","code":""},{"path":"/reference/carlson.germination.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(carlson.germination) dat <- carlson.germination dat$germ <- dat$germ/100 # Convert to percent # Separate response curve for each genotype. # Really, we should use a glmm with random int/slope for each genotype m1 <- glm(germ~ 0 + gen*nacl, data=dat, family=quasibinomial) # Plot data and fitted model libs(latticeExtra) newd <- data.frame(expand.grid(gen=levels(dat$gen), nacl=seq(0,2,length=100))) newd$pred <- predict(m1, newd, type=\"response\") xyplot(germ~nacl|gen, dat, as.table=TRUE, main=\"carlson.germination\", xlab=\"Percent NaCl\", ylab=\"Fraction germinated\") + xyplot(pred~nacl|gen, newd, type='l', grid=list(h=1,v=0)) # Calculate LD50 values. Note, Carlson et al used quadratics, not glm. # MASS::dose.p cannot handle multiple slopes, so do a separate fit for # each genotype. Results are vaguely similar to Carlson table 5. ## libs(MASS) ## for(ii in unique(dat$gen)){ ## cat(\"\\n\", ii, \"\\n\") ## mm <- glm(germ ~ 1 + nacl, data=dat, subset=gen==ii, family=quasibinomial(link=\"probit\")) ## print(dose.p(mm)) ## } ## Dose SE ## Anchor 1.445728 0.05750418 ## Apollo 1.305804 0.04951644 ## Baker 1.444153 0.07653989 ## Drylander 1.351201 0.03111795 ## Grimm 1.395735 0.04206377 } # }"},{"path":"/reference/carmer.density.html","id":null,"dir":"Reference","previous_headings":"","what":"Nonlinear maize yield-density model — carmer.density","title":"Nonlinear maize yield-density model — carmer.density","text":"Nonlinear maize yield-density model.","code":""},{"path":"/reference/carmer.density.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Nonlinear maize yield-density model — carmer.density","text":"data frame 32 observations following 3 variables. gen genotype/hybrid, 8 levels pop population (plants) yield yield, pounds per hill","code":""},{"path":"/reference/carmer.density.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Nonlinear maize yield-density model — carmer.density","text":"Eight single-cross hybrids experiment–Hy2xOh7 WF9xC103 included believed optimum yields relatively high low populations. Planted 1963. Plots thinned 2, 4, 6, 8 plants per hill, giving densities 8, 16, 24, 32 thousand plants per acre. Hills rows 40 inches apart. One hill = 1/4000 acre. Split-plot design 5 reps, density main plot subplot hybrid.","code":""},{"path":"/reference/carmer.density.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Nonlinear maize yield-density model — carmer.density","text":"S G Carmer J Jackobs (1965). Exponential Model Predicting Optimum Plant Density Maximum Corn Yield. Agronomy Journal, 57, 241–244. https://doi.org/10.2134/agronj1965.00021962005700030003x","code":""},{"path":"/reference/carmer.density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nonlinear maize yield-density model — carmer.density","text":"","code":"library(agridat) data(carmer.density) dat <- carmer.density dat$gen <- factor(dat$gen, levels=c('Hy2x0h7','WF9xC103','R61x187-2', 'WF9x38-11','WF9xB14','C103xB14', '0h43xB37','WF9xH60')) # Separate analysis for each hybrid # Model: y = x * a * k^x. Table 1 of Carmer and Jackobs. out <- data.frame(a=rep(NA,8), k=NA) preds <- NULL rownames(out) <- levels(dat$gen) newdat <- data.frame(pop=seq(2,8,by=.1)) for(i in levels(dat$gen)){ print(i) dati <- subset(dat, gen==i) mi <- nls(yield ~ pop * a * k^pop, data=dati, start=list(a=10,k=1)) out[i, ] <- mi$m$getPars() # Predicted values pi <- cbind(gen=i, newdat, pred= predict(mi, newdat=newdat)) preds <- rbind(preds, pi) } #> [1] \"Hy2x0h7\" #> [1] \"WF9xC103\" #> [1] \"R61x187-2\" #> [1] \"WF9x38-11\" #> [1] \"WF9xB14\" #> [1] \"C103xB14\" #> [1] \"0h43xB37\" #> [1] \"WF9xH60\" # Optimum plant density is -1/log(k) out$pop.opt <- -1/log(out$k) round(out, 3) #> a k pop.opt #> Hy2x0h7 0.782 0.865 6.875 #> WF9xC103 1.039 0.825 5.192 #> R61x187-2 0.998 0.798 4.441 #> WF9x38-11 1.042 0.825 5.203 #> WF9xB14 1.067 0.806 4.647 #> C103xB14 0.813 0.860 6.653 #> 0h43xB37 0.673 0.862 6.740 #> WF9xH60 0.858 0.854 6.358 ## a k pop.opt ## Hy2x0h7 0.782 0.865 6.875 ## WF9xC103 1.039 0.825 5.192 ## R61x187-2 0.998 0.798 4.441 ## WF9x38-11 1.042 0.825 5.203 ## WF9xB14 1.067 0.806 4.647 ## C103xB14 0.813 0.860 6.653 ## 0h43xB37 0.673 0.862 6.740 ## WF9xH60 0.858 0.854 6.358 # Fit an overall fixed-effect with random deviations for each hybrid. libs(nlme) m1 <- nlme(yield ~ pop * a * k^pop, fixed = a + k ~ 1, random = a + k ~ 1|gen, data=dat, start=c(a=10,k=1)) # summary(m1) # Random effect for 'a' probably not needed libs(latticeExtra) # Plot Data, fixed-effect prediction, random-effect prediction. pdat <- expand.grid(gen=levels(dat$gen), pop=seq(2,8,length=50)) pdat$pred <- predict(m1, pdat) pdat$predf <- predict(m1, pdat, level=0) xyplot(yield~pop|gen, dat, pch=16, as.table=TRUE, main=\"carmer.density models\", key=simpleKey(text=c(\"Data\", \"Fixed effect\",\"Random effect\"), col=c(\"blue\", \"red\",\"darkgreen\"), columns=3, points=FALSE)) + xyplot(predf~pop|gen, pdat, type='l', as.table=TRUE, col=\"red\") + xyplot(pred~pop|gen, pdat, type='l', col=\"darkgreen\", lwd=2)"},{"path":"/reference/cate.potassium.html","id":null,"dir":"Reference","previous_headings":"","what":"Relative cotton yield for different soil potassium concentrations — cate.potassium","title":"Relative cotton yield for different soil potassium concentrations — cate.potassium","text":"Relative cotton yield different soil potassium concentrations","code":""},{"path":"/reference/cate.potassium.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Relative cotton yield for different soil potassium concentrations — cate.potassium","text":"data frame 24 observations following 2 variables. yield Relative yield potassium Soil potassium, ppm","code":""},{"path":"/reference/cate.potassium.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Relative cotton yield for different soil potassium concentrations — cate.potassium","text":"Cate & Nelson used data determine minimum optimal amount soil potassium achieve maximum yield. Note, Fig 1 Cate & Nelson match data Table 2. sort appears points high-concentrations potassium shifted left truncation point. Also, calculations quite match results Table 1. Perhaps published data rounded?","code":""},{"path":"/reference/cate.potassium.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Relative cotton yield for different soil potassium concentrations — cate.potassium","text":"Cate, R.B. Nelson, L.. (1971). simple statistical procedure partitioning soil test correlation data two classes. Soil Science Society America Journal, 35, 658–660. https://doi.org/10.2136/sssaj1971.03615995003500040048x","code":""},{"path":"/reference/cate.potassium.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Relative cotton yield for different soil potassium concentrations — cate.potassium","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cate.potassium) dat <- cate.potassium names(dat) <- c('y','x') CateNelson <- function(dat){ dat <- dat[order(dat$x),] # Sort the data by x x <- dat$x y <- dat$y # Create a data.frame to store the results out <- data.frame(x=NA, mean1=NA, css1=NA, mean2=NA, css2=NA, r2=NA) css <- function(x) { var(x) * (length(x)-1) } tcss <- css(y) # Total corrected sum of squares for(i in 2:(length(y)-2)){ y1 <- y[1:i] y2 <- y[-(1:i)] out[i, 'x'] <- x[i] out[i, 'mean1'] <- mean(y1) out[i, 'mean2'] <- mean(y2) out[i, 'css1'] <- css1 <- css(y1) out[i, 'css2'] <- css2 <- css(y2) out[i, 'r2'] <- ( tcss - (css1+css2)) / tcss } return(out) } cn <- CateNelson(dat) ix <- which.max(cn$r2) with(dat, plot(y~x, ylim=c(0,110), xlab=\"Potassium\", ylab=\"Yield\")) title(\"cate.potassium - Cate-Nelson analysis\") abline(v=dat$x[ix], col=\"skyblue\") abline(h=(dat$y[ix] + dat$y[ix+1])/2, col=\"skyblue\") # another approach with similar results # https://joe.org/joe/2013october/tt1.php libs(\"rcompanion\") cateNelson(dat$x, dat$y, plotit=0) } # }"},{"path":"/reference/chakravertti.factorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"Factorial experiment rice, 3x5x3x3.","code":""},{"path":"/reference/chakravertti.factorial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"","code":"data(\"chakravertti.factorial\")"},{"path":"/reference/chakravertti.factorial.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"data frame 405 observations following 7 variables. block block/field yield yield date planting date, 5 levels gen genotype/variety, 3 levels treat treatment combination, 135 levels seeds number seeds per hole, 3 levels spacing spacing, inches, 3 levels","code":""},{"path":"/reference/chakravertti.factorial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"4 treatment factors: 3 Genotypes (varieties): Nehara, Bhasamanik, Bhasakalma 5 Planting dates: Jul 16, Aug 1, Aug 16, Sep 1, Sep 16 3 Spacings: 6 , 9 , 12 inches 3 Seedlings per hole: 1, 2, local method 3x5x3x3=135 treatment combinations. experiment divided 3 blocks (fields). Total 405 plots. \"plots sowing date within block grouped together, position occupied sowing date groups within Within blocks determined random. grouping together plots sewing date adopted facilitate cultural operations. reason, three varieties also laid compact rows. nine combinations spacings seedling numbers thrown random within combination date planting variety shown diagram.\" Note: diagram appears show treatment combinations, physical layout. Basically, date whole-plot effect, genotype sub-plot effect, 9 treatments (spacings * seedlings) completely randomized withing sub-plot effect.","code":""},{"path":"/reference/chakravertti.factorial.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"Chakravertti, S. C. S. S. Bose P. C. Mahalanobis (1936). complex experiment rice Chinsurah farm, Bengal, 1933-34. Indian Journal Agricultural Science, 6, 34-51. https://archive.org/details/.ernet.dli.2015.271737/page/n83/mode/2up","code":""},{"path":"/reference/chakravertti.factorial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"None","code":""},{"path":"/reference/chakravertti.factorial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"","code":"if (FALSE) { # \\dontrun{ libs(agridat) data(chakravertti.factorial) dat <- chakravertti.factorial # Simple means for each factor. Same as Chakravertti Table 3 group_by(dat, gen) group_by(dat, date) group_by(dat, spacing) group_by(dat, seeds) libs(HH) interaction2wt(yield ~ gen + date + spacing + seeds, data=dat, main=\"chakravertti.factorial\") # ANOVA matches Chakravertti table 2 # This has a very interesting error structure. # block:date is error term for date # block:date:gen is error term for gen and date:gen # Residual is error term for all other tests (not needed inside Error()) dat <- transform(dat,spacing=factor(spacing)) m2 <- aov(yield ~ block + date + gen + date:gen + spacing + seeds + seeds:spacing + date:seeds + date:spacing + gen:seeds + gen:spacing + date:gen:seeds + date:gen:spacing + date:seeds:spacing + gen:seeds:spacing + date:gen:seeds:spacing + Error(block/(date + date:gen)), data=dat) summary(m2) } # }"},{"path":"/reference/chinloy.fractionalfactorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"Fractional factorial sugarcane, 1/3 3^5 = 3x3x3x3x3.","code":""},{"path":"/reference/chinloy.fractionalfactorial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"","code":"data(\"chinloy.fractionalfactorial\")"},{"path":"/reference/chinloy.fractionalfactorial.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"data frame 81 observations following 10 variables. yield yield block block row row position col column position trt treatment code n nitrogen treatment, 3 levels 0, 1, 2 p phosphorous treatment, 3 levels 0, 1, 2 k potassium treatment, 3 levels 0, 1, 2 b bagasse treatment, 3 levels 0, 1, 2 m filter press mud treatment, 3 levels 0, 1, 2","code":""},{"path":"/reference/chinloy.fractionalfactorial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"experiment grown 1949 Worthy Park Estate Jamaica. 5 treatment factors: 3 Nitrogen levels: 0, 3, 6 hundred-weight per acre. 3 Phosphorous levels: 0, 4, 8 hundred-weight per acre. 3 Potassium (muriate potash) levels: 0, 1, 2 hundred-weight per acre. 3 Bagasse (applied pre-plant) levels: 0, 20, 40 tons per acre. 3 Filter press mud (applied pre-plant) levels: 0, 10, 20 tons per acre. plot 18 yards long 6 yards (3 rows) wide. Plots arranged nine columns nine, 2-yard space separating plots along rows two guard rows separating plots across rows. Field width: 6 yards * 9 plots + 4 yards * 8 gaps = 86 yards Field length: 18 yards * 9 plots + 2 yards * 8 gaps = 178 yards","code":""},{"path":"/reference/chinloy.fractionalfactorial.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"T. Chinloy, R. F. Innes D. J. Finney. (1953). example fractional replication experiment sugar cane manuring. Journ Agricultural Science, 43, 1-11. https://doi.org/10.1017/S0021859600044567","code":""},{"path":"/reference/chinloy.fractionalfactorial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"None","code":""},{"path":"/reference/chinloy.fractionalfactorial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(chinloy.fractionalfactorial) dat <- chinloy.fractionalfactorial # Treatments are coded with levels 0,1,2. Make sure they are factors dat <- transform(dat, n=factor(n), p=factor(p), k=factor(k), b=factor(b), m=factor(m)) # Experiment layout libs(desplot) desplot(dat, yield ~ col*row, out1=block, text=trt, shorten=\"no\", cex=0.6, aspect=178/86, main=\"chinloy.fractionalfactorial\") # Main effect and some two-way interactions. These match Chinloy table 6. # Not sure how to code terms like p^2k=b^2m m1 <- aov(yield ~ block + n + p + k + b + m + n:p + n:k + n:b + n:m, dat) anova(m1) } # }"},{"path":"/reference/christidis.competition.html","id":null,"dir":"Reference","previous_headings":"","what":"Competition between varieties in cotton — christidis.competition","title":"Competition between varieties in cotton — christidis.competition","text":"Competition varieties cotton, measurements taken row.","code":""},{"path":"/reference/christidis.competition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Competition between varieties in cotton — christidis.competition","text":"","code":"data(\"christidis.competition\")"},{"path":"/reference/christidis.competition.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Competition between varieties in cotton — christidis.competition","text":"data frame 270 observations following 8 variables. plot plot plotrow row within plot block block row row, 1 row col column gen genotype yield yield, kg height height, cm","code":""},{"path":"/reference/christidis.competition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Competition between varieties in cotton — christidis.competition","text":"Nine genotypes/varieties cotton used variety test. plots 100 meters long 2.40 meters wide, plot 3 rows 0.80 meters apart. layout RCB 5 blocks, block 2 replicates every variety (original intention trying 2 seed treatments). row harvested/weighed separately. leaves plants dried fallen, mean height row measured. Christidis found significant competition varieties, due height differences. Crude analysis. TODO: Find better analysis data incorporates field trends competition effects, maybe including random effect border rows genotype pairs (neighbors)?","code":""},{"path":"/reference/christidis.competition.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Competition between varieties in cotton — christidis.competition","text":"Christidis, Basil G (1935). Intervarietal competition yield trials cotton. Journal Agricultural Science, 25, 231-237. Table 1. https://doi.org/10.1017/S0021859600009710","code":""},{"path":"/reference/christidis.competition.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Competition between varieties in cotton — christidis.competition","text":"None","code":""},{"path":"/reference/christidis.competition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Competition between varieties in cotton — christidis.competition","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(christidis.competition) dat <- christidis.competition # Match Christidis Table 2 means # aggregate(yield ~ gen, aggregate(yield ~ gen+plot, dat, sum), mean) # Each RCB block has 2 replicates of each genotype # with(dat, table(block,gen)) libs(lattice) # Tall plants yield more # xyplot(yield ~ height|gen, data=dat) # Huge yield variation across field. Also heterogeneous variance. xyplot(yield ~ col, dat, group=gen, auto.key=list(columns=5), main=\"christidis.competition\") libs(mgcv) if(is.element(\"package:gam\", search())) detach(\"package:gam\") # Simple non-competition model to remove main effects m1 <- gam(yield ~ gen + s(col), data=dat) p1 <- as.data.frame(predict(m1, type=\"terms\")) names(p1) <- c('geneff','coleff') dat2 <- cbind(dat, p1) dat2 <- transform(dat2, res=yield-geneff-coleff) libs(lattice) xyplot(res ~ col, data=dat2, group=gen, main=\"christidis.competition - residuals\") } # }"},{"path":"/reference/christidis.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton — christidis.cotton.uniformity","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"Uniformity trial cotton Greece, 1938","code":""},{"path":"/reference/christidis.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"","code":"data(\"christidis.cotton.uniformity\")"},{"path":"/reference/christidis.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"data frame 1024 observations following 4 variables. col column row row yield yield, kg/unit block block factor","code":""},{"path":"/reference/christidis.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"experiment conducted 1938 Sindos Greek Cotton Research Institute. block consisted 20 rows, 1 meter apart 66 meters long. Two rows side 1 meter end removed borders. row divided 4 meter-lengths harvested separately. 4 blocks, oriented 0, 30, 60, 90 degrees. block contained 16 rows, 64 meters long. Field width: 16 units * 4 m = 64 m Field depth: 16 rows * 1 m = 16 m","code":""},{"path":"/reference/christidis.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"Christidis, B. G. (1939). Variability Plots Various Shapes Affected Plot Orientation. Empire Journal Experimental Agriculture 7: 330-342. Table 1.","code":""},{"path":"/reference/christidis.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"None","code":""},{"path":"/reference/christidis.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(christidis.cotton.uniformity) dat <- christidis.cotton.uniformity # Match the mean yields in table 2. Not sure why '16' is needed # sapply(split(dat$yield, dat$block), mean)*16 libs(desplot) dat$yld <- dat$yield/4*1000 # re-scale to match Christidis fig 1 desplot(dat, yld ~ col*row|block, flip=TRUE, aspect=(16)/(64), main=\"christidis.cotton.uniformity\") } # }"},{"path":"/reference/christidis.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — christidis.wheat.uniformity","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"Uniformity trial wheat Cambridge, UK 1931.","code":""},{"path":"/reference/christidis.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"","code":"data(\"christidis.wheat.uniformity\")"},{"path":"/reference/christidis.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"data frame 288 observations following 3 variables. row row col column yield yield","code":""},{"path":"/reference/christidis.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"Two blocks, 24 rows . block , 90-foot row divided 12 units, unit 7.5 feet long. Rows 8 inches wide. Field width: 12 units * 7.5 feet = 90 feet Field length: 24 rows * 8 inches = 16 feet","code":""},{"path":"/reference/christidis.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"Christidis, Basil G (1931). importance shape plots field experimentation. Journal Agricultural Science, 21, 14-37. Table VI, p. 28. https://dx.doi.org/10.1017/S0021859600007942","code":""},{"path":"/reference/christidis.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"None","code":""},{"path":"/reference/christidis.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(christidis.wheat.uniformity) dat <- christidis.wheat.uniformity # sum(dat$yield) # Matches Christidis libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=16/90, # true aspect main=\"christidis.wheat.uniformity\") } # }"},{"path":"/reference/cleveland.soil.html","id":null,"dir":"Reference","previous_headings":"","what":"Soil resistivity in a field — cleveland.soil","title":"Soil resistivity in a field — cleveland.soil","text":"Soil resistivity field","code":""},{"path":"/reference/cleveland.soil.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Soil resistivity in a field — cleveland.soil","text":"data frame 8641 observations following 5 variables. northing y ordinate easting x ordinate resistivity Soil resistivity, ohms .ns Indicator north/south track track Track number","code":""},{"path":"/reference/cleveland.soil.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Soil resistivity in a field — cleveland.soil","text":"Resistivity related soil salinity. Electronic version data retrieved http://lib.stat.cmu.edu/datasets/Andrews/ Cleaned version Luke Tierney https://homepage.stat.uiowa.edu/~luke/classes/248/examples/soil","code":""},{"path":"/reference/cleveland.soil.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Soil resistivity in a field — cleveland.soil","text":"William Cleveland, (1993). Visualizing Data.","code":""},{"path":"/reference/cleveland.soil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Soil resistivity in a field — cleveland.soil","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cleveland.soil) dat <- cleveland.soil # Similar to Cleveland fig 4.64 ## libs(latticeExtra) ## redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) ## levelplot(resistivity ~ easting + northing, data = dat, ## col.regions=redblue, ## panel=panel.levelplot.points, ## aspect=2.4, xlab= \"Easting (km)\", ylab= \"Northing (km)\", ## main=\"cleveland\") # 2D loess plot. Cleveland fig 4.68 sg1 <- expand.grid(easting = seq(.15, 1.410, by = .02), northing = seq(.150, 3.645, by = .02)) lo1 <- loess(resistivity~easting*northing, data=dat, span = 0.1, degree = 2) fit1 <- predict(lo1, sg1) libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(fit1 ~ sg1$easting * sg1$northing, col.regions=redblue, cuts = 9, aspect=2.4, xlab = \"Easting (km)\", ylab = \"Northing (km)\", main=\"cleveland.soil - 2D smooth of Resistivity\") # 3D loess plot with data overlaid libs(rgl) bg3d(color = \"white\") clear3d() points3d(dat$easting, dat$northing, dat$resistivity / 100, col = rep(\"gray50\", nrow(dat))) rgl::surface3d(seq(.15, 1.410, by = .02), seq(.150, 3.645, by = .02), fit1/100, alpha=0.9, col=rep(\"wheat\", length(fit1)), front=\"fill\", back=\"fill\") close3d() } # }"},{"path":"/reference/cochran.beets.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"Yield number plants sugarbeet fertilizer experiment.","code":""},{"path":"/reference/cochran.beets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"","code":"data(\"cochran.beets\")"},{"path":"/reference/cochran.beets.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"data frame 42 observations following 4 variables. fert fertilizer treatment block block yield yield, tons/acres plants number plants per plot","code":""},{"path":"/reference/cochran.beets.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"Yield (tons/acre) number beets per plot. Fertilizer treatments combine superphosphate (P), muriate potash (K), sodium nitrate (N).","code":""},{"path":"/reference/cochran.beets.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"George Snedecor (1946). Statisitcal Methods, 4th ed. Table 12.13, p. 332.","code":""},{"path":"/reference/cochran.beets.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"H. Fairfield Smith (1957). Interpretation Adjusted Treatment Means Regressions Analysis Covariance. Biometrics, 13, 282-308. https://doi.org/10.2307/2527917","code":""},{"path":"/reference/cochran.beets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.beets) dat = cochran.beets # P has strong effect libs(lattice) xyplot(yield ~ plants|fert, dat, main=\"cochran.beets\") } # }"},{"path":"/reference/cochran.bib.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"Balanced incomplete block design corn","code":""},{"path":"/reference/cochran.bib.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"data frame 52 observations following 3 variables. loc location/block, 13 levels gen genotype/line, 13 levels yield yield, pounds/plot","code":""},{"path":"/reference/cochran.bib.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"Incomplete block design. loc/block 4 genotypes/lines. blocks planted different locations. Conducted 1943 North Carolina.","code":""},{"path":"/reference/cochran.bib.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"North Carolina Agricultural Experiment Station, United States Department Agriculture.","code":""},{"path":"/reference/cochran.bib.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"Cochran, W.G. Cox, G.M. (1957), Experimental Designs, 2nd ed., Wiley Sons, New York, p. 448.","code":""},{"path":"/reference/cochran.bib.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.bib) dat <- cochran.bib # Show the incomplete-block structure libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(yield~loc*gen, dat, col.regions=redblue, xlab=\"loc (block)\", main=\"cochran.bib - incomplete blocks\") with(dat, table(gen,loc)) rowSums(as.matrix(with(dat, table(gen,loc)))) colSums(as.matrix(with(dat, table(gen,loc)))) m1 = aov(yield ~ gen + Error(loc), data=dat) summary(m1) libs(nlme) m2 = lme(yield ~ -1 + gen, data=dat, random=~1|loc) } # }"},{"path":"/reference/cochran.crd.html","id":null,"dir":"Reference","previous_headings":"","what":"Potato scab infection with sulfur treatments — cochran.crd","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"Potato scab infection sulfur treatments","code":""},{"path":"/reference/cochran.crd.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"data frame 32 observations following 5 variables. inf infection percent trt treatment factor row row col column","code":""},{"path":"/reference/cochran.crd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"experiment conducted investigate effect sulfur controlling scab disease potatoes. seven treatments. Control, plus spring fall application 300, 600, 1200 pounds/acre sulfur. response variable infection percent surface area covered scab. completely randomized design used 8 replications control 4 replications treatments. Although original analysis show significant differences sulfur treatments, including polynomial trend model uncovered significant differences (Tamura, 1988).","code":""},{"path":"/reference/cochran.crd.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"W.G. Cochran G. Cox, 1957. Experimental Designs, 2nd ed. John Wiley, New York.","code":""},{"path":"/reference/cochran.crd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"Tamura, R.N. Nelson, L.. Naderman, G.C., (1988). investigation validity usefulness trend analysis field plot data. Agronomy Journal, 80, 712-718. https://doi.org/10.2134/agronj1988.00021962008000050003x","code":""},{"path":"/reference/cochran.crd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.crd) dat <- cochran.crd # Field plan libs(desplot) desplot(dat, inf~col*row, text=trt, cex=1, # aspect unknown main=\"cochran.crd\") # CRD anova. Table 6 of Tamura 1988 contrasts(dat$trt) <- cbind(c1=c(1,1,1,-6,1,1,1), # Control vs Sulf c2=c(-1,-1,-1,0,1,1,1)) # Fall vs Sp m1 <- aov(inf ~ trt, data=dat) anova(m1) summary(m1, split=list(trt=list(\"Control vs Sulf\"=1, \"Fall vs Spring\"=2))) # Quadratic polynomial for columns...slightly different than Tamura 1988 m2 <- aov(inf ~ trt + poly(col,2), data=dat) anova(m2) summary(m2, split=list(trt=list(\"Control vs Sulf\"=1, \"Fall vs Spring\"=2))) } # }"},{"path":"/reference/cochran.eelworms.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"Counts eelworms fumigant treatments","code":""},{"path":"/reference/cochran.eelworms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"data frame 48 observations following 7 variables. block block factor, 4 levels row row col column fumigant fumigant factor dose dose, Numeric 0,1,2. Maybe factor? initial count eelworms pre-treatment final count eelworms post-treatment grain grain yield pounds straw straw yield pounds weeds ratio weeds total oats","code":""},{"path":"/reference/cochran.eelworms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"soil fumigation experiment Spring Oats, conducted 1935. plot 30 links x 41.7 links, clear side plot specific length. Treatment codes: Con = Control, Chl = Chlorodinitrobenzen, Cym = Cymag, Car = Carbon Disulphide jelly, See = Seekay. Experiment conducted 1935 Rothamsted Experiment Station. early March 400 grams soil (4 x 100g) sampled number eelworm cysts counted. Fumigants added soil, oats sown later harvested. October, plots sampled final count cysts recorded. Rothamsted report concludes \"Car\" \"Cym\" produced higher yields, due partly nitrogen fumigant, \"Chl\" decreased yield. fumigants reduced weeds. crop 'unusually weedy'. \"Car\" \"See\" decreased number eelworm cysts soil. original data can found Rothamsted Report. report notes position blocks field slightly different shown. experiment plan shown Bailey (2008, p. 73), shows columns 9-11 shifted slightly upward. clear . Thanks U.Genschel identifying typo.","code":""},{"path":"/reference/cochran.eelworms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"Cochran Cox, 1950. Experimental Designs. Table 3.1.","code":""},{"path":"/reference/cochran.eelworms.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"R. . Bailey (2008). Design Comparative Experiments. Cambridge. Experiments Rothamsted (1936). Report 1935, Rothamsted Research. pp 174 - 193. https://doi.org/10.23637/ERADOC-1-67","code":""},{"path":"/reference/cochran.eelworms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.eelworms) dat <- cochran.eelworms libs(lattice) splom(dat[ , 5:10], group=dat$fumigant, auto.key=TRUE, main=\"cochran.eelworms\") libs(desplot) desplot(dat, fumigant~col*row, text=dose, flip=TRUE, cex=2) # Very strong spatial trends desplot(dat, initial ~ col*row, flip=TRUE, # aspect unknown main=\"cochran.eelworms\") # final counts are strongly related to initial counts libs(lattice) xyplot(final~initial|factor(dose), data=dat, group=fumigant, main=\"cochran.eelworms - by dose (panel) & fumigant\", xlab=\"Initial worm count\", ylab=\"Final worm count\", auto.key=list(columns=5)) # One approach...log transform, use 'initial' as covariate, create 9 treatments dat <- transform(dat, trt=factor(paste0(fumigant, dose))) m1 <- aov(log(final) ~ block + trt + log(initial), data=dat) anova(m1) } # }"},{"path":"/reference/cochran.factorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"Factorial experiment beans, 2x2x2x2.","code":""},{"path":"/reference/cochran.factorial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"","code":"data(\"cochran.factorial\")"},{"path":"/reference/cochran.factorial.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"data frame 32 observations following 4 variables. rep rep factor block block factor trt treatment factor, 16 levels yield yield (pounds) d dung treatment, 2 levels n nitrogen treatment, 2 levels p phosphorous treatment, 2 levels k potassium treatment, 2 levels","code":""},{"path":"/reference/cochran.factorial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"Conducted Rothamsted Experiment Station 1936. 4 treatment factors: 2 d dung levels: None, 10 tons/acre. 2 n nitrochalk levels: None, 0.4 hundredweight nitrogen per acre. 2 p superphosphate levels: None, 0.6 hundredweight per acre 2 k muriate potash levels: None, 1 hundredweight K20 per acres. response variable yield beans.","code":""},{"path":"/reference/cochran.factorial.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"Cochran, W.G. Cox, G.M. (1957), Experimental Designs, 2nd ed., Wiley Sons, New York, p. 160.","code":""},{"path":"/reference/cochran.factorial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.factorial) dat <- cochran.factorial # Ensure factors dat <- transform(dat, d=factor(d), n=factor(n), p=factor(p), k=factor(k)) # Cochran table 6.5. m1 <- lm(yield ~ rep * block + (d+n+p+k)^3, data=dat) anova(m1) libs(FrF2) aliases(m1) MEPlot(m1, select=3:6, main=\"cochran.factorial - main effects plot\") } # }"},{"path":"/reference/cochran.latin.html","id":null,"dir":"Reference","previous_headings":"","what":"Latin square design in wheat — cochran.latin","title":"Latin square design in wheat — cochran.latin","text":"Six wheat plots sampled six operators shoot heights measured. operators sampled plots six ordered sequences. dependent variate difference measured height true height plot.","code":""},{"path":"/reference/cochran.latin.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Latin square design in wheat — cochran.latin","text":"data frame 36 observations following 4 variables. row row col column operator operator factor diff difference measured height true height","code":""},{"path":"/reference/cochran.latin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latin square design in wheat — cochran.latin","text":"Cochran, W.G. Cox, G.M. (1957), Experimental Designs, 2nd ed., Wiley Sons, New York.","code":""},{"path":"/reference/cochran.latin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Latin square design in wheat — cochran.latin","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.latin) dat <- cochran.latin libs(desplot) desplot(dat, diff~col*row, text=operator, cex=1, # aspect unknown main=\"cochran.latin\") dat <- transform(dat, rf=factor(row), cf=factor(col)) aov.dat <- aov(diff ~ operator + Error(rf*cf), dat) summary(aov.dat) model.tables(aov.dat, type=\"means\") } # }"},{"path":"/reference/cochran.lattice.html","id":null,"dir":"Reference","previous_headings":"","what":"Balanced lattice experiment in cotton — cochran.lattice","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"Balanced lattice experiment cotton","code":""},{"path":"/reference/cochran.lattice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"","code":"data(\"cochran.lattice\")"},{"path":"/reference/cochran.lattice.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"data frame 80 observations following 5 variables. y percent affected flower buds rep replicate row row col column trt treatment factor","code":""},{"path":"/reference/cochran.lattice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"experiment balanced lattice square 16 treatments 4x4 layout 5 replicates. treatments applied cotton plants. plot ten rows wide 70 feet long (1/18 acre). (Estimated plot width 34.5 feet.) Data collected middle 4 rows. data percentages squares showing attack boll weevils. 'square' name given young flower bud. plot orientation clear.","code":""},{"path":"/reference/cochran.lattice.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"William G. Cochran, Gertrude M. Cox. Experimental Designs, 2nd Edition. Page 490. Originally : F. M. Wadley (1946). Incomplete block designs insect population problems. J. Economic Entomology, 38, 651–654.","code":""},{"path":"/reference/cochran.lattice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"Walter Federer. Combining Standard Block Analyses Spatial Analyses Random Effects Model. Cornell Univ Tech Report BU-1373-MA. https://hdl.handle.net/1813/31971","code":""},{"path":"/reference/cochran.lattice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.lattice) dat <- cochran.lattice libs(desplot) desplot(dat, y~row*col|rep, text=trt, # aspect unknown, should be 2 or .5 main=\"cochran.lattice\") # Random rep,row,column model often used by Federer libs(lme4) dat <- transform(dat, rowf=factor(row), colf=factor(col)) m1 <- lmer(y ~ trt + (1|rep) + (1|rep:row) + (1|rep:col), data=dat) summary(m1) } # }"},{"path":"/reference/cochran.wireworms.html","id":null,"dir":"Reference","previous_headings":"","what":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"Wireworms controlled fumigants latin square","code":""},{"path":"/reference/cochran.wireworms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"data frame 25 observations following 4 variables. row row col column trt fumigant treatment, 5 levels worms count wireworms per plot","code":""},{"path":"/reference/cochran.wireworms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"Plots approximately 22 cm 13 cm. Layout experiment latin square. number wireworms plot counted, following soil fumigation previous year.","code":""},{"path":"/reference/cochran.wireworms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"W. G. Cochran (1938). difficulties statistical analysis replicated experiments. Empire Journal Experimental Agriculture, 6, 157–175.","code":""},{"path":"/reference/cochran.wireworms.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"Ron Snee (1980). Graphical Display Means. American Statistician, 34, 195-199. https://www.jstor.org/stable/2684060 https://doi.org/10.1080/00031305.1980.10483028 W. Cochran (1940). analysis variance experimental errors follow Poisson binomial laws. Annals Mathematical Statistics, 11, 335-347. https://www.jstor.org/stable/2235680 G W Snedecor W G Cochran, 1980. Statistical Methods, Iowa State University Press. Page 288.","code":""},{"path":"/reference/cochran.wireworms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.wireworms) dat <- cochran.wireworms libs(desplot) desplot(dat, worms ~ col*row, text=trt, cex=1, # aspect unknown main=\"cochran.wireworms\") # Trt K is effective, but not the others. Really, this says it all. libs(lattice) bwplot(worms ~ trt, dat, main=\"cochran.wireworms\", xlab=\"Treatment\") # Snedecor and Cochran do ANOVA on sqrt(x+1). dat <- transform(dat, rowf=factor(row), colf=factor(col)) m1 <- aov(sqrt(worms+1) ~ rowf + colf + trt, data=dat) anova(m1) # Instead of transforming, use glm m2 <- glm(worms ~ trt + rowf + colf, data=dat, family=\"poisson\") anova(m2) # GLM with random blocking. libs(lme4) m3 <- glmer(worms ~ -1 +trt +(1|rowf) +(1|colf), data=dat, family=\"poisson\") summary(m3) ## Fixed effects: ## Estimate Std. Error z value Pr(>|z|) ## trtK 0.1393 0.4275 0.326 0.745 ## trtM 1.7814 0.2226 8.002 1.22e-15 *** ## trtN 1.9028 0.2142 8.881 < 2e-16 *** ## trtO 1.7147 0.2275 7.537 4.80e-14 *** } # }"},{"path":"/reference/connolly.potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Potato yields in single-drill plots — connolly.potato","title":"Potato yields in single-drill plots — connolly.potato","text":"Potato yields single-drill plots","code":""},{"path":"/reference/connolly.potato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Potato yields in single-drill plots — connolly.potato","text":"","code":"data(\"connolly.potato\")"},{"path":"/reference/connolly.potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Potato yields in single-drill plots — connolly.potato","text":"data frame 80 observations following 6 variables. rep block gen variety row row col column yield yield, kg/ha matur maturity group","code":""},{"path":"/reference/connolly.potato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Potato yields in single-drill plots — connolly.potato","text":"Connolly et el use data illustrate yield can affected competition neighboring plots. data uses M1, M2, M3 maturity, Connolly et al use FE (first early), SE (second early) M (maincrop). trial 20 sections, independent row 20 drills. data four reps single-drill plots sections 1, 6, 11, 16. neighbor covariate plot defined average plots left right. drills edge trial, covariate average one neighboring plot yield section (.e. rep) mean. interesting fit model uses differences maturity plot neighbor actual covariate. https://doi.org/10.1111/j.1744-7348.1993.tb04099.x Used permission Iain Currie.","code":""},{"path":"/reference/connolly.potato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Potato yields in single-drill plots — connolly.potato","text":"Connolly, T Currie, ID Bradshaw, JE McNicol, JW. (1993). Inter-plot competition yield trials potatoes Solanum tuberosum L. single-drill plots. Annals Applied Biology, 123, 367-377.","code":""},{"path":"/reference/connolly.potato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Potato yields in single-drill plots — connolly.potato","text":"","code":"library(agridat) data(connolly.potato) dat <- connolly.potato # Field plan libs(desplot) desplot(dat, yield~col*row, out1=rep, # aspect unknown main=\"connolly.potato yields (reps not contiguous)\") # Later maturities are higher yielding libs(lattice) bwplot(yield~matur, dat, main=\"connolly.potato yield by maturity\") # Observed raw means. Matches Connolly table 2. mn <- aggregate(yield~gen, data=dat, FUN=mean) mn[rev(order(mn$yield)),] #> gen yield #> 8 V08 16.200 #> 19 V19 14.450 #> 10 V10 13.925 #> 12 V12 13.500 #> 7 V07 13.300 #> 20 V20 12.975 #> 14 V14 12.975 #> 6 V06 12.625 #> 11 V11 12.575 #> 16 V16 11.900 #> 3 V03 11.650 #> 9 V09 11.500 #> 1 V01 11.275 #> 18 V18 10.650 #> 2 V02 10.325 #> 17 V17 10.200 #> 15 V15 10.125 #> 13 V13 10.050 #> 4 V04 9.425 #> 5 V05 9.275 # Create a covariate which is the average of neighboring plot yields libs(reshape2) mat <- acast(dat, row~col, value.var='yield') mat2 <- matrix(NA, nrow=4, ncol=20) mat2[,2:19] <- (mat[ , 1:18] + mat[ , 3:20])/2 mat2[ , 1] <- (mat[ , 1] + apply(mat, 1, mean))/2 mat2[ , 20] <- (mat[ , 20] + apply(mat, 1, mean))/2 dat2 <- melt(mat2) colnames(dat2) <- c('row','col','cov') dat <- merge(dat, dat2) # xyplot(yield ~ cov, data=dat, type=c('p','r')) # Connolly et al fit a model with avg neighbor yield as a covariate m1 <- lm(yield ~ 0 + gen + rep + cov, data=dat) coef(m1)['cov'] # = -.303 (Connolly obtained -.31) #> cov #> -0.3030545 # Block names and effects bnm <- c(\"R1\",\"R2\",\"R3\",\"R4\") beff <- c(0, coef(m1)[c('repR2','repR3','repR4')]) # Variety names and effects vnm <- paste0(\"V\", formatC(1:20, width=2, flag='0')) veff <- coef(m1)[1:20] # Adjust yield for variety and block effects dat <- transform(dat, yadj = yield - beff[match(rep,bnm)] - veff[match(gen,vnm)]) # Similar to Connolly Fig 1. Point pattern doesn't quite match xyplot(yadj~cov, data=dat, type=c('p','r'), main=\"connolly.potato\", xlab=\"Avg yield of nearest neighbors\", ylab=\"Yield, adjusted for variety and block effects\")"},{"path":"/reference/coombs.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"Uniformity trial rice Malaysia","code":""},{"path":"/reference/coombs.rice.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"","code":"data(\"coombs.rice.uniformity\")"},{"path":"/reference/coombs.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"data frame 54 observations following 3 variables. row row col column yield yield gantangs per plot","code":""},{"path":"/reference/coombs.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"Estimated harvest date 1915 earlier. Field length, 18 plots * 1/2 chain. Field width, 3 plots * 1/2 chain.","code":""},{"path":"/reference/coombs.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"Coombs, G. E. J. Grantham (1916). Field Experiments Interpretation results. Agriculture Bulletin Federated Malay States, 7. https://www.google.com/books/edition/The_Agricultural_Bulletin_of_the_Federat/M2E4AQAAMAAJ","code":""},{"path":"/reference/coombs.rice.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"None","code":""},{"path":"/reference/coombs.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(coombs.rice.uniformity) dat <- coombs.rice.uniformity # Data check. Matches Coombs 709.4 # sum(dat$yield) # There are an excess number of 12s and 14s in the yield libs(lattice) qqmath( ~ yield, dat) # weird libs(desplot) desplot(dat, yield ~ col*row, main=\"coombs.rice.uniformity\", flip=TRUE, aspect=(18 / 3)) } # }"},{"path":"/reference/cornelius.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"Maize yields 9 cultivars 20 locations.","code":""},{"path":"/reference/cornelius.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"","code":"data(\"cornelius.maize\")"},{"path":"/reference/cornelius.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"data frame 180 observations following 3 variables. env environment factor, 20 levels gen genotype/cultivar, 9 levels yield yield, kg/ha","code":""},{"path":"/reference/cornelius.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"Cell means (kg/hectare) CIMMYT EVT16B maize yield trial.","code":""},{"path":"/reference/cornelius.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"P L Cornelius J Crossa M S Seyedsadr. (1996). Statistical Tests Estimators Multiplicative Models Genotype--Environment Interaction. Book: Genotype--Environment Interaction. Pages 199-234.","code":""},{"path":"/reference/cornelius.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"Forkman, Johannes Piepho, Hans-Peter. (2014). Parametric bootstrap methods testing multiplicative terms GGE AMMI models. Biometrics, 70(3), 639-647. https://doi.org/10.1111/biom.12162","code":""},{"path":"/reference/cornelius.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cornelius.maize) dat <- cornelius.maize # dotplot(gen~yield|env,dat) # We cannot compare genotype yields easily # Subtract environment mean from each observation libs(reshape2) mat <- acast(dat, gen~env) mat <- scale(mat, scale=FALSE) dat2 <- melt(mat) names(dat2) <- c('gen','env','yield') libs(lattice) bwplot(yield ~ gen, dat2, main=\"cornelius.maize - environment centered yields\") if(0){ # This reproduces the analysis of Forkman and Piepho. test.pc <- function(Y0, type=\"AMMI\", n.boot=10000, maxpc=6) { # Test the significance of Principal Components in GGE/AMMI # Singular value decomposition of centered/double-centered Y Y <- sweep(Y0, 1, rowMeans(Y0)) # subtract environment means if(type==\"AMMI\") { Y <- sweep(Y, 2, colMeans(Y0)) # subtract genotype means Y <- Y + mean(Y0) } lam <- svd(Y)$d # Observed value of test statistic. # t.obs[k] is the proportion of variance explained by the kth term out of # the k...M terms, e.g. t.obs[2] is lam[2]^2 / sum(lam[2:M]^2) t.obs <- { lam^2/rev(cumsum(rev(lam^2))) } [1:(M-1)] t.boot <- matrix(NA, nrow=n.boot, ncol=M-1) # Centering rows/columns reduces the rank by 1 in each direction. I <- if(type==\"AMMI\") nrow(Y0)-1 else nrow(Y0) J <- ncol(Y0)-1 M <- min(I, J) # rank of Y, maximum number of components M <- min(M, maxpc) # Optional step: No more than 5 components for(K in 0:(M-2)){ # 'K' multiplicative components in the svd for(bb in 1:n.boot){ E.b <- matrix(rnorm((I-K) * (J-K)), nrow = I-K, ncol = J-K) lam.b <- svd(E.b)$d t.boot[bb, K+1] <- lam.b[1]^2 / sum(lam.b^2) } } # P-value for each additional multiplicative term in the SVD. # P-value is the proportion of time bootstrap values exceed t.obs colMeans(t.boot > matrix(rep(t.obs, n.boot), nrow=n.boot, byrow=TRUE)) } dat <- cornelius.maize # Convert to matrix format libs(reshape2) dat <- acast(dat, env~gen, value.var='yield') ## R> test.pc(dat,\"AMMI\") ## [1] 0.0000 0.1505 0.2659 0.0456 0.1086 # Forkman: .00 .156 .272 .046 .111 ## R> test.pc(dat,\"GGE\") ## [1] 0.0000 0.2934 0.1513 0.0461 0.2817 # Forkman: .00 .296 .148 .047 .285 } } # }"},{"path":"/reference/corsten.interaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn — corsten.interaction","title":"Multi-environment trial of corn — corsten.interaction","text":"data yield (kg/acre) 20 genotypes corn 7 locations.","code":""},{"path":"/reference/corsten.interaction.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn — corsten.interaction","text":"data frame 140 observations following 3 variables. gen genotype, 20 levels loc location, 7 levels yield yield, kg/acre","code":""},{"path":"/reference/corsten.interaction.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn — corsten.interaction","text":"data used Corsten & Denis (1990) illustrate two-way clustering minimizing interaction sum squares. paper, labels location dendrogram slight typo. order loc labels shown 1 2 3 4 5 6 7. correct order loc labels 1 2 4 5 6 7 3. Used permission Jean-Baptiste Denis.","code":""},{"path":"/reference/corsten.interaction.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn — corsten.interaction","text":"L C Corsten J B Denis, (1990). Structuring Interaction Two-Way Tables Clustering. Biometrics, 46, 207–215. Table 1. https://doi.org/10.2307/2531644","code":""},{"path":"/reference/corsten.interaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn — corsten.interaction","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(corsten.interaction) dat <- corsten.interaction libs(reshape2) m1 <- melt(dat, measure.var='yield') dmat <- acast(m1, loc~gen) # Corsten (1990) uses this data to illustrate simultaneous row and # column clustering based on interaction sums-of-squares. # There is no (known) function in R to reproduce this analysis # (please contact the package maintainer if this is not true). # For comparison, the 'heatmap' function clusters the rows and # columns _independently_ of each other. heatmap(dmat, main=\"corsten.interaction\") } # }"},{"path":"/reference/cox.stripsplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"Strip-split-plot barley fertilizer, calcium, soil factors.","code":""},{"path":"/reference/cox.stripsplit.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"data frame 96 observations following 5 variables. rep replicate, 4 levels soil soil, 3 levels fert fertilizer, 4 levels calcium calcium, 2 levels yield yield winter barley","code":""},{"path":"/reference/cox.stripsplit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"Four different fertilizer treatments laid vertical strips, split subplots different levels calcium. Soil type stripped across split-plot experiment, entire experiment replicated three times. Sometimes called split-block design.","code":""},{"path":"/reference/cox.stripsplit.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"Comes notes Gertrude Cox . Rotti.","code":""},{"path":"/reference/cox.stripsplit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"SAS/STAT(R) 9.2 User's Guide, Second Edition. Example 23.5 Strip-Split Plot. https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_anova_sect030.htm","code":""},{"path":"/reference/cox.stripsplit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cox.stripsplit) dat <- cox.stripsplit # Raw means # aggregate(yield ~ calcium, data=dat, mean) # aggregate(yield ~ soil, data=dat, mean) # aggregate(yield ~ calcium, data=dat, mean) libs(HH) interaction2wt(yield ~ rep + soil + fert + calcium, dat, x.between=0, y.between=0, main=\"cox.stripsplit\") # Traditional AOV m1 <- aov(yield~ fert*calcium*soil + Error(rep/(fert+soil+calcium:fert+soil:fert)), data=dat) summary(m1) # With balanced data, the following are all basically identical libs(lme4) # The 'rep:soil:fert' term causes problems...so we drop it. m2 <- lmer(yield ~ fert*soil*calcium + (1|rep) + (1|rep:fert) + (1|rep:soil) + (1|rep:fert:calcium), data=dat) if(0){ # afex uses Kenword-Rogers approach for denominator d.f. libs(afex) mixed(yield ~ fert*soil*calcium + (1|rep) + (1|rep:fert) + (1|rep:soil) + (1|rep:fert:calcium) + (1|rep:soil:fert), data=dat, control=lmerControl(check.nobs.vs.rankZ=\"ignore\")) ## Effect stat ndf ddf F.scaling p.value ## 1 (Intercept) 1350.8113 1 3.0009 1 0.0000 ## 2 fert 3.5619 3 9.0000 1 0.0604 ## 3 soil 3.4659 2 6.0000 1 0.0999 ## 4 calcium 1.8835 1 12.0000 1 0.1950 ## 5 fert:soil 1.2735 6 18.0000 1 0.3179 ## 6 fert:calcium 4.4457 3 12.0000 1 0.0255 ## 7 soil:calcium 0.2494 2 24.0000 1 0.7813 ## 8 fert:soil:calcium 0.3504 6 24.0000 1 0.9027 } } # }"},{"path":"/reference/cramer.cucumber.html","id":null,"dir":"Reference","previous_headings":"","what":"Cucumber yields and quantitative traits — cramer.cucumber","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"Cucumber yields quantitative traits","code":""},{"path":"/reference/cramer.cucumber.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"","code":"data(\"cramer.cucumber\")"},{"path":"/reference/cramer.cucumber.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"data frame 24 observations following 9 variables. cycle cycle rep replicate plants plants per plot flowers number pistillate flowers branches number branches leaves number leaves totalfruit total fruit number culledfruit culled fruit number earlyfruit early fruit number","code":""},{"path":"/reference/cramer.cucumber.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"data used illustrate path analysis correlations phenotypic traits. Used permission Christopher Cramer.","code":""},{"path":"/reference/cramer.cucumber.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"Christopher S. Cramer, Todd C. Wehner, Sandra B. Donaghy. 1999. Path Coefficient Analysis Quantitative Traits. : Handbook Formulas Software Plant Geneticists Breeders, page 89.","code":""},{"path":"/reference/cramer.cucumber.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"Cramer, C. S., T. C. Wehner, S. B. Donaghy. 1999. PATHSAS: SAS computer program path coefficient analysis quantitative data. J. Hered, 90, 260-262 https://doi.org/10.1093/jhered/90.1.260","code":""},{"path":"/reference/cramer.cucumber.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cramer.cucumber) dat <- cramer.cucumber libs(lattice) splom(dat[3:9], group=dat$cycle, main=\"cramer.cucumber - traits by cycle\", auto.key=list(columns=3)) # derived traits dat <- transform(dat, marketable = totalfruit-culledfruit, branchesperplant = branches/plants, nodesperbranch = leaves/(branches+plants), femalenodes = flowers+totalfruit) dat <- transform(dat, perfenod = (femalenodes/leaves), fruitset = totalfruit/flowers, fruitperplant = totalfruit / plants, marketableperplant = marketable/plants, earlyperplant=earlyfruit/plants) # just use cycle 1 dat1 <- subset(dat, cycle==1) # define independent and dependent variables indep <- c(\"branchesperplant\", \"nodesperbranch\", \"perfenod\", \"fruitset\") dep0 <- \"fruitperplant\" dep <- c(\"marketable\",\"earlyperplant\") # standardize trait data for cycle 1 sdat <- data.frame(scale(dat1[1:8, c(indep,dep0,dep)])) # slopes for dep0 ~ indep X <- as.matrix(sdat[,indep]) Y <- as.matrix(sdat[,c(dep0)]) # estdep <- solve(t(X) estdep <- solve(crossprod(X), crossprod(X,Y)) estdep ## branchesperplant 0.7160269 ## nodesperbranch 0.3415537 ## perfenod 0.2316693 ## fruitset 0.2985557 # slopes for dep ~ dep0 X <- as.matrix(sdat[,dep0]) Y <- as.matrix(sdat[,c(dep)]) # estind2 <- solve(t(X) estind2 <- solve(crossprod(X), crossprod(X,Y)) estind2 ## marketable earlyperplant ## 0.97196 0.8828393 # correlation coefficients for indep variables corrind=cor(sdat[,indep]) round(corrind,2) ## branchesperplant nodesperbranch perfenod fruitset ## branchesperplant 1.00 0.52 -0.24 0.09 ## nodesperbranch 0.52 1.00 -0.44 0.14 ## perfenod -0.24 -0.44 1.00 0.04 ## fruitset 0.09 0.14 0.04 1.00 # Correlation coefficients for dependent variables corrdep=cor(sdat[,c(dep0, dep)]) round(corrdep,2) ## fruitperplant marketable earlyperplant ## fruitperplant 1.00 0.97 0.88 ## marketable 0.97 1.00 0.96 ## earlyperplant 0.88 0.96 1.00 result = corrind result = result*matrix(estdep,ncol=4,nrow=4,byrow=TRUE) round(result,2) # match SAS output columns 1-4 ## branchesperplant nodesperbranch perfenod fruitset ## branchesperplant 0.72 0.18 -0.06 0.03 ## nodesperbranch 0.37 0.34 -0.10 0.04 ## perfenod -0.17 -0.15 0.23 0.01 ## fruitset 0.07 0.05 0.01 0.30 resdep0 = rowSums(result) resdep <- cbind(resdep0,resdep0)*matrix(estind2, nrow=4,ncol=2,byrow=TRUE) colnames(resdep) <- dep # slightly different from SAS output last 2 columns round(cbind(fruitperplant=resdep0, round(resdep,2)),2) ## fruitperplant marketable earlyperplant ## branchesperplant 0.87 0.84 0.76 ## nodesperbranch 0.65 0.63 0.58 ## perfenod -0.08 -0.08 -0.07 ## fruitset 0.42 0.41 0.37 } # }"},{"path":"/reference/crampton.pig.html","id":null,"dir":"Reference","previous_headings":"","what":"Weight gain in pigs for different treatments — crampton.pig","title":"Weight gain in pigs for different treatments — crampton.pig","text":"Weight gain pigs different treatments, initial weight feed eaten covariates.","code":""},{"path":"/reference/crampton.pig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weight gain in pigs for different treatments — crampton.pig","text":"","code":"data(\"crampton.pig\")"},{"path":"/reference/crampton.pig.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weight gain in pigs for different treatments — crampton.pig","text":"data frame 50 observations following 5 variables. treatment feed treatment rep replicate weight1 initial weight feed feed eaten weight2 final weight","code":""},{"path":"/reference/crampton.pig.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Weight gain in pigs for different treatments — crampton.pig","text":"study effect initial weight feed eaten weight gaining ability pigs different feed treatments. data extracted Ostle. clear 'replicate' actually blocking replicate opposed repeated measurement. original source document needs consulted.","code":""},{"path":"/reference/crampton.pig.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Weight gain in pigs for different treatments — crampton.pig","text":"Crampton, EW Hopkins, JW. (1934). Use Method Partial Regression Analysis Comparative Feeding Trial Data, Part II. Journal Nutrition, 8, 113-123. https://doi.org/10.1093/jn/8.3.329","code":""},{"path":"/reference/crampton.pig.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Weight gain in pigs for different treatments — crampton.pig","text":"Bernard Ostle. Statistics Research, Page 458. https://archive.org/details/secondeditionsta001000mbp Goulden (1939). Methods Statistical Analysis, 1st ed. Page 256-259. https://archive.org/details/methodsofstatist031744mbp","code":""},{"path":"/reference/crampton.pig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weight gain in pigs for different treatments — crampton.pig","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(crampton.pig) dat <- crampton.pig dat <- transform(dat, gain=weight2-weight1) libs(lattice) # Trt 4 looks best xyplot(gain ~ feed, dat, group=treatment, type=c('p','r'), auto.key=list(columns=5), xlab=\"Feed eaten\", ylab=\"Weight gain\", main=\"crampton.pig\") # Basic Anova without covariates m1 <- lm(weight2 ~ treatment + rep, data=dat) anova(m1) # Add covariates m2 <- lm(weight2 ~ treatment + rep + weight1 + feed, data=dat) anova(m2) # Remove treatment, test this nested model for significant treatments m3 <- lm(weight2 ~ rep + weight1 + feed, data=dat) anova(m2,m3) # p-value .07. F=2.34 matches Ostle } # }"},{"path":"/reference/crossa.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"Wheat yields 18 genotypes 25 locations","code":""},{"path":"/reference/crossa.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"data frame 450 observations following 3 variables. loc location locgroup location group: Grp1-Grp2 gen genotype gengroup genotype group: W1, W2, W3 yield grain yield, tons/ha","code":""},{"path":"/reference/crossa.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"Grain yield 8th Elite Selection Wheat Yield Trial evaluate 18 bread wheat genotypes 25 locations 15 countries. Cross et al. used data cluster loctions 2 mega-environments clustered genotypes 3 wheat clusters. Locations Used permission Jose' Crossa.","code":""},{"path":"/reference/crossa.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"Crossa, J Fox, PN Pfeiffer, WH Rajaram, S Gauch Jr, HG. (1991). AMMI adjustment statistical analysis international wheat yield trial. Theoretical Applied Genetics, 81, 27–37. https://doi.org/10.1007/BF00226108","code":""},{"path":"/reference/crossa.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"Jean-Louis Laffont, Kevin Wright Mohamed Hanafi (2013). Genotype + Genotype x Block Environments (GGB) Biplots. Crop Science, 53, 2332-2341. https://doi.org/10.2135/cropsci2013.03.0178","code":""},{"path":"/reference/crossa.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(crossa.wheat) dat <- crossa.wheat # AMMI biplot. Fig 3 of Crossa et al. libs(agricolae) m1 <- with(dat, AMMI(E=loc, G=gen, R=1, Y=yield)) b1 <- m1$biplot[,1:4] b1$PC1 <- -1 * b1$PC1 # Flip vertical plot(b1$yield, b1$PC1, cex=0.0, text(b1$yield, b1$PC1, cex=.5, labels=row.names(b1),col=\"brown\"), main=\"crossa.wheat AMMI biplot\", xlab=\"Average yield\", ylab=\"PC1\", frame=TRUE) mn <- mean(b1$yield) abline(h=0, v=mn, col='wheat') g1 <- subset(b1,type==\"GEN\") text(g1$yield, g1$PC1, rownames(g1), col=\"darkgreen\", cex=.5) e1 <- subset(b1,type==\"ENV\") arrows(mn, 0, 0.95*(e1$yield - mn) + mn, 0.95*e1$PC1, col= \"brown\", lwd=1.8,length=0.1) # GGB example library(agridat) data(crossa.wheat) dat2 <- crossa.wheat libs(gge) # Specify env.group as column in data frame m2 <- gge(dat2, yield~gen*loc, env.group=locgroup, gen.group=gengroup, scale=FALSE) biplot(m2, main=\"crossa.wheat - GGB biplot\") } # }"},{"path":"/reference/crowder.seeds.html","id":null,"dir":"Reference","previous_headings":"","what":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"Number Orobanche seeds tested/germinated two genotypes two treatments.","code":""},{"path":"/reference/crowder.seeds.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"plate Factor replication gen Factor genotype levels O73, O75 extract Factor extract bean, cucumber germ Number seeds germinated n Total number seeds tested","code":""},{"path":"/reference/crowder.seeds.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"Egyptian broomrape, orobanche aegyptiaca parasitic plant family. plants chlorophyll grow roots plants. seeds remain dormant soil certain compounds living plants stimulate germination. Two genotypes studied experiment, O. aegyptiaca 73 O. aegyptiaca 75. seeds brushed one two extracts prepared either bean plant cucmber plant. experimental design 2x2 factorial, 5 6 reps plates.","code":""},{"path":"/reference/crowder.seeds.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"Crowder, M.J., 1978. Beta-binomial anova proportions. Appl. Statist., 27, 34-37. https://doi.org/10.2307/2346223","code":""},{"path":"/reference/crowder.seeds.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"N. E. Breslow D. G. Clayton. 1993. Approximate inference generalized linear mixed models. Journal American Statistical Association, 88:9-25. https://doi.org/10.2307/2290687 Y. Lee J. . Nelder. 1996. Hierarchical generalized linear models discussion. J. R. Statist. Soc. B, 58:619-678.","code":""},{"path":"/reference/crowder.seeds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(crowder.seeds) dat <- crowder.seeds m1.glm <- m1.glmm <- m1.glmmtmb <- m1.hglm <- NA # ----- Graphic libs(lattice) dotplot(germ/n~gen|extract, dat, main=\"crowder.seeds\") # --- GLMM. Assumes Gaussian random effects libs(MASS) m1.glmm <- glmmPQL(cbind(germ, n-germ) ~ gen*extract, random= ~1|plate, family=binomial(), data=dat) summary(m1.glmm) ## round(summary(m1.glmm)$tTable,2) ## Value Std.Error DF t-value p-value ## (Intercept) -0.44 0.25 17 -1.80 0.09 ## genO75 -0.10 0.31 17 -0.34 0.74 ## extractcucumber 0.52 0.34 17 1.56 0.14 ## genO75:extractcucumber 0.80 0.42 17 1.88 0.08 # ----- glmmTMB libs(glmmTMB) m1.glmmtmb <- glmmTMB(cbind(germ, n-germ) ~ gen*extract + (1|plate), data=dat, family=binomial) summary(m1.glmmtmb) ## round(summary(m1.glmmtmb)$coefficients$cond , 2) ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -0.45 0.22 -2.03 0.04 ## genO75 -0.10 0.28 -0.35 0.73 ## extractcucumber 0.53 0.30 1.74 0.08 ## genO75:extractcucumber 0.81 0.38 2.11 0.04 } # }"},{"path":"/reference/cullis.earlygen.html","id":null,"dir":"Reference","previous_headings":"","what":"Early generation variety trial in wheat — cullis.earlygen","title":"Early generation variety trial in wheat — cullis.earlygen","text":"Early generation variety trial wheat","code":""},{"path":"/reference/cullis.earlygen.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Early generation variety trial in wheat — cullis.earlygen","text":"data frame 670 observations following 5 variables. gen genotype factor row row col column entry entry (genotype) number yield yield plot, kg/ha weed weed score","code":""},{"path":"/reference/cullis.earlygen.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Early generation variety trial in wheat — cullis.earlygen","text":"data field experiment conducted Tullibigeal, New South Wales, Australia 1987-88. aim trials identify retain top (10-20 percent) lines testing. genotypes unreplicated, augmented genotypes. row, every 6th plot variety 526 = 'Kite'. Six varieties 527-532 randomly placed trial, 3 5 plots . plot 15m x 1.8m, \"oriented longest side rows\". 'weed' variable visual score 0 10 scale, 0 = weeds, 10 = 100 percent weeds. Cullis et al. (1989) presented analysis early generation variety trials included one-dimensional spatial analysis. , two-dimensional spatial analysis presented. Note: 'row' 'col' variables VSN link (switched compared paper Cullis et al.) Field width: 10 rows * 15 m = 150 m Field length: 67 plots * 1.8 m = 121 m orientation certain, alternative orientation field roughly 20m x 1000m, seems unlikely.","code":""},{"path":"/reference/cullis.earlygen.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Early generation variety trial in wheat — cullis.earlygen","text":"Brian R. Cullis, Warwick J. Lill, John . Fisher, Barbara J. Read Alan C. Gleeson (1989). New Procedure Analysis Early Generation Variety Trials. Journal Royal Statistical Society. Series C (Applied Statistics), 38, 361-375. https://doi.org/10.2307/2348066","code":""},{"path":"/reference/cullis.earlygen.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Early generation variety trial in wheat — cullis.earlygen","text":"Unreplicated early generation variety trial Wheat. https://www.vsni.co.uk/software/asreml/htmlhelp/asreml/xwheat.htm","code":""},{"path":"/reference/cullis.earlygen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Early generation variety trial in wheat — cullis.earlygen","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cullis.earlygen) dat <- cullis.earlygen # Show field layout of checks. Cullis Table 1. dat$check <- ifelse(dat$entry < 8, dat$entry, NA) libs(desplot) desplot(dat, check ~ col*row, num=entry, cex=0.5, flip=TRUE, aspect=121/150, # true aspect main=\"cullis.earlygen (yield)\") desplot(dat, yield ~ col*row, num=\"check\", cex=0.5, flip=TRUE, aspect=121/150, # true aspect main=\"cullis.earlygen (yield)\") grays <- colorRampPalette(c(\"white\",\"#252525\")) desplot(dat, weed ~ col*row, at=0:6-0.5, col.regions=grays(7)[-1], flip=TRUE, aspect=121/150, # true aspect main=\"cullis.earlygen (weed)\") libs(lattice) bwplot(yield ~ as.character(weed), dat, horizontal=FALSE, xlab=\"Weed score\", main=\"cullis.earlygen\") # Moving Grid libs(mvngGrAd) shape <- list(c(1), c(1), c(1:4), c(1:4)) # sketchGrid(10,10,20,20,shapeCross=shape, layers=1, excludeCenter=TRUE) m0 <- movingGrid(rows=dat$row, columns=dat$col, obs=dat$yield, shapeCross=shape, layers=NULL) dat$mov.avg <- fitted(m0) if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # Start with the standard AR1xAR1 analysis dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf, dat$yf),] m2 <- asreml(yield ~ weed, data=dat, random= ~gen, resid = ~ ar1(xf):ar1(yf)) # Variogram suggests a polynomial trend m3 <- update(m2, fixed= yield~weed+pol(col,-1)) # Now add a nugget variance m4 <- update(m3, random= ~ gen + units) lucid::vc(m4) ## effect component std.error z.ratio bound ## gen 73780 10420 7.1 P 0 ## units 30440 8073 3.8 P 0.1 ## xf:yf(R) 54730 10630 5.1 P 0 ## xf:yf!xf!cor 0.38 0.115 3.3 U 0 ## xf:yf!yf!cor 0.84 0.045 19 U 0 ## # Predictions from models m3 and m4 are non-estimable. Why? ## # Use model m2 for predictions ## predict(m2, classify=\"gen\")$pvals ## ## gen predicted.value std.error status ## ## 1 Banks 2723.534 93.14719 Estimable ## ## 2 Eno008 2981.056 162.85241 Estimable ## ## 3 Eno009 2978.008 161.57129 Estimable ## ## 4 Eno010 2821.399 153.96943 Estimable ## ## 5 Eno011 2991.612 161.53507 Estimable ## # Compare AR1 with Moving Grid ## dat$ar1 <- fitted(m2) ## head(dat[ , c('yield','ar1','mov.avg')]) ## ## yield ar1 mg ## ## 1 2652 2467.980 2531.998 ## ## 11 3394 3071.681 3052.160 ## ## 21 3148 2826.188 2807.031 ## ## 31 3426 3026.985 3183.649 ## ## 41 3555 3070.102 3195.910 ## ## 51 3453 3006.352 3510.511 ## pairs(dat[ , c('yield','ar1','mg')]) } } # }"},{"path":"/reference/damesa.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"Incomplete-block experiment maize Ethiopia.","code":""},{"path":"/reference/damesa.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"","code":"data(\"damesa.maize\")"},{"path":"/reference/damesa.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"data frame 264 observations following 8 variables. site site, 4 levels rep replicate, 3 levels block incomplete block plot plot number gen genotype, 22 levels row row ordinate col column ordinate yield yield, t/ha","code":""},{"path":"/reference/damesa.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"experiment harvested 2012, evaluating drought-tolerant maize hybrids 4 sites Ethiopia. site, incomplete-block design used. Damesa et al use data compare single-stage two-stage analyses.","code":""},{"path":"/reference/damesa.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"Tigist Mideksa Damesa, Jens Möhring, Mosisa Worku, Hans-Peter Piepho (2017). One Step Time: Stage-Wise Analysis Series Experiments. Agronomy J, 109, 845-857. https://doi.org/10.2134/agronj2016.07.0395","code":""},{"path":"/reference/damesa.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"None","code":""},{"path":"/reference/damesa.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(damesa.maize) libs(desplot) desplot(damesa.maize, yield ~ col*row|site, main=\"damesa.maize\", out1=rep, out2=block, num=gen, cex=1) if(require(\"asreml\", quietly=TRUE)) { # Fit the single-stage model in Damesa libs(asreml,lucid) m0 <- asreml(data=damesa.maize, fixed = yield ~ gen, random = ~ site + gen:site + at(site):rep/block, residual = ~ dsum( ~ units|site) ) lucid::vc(m0) # match Damesa table 1 column 3 ## effect component std.error z.ratio bound ## at(site, S1):rep 0.08819 0.1814 0.49 P 0 ## at(site, S2):rep 1.383 1.426 0.97 P 0 ## at(site, S3):rep 0 NA NA B 0 ## at(site, S4):rep 0.01442 0.02602 0.55 P 0 ## site 10.45 8.604 1.2 P 0.1 ## gen:site 0.1054 0.05905 1.8 P 0.1 ## at(site, S1):rep:block 0.3312 0.3341 0.99 P 0 ## at(site, S2):rep:block 0.4747 0.1633 2.9 P 0 ## at(site, S3):rep:block 0 NA NA B 0 ## at(site, S4):rep:block 0.06954 0.04264 1.6 P 0 ## site_S1!R 1.346 0.3768 3.6 P 0 ## site_S2!R 0.1936 0.06628 2.9 P 0 ## site_S3!R 1.153 0.2349 4.9 P 0 ## site_S4!R 0.1112 0.03665 3 P 0 } } # }"},{"path":"/reference/darwin.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"Darwin's maize data crossed/inbred plant heights.","code":""},{"path":"/reference/darwin.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"data frame 30 observations following 4 variables. pot Pot factor, 4 levels pair Pair factor, 12 levels type Type factor, self-pollinated, cross-pollinated height Height, inches (measured 1/8 inch)","code":""},{"path":"/reference/darwin.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"Charles Darwin, 1876, reported data experiment conducted heights corn plants. seeds came parents, seeds produced self-fertilized parents seeds produced cross-fertilized parents. Pairs seeds planted pots. Darwin hypothesized cross-fertilization produced produced robust vigorous offspring. Darwin wrote, \"long doubted whether worth give measurements separate plant, decided , order may seen superiority crossed plants self-fertilised, commonly depend presence two three extra fine plants one side, poor plants side. Although several observers insisted general terms offspring intercrossed varieties superior either parent-form, precise measurements given;* met observations effects crossing self-fertilising individuals variety. Moreover, experiments kind require much time–mine continued eleven years–likely soon repeated.\" Darwin asked cousin Francis Galton help understanding data. Galton modern statistical methods approach problem said, \"doubt, making many tests, whether possible derive useful conclusions observations. least 50 plants case, order position deduce fair results\". Later, R. . Fisher used Darwin's data book design experiments showed t-test exhibits significant difference two groups.","code":""},{"path":"/reference/darwin.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"Darwin, C. R. 1876. effects cross self fertilisation vegetable kingdom. London: John Murray. Page 16. https://darwin-online.org.uk/converted/published/1881_Worms_F1357/1876_CrossandSelfFertilisation_F1249/1876_CrossandSelfFertilisation_F1249.html","code":""},{"path":"/reference/darwin.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"R. . Fisher, (1935) Design Experiments, Oliver Boyd. Page 30.","code":""},{"path":"/reference/darwin.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(darwin.maize) dat <- darwin.maize # Compare self-pollination with cross-pollination libs(lattice) bwplot(height~type, dat, main=\"darwin.maize\") libs(reshape2) dm <- melt(dat) d2 <- dcast(dm, pot+pair~type) d2$diff <- d2$cross-d2$self t.test(d2$diff) ## One Sample t-test ## t = 2.148, df = 14, p-value = 0.0497 ## alternative hypothesis: true mean is not equal to 0 ## 95 percent confidence interval: ## 0.003899165 5.229434169 } # }"},{"path":"/reference/dasilva.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize — dasilva.maize","title":"Multi-environment trial of maize — dasilva.maize","text":"Multi-environment trial maize 3 reps.","code":""},{"path":"/reference/dasilva.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize — dasilva.maize","text":"","code":"data(\"dasilva.maize\")"},{"path":"/reference/dasilva.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize — dasilva.maize","text":"data frame 1485 observations following 4 variables. env environment rep replicate block, 3 per env gen genotype yield yield (tons/hectare)","code":""},{"path":"/reference/dasilva.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize — dasilva.maize","text":"location 3 blocks. Block numbers unique across environments. NOTE! environment codes supplemental data file da Silva 2015 quite match environment codes paper, mostly 1. DaSilva Table 1 footnote \"Machado et al 2007\". reference appears : Machado et al. Estabilidade de producao de hibridos simples e duplos de milhooriundos de um mesmo conjunto genico. Bragantia, 67, 3. www.scielo.br/pdf/brag/v67n3/a10v67n3.pdf DaSilva Table 1, mean E1 10.803. appears copy mean row 1 Table 1 Machado. Using supplemental data paper, correct mean 8.685448.","code":""},{"path":"/reference/dasilva.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize — dasilva.maize","text":"Bayesian Shrinkage Approach AMMI Models. Carlos Pereira da Silva, Luciano Antonio de Oliveira, Joel Jorge Nuvunga, Andrezza Kellen Alves Pamplona, Marcio Balestre. Plos One. Supplemental material. https://doi.org/10.1371/journal.pone.0131414 Used via license: Creative Commons -SA.","code":""},{"path":"/reference/dasilva.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize — dasilva.maize","text":"J.J. Nuvunga, L.. Oliveira, .K.. Pamplona, C.P. Silva, R.R. Lima M. Balestre. Factor analysis using mixed models multi-environment trials different levels unbalancing. Genet. Mol. Res. 14.","code":""},{"path":"/reference/dasilva.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize — dasilva.maize","text":"","code":"library(agridat) data(dasilva.maize) dat <- dasilva.maize # Try to match Table 1 of da Silva 2015. # aggregate(yield ~ env, data=dat, FUN=mean) ## env yield ## 1 E1 6.211817 # match E2 in Table 1 ## 2 E2 4.549104 # E3 ## 3 E3 5.152254 # E4 ## 4 E4 6.245904 # E5 ## 5 E5 8.084609 # E6 ## 6 E6 13.191890 # E7 ## 7 E7 8.895721 # E8 ## 8 E8 8.685448 ## 9 E9 8.737089 # E9 # Unable to match CVs in Table 2, but who knows what they used # for residual variance. # aggregate(yield ~ env, data=dat, FUN=function(x) 100*sd(x)/mean(x)) # Match DaSilva supplement 2, ANOVA # m1 <- aov(yield ~ env + gen + rep:env + gen:env, dat) # anova(m1) ## Response: yield ## Df Sum Sq Mean Sq F value Pr(>F) ## env 8 8994.2 1124.28 964.1083 < 2.2e-16 *** ## gen 54 593.5 10.99 9.4247 < 2.2e-16 *** ## env:rep 18 57.5 3.19 2.7390 0.0001274 *** ## env:gen 432 938.1 2.17 1.8622 1.825e-15 *** ## Residuals 972 1133.5 1.17"},{"path":"/reference/dasilva.soybean.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of soybean — dasilva.soybean.uniformity","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"Uniformity trial soybean Brazil, 1970.","code":""},{"path":"/reference/dasilva.soybean.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"","code":"data(\"dasilva.soybean.uniformity\")"},{"path":"/reference/dasilva.soybean.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"data frame 1152 observations following 3 variables. row row col column yield yield, grams/plot","code":""},{"path":"/reference/dasilva.soybean.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"Field length: 48 rows * .6 m = 28.8 m Field width: 24 columns * .6 m = 14.4 m","code":""},{"path":"/reference/dasilva.soybean.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"Enedino Correa da Silva. (1974). Estudo tamanho e forma de parcelas para experimentos de soja (Plot size shape soybean yield trials). Pesquisa Agropecuaria Brasileira, Serie Agronomia, 9, 49-59. Table 3, page 52-53. https://seer.sct.embrapa.br/index.php/pab/article/view/17250","code":""},{"path":"/reference/dasilva.soybean.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"Humada-Gonzalez, G.G. (2013). Estimação tamanho otimo de parcela experimental em experimento com soja. Dissertation, Universidade Federal de Lavras. http://repositorio.ufla.br/jspui/handle/1/744","code":""},{"path":"/reference/dasilva.soybean.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(dasilva.soybean.uniformity) dat <- dasilva.soybean.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=28.8/14.4, main=\"dasilva.soybean.uniformity\") } # }"},{"path":"/reference/davidian.soybean.html","id":null,"dir":"Reference","previous_headings":"","what":"Growth of soybean varieties in 3 years — davidian.soybean","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"Growth soybean varieties 3 years","code":""},{"path":"/reference/davidian.soybean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"","code":"data(\"davidian.soybean\")"},{"path":"/reference/davidian.soybean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"data frame 412 observations following 5 variables. plot plot code variety variety, F P year 1988-1990 day days planting weight weight soybean leaves","code":""},{"path":"/reference/davidian.soybean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"experiment compared growth patterns two genotypes soybean varieties: F=Forrest (commercial variety) P=Plant Introduction number 416937 (experimental variety). Data collected 3 consecutive years. start growing season, 16 plots seeded (8 variety). Data collected approximately weekly. timepoint, six plants randomly selected plot. leaves 6 plants weighed, average leaf weight per plant reported. (assume data collection destructive different plants sampled date). Note: data \"nlme::Soybean\" data.","code":""},{"path":"/reference/davidian.soybean.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"Marie Davidian D. M. Giltinan, (1995). Nonlinear Models Repeated Measurement Data. Chapman Hall, London. Electronic version retrieved https://www4.stat.ncsu.edu/~davidian/data/soybean.dat","code":""},{"path":"/reference/davidian.soybean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"Pinheiro, J. C. Bates, D. M. (2000). Mixed-Effects Models S S-PLUS. Springer, New York.","code":""},{"path":"/reference/davidian.soybean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(davidian.soybean) dat <- davidian.soybean dat$year <- factor(dat$year) libs(lattice) xyplot(weight ~ day|variety*year, dat, group=plot, type='l', main=\"davidian.soybean\") # The only way to keep your sanity with nlme is to use groupedData objects # Well, maybe not. When I use \"devtools::run_examples\", # the \"groupedData\" function creates a dataframe with/within(?) an # environment, and then \"nlsList\" cannot find datg, even though # ls() shows datg is visible and head(datg) is fine. # Also works fine in interactive mode. It is driving me insane. # reid.grasses has the same problem # Use if(0){} to block this code from running. if(0){ libs(nlme) datg <- groupedData(weight ~ day|plot, dat) # separate fixed-effect model for each plot # 1988P6 gives unusual estimates m1 <- nlsList(SSlogis, data=datg, subset = plot != \"1988P6\") # plot(m1) # seems heterogeneous plot(intervals(m1), layout=c(3,1)) # clear year,variety effects in Asym # A = maximum, B = time of half A = steepness of curve # C = sharpness of curve (smaller = sharper curve) # switch to mixed effects m2 <- nlme(weight ~ A / (1+exp(-(day-B)/C)), data=datg, fixed=list(A ~ 1, B ~ 1, C ~ 1), random = A +B +C ~ 1, start=list(fixed = c(17,52,7.5))) # no list! # add covariates for A,B,C effects, correlation, weights # not necessarily best model, but it shows the syntax m3 <- nlme(weight ~ A / (1+exp(-(day-B)/C)), data=datg, fixed=list(A ~ variety + year, B ~ year, C ~ year), random = A +B +C ~ 1, start=list(fixed= c(19,0,0,0, 55,0,0, 8,0,0)), correlation = corAR1(form = ~ 1|plot), weights=varPower(), # really helps control=list(mxMaxIter=200)) plot(augPred(m3), layout=c(8,6), main=\"davidian.soybean - model 3\") } # end if(0) } # }"},{"path":"/reference/davies.pasture.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of pasture. — davies.pasture.uniformity","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"Uniformity trial pasture Australia.","code":""},{"path":"/reference/davies.pasture.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"","code":"data(\"davies.pasture.uniformity\")"},{"path":"/reference/davies.pasture.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"data frame 760 observations following 3 variables. row row col column yield yield per plot, grams","code":""},{"path":"/reference/davies.pasture.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"Conducted Waite Agricultural Research Institute 1928. rectangle 250 x 200 links selected, divided 1000 plots measuring 10 x 5 links, 1/2000th acre. Plots hand harvested herbage air-dried. Cutting began Tue, 25 Sep ended Sat, 29 Sep, time 760 plots harvested. Rain fell, harvesting ceased. minimum recommended plot size 150 square links. optimum recommended plot size 450 square links, 5 x 90 links size. Note, 4 digits hard read original document. Best estimates digits used yields affects plots. yields digitally watermarked extra .01 added yield value. botanical composition species clearly influenced total herbage. Field length: 40 plots * 5 links = 200 links Field width: 19 plots * 10 links = 190 links","code":""},{"path":"/reference/davies.pasture.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"J. Griffiths Davies (1931). Experimental Error Yield Small Plots Natural Pasture. Council Scientific Industrial Research (Aust.) Bulletin 48. Table 1.","code":""},{"path":"/reference/davies.pasture.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"None","code":""},{"path":"/reference/davies.pasture.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(davies.pasture.uniformity) dat <- davies.pasture.uniformity # range(dat$yield) # match Davies # mean(dat$yield) # 227.77, Davies has 221.7 # sd(dat$yield)/mean(dat$yield) # 33.9, Davies has 32.5 # libs(lattice) # qqmath( ~ yield, dat) # clearly non-normal, skewed right libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(40*5)/(19*10), # true aspect main=\"davies.pasture.uniformity\") } # }"},{"path":"/reference/day.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — day.wheat.uniformity","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"Uniformity trial wheat 1903 Missouri.","code":""},{"path":"/reference/day.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"","code":"data(\"day.wheat.uniformity\")"},{"path":"/reference/day.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"data frame 3090 observations following 4 variables. row row col col grain grain weight, grams per plot straw straw weight, grams per plot","code":""},{"path":"/reference/day.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"data Shelbina field Missouri Agricultural Experiment Station. field (plat) 1/4 acre area apparently uniform throughout. fall 1912, wheat drilled rows 8 inches apart, row 155 feet long. wheat harvested June, 5-foot segments. gross weight grain weight measured, straw weight calculated subtraction. Field width: 31 series * 5 feet = 155 feet Field length: 100 rows, 8 inches apart = 66.66 feet","code":""},{"path":"/reference/day.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"James Westbay Day (1916). relation size, shape, number replications plats probable error field experimentation. Dissertation, University Missouri. Table 1, page 22. https://hdl.handle.net/10355/56391","code":""},{"path":"/reference/day.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"James W. Day (1920). relation size, shape, number replications plats probable error field experimentation. Agronomy Journal, 12, 100-105. https://doi.org/10.2134/agronj1920.00021962001200030002x","code":""},{"path":"/reference/day.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(day.wheat.uniformity) dat <- day.wheat.uniformity libs(desplot) desplot(dat, grain~col*row, flip=TRUE, aspect=(100*8)/(155*12), # true aspect main=\"day.wheat.uniformity - grain yield\") # similar to Day table IV libs(lattice) xyplot(grain~straw, data=dat, main=\"day.wheat.uniformity\", type=c('p','r')) # cor(dat$grain, dat$straw) # .9498 # Day calculated 0.9416 libs(desplot) desplot(dat, straw~col*row, flip=TRUE, aspect=(100*8)/(155*12), # true aspect main=\"day.wheat.uniformity - straw yield\") # Day fig 2 coldat <- aggregate(grain~col, dat, sum) xyplot(grain ~ col, coldat, type='l', ylim=c(2500,6500)) dat$rowgroup <- round((dat$row +1)/3,0) rowdat <- aggregate(grain~rowgroup, dat, sum) xyplot(grain ~ rowgroup, rowdat, type='l', ylim=c(2500,6500)) } # }"},{"path":"/reference/denis.missing.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial with structured missing values — denis.missing","title":"Multi-environment trial with structured missing values — denis.missing","text":"Grain yield measured 5 genotypes 26 environments. Missing values non-random, structured.","code":""},{"path":"/reference/denis.missing.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial with structured missing values — denis.missing","text":"env environment, 26 levels gen genotype factor, 5 levels yield yield Used permission Jean-Baptists Denis.","code":""},{"path":"/reference/denis.missing.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial with structured missing values — denis.missing","text":"Denis, J. B. C P Baril, 1992, Sophisticated models numerous missing values: multiplicative interaction model example. Biul. Oceny Odmian, 24–25, 7–31.","code":""},{"path":"/reference/denis.missing.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial with structured missing values — denis.missing","text":"H P Piepho, (1999) Stability analysis using SAS system, Agron Journal, 91, 154–160. https://doi.og/10.2134/agronj1999.00021962009100010024x","code":""},{"path":"/reference/denis.missing.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial with structured missing values — denis.missing","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(denis.missing) dat <- denis.missing # view missingness structure libs(reshape2) acast(dat, env~gen, value.var='yield') libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(yield ~ gen*env, data=dat, col.regions=redblue, main=\"denis.missing - incidence heatmap\") # stability variance (Table 3 in Piepho) libs(nlme) m1 <- lme(yield ~ -1 + gen, data=dat, random= ~ 1|env, weights = varIdent(form= ~ 1|gen), na.action=na.omit) svar <- m1$sigma^2 * c(1, coef(m1$modelStruct$varStruct, unc = FALSE))^2 round(svar, 2) ## G5 G3 G1 G2 ## 39.25 22.95 54.36 12.17 23.77 } # }"},{"path":"/reference/denis.ryegrass.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"Plant strength perennial ryegrass France 21 genotypes 7 locations.","code":""},{"path":"/reference/denis.ryegrass.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"data frame 147 observations following 3 variables. gen genotype, 21 levels loc location, 7 levels strength average plant strength * 100","code":""},{"path":"/reference/denis.ryegrass.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"INRA conducted breeding trial western France 21 genotypes 7 locations. observed data 'strength' averaged 7-10 plants per plot three plots per location (adjusting blocking effects). plant scored scale 0-9. original data value 86.0 genotype G1 location L4–replaced additive estimated value 361.2 Gower Hand (1996).","code":""},{"path":"/reference/denis.ryegrass.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"Jean-Baptiste Denis John C. Gower, 1996. Asymptotic confidence regions biadditive models: interpreting genotype-environment interaction, Applied Statistics, 45, 479-493. https://doi.org/10.2307/2986069","code":""},{"path":"/reference/denis.ryegrass.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"Gower, J.C. Hand, D.J., 1996. Biplots. Chapman Hall.","code":""},{"path":"/reference/denis.ryegrass.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"","code":"library(agridat) data(denis.ryegrass) dat <- denis.ryegrass # biplots (without ellipses) similar to Denis figure 1 libs(gge) #> #> Attaching package: ‘gge’ #> The following object is masked from ‘package:desplot’: #> #> RedGrayBlue m1 <- gge(dat, strength ~ gen*loc, scale=FALSE) biplot(m1, main=\"denis.ryegrass biplot\")"},{"path":"/reference/depalluel.sheep.html","id":null,"dir":"Reference","previous_headings":"","what":"Latin square of four breeds of sheep with four diets — depalluel.sheep","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"Latin square four breeds sheep four diets","code":""},{"path":"/reference/depalluel.sheep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"","code":"data(\"depalluel.sheep\")"},{"path":"/reference/depalluel.sheep.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"data frame 32 observations following 5 variables. food diet animal animal number breed sheep breed weight weight, pounds date months start","code":""},{"path":"/reference/depalluel.sheep.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"may earliest known Latin Square experiment. Four sheep four breeds randomized four feeds four slaughter dates. Sheep eat roots eat sheep eating corn, acre land produces roots corn. de Palleuel said: short, adopting use roots, instead corn, fattening sorts cattle, farmers neighborhood capital gain great profit , also much benefit public supplying great city resources, preventing sudden rise meat markets, often considerable.","code":""},{"path":"/reference/depalluel.sheep.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"M. Crette de Palluel (1788). advantage economy feeding sheep house roots. Annals Agriculture, 14, 133-139. https://books.google.com/books?id=LXIqAAAAYAAJ&pg=PA133","code":""},{"path":"/reference/depalluel.sheep.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"None","code":""},{"path":"/reference/depalluel.sheep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(depalluel.sheep) dat <- depalluel.sheep # Not the best view...weight gain is large in the first month, then slows down # and the linear line hides this fact libs(lattice) xyplot(weight ~ date|food, dat, group=animal, type='l', auto.key=list(columns=4), xlab=\"Months since start\", main=\"depalluel.sheep\") } # }"},{"path":"/reference/devries.pine.html","id":null,"dir":"Reference","previous_headings":"","what":"Graeco-Latin Square experiment in pine — devries.pine","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"Graeco-Latin Square experiment pine","code":""},{"path":"/reference/devries.pine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"","code":"data(\"devries.pine\")"},{"path":"/reference/devries.pine.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"data frame 36 observations following 6 variables. block block row row col column spacing spacing treatment thinning thinning treatment volume stem volume m^3/ha growth annual stem volume increment m^3/ha age 11","code":""},{"path":"/reference/devries.pine.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"Experiment conducted Caribbean Pine Coebiti Surinam (Long 55 28 30 W, Lat 5 18 5 N). Land cleared Jan 1965 planted May 1965. experimental plot 60m x 60m. Roads 10 m wide run rows. block thus 180m wide 200m deep. Data collected 40m x 40m plots center experimental unit. Plots thinned 1972 1975. two treatment factors (spacing, thinning) assigned Graeco-Latin Square design. Spacing: =2.5, B=3, C=3.5. Thinning: Z=low, M=medium, S=heavy. Field width: 4 blocks x 180 m = 720 m Field length: 1 block x 200 m = 200 m.","code":""},{"path":"/reference/devries.pine.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"P.G. De Vries, J.W. Hildebrand, N.R. De Graaf. (1978). Analysis 11 years growth carribbean pine replicated Graeco-Latin square spacing-thinning experiment Surinam. Page 46, 51. https://edepot.wur.nl/287590","code":""},{"path":"/reference/devries.pine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"None","code":""},{"path":"/reference/devries.pine.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(devries.pine) dat <- devries.pine libs(desplot) desplot(dat, volume ~ col*row, main=\"devries.pine - expt design and tree volume\", col=spacing, num=thinning, cex=1, out1=block, aspect=200/720) libs(HH) HH::interaction2wt(volume ~ spacing+thinning, dat, main=\"devries.pine\") # ANOVA matches appendix 5 of DeVries m1 <- aov(volume ~ block + spacing + thinning + block:factor(row) + block:factor(col), data=dat) anova(m1) } # }"},{"path":"/reference/digby.jointregression.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat — digby.jointregression","title":"Multi-environment trial of wheat — digby.jointregression","text":"Yield 10 spring wheat varieties 17 locations 1976.","code":""},{"path":"/reference/digby.jointregression.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat — digby.jointregression","text":"data frame 134 observations following 3 variables. gen genotype, 10 levels env environment, 17 levels yield yield (t/ha)","code":""},{"path":"/reference/digby.jointregression.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat — digby.jointregression","text":"Yield 10 spring wheat varieties 17 locations 1976. Used illustrate modified joint regression.","code":""},{"path":"/reference/digby.jointregression.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat — digby.jointregression","text":"Digby, P.G.N. (1979). Modified joint regression analysis incomplete variety x environment data. Journal Agricultural Science, 93, 81-86. https://doi.org/10.1017/S0021859600086159","code":""},{"path":"/reference/digby.jointregression.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat — digby.jointregression","text":"Hans-Pieter Piepho, 1997. Analyzing Genotype-Environment Data Mixed-Models Multiplicative Terms. Biometrics, 53, 761-766. https://doi.org/10.2307/2533976 RJOINT procedure GenStat. https://www.vsni.co.uk/software/genstat/htmlhelp/server/RJOINT.htm","code":""},{"path":"/reference/digby.jointregression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat — digby.jointregression","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(digby.jointregression) dat <- digby.jointregression # Simple gen means, ignoring unbalanced data. # Matches Digby table 2, Unadjusted Mean round(tapply(dat$yield, dat$gen, mean),3) # Two-way model. Matches Digby table 2, Fitting Constants m00 <- lm(yield ~ 0 + gen + env, dat) round(coef(m00)[1:10]-2.756078+3.272,3) # Adjust intercept # genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10 # 3.272 3.268 4.051 3.724 3.641 3.195 3.232 3.268 3.749 3.179 n.gen <- nlevels(dat$gen) n.env <- nlevels(dat$env) # Estimate theta (env eff) m0 <- lm(yield ~ -1 + env + gen, dat) thetas <- coef(m0)[1:n.env] thetas <- thetas-mean(thetas) # center env effects # Add env effects to the data dat$theta <- thetas[match(paste(\"env\",dat$env,sep=\"\"), names(thetas))] # Initialize beta (gen slopes) at 1 betas <- rep(1, n.gen) done <- FALSE while(!done){ betas0 <- betas # M1: Fix thetas (env effects), estimate beta (gen slope) m1 <- lm(yield ~ -1 + gen + gen:theta, data=dat) betas <- coef(m1)[-c(1:n.gen)] dat$beta <- betas[match(paste(\"gen\",dat$gen,\":theta\",sep=\"\"), names(betas))] # print(betas) # M2: Fix betas (gen slopes), estimate theta (env slope) m2 <- lm(yield ~ env:beta + gen -1, data=dat) thetas <- coef(m2)[-c(1:n.gen)] thetas[is.na(thetas)] <- 0 # Change last coefficient from NA to 0 dat$theta <- thetas[match(paste(\"env\",dat$env,\":beta\",sep=\"\"), names(thetas))] # print(thetas) # Check convergence chg <- sum(((betas-betas0)/betas0)^2) cat(\"Relative change in betas\",chg,\"\\n\") if(chg < .0001) done <- TRUE } libs(lattice) xyplot(yield ~ theta|gen, data=dat, xlab=\"theta (environment effect)\", main=\"digby.jointregression - stability plot\") # Dibgy Table 2, modified joint regression # Genotype sensitivities (slopes) round(betas,3) # Match Digby table 2, Modified joint regression sensitivity # genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10 # 0.953 0.739 1.082 1.024 1.142 0.877 1.089 0.914 1.196 0.947 # Env effects. Match Digby table 3, Modified joint reg round(thetas,3)+1.164-.515 # Adjust intercept to match # envE01 envE02 envE03 envE04 envE05 envE06 envE07 envE08 envE09 envE10 # -0.515 -0.578 -0.990 -1.186 1.811 1.696 -1.096 0.046 0.057 0.825 # envE11 envE12 envE13 envE14 envE15 envE16 envE17 # -0.576 1.568 -0.779 -0.692 0.836 -1.080 0.649 # Using 'gnm' gives similar results. # libs(gnm) # m3 <- gnm(yield ~ gen + Mult(gen,env), data=dat) # slopes negated # round(coef(m3)[11:20],3) # Using 'mumm' gives similar results, though gen is random and the # coeffecients are shrunk toward 0 a bit. if(require(\"mumm\", quietly=TRUE)) { libs(mumm) m1 <- mumm(yield ~ -1 + env + mp(gen, env), dat) round(1 + ranef(m1)$`mp gen:env`,2) } } # }"},{"path":"/reference/diggle.cow.html","id":null,"dir":"Reference","previous_headings":"","what":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"Bodyweight cows 2--2 factorial experiment.","code":""},{"path":"/reference/diggle.cow.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"data frame 598 observations following 5 variables. animal Animal factor, 26 levels iron Factor levels Iron, NoIron infect Factor levels Infected, NonInfected weight Weight (rounded nearest 5) kilograms day Days birth","code":""},{"path":"/reference/diggle.cow.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"Diggle et al., 1994, pp. 100-101, consider experiment studied iron dosing (none/standard) micro-organism (infected non-infected) influence weight cows. Twenty-eight cows allocated 2--2 factorial design factors. calves inoculated tuberculosis six weeks age. six months, calves maintained supplemental iron diet 27 months. weight animal measured 23 times, unequally spaced. One cow died study data another cow removed.","code":""},{"path":"/reference/diggle.cow.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"Diggle, P. J., Liang, K.-Y., & Zeger, S. L. (1994). Analysis Longitudinal Data. Page 100-101. Retrieved Oct 2011 https://www.maths.lancs.ac.uk/~diggle/lda/Datasets/","code":""},{"path":"/reference/diggle.cow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"Lepper, AWD Lewis, VM, 1989. Effects altered dietary iron intake Mycobacterium paratuberculosis-infected dairy cattle: sequential observations growth, iron copper metabolism development paratuberculosis. Research veterinary science, 46, 289–296. Arunas P. Verbyla Brian R. Cullis Michael G. Kenward Sue J. Welham, (1999), analysis designed experiments longitudinal data using smoothing splines. Appl. Statist., 48, 269–311. SAS/STAT(R) 9.2 User's Guide, Second Edition. https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glimmix_sect018.htm","code":""},{"path":"/reference/diggle.cow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(diggle.cow) dat <- diggle.cow # Figure 1 of Verbyla 1999 libs(latticeExtra) useOuterStrips(xyplot(weight ~ day|iron*infect, dat, group=animal, type='b', cex=.5, main=\"diggle.cow\")) # Scaling dat <- transform(dat, time = (day-122)/10) if(require(\"asreml\", quietly=TRUE)) { libs(asreml, latticeExtra) ## # Smooth for each animal. No treatment effects. Similar to SAS Output 38.6.9 m1 <- asreml(weight ~ 1 + lin(time) + animal + animal:lin(time), data=dat, random = ~ animal:spl(time)) p1 <- predict(m1, data=dat, classify=\"animal:time\", design.points=list(time=seq(0,65.9, length=50))) p1 <- p1$pvals p1 <- merge(dat, p1, all=TRUE) # to get iron/infect merged in foo1 <- xyplot(weight ~ day|iron*infect, dat, group=animal, main=\"diggle.cow\") foo2 <- xyplot(predicted.value ~ day|iron*infect, p1, type='l', group=animal) print(foo1+foo2) } } # }"},{"path":"/reference/draper.safflower.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of safflower — draper.safflower.uniformity","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"Uniformity trial safflower Arizona 1958.","code":""},{"path":"/reference/draper.safflower.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"","code":"data(\"draper.safflower.uniformity\")"},{"path":"/reference/draper.safflower.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"data frame 640 observations following 4 variables. expt experiment row row col column yield yield per plot (grams)","code":""},{"path":"/reference/draper.safflower.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"Experiments conducted Agricultural Experiment Station Farm Eloy, Arizona. crop harvested July 1958. crop planted two rows 12 inches apart vegetable beds 40 inches center center. test, end ranges one row plots one side next alleys, plots gave estimates border effects. Experiment E4 (four foot test) Sandy streaks present field. Average yield 1487 lb/ac. diagonal fertility gradient field. Widening plot equally effective lengthening plot reduce variability. optimum plot size 1 bed wide, 24 feet long. Considering economic costs, optimum size 1 bed, 12 feet long. Field width: 16 beds * 3.33 feet = 53 feet Field length: 18 ranges * 4 feet = 72 feet Experiment E5 (five foot test) Average yield 2517 lb/ac, typical crop. Combining plots lengthwise effective widening plots, order reduce variability. optimum plot size 1 bed wide, 25 feet long. Considering economic costs, optimum size 1 bed, 18 feet long. Field width: 14 beds * 3.33 feet = 46.6 feet. Field length: 18 ranges * 5 feet = 90 feet. Data Table & B Draper, p. 53-56. Typed K.Wright.","code":""},{"path":"/reference/draper.safflower.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"Arlen D. Draper. (1959). Optimum plot size shape safflower yield tests. Dissertation. University Arizona. https://hdl.handle.net/10150/319371 Page 53-56.","code":""},{"path":"/reference/draper.safflower.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"None","code":""},{"path":"/reference/draper.safflower.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(draper.safflower.uniformity) dat4 <- subset(draper.safflower.uniformity, expt==\"E4\") dat5 <- subset(draper.safflower.uniformity, expt==\"E5\") libs(desplot) desplot(dat4, yield~col*row, flip=TRUE, tick=TRUE, aspect=72/53, # true aspect main=\"draper.safflower.uniformity (four foot)\") desplot(dat5, yield~col*row, flip=TRUE, tick=TRUE, aspect=90/46, # true aspect main=\"draper.safflower.uniformity (five foot)\") # Draper appears to removed the border plots, but it is difficult to # match his results exactly dat4 <- subset(dat4, row>1 & row<20) dat4 <- subset(dat4, col>1 & col<17) dat5 <- subset(dat5, row>1 & row<20) dat5 <- subset(dat5, col<15) # Convert gm/plot to pounds/acre. Draper (p. 20) says 1487 pounds/acre mean(dat4$yield) / 453.592 / (3.33*4) * 43560 # 1472 lb/ac libs(agricolae) libs(reshape2) s4 <- index.smith(acast(dat4, row~col, value.var='yield'), main=\"draper.safflower.uniformity (four foot)\", col=\"red\")$uni s4 # match Draper table 2, p 22 ## s5 <- index.smith(acast(dat5, row~col, value.var='yield'), ## main=\"draper.safflower.uniformity (five foot)\", ## col=\"red\")$uni ## s5 # match Draper table 1, p 21 } # }"},{"path":"/reference/ducker.groundnut.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of groundnut — ducker.groundnut.uniformity","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"Uniformity trial groundnut.","code":""},{"path":"/reference/ducker.groundnut.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"","code":"data(\"ducker.groundnut.uniformity\")"},{"path":"/reference/ducker.groundnut.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"data frame 215 observations following 3 variables. row row ordinate col column ordinate yield yield, pounds per plot","code":""},{"path":"/reference/ducker.groundnut.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"experiment grown Nyasaland, Cotton Experiment Station, Domira Bay, 1942-43. 44x5 identical plots, 1/220 acre area. Single ridge plots one chain length, one yard apart. Two rows groundnuts planted per ridge, staggered 1 foot holes. Holes spaced 18 inches x 12 inches. Two seeds planted per hole. yield values pounds nuts shell. Field length: 5 plots, 22 yards = 110 yards. Field width: 44 plots, 1 yard = 44 yards. data made available special help staff Rothamsted Research Library. Data typed K.Wright checked hand.","code":""},{"path":"/reference/ducker.groundnut.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 2.","code":""},{"path":"/reference/ducker.groundnut.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"None","code":""},{"path":"/reference/ducker.groundnut.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ducker.groundnut.uniformity) dat <- ducker.groundnut.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=110/44, main=\"ducker.groundnut.uniformity\") } # }"},{"path":"/reference/durban.competition.html","id":null,"dir":"Reference","previous_headings":"","what":"Sugar beet yields with competition effects — durban.competition","title":"Sugar beet yields with competition effects — durban.competition","text":"Sugar beet yields competition effects","code":""},{"path":"/reference/durban.competition.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sugar beet yields with competition effects — durban.competition","text":"data frame 114 observations following 5 variables. gen Genotype factor, 36 levels plus Border col Column block Row/Block wheel Position relative wheel tracks yield Root yields, kg/plot","code":""},{"path":"/reference/durban.competition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sugar beet yields with competition effects — durban.competition","text":"sugar-beet trial conducted 1979. Single-row plots, 12 m long, 0.5 m rows. block made 36 genotypes laid side side. Guard/border plots end. Root yields collected. Wheel tracks located columns 1 2, columns 5 6, set six plots. genotype randomly allocated pair plots (1,6), (2,5), (3,4) across three reps. Wheel effect significant _this_ trial. Field width: 18m + 1m guard rows = 19m Field length: 3 blocks * 12m + 2*0.5m spacing = 37m Retrieved https://www.ma.hw.ac.uk/~iain/research/JAgSciData/data/Trial1.dat Used permission Iain Currie.","code":""},{"path":"/reference/durban.competition.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sugar beet yields with competition effects — durban.competition","text":"Durban, M., Currie, . R. Kempton, 2001. Adjusting fertility competition variety trials. J. Agricultural Science, 136, 129–140.","code":""},{"path":"/reference/durban.competition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sugar beet yields with competition effects — durban.competition","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(durban.competition) dat <- durban.competition # Check that genotypes were balanced across wheel tracks. with(dat, table(gen,wheel)) libs(desplot) desplot(dat, yield ~ col*block, out1=block, text=gen, col=wheel, aspect=37/19, # true aspect main=\"durban.competition\") # Calculate residual after removing block/genotype effects m1 <- lm(yield ~ gen + block, data=dat) dat$res <- resid(m1) ## desplot(dat, res ~ col*block, out1=block, text=gen, col=wheel, ## main=\"durban.competition - residuals\") # Calculate mean of neighboring plots dat$comp <- NA dat$comp[3:36] <- ( dat$yield[2:35] + dat$yield[4:37] ) / 2 dat$comp[41:74] <- ( dat$yield[40:73] + dat$yield[42:75] ) / 2 dat$comp[79:112] <- ( dat$yield[78:111] + dat$yield[80:113] ) / 2 # Demonstrate the competition effect # Competitor plots have low/high yield -> residuals are negative/positive libs(lattice) xyplot(res~comp, dat, type=c('p','r'), main=\"durban.competition\", xlab=\"Average yield of neighboring plots\", ylab=\"Residual\") } # }"},{"path":"/reference/durban.rowcol.html","id":null,"dir":"Reference","previous_headings":"","what":"Row-column experiment of spring barley, many varieties — durban.rowcol","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"Row-column experiment spring barley, many varieties","code":""},{"path":"/reference/durban.rowcol.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"data frame 544 observations following 5 variables. row row bed bed (column) rep rep, 2 levels gen genotype, 272 levels yield yield, tonnes/ha","code":""},{"path":"/reference/durban.rowcol.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"Spring barley variety trial 272 entries (260 new varieties, 12 control). Grown Scottish Crop Research Institute 1998. Row-column design 2 reps, 16 rows (north/south) 34 beds (east/west). land sloped downward row 16 row 1. Plot yields converted tonnes per hectare. Plot dimensions given. Used permission Maria Durban.","code":""},{"path":"/reference/durban.rowcol.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"Durban, Maria Hackett, Christine McNicol, James Newton, Adrian Thomas, William Currie, Iain. 2003. practical use semiparametric models field trials, Journal Agric Biological Envir Stats, 8, 48-66. https://doi.org/10.1198/1085711031265","code":""},{"path":"/reference/durban.rowcol.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"Edmondson, Rodney (2020). Multi-level Block Designs Comparative Experiments. J Agric, Biol, Env Stats. https://doi.org/10.1007/s13253-020-00416-0","code":""},{"path":"/reference/durban.rowcol.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(durban.rowcol) dat <- durban.rowcol libs(desplot) desplot(dat, yield~bed*row, out1=rep, num=gen, # aspect unknown main=\"durban.rowcol\") # Durban 2003 Figure 1 m10 <- lm(yield~gen, data=dat) dat$resid <- m10$resid ## libs(lattice) ## xyplot(resid~row, dat, type=c('p','smooth'), main=\"durban.rowcol\") ## xyplot(resid~bed, dat, type=c('p','smooth'), main=\"durban.rowcol\") # Figure 3 libs(lattice) xyplot(resid ~ bed|factor(row), data=dat, main=\"durban.rowcol\", type=c('p','smooth')) # Figure 5 - field trend # note, Durban used gam package like this # m1lo <- gam(yield ~ gen + lo(row, span=10/16) + lo(bed, span=9/34), data=dat) libs(mgcv) m1lo <- gam(yield ~ gen + s(row) + s(bed, k=5), data=dat) new1 <- expand.grid(row=unique(dat$row),bed=unique(dat$bed)) new1 <- cbind(new1, gen=\"G001\") p1lo <- predict(m1lo, newdata=new1) libs(lattice) wireframe(p1lo~row+bed, new1, aspect=c(1,.5), main=\"Field trend\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml) dat <- transform(dat, rowf=factor(row), bedf=factor(bed)) dat <- dat[order(dat$rowf, dat$bedf),] m1a1 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat, random=~spl(rowf) + spl(bedf) + units, family=asr_gaussian(dispersion=1)) m1a2 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat, random=~spl(rowf) + spl(bedf) + units, resid = ~ar1(rowf):ar1(bedf)) m1a2 <- update(m1a2) m1a3 <- asreml(yield~gen, data=dat, random=~units, resid = ~ar1(rowf):ar1(bedf)) # Figure 7 libs(lattice) v7a <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1a3$residuals) wireframe(gamma ~ x*y, v7a, aspect=c(1,.5)) # Fig 7a v7b <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1a2$residuals) wireframe(gamma ~ x*y, v7b, aspect=c(1,.5)) # Fig 7b v7c <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1lo$residuals) wireframe(gamma ~ x*y, v7c, aspect=c(1,.5)) # Fig 7c } } # }"},{"path":"/reference/durban.splitplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","title":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","text":"Split-plot experiment barley fungicide treatments","code":""},{"path":"/reference/durban.splitplot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","text":"data frame 560 observations following 6 variables. yield yield, tonnes/ha block block, 4 levels gen genotype, 70 levels fung fungicide, 2 levels row row bed bed (column)","code":""},{"path":"/reference/durban.splitplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","text":"Grown 1995-1996 Scottish Crop Research Institute. Split-plot design 4 blocks, 2 whole-plot fungicide treatments, 70 barley varieties variety mixes. Total area 10 rows (north/south) 56 beds (east/west). Used permission Maria Durban.","code":""},{"path":"/reference/durban.splitplot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","text":"Durban, Maria Hackett, Christine McNicol, James Newton, Adrian Thomas, William Currie, Iain. 2003. practical use semiparametric models field trials, Journal Agric Biological Envir Stats, 8, 48-66. https://doi.org/10.1198/1085711031265.","code":""},{"path":"/reference/durban.splitplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(durban.splitplot) dat <- durban.splitplot libs(desplot) desplot(dat, yield~bed*row, out1=block, out2=fung, num=gen, # aspect unknown main=\"durban.splitplot\") # Durban 2003, Figure 2 m20 <- lm(yield~gen + fung + gen:fung, data=dat) dat$resid <- m20$resid ## libs(lattice) ## xyplot(resid~row, dat, type=c('p','smooth'), main=\"durban.splitplot\") ## xyplot(resid~bed, dat, type=c('p','smooth'), main=\"durban.splitplot\") # Figure 4 doesn't quite match due to different break points libs(lattice) xyplot(resid ~ bed|factor(row), data=dat, main=\"durban.splitplot\", type=c('p','smooth')) # Figure 6 - field trend # note, Durban used gam package like this # m2lo <- gam(yield ~ gen*fung + lo(row, bed, span=.082), data=dat) libs(mgcv) m2lo <- gam(yield ~ gen*fung + s(row, bed,k=45), data=dat) new2 <- expand.grid(row=unique(dat$row), bed=unique(dat$bed)) new2 <- cbind(new2, gen=\"G01\", fung=\"F1\") p2lo <- predict(m2lo, newdata=new2) libs(lattice) wireframe(p2lo~row+bed, new2, aspect=c(1,.5), main=\"durban.splitplot - Field trend\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # Table 5, variance components. Table 6, F tests dat <- transform(dat, rowf=factor(row), bedf=factor(bed)) dat <- dat[order(dat$rowf, dat$bedf),] m2a2 <- asreml(yield ~ gen*fung, random=~block/fung+units, data=dat, resid =~ar1v(rowf):ar1(bedf)) m2a2 <- update(m2a2) lucid::vc(m2a2) ## effect component std.error z.ratio bound ## block 0 NA NA B NA ## block:fung 0.01206 0.01512 0.8 P 0 ## units 0.02463 0.002465 10 P 0 ## rowf:bedf(R) 1 NA NA F 0 ## rowf:bedf!rowf!cor 0.8836 0.03646 24 U 0 ## rowf:bedf!rowf!var 0.1261 0.04434 2.8 P 0 ## rowf:bedf!bedf!cor 0.9202 0.02846 32 U 0 wald(m2a2) } } # }"},{"path":"/reference/eden.nonnormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Height of barley plants in a study of non-normal data — eden.nonnormal","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"Height barley plants study non-normal data.","code":""},{"path":"/reference/eden.nonnormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"","code":"data(\"eden.nonnormal\")"},{"path":"/reference/eden.nonnormal.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"data frame 256 observations following 3 variables. pos position within block block block (numeric) height height wheat plant","code":""},{"path":"/reference/eden.nonnormal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"data used early example permutation test. Eden & Yates used data consider impact non-normal data validity hypothesis test assumes normality. concluded skew data negatively affect analysis variance. Grown Rothamsted. Eight blocks Yeoman II wheat. Sampling blocks quarter-meter rows, four times row. Rows selected random. Position within rows partly controlled make use whole length block. Plants ends sub-unit measured. Shoot height measured ground level auricle last expanded leaf.","code":""},{"path":"/reference/eden.nonnormal.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"T. Eden, F. Yates (1933). validity Fisher's z test applied actual example non-normal data. Journal Agric Science, 23, 6-17. https://doi.org/10.1017/S0021859600052862","code":""},{"path":"/reference/eden.nonnormal.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"Kenneth J. Berry, Paul W. Mielke, Jr., Janis E. Johnston Permutation Statistical Methods: Integrated Approach.","code":""},{"path":"/reference/eden.nonnormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(eden.nonnormal) dat <- eden.nonnormal mean(dat$height) # 55.23 matches Eden table 1 # Eden figure 2 libs(dplyr, lattice) # Blocks had different means, so substract block mean from each datum dat <- group_by(dat, block) dat <- mutate(dat, blkmn=mean(height)) dat <- transform(dat, dev=height-blkmn) histogram( ~ dev, data=dat, breaks=seq(from=-40, to=30, by=2.5), xlab=\"Deviations from block means\", main=\"eden.nonnormal - heights skewed left\") # calculate skewness, permutation libs(dplyr, lattice, latticeExtra) # Eden table 1 # anova(aov(height ~ factor(block), data=dat)) # Eden table 2,3. Note, this may be a different definition of skewness # than is commonly used today (e.g. e1071::skewness). skew <- function(x){ n <- length(x) x <- x - mean(x) s1 = sum(x) s2 = sum(x^2) s3 = sum(x^3) k3=n/((n-1)*(n-2)) * s3 -3/n*s2*s1 + 2/n^2 * s1^3 return(k3) } # Negative values indicate data are skewed left dat <- group_by(dat, block) summarize(dat, s1=sum(height),s2=sum(height^2), mean2=var(height), k3=skew(height)) ## block s1 s2 mean2 k3 ## ## 1 1 1682.0 95929.5 242.56048 -1268.5210 ## 2 2 1858.0 111661.5 121.97984 -1751.9919 ## 3 3 1809.5 108966.8 214.36064 -3172.5284 ## 4 4 1912.0 121748.5 242.14516 -2548.2194 ## 5 5 1722.0 99026.5 205.20565 -559.0629 ## 6 6 1339.0 63077.0 227.36190 -801.2740 ## 7 7 1963.0 123052.5 84.99093 -713.2595 ## 8 8 1854.0 112366.0 159.67339 -1061.9919 # Another way to view skewness with qq plot. Panel 3 most skewed. qqmath( ~ dev|factor(block), data=dat, as.table=TRUE, ylab=\"Deviations from block means\", panel = function(x, ...) { panel.qqmathline(x, ...) panel.qqmath(x, ...) }) # Now, permutation test. # Eden: \"By a process of amalgamation the eight sets of 32 observations were # reduced to eight sets of four and the data treated as a potential # layout for a 32-plot trial\". dat2 <- transform(dat, grp = rep(1:4, each=8)) dat2 <- aggregate(height ~ grp+block, dat2, sum) dat2$trt <- rep(letters[1:4], 8) dat2$block <- factor(dat2$block) # Treatments were assigned at random 1000 times set.seed(54323) fobs <- rep(NA, 1000) for(i in 1:1000){ # randomize treatments within each block # trick from https://stackoverflow.com/questions/25085537 dat2$trt <- with(dat2, ave(trt, block, FUN = sample)) fobs[i] <- anova(aov(height ~ block + trt, dat2))[\"trt\",\"F value\"] } # F distribution with 3,21 deg freedom # Similar to Eden's figure 4, but on a different horizontal scale xval <- seq(from=0,to=max(fobs), length=50) yval <- df(xval, df1 = 3, df2 = 21) # Re-scale, 10 = max of historgram, 0.7 = max of density histogram( ~ fobs, breaks=xval, xlab=\"F value\", main=\"Observed (histogram) & theoretical (line) F values\") + xyplot((10/.7)* yval ~ xval, type=\"l\", lwd=2) } # }"},{"path":"/reference/eden.potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"Potato yields response potash nitrogen fertilizer. Data Fisher's 1929 paper Studies Crop Variation 6. different design used year.","code":""},{"path":"/reference/eden.potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"data frame 225 observations following 9 variables. year year/type factor yield yield, pounds per plot block block row row col column trt treatment factor nitro nitrogen fertilizer, cwt/acre potash potash fertilizer, cwt/acre ptype potash type","code":""},{"path":"/reference/eden.potato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"data interest show gradual development experimental designs agriculture. 1925/1926 potato variety Kerr's Pink. 1927 Arran Comrade. 1925a/1926a qualitative experiments, treatments O=None, S=Sulfate, M=Muriate, P=Potash manure salts. design Latin Square. 1925/1926b/1927 experiments RCB designs treatment codes defining amount type fertilizer used. Note: 't' treatment defined original paper.","code":""},{"path":"/reference/eden.potato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"T Eden R Fisher, 1929. Studies Crop Variation. VI. Experiments response potato potash nitrogen. Journal Agricultural Science, 19: 201-213.","code":""},{"path":"/reference/eden.potato.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667","code":""},{"path":"/reference/eden.potato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(eden.potato) dat <- eden.potato # 1925 qualitative d5a <- subset(dat, year=='1925a') libs(desplot) desplot(d5a, trt~col*row, text=yield, cex=1, shorten='no', # aspect unknown main=\"eden.potato: 1925 qualitative\") anova(m5a <- aov(yield~trt+factor(row)+factor(col), d5a)) # table 2 # 1926 qualitative d6a <- subset(dat, year=='1926a') libs(desplot) desplot(d6a, trt~col*row, text=yield, cex=1, shorten='no', # aspect unknown main=\"eden.potato: 1926 qualitative\") anova(m6a <- aov(yield~trt+factor(row)+factor(col), d6a)) # table 4 # 1925 quantitative d5 <- subset(dat, year=='1925b') libs(desplot) desplot(d5, yield ~ col*row, out1=block, text=trt, cex=1, # aspect unknown main=\"eden.potato: 1925 quantitative\") # Trt 't' not defined, seems to be the same as 'a' libs(lattice) dotplot(trt~yield|block, d5, # aspect unknown main=\"eden.potato: 1925 quantitative\") anova(m5 <- aov(yield~trt+block, d5)) # table 6 # 1926 quantitative d6 <- subset(dat, year=='1926b') libs(desplot) desplot(d6, yield ~ col*row, out1=block, text=trt, cex=1, # aspect unknown main=\"eden.potato: 1926 quantitative\") anova(m6 <- aov(yield~trt+block, d6)) # table 7 # 1927 qualitative + quantitative d7 <- droplevels(subset(dat, year==1927)) libs(desplot) desplot(d7, yield ~ col*row, out1=block, text=trt, cex=1, col=ptype, # aspect unknown main=\"eden.potato: 1927 qualitative + quantitative\") # Table 8. Anova, mean yield tons / acre anova(m7 <- aov(yield~trt+block+ptype + ptype:potash, d7)) libs(reshape2) me7 <- melt(d7, measure.vars='yield') acast(me7, potash~nitro, fun=mean) * 40/2240 # English ton = 2240 pounds acast(me7, potash~ptype, fun=mean) * 40/2240 } # }"},{"path":"/reference/eden.tea.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of tea — eden.tea.uniformity","title":"Uniformity trial of tea — eden.tea.uniformity","text":"Uniformity trial tea Ceylon.","code":""},{"path":"/reference/eden.tea.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of tea — eden.tea.uniformity","text":"","code":"data(\"eden.tea.uniformity\")"},{"path":"/reference/eden.tea.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of tea — eden.tea.uniformity","text":"data frame 144 observations following 4 variables. entry entry number yield yield row row col column","code":""},{"path":"/reference/eden.tea.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of tea — eden.tea.uniformity","text":"Tea plucking Ceylon extended 20 Apr 1928 10 Dec 1929. 42 pluckings. clear units , paper mentions \"quarter pound\". field divided 144 plots 1/72 acre = 605 sq ft. plot contained 6 rows bushes, approximately 42 bushes. ( row thus 7 bushes). Plots row 12 high hillside, plots row 1 low hill. Note: assume plots roughly square: 6 rows 7 bushes. Field width: 12 plots * 24.6 feet = 295 feet Field length: 12 plots * 24.6 feet = 295 feet Data typed K.Wright. Although pdf paper crease across page hid digits, row column totals included paper allowed re-construction missing digits.","code":""},{"path":"/reference/eden.tea.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of tea — eden.tea.uniformity","text":"T. Eden. (1931). Studies yield tea. 1. experimental errors field experiments tea. Agricultural Science, 21, 547-573. https://doi.org/10.1017/S0021859600088511","code":""},{"path":"/reference/eden.tea.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of tea — eden.tea.uniformity","text":"None","code":""},{"path":"/reference/eden.tea.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of tea — eden.tea.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(eden.tea.uniformity) dat <- eden.tea.uniformity # sum(dat$yield) # 140050.6 matches total yield in appendix A # mean(dat$yield) # 972.574 match page 5554 m1 <- aov(yield ~ factor(entry) + factor(row) + factor(col), data=dat) summary(m1) libs(desplot) desplot(dat, yield ~ col*row, aspect=1, main=\"eden.tea.uniformity\") } # }"},{"path":"/reference/edwards.oats.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"Multi-environment trial oats 5 locations, 7 years, 3 replicates trial.","code":""},{"path":"/reference/edwards.oats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"","code":"data(\"edwards.oats\")"},{"path":"/reference/edwards.oats.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"data frame 3694 observations following 7 variables. eid Environment identification (factor) year Year loc Location name block Block gen Genotype name yield Yield testwt Test weight","code":""},{"path":"/reference/edwards.oats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"data comes breeding program, usual pattern (1) genotypes entering/leaving program (2) check genotypes remain throughout duration program. Experiments conducted Iowa State University Oat Variety Trial years 1997 2003. year 40 genotypes, 30 released checks 10 experimental lines. genotype appeared range 3 34 year-loc combinations. trials grown five locations Iowa: Ames, Nashua, Crawfordsville, Lewis, Sutherland. 1998 trial grown Sutherland. 3 blocks trial. Five genotypes removed data low yields (included ). environment identifaction values Edwards (2006) table 1. Electronic data supplied Jode Edwards.","code":""},{"path":"/reference/edwards.oats.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"Jode W. Edwards, Jean-Luc Jannink (2006). Bayesian Modeling Heterogeneous Error Genotype x Environment Interaction Variances. Crop Science, 46, 820-833. https://dx.doi.org/10.2135/cropsci2005.0164","code":""},{"path":"/reference/edwards.oats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"None","code":""},{"path":"/reference/edwards.oats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(dplyr,lattice, reshape2, stringr) data(edwards.oats) dat <- edwards.oats dat$env <- paste0(dat$year,\".\",dat$loc) dat$eid <- factor(dat$eid) mat <- reshape2::acast(dat, env ~ gen, fun.aggregate=mean, value.var=\"yield\", na.rm=TRUE) lattice::levelplot(mat, aspect=\"m\", main=\"edwards.oats\", xlab=\"environment\", ylab=\"genotype\", scales=list(x=list(rot=90))) # Calculate BLUEs of gen/env effects m1 <- lm(yield ~ gen+eid, dat) gg <- coef(m1)[2:80] names(gg) <- stringr::str_replace(names(gg), \"gen\", \"\") gg <- c(0,gg) names(gg)[1] <- \"ACStewart\" ee <- coef(m1)[81:113] names(ee) <- stringr::str_replace(names(ee), \"eid\", \"\") ee <- c(0,ee) names(ee)[1] <- \"1\" # Subtract gen/env coefs from yield values dat2 <- dat dat2$gencoef <- gg[match(dat2$gen, names(gg))] dat2$envcoef <- ee[match(dat2$eid, names(ee))] dat2 <- dplyr::mutate(dat2, y = yield - gencoef - envcoef) # Calculate variance for each gen*env. Shape of the graph is vaguely # similar to Fig 2 of Edwards et al (2006), who used a Bayesian model dat2 <- group_by(dat2, gen, eid) dat2sum <- summarize(dat2, stddev = sd(y)) bwplot(stddev ~ eid, dat2sum) } # }"},{"path":"/reference/engelstad.nitro.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"Corn yield response nitrogen fertilizer single variety corn two locations five years","code":""},{"path":"/reference/engelstad.nitro.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"data frame 60 observations following 4 variables. loc location, 2 levels year year, 1962-1966 nitro nitrogen fertilizer kg/ha yield yield, quintals/ha","code":""},{"path":"/reference/engelstad.nitro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"Corn yield response nitrogen fertilizer single variety corn two locations Tennessee five years. yield data mean 9 replicates. original paper fits quadratic curves data. Schabenberger Pierce fit multiple models including linear plateau. example fits quadratic plateau one year/loc. original paper, 1965 1966 data Knoxville location used appeared response due nitrogen minimal 1965 nonexistant 1966. economic optimum can found setting tangent equal ratio (fertilizer price)/(grain price).","code":""},{"path":"/reference/engelstad.nitro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"Engelstad, OP Parks, WL. 1971. Variability Optimum N Rates Corn. Agronomy Journal, 63, 21–23.","code":""},{"path":"/reference/engelstad.nitro.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"Schabenberger, O. Pierce, F.J., 2002. Contemporary statistical models plant soil sciences, CRC. Page 254-259.","code":""},{"path":"/reference/engelstad.nitro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"","code":"library(agridat) data(engelstad.nitro) dat <- engelstad.nitro libs(latticeExtra) useOuterStrips(xyplot(yield ~ nitro | factor(year)*loc, dat, main=\"engelstad.nitro\")) # Fit a quadratic plateau model to one year/loc j62 <- droplevels(subset(dat, loc==\"Jackson\" & year==1962)) # ymax is maximum yield, M is the change point, k affects curvature m1 <- nls(yield ~ ymax*(nitro > M) + (ymax - (k/2) * (M-nitro)^2) * (nitro < M), data= j62, start=list(ymax=80, M=150, k=.01)) # Plot the raw data and model newdat <- data.frame(nitro=seq(0,max(dat$nitro))) p1 <- predict(m1, new=newdat) plot(yield ~ nitro, j62) lines(p1 ~ newdat$nitro, col=\"blue\") title(\"engelstad.nitro: quadratic plateau at Jackson 1962\") # Optimum nitro level ignoring prices = 225 coef(m1)['M'] #> M #> 225.3404 # Optimum nitro level using $0.11 for N cost, $1.15 for grain price = 140 # Set the first derivative equal to N/corn price, k(M-nitro)=.11/1.15 coef(m1)['M']-(.11/1.15)/coef(m1)['k'] #> M #> 140.7837"},{"path":"/reference/evans.sugarcane.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"Uniformity trial sugarcane Mauritius.","code":""},{"path":"/reference/evans.sugarcane.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"","code":"data(\"evans.sugarcane.uniformity\")"},{"path":"/reference/evans.sugarcane.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"data frame 710 observations following 3 variables. row row ordinate col column ordinate yield plot yield","code":""},{"path":"/reference/evans.sugarcane.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"field ratoon canes harvested 20-hole plots. Described letter Frank Yates written 21 May 1935. Field length: 5 plots x 50 feet (20 stools per plot; 30 inches stools) = 250 feet Field width: 142 plots x 5 feet = 710 feet data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/evans.sugarcane.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 8.","code":""},{"path":"/reference/evans.sugarcane.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"None.","code":""},{"path":"/reference/evans.sugarcane.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ data(evans.sugarcane.uniformity) dat <- evans.sugarcane.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(5*50)/(142*5), # true aspect main=\"evans.sugarcane.uniformity\") table( substring(dat$yield,3) ) # yields ending in 0,5 are much more common } # }"},{"path":"/reference/fan.stability.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize hybrids in China — fan.stability","title":"Multi-environment trial of maize hybrids in China — fan.stability","text":"Yield 13 hybrids, grown 10 locations across 2 years. Conducted Yunnan, China.","code":""},{"path":"/reference/fan.stability.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize hybrids in China — fan.stability","text":"data frame 260 observations following 5 variables. gen genotype maturity maturity, days year year loc location yield yield, Mg/ha","code":""},{"path":"/reference/fan.stability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize hybrids in China — fan.stability","text":"Data mean 3 reps. data used conduct stability analysis yield. Used permission Manjit Kang.","code":""},{"path":"/reference/fan.stability.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize hybrids in China — fan.stability","text":"Fan, X.M. Kang, M.S. Chen, H. Zhang, Y. Tan, J. Xu, C. (2007). Yield stability maize hybrids evaluated multi-environment trials Yunnan, China. Agronomy Journal, 99, 220-228. https://doi.org/10.2134/agronj2006.0144","code":""},{"path":"/reference/fan.stability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize hybrids in China — fan.stability","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(fan.stability) dat <- fan.stability dat$env <- factor(paste(dat$loc, dat$year, sep=\"\")) libs(lattice) dotplot(gen~yield|env, dat, main=\"fan.stability\") libs(reshape2, agricolae) dm <- acast(dat, gen~env, value.var='yield') # Use 0.464 as pooled error from ANOVA. Calculate yield mean/stability. stability.par(dm, rep=3, MSerror=0.464) # Table 5 of Fan et al. } # }"},{"path":"/reference/federer.diagcheck.html","id":null,"dir":"Reference","previous_headings":"","what":"Wheat experiment with diagonal checks — federer.diagcheck","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"Wheat experiment augmented two check varieties diagonal strips.","code":""},{"path":"/reference/federer.diagcheck.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"data frame 180 observations following 4 variables. row row col column gen genotype, 120 levels yield yield","code":""},{"path":"/reference/federer.diagcheck.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"experiment conducted Matthew Reynolds, CIMMYT. 180 plots field, 60 diagonal checks (G121 G122) 120 new varieties. Federer used data multiple papers illustrate use orthogonal polynomials model field trends related genetic effects. Note: Federer Wolfinger (2003) provide SAS program analysis data. However, SAS program used analyze data, results match results given Federer (1998) Federer Wolfinger (2003). differences slight, suggests typographical error presentation data. R code provides results consistent SAS code Federer & Wolfinger (2003) applied version data. Plot dimensions given.","code":""},{"path":"/reference/federer.diagcheck.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"Federer, Walter T. 1998. Recovery interblock, intergradient, intervariety information incomplete block lattice rectangle design experiments. Biometrics, 54, 471–481. https://doi.org/10.2307/3109756","code":""},{"path":"/reference/federer.diagcheck.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"Walter T Federer Russell D Wolfinger, 2003. Augmented Row-Column Design Trend Analysis, chapter 28 Handbook Formulas Software Plant Geneticists Breeders, Haworth Press.","code":""},{"path":"/reference/federer.diagcheck.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(federer.diagcheck) dat <- federer.diagcheck dat$check <- ifelse(dat$gen == \"G121\" | dat$gen==\"G122\", \"C\",\"N\") # Show the layout as in Federer 1998. libs(desplot) desplot(dat, yield ~ col*row, text=gen, show.key=FALSE, # aspect unknown shorten='no', col=check, cex=.8, col.text=c(\"yellow\",\"gray\"), main=\"federer.diagcheck\") # Now reproduce the analysis of Federer 2003. # Only to match SAS results dat$row <- 16 - dat$row dat <- dat[order(dat$col, dat$row), ] # Add row / column polynomials to the data. # The scaling factors sqrt() are arbitrary, but used to match SAS nr <- length(unique(dat$row)) nc <- length(unique(dat$col)) rpoly <- poly(dat$row, degree=10) * sqrt(nc) cpoly <- poly(dat$col, degree=10) * sqrt(nr) dat <- transform(dat, c1 = cpoly[,1], c2 = cpoly[,2], c3 = cpoly[,3], c4 = cpoly[,4], c6 = cpoly[,6], c8 = cpoly[,8], r1 = rpoly[,1], r2 = rpoly[,2], r3 = rpoly[,3], r4 = rpoly[,4], r8 = rpoly[,8], r10 = rpoly[,10]) dat$trtn <- ifelse(dat$gen == \"G121\" | dat$gen==\"G122\", dat$gen, \"G999\") dat$new <- ifelse(dat$gen == \"G121\" | dat$gen==\"G122\", \"N\", \"Y\") dat <- transform(dat, trtn=factor(trtn), new=factor(new)) m1 <- lm(yield ~ c1 + c2 + c3 + c4 + c6 + c8 + r1 + r2 + r4 + r8 + r10 + c1:r1 + c2:r1 + c3:r1 + gen, data = dat) # To get Type III SS use the following # libs(car) # car::Anova(m1, type=3) # Matches PROC GLM output ## Sum Sq Df F value Pr(>F) ## (Intercept) 538948 1 159.5804 3.103e-16 *** ## c1 13781 1 4.0806 0.0494940 * ## c2 51102 1 15.1312 0.0003354 *** ## c3 45735 1 13.5419 0.0006332 *** ## c4 24670 1 7.3048 0.0097349 ** ## ... # lmer libs(lme4,lucid) # \"group\" for all data dat$one <- factor(rep(1, nrow(dat))) # lmer with bobyqa (default) m2b <- lmer(yield ~ trtn + (0 + r1 + r2 + r4 + r8 + r10 + c1 + c2 + c3 + c4 + c6 + c8 + r1:c1 + r1:c2 + r1:c3 || one) + (1|new:gen), data = dat, control=lmerControl(check.nlev.gtr.1=\"ignore\")) vc(m2b) ## grp var1 var2 vcov sdcor ## new.gen (Intercept) 2869 53.57 ## one r1:c3 5532 74.37 ## one.1 r1:c2 58230 241.3 ## one.2 r1:c1 128000 357.8 ## one.3 c8 6456 80.35 ## one.4 c6 1400 37.41 ## one.5 c4 1792 42.33 ## one.6 c3 2549 50.49 ## one.7 c2 5942 77.08 ## one.8 c1 0 0 ## one.9 r10 1133 33.66 ## one.10 r8 1355 36.81 ## one.11 r4 2269 47.63 ## one.12 r2 241.8 15.55 ## one.13 r1 9200 95.92 ## Residual 4412 66.42 # lmer with Nelder_Mead gives 'wrong' results ## m2n <- lmer(yield ~ trtn + (0 + r1 + r2 + r4 + r8 + r10 + ## c1 + c2 + c3 + c4 + c6 + c8 + r1:c1 + r1:c2 + r1:c3 || one) + ## (1|new:gen) ## , data = dat, ## control=lmerControl(optimizer=\"Nelder_Mead\", ## check.nlev.gtr.1=\"ignore\")) ## vc(m2n) ## groups name variance stddev ## new.gen (Intercept) 3228 56.82 ## one r1:c3 7688 87.68 ## one.1 r1:c2 69750 264.1 ## one.2 r1:c1 107400 327.8 ## one.3 c8 6787 82.38 ## one.4 c6 1636 40.45 ## one.5 c4 12270 110.8 ## one.6 c3 2686 51.83 ## one.7 c2 7645 87.43 ## one.8 c1 0 0.0351 ## one.9 r10 1976 44.45 ## one.10 r8 1241 35.23 ## one.11 r4 2811 53.02 ## one.12 r2 928.2 30.47 ## one.13 r1 10360 101.8 ## Residual 4127 64.24 if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) m3 <- asreml(yield ~ -1 + trtn, data=dat, random = ~ r1 + r2 + r4 + r8 + r10 + c1 + c2 + c3 + c4 + c6 + c8 + r1:c1 + r1:c2 + r1:c3 + new:gen) ## coef(m3) ## # REML cultivar means. Very similar to Federer table 2. ## rev(sort(round(coef(m3)$fixed[3] + coef(m3)$random[137:256,],0))) ## ## gen_G060 gen_G021 gen_G011 gen_G099 gen_G002 ## ## 974 949 945 944 942 ## ## gen_G118 gen_G058 gen_G035 gen_G111 gen_G120 ## ## 938 937 937 933 932 ## ## gen_G046 gen_G061 gen_G082 gen_G038 gen_G090 ## ## 932 931 927 927 926 ## vc(m3) ## ## effect component std.error z.ratio constr ## ## r1!r1.var 9201 13720 0.67 pos ## ## r2!r2.var 241.7 1059 0.23 pos ## ## r4!r4.var 2269 3915 0.58 pos ## ## r8!r8.var 1355 2627 0.52 pos ## ## r10!r10.var 1133 2312 0.49 pos ## ## c1!c1.var 0.01 0 4.8 bound ## ## c2!c2.var 5942 8969 0.66 pos ## ## c3!c3.var 2549 4177 0.61 pos ## ## c4!c4.var 1792 3106 0.58 pos ## ## c6!c6.var 1400 2551 0.55 pos ## ## c8!c8.var 6456 9702 0.67 pos ## ## r1:c1!r1.var 128000 189700 0.67 pos ## ## r1:c2!r1.var 58230 90820 0.64 pos ## ## r1:c3!r1.var 5531 16550 0.33 pos ## ## new:gen!new.var 2869 1367 2.1 pos ## ## R!variance 4412 915 4.8 pos } } # }"},{"path":"/reference/federer.tobacco.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"RCB tobacco, height plants exposed radiation","code":""},{"path":"/reference/federer.tobacco.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"data frame 56 observations following 4 variables. row row block block, numeric dose radiation dose, roentgens height height 20 plants, cm","code":""},{"path":"/reference/federer.tobacco.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"experiment conducted 1951 described Federer (1954). treatment involved exposing tobacco seeds seven different doses radiation. seedlings transplanted field RCB experiment 7 treatments 8 blocks. physical layout experiment 8 rows 7 columns. Shortly plants transplanted field became apparent environmental gradient existed. response variable total height (centimeters) 20 plants.","code":""},{"path":"/reference/federer.tobacco.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"Walter T Federer C S Schlottfeldt, 1954. use covariance control gradients experiments. Biometrics, 10, 282–290. https://doi.org/10.2307/3001881","code":""},{"path":"/reference/federer.tobacco.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"R. D. Cook S. Weisberg (1999). Applied Regression Including Computing Graphics. Walter T Federer Russell D Wolfinger, 2003. PROC GLM PROC MIXED Codes Trend Analyses Row-Column Designed Experiments, Handbook Formulas Software Plant Geneticists Breeders, Haworth Press. Paul N Hinz, (1987). Nearest-Neighbor Analysis Practice, Iowa State Journal Research, 62, 199–217. https://lib.dr.iastate.edu/iowastatejournalofresearch/vol62/iss2/1","code":""},{"path":"/reference/federer.tobacco.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(federer.tobacco) dat <- federer.tobacco # RCB analysis. Treatment factor not signficant. dat <- transform(dat, dosef=factor(dose), rowf=factor(row), blockf=factor(block)) m1 <- lm(height ~ blockf + dosef, data=dat) anova(m1) # RCB residuals show strong spatial trends libs(desplot) dat$resid <- resid(m1) desplot(dat, resid ~ row * block, # aspect unknown main=\"federer.tobacco\") # Row-column analysis. Treatment now significant m2 <- lm(height ~ rowf + blockf + dosef, data=dat) anova(m2) } # }"},{"path":"/reference/fisher.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"Multi-environment trial 5 barley varieties, 6 locations, 2 years","code":""},{"path":"/reference/fisher.barley.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"","code":"data(\"fisher.barley\")"},{"path":"/reference/fisher.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"data frame 60 observations following 4 variables. yield yield, bu/ac gen genotype/variety, 5 levels env environment/location, 2 levels year year, 1931/1932","code":""},{"path":"/reference/fisher.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"Trials 5 varieties barley conducted 6 stations Minnesota years 1931-1932. subset Immer's barley data. yield values totals 3 reps (Immer gave average yield 3 reps).","code":""},{"path":"/reference/fisher.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"Ronald Fisher (1935). Design Experiments.","code":""},{"path":"/reference/fisher.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"George Fernandez (1991). Analysis Genotype x Environment Interaction Stability Estimates. Hort Science, 26, 947-950. F. Yates & W. G. Cochran (1938). Analysis Groups Experiments. Journal Agricultural Science, 28, 556-580, table 1. https://doi.org/10.1017/S0021859600050978 G. K. Shukla, 1972. statistical aspects partitioning genotype-environmental components variability. Heredity, 29, 237-245. Table 1. https://doi.org/10.1038/hdy.1972.87","code":""},{"path":"/reference/fisher.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(fisher.barley) dat <- fisher.barley libs(dplyr,lattice) # Yates 1938 figure 1. Regression on env mean # Sum years within loc dat2 <- aggregate(yield ~ gen + env, data=dat, FUN=sum) # Avg within env emn <- aggregate(yield ~ env, data=dat2, FUN=mean) dat2$envmn <- emn$yield[match(dat2$env, emn$env)] xyplot(yield ~ envmn, dat2, group=gen, type=c('p','r'), main=\"fisher.barley - stability regression\", xlab=\"Environment total\", ylab=\"Variety mean\", auto.key=list(columns=3)) # calculate stability according to the sum-of-squares approach used by # Shukla (1972), eqn 11. match to Shukla, Table 4, M.S. column # also matches fernandez, table 3, stabvar column libs(dplyr) dat2 <- dat dat2 <- group_by(dat2, gen,env) dat2 <- summarize(dat2, yield=sum(yield)) # means across years dat2 <- group_by(dat2, env) dat2 <- mutate(dat2, envmn=mean(yield)) # env means dat2 <- group_by(dat2, gen) dat2 <- mutate(dat2, genmn=mean(yield)) # gen means dat2 <- ungroup(dat2) dat2 <- mutate(dat2, grandmn=mean(yield)) # grand mean # correction factor overall dat2 <- mutate(dat2, cf = sum((yield - genmn - envmn + grandmn)^2)) t=5; s=6 # t genotypes, s environments dat2 <- group_by(dat2, gen) dat2 <- mutate(dat2, ss=sum((yield-genmn-envmn+grandmn)^2)) # divide by 6 to scale down to plot-level dat2 <- mutate(dat2, sig2i = 1/((s-1)*(t-1)*(t-2)) * (t*(t-1)*ss-cf)/6) dat2[!duplicated(dat2$gen),c('gen','sig2i')] ## ## 1 Manchuria 25.87912 ## 2 Peatland 75.68001 ## 3 Svansota 19.59984 ## 4 Trebi 225.52866 ## 5 Velvet 22.73051 if(require(\"asreml\", quietly=TRUE)) { # mixed model approach gives similar results (but not identical) libs(asreml,lucid) dat2 <- dat dat2 <- dplyr::group_by(dat2, gen,env) dat2 <- dplyr::summarize(dat2, yield=sum(yield)) # means across years dat2 <- dplyr::arrange(dat2, gen) # G-side m1g <- asreml(yield ~ gen, data=dat2, random = ~ env + at(gen):units, family=asr_gaussian(dispersion=1.0)) m1g <- update(m1g) summary(m1g)$varcomp[-1,1:2]/6 # component std.error # at(gen, Manchuria):units 33.8145031 27.22721 # at(gen, Peatland):units 70.4489092 50.52680 # at(gen, Svansota):units 25.2728568 21.92919 # at(gen, Trebi):units 231.6981702 150.80464 # at(gen, Velvet):units 13.9325646 16.58571 # units!R 0.1666667 NA # R-side estimates = G-side estimate + 0.1666 (resid variance) m1r <- asreml(yield ~ gen, data=dat2, random = ~ env, residual = ~ dsum( ~ units|gen)) m1r <- update(m1r) summary(m1r)$varcomp[-1,1:2]/6 # component std.error # gen_Manchuria!R 34.00058 27.24871 # gen_Peatland!R 70.65501 50.58925 # gen_Svansota!R 25.42022 21.88606 # gen_Trebi!R 231.85846 150.78756 # gen_Velvet!R 14.08405 16.55558 } } # }"},{"path":"/reference/fisher.latin.html","id":null,"dir":"Reference","previous_headings":"","what":"Latin square experiment on mangolds — fisher.latin","title":"Latin square experiment on mangolds — fisher.latin","text":"Latin square experiment mangolds. Used R. . Fisher.","code":""},{"path":"/reference/fisher.latin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latin square experiment on mangolds — fisher.latin","text":"","code":"data(\"fisher.latin\")"},{"path":"/reference/fisher.latin.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Latin square experiment on mangolds — fisher.latin","text":"data frame 25 observations following 4 variables. trt treatment factor, 5 levels yield yield row row col column","code":""},{"path":"/reference/fisher.latin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Latin square experiment on mangolds — fisher.latin","text":"Yields root weights. Data originally collected Mercer Hall part uniformity trial. data data columns 1-5, rows 16-20, mercer.mangold.uniformity data package. Unsurprisingly, significant treatment differences.","code":""},{"path":"/reference/fisher.latin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latin square experiment on mangolds — fisher.latin","text":"Mercer, WB Hall, AD, 1911. experimental error field trials Journal Agricultural Science, 4, 107-132. Table 1. http::/doi.org/10.1017/S002185960000160X R. . Fisher. Statistical Methods Research Workers.","code":""},{"path":"/reference/fisher.latin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Latin square experiment on mangolds — fisher.latin","text":"","code":"library(agridat) data(fisher.latin) dat <- fisher.latin # Standard latin-square analysis m1 <- lm(yield ~ trt + factor(row) + factor(col), data=dat) anova(m1) #> Analysis of Variance Table #> #> Response: yield #> Df Sum Sq Mean Sq F value Pr(>F) #> trt 4 330.2 82.56 0.5647 0.692978 #> factor(row) 4 4240.2 1060.06 7.2511 0.003294 ** #> factor(col) 4 701.8 175.46 1.2002 0.360412 #> Residuals 12 1754.3 146.19 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"},{"path":"/reference/forster.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"Uniformity trial wheat Australia.","code":""},{"path":"/reference/forster.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"","code":"data(\"forster.wheat.uniformity\")"},{"path":"/reference/forster.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"data frame 160 observations following 3 variables. row row ordinate col column ordinate yield yield, ounces per plot","code":""},{"path":"/reference/forster.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"experiment repeat classic experiment Mercer Hall. Conducted State Research Farm, Werribee, Victoria, Australia. Planted 1926. Harvested 1927. acre land selected. plot one double-sown row. plot 30 x 20 links. whole experiment 300 x 320 links. Near west edge, strip damaged cart tracks excluded. field marked quarters one quarter subdivided harvested time. quarter cut 5 strips 8 plots. Field length: 16 plots * 20 links = 320 links (211 feet). Field width: 10 plots * 30 links = 300 links (197 feet). Note: clear strip \"yards wide\" omitted yet dimensions whole area still 300 x 320 links. Since omitted strip 1/3 width plot, (agridat authors) decided ignore omitted strip. electronic data manually typed source 2023-04-12. Summary statistics electronic data differ slightly summaries Forster, indicating possible typos rounding printed yield values paper. Values checked hand match paper.","code":""},{"path":"/reference/forster.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"Forster, H. C. (Howard Carlyle), - Vasey, . J. (1928). Experimental error field trials Australia. Proceedings Royal Society Victoria. New series, 40, 70–80. Table 1. https://www.biodiversitylibrary.org/page/54367272","code":""},{"path":"/reference/forster.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"None","code":""},{"path":"/reference/forster.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ require(agridat) data(forster.wheat.uniformity) dat <- forster.wheat.uniformity mean(dat$yield) # 135.97 # Forster says 136.5 sd(dat$yield) # 10.68 # Forster says 10.9 # Compare to Forster table 3. Slight differences. table( cut(dat$yield, breaks = c(106,111,116,121,126,131,136,141, 146,151,156,161,166)+.5) ) # Forster has 5 plots in the 157-161 bin, but we show 6. # I filtered the data for this bin and verified our data # matches the layout in the paper. filter(dat, yield>156.5, yield<161.5) libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(16*20)/(10*30), # true aspect main=\"forster.wheat.uniformity\") } # }"},{"path":"/reference/foulley.calving.html","id":null,"dir":"Reference","previous_headings":"","what":"Calving difficulty by calf sex and age of dam — foulley.calving","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"Calving difficulty calf sex age dam","code":""},{"path":"/reference/foulley.calving.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"","code":"data(\"foulley.calving\")"},{"path":"/reference/foulley.calving.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"data frame 54 observations following 4 variables. sex calf gender age dam age factor, 9 levels score score birthing difficulty, S1 < S2 < S3 count count births category","code":""},{"path":"/reference/foulley.calving.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"data calving difficulty scores purebred US Simmental cows. raw data show greatest calving difficulty young dams male calves. Differences male/female calves decreased age dam. goodness fit can improved using scaling effect age dam. Note: paper Foulley Gianola '21943' count score 1, F, >8. data uses '20943' marginal totals data match marginal totals given paper. Used permission Jean-Louis Foulley.","code":""},{"path":"/reference/foulley.calving.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"JL Foulley, D Gianola (1996). Statistical Analysis Ordered Categorical Data via Structured Heteroskedastic Threshold Model. Genet Sel Evol, 28, 249–273. https://doi.org/10.1051/gse:19960304","code":""},{"path":"/reference/foulley.calving.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(foulley.calving) dat <- foulley.calving ## Plot d2 <- transform(dat, age=ordered(age, levels=c(\"0.0-2.0\",\"2.0-2.5\",\"2.5-3.0\", \"3.0-3.5\",\"3.5-4.0\", \"4.0-4.5\",\"4.5-5.0\",\"5.0-8.0\",\"8.0+\")), score=ordered(score, levels=c('S1','S2','S3'))) libs(reshape2) d2 <- acast(dat, sex+age~score, value.var='count') d2 <- prop.table(d2, margin=1) libs(lattice) thm <- simpleTheme(col=c('skyblue','gray','pink')) barchart(d2, par.settings=thm, main=\"foulley.calving\", xlab=\"Frequency of calving difficulty\", ylab=\"Calf gender and dam age\", auto.key=list(columns=3, text=c(\"Easy\",\"Assited\",\"Difficult\"))) ## Ordinal multinomial model libs(ordinal) m2 <- clm(score ~ sex*age, data=dat, weights=count, link='probit') summary(m2) ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## sexM 0.500605 0.015178 32.982 < 2e-16 *** ## age2.0-2.5 -0.237643 0.013846 -17.163 < 2e-16 *** ## age2.5-3.0 -0.681648 0.018894 -36.077 < 2e-16 *** ## age3.0-3.5 -0.957138 0.018322 -52.241 < 2e-16 *** ## age3.5-4.0 -1.082520 0.024356 -44.446 < 2e-16 *** ## age4.0-4.5 -1.146834 0.022496 -50.981 < 2e-16 *** ## age4.5-5.0 -1.175312 0.028257 -41.594 < 2e-16 *** ## age5.0-8.0 -1.280587 0.016948 -75.559 < 2e-16 *** ## age8.0+ -1.323749 0.024079 -54.974 < 2e-16 *** ## sexM:age2.0-2.5 0.003035 0.019333 0.157 0.87527 ## sexM:age2.5-3.0 -0.076677 0.026106 -2.937 0.00331 ** ## sexM:age3.0-3.5 -0.080657 0.024635 -3.274 0.00106 ** ## sexM:age3.5-4.0 -0.135774 0.032927 -4.124 3.73e-05 *** ## sexM:age4.0-4.5 -0.124303 0.029819 -4.169 3.07e-05 *** ## sexM:age4.5-5.0 -0.198897 0.038309 -5.192 2.08e-07 *** ## sexM:age5.0-8.0 -0.135524 0.022804 -5.943 2.80e-09 *** ## sexM:age8.0+ -0.131033 0.031852 -4.114 3.89e-05 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Threshold coefficients: ## Estimate Std. Error z value ## S1|S2 0.82504 0.01083 76.15 ## S2|S3 1.52017 0.01138 133.62 ## Note 1.52017 - 0.82504 = 0.695 matches Foulley's '2-3' threshold estimate predict(m2) # probability of each category } # }"},{"path":"/reference/fox.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"Wheat yields 22 varieties 14 sites Australia","code":""},{"path":"/reference/fox.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"","code":"data(\"fox.wheat\")"},{"path":"/reference/fox.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"data frame 308 observations following 4 variables. gen genotype/variety factor, 22 levels site site factor, 14 levels yield yield, tonnes/ha state state Australia","code":""},{"path":"/reference/fox.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"1975 Interstate Wheat Variety trial Australia used RCB design 4 blocks, 22 varieties 14 sites. Wagga represented twice, trials sown May June. 22 varieties highly selected represent considerable genetic diversity four different groups. () University Sydney: Timson, Songlen, Gamenya. (ii) widely grown Mallee soils: Heron Halberd. (iii) late maturing varieties Victoria: Pinnacle, KL-21, JL-157. (iv) Mexican parentage: WW-15 Oxley.","code":""},{"path":"/reference/fox.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"Fox, P.N. Rathjen, .J. (1981). Relationships sites used interstate wheat variety trials. Australian Journal Agricultural Research, 32, 691-702. Electronic version supplied Jonathan Godfrey.","code":""},{"path":"/reference/fox.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(fox.wheat) dat <- fox.wheat # Means of varieties. Slight differences from Fox and Rathjen suggest # they had more decimals of precision than shown. tapply(dat$yield, dat$gen, mean) # Calculate genotype means, merge into the data genm <- tapply(dat$yield, dat$gen, mean) dat$genm <- genm[match(dat$gen, names(genm))] # Calculate slopes for each site. Matches Fox, Table 2, Col A. m1 <- lm(yield~site+site:genm, data=dat) sort(round(coef(m1)[15:28],2), dec=TRUE) # Figure 1 of Fox libs(lattice) xyplot(yield~genm|state, data=dat, type=c('p','r'), group=site, auto.key=list(columns=4), main=\"fox.wheat\", xlab=\"Variety mean across all sites\", ylab=\"Variety yield at each site within states\") } # }"},{"path":"/reference/garber.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"Uniformity trials oat hay wheat grain, West Virginia Agricultural Experiment Station, 1923-1924, land.","code":""},{"path":"/reference/garber.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"data frame 270 observations following 4 variables. row row col column plot plot number year year crop crop yield yield (pounds bu/ac)","code":""},{"path":"/reference/garber.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"experiments conducted West Virginia Agricultural Experiment Station Maggie, West Virginia. Note, Garber et al (1926) Garber et al (1931) describe uniformity trials field, experimental plot numbers two papers different, indicating different parts field. data 1923 1924 given Garber (1926). data 1927, 1928, 1929 given Garber (1931). data given source papers relative deviations mean, converted absolute yields package. First paper: Garber (1926) plot 68 feet x 21 feet. discarding 3.5 foot border sides, harvested area 61 feet x 14 feet. plots laid double series 14-foot roadway plots. example, columns 1 & 2 side--side, 14 foot road, columns 3 & 4, 14 foot road, columns 5 & 6. Note: orientation plots (68x21) educated guess. orientation 21x68, field extremely narrow long. Field width: 6 plots * 68 feet + 14 ft/roadway * 2 = 436 feet Field length: 45 plots * 21 feet/plot = 945 feet Garber said: \"Plots 211 214, 261 264, [note, rows 11-14, columns 5-6] inclusive, eliminated study fact years ago straw stack stood vicinity...undoubtedly accounts relatively high yields plots 261 264, inclusive.\" 1923 oat hay, yield pounds per acre data oat hay given Table 5 mean-subtracted yields pounds per acre plot. oat yield row 22, column 5 given +59.7. obviously incorrect, since negative yields end '.7' positive yields ended '.3'. used -59.7 centered yield value added mean 1883.7 (p. 259) centered yields obtain absolute yields pounds per acre. 1924 wheat, yield bushels per acre data wheat given bushels per acre, expressed deviations mean yield (15.6 bu). added mean plot data. Second paper: Garber (1926) 1927 corn, 1928 oats, 1929 wheat field 10 plots wide, 84 plots tall. Field width: 10 plots * 68 feet + 4 roads * 14 feet = 736 feet. Field length: 84 plots * 21 feet + 3 roads * 14 feet = 1806 feet.","code":""},{"path":"/reference/garber.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"Garber, R.J. Mcllvaine, T.C. Hoover, M.M. (1926). study soil heterogeneity experiment plots. Jour Agr Res, 33, 255-268. Tables 3, 5. https://naldc.nal.usda.gov/download/IND43967148/PDF Garber, R. J. T. C. McIlvaine M. M. Hoover (1931). Method Laying Experimental Plats. Journal American Society Agronomy, 23, 286-298, https://archive.org/details/.ernet.dli.2015.229753/page/n299","code":""},{"path":"/reference/garber.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"None","code":""},{"path":"/reference/garber.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(garber.multi.uniformity) dat <- garber.multi.uniformity ## aggregate(yield~year, data=dat, FUN=mean) ## year yield ## 1 1923 1883.30741 ## 2 1924 15.58296 ## 3 1927 76.28965 ## 4 1928 32.81415 ## 5 1929 19.44650 libs(desplot) desplot(dat, yield ~ col*row, subset=year==1923, flip=TRUE, tick=TRUE, aspect=945/436, # true aspect main=\"garber.multi.uniformity 1923 oats\") desplot(dat, yield ~ col*row, subset=year==1924, flip=TRUE, tick=TRUE, aspect=945/436, # true aspect main=\"garber.multi.uniformity 1924 wheat\") desplot(dat, yield ~ col*row|year, subset=year >= 1927, flip=TRUE, tick=TRUE, aspect=1806/736, # true aspect main=\"garber.multi.uniformity 1927-1929\") # Correlation of same plots in 1923 vs 1924. Garber has 0.37 # cor(subset(dat, year==1923)$yield, # subset(dat, year==1924)$yield ) # .37 # Garber 1931 table 2 has .58, .20 # cor(subset(dat, year==1927)$yield, # subset(dat, year==1928)$yield, use=\"pair\" ) # .58 # cor(subset(dat, year==1927)$yield, # subset(dat, year==1929)$yield, use=\"pair\" ) # .19 } # }"},{"path":"/reference/gartner.corn.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield monitor data from a corn field in Minnesota — gartner.corn","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"Yield monitor data corn field Minnesota","code":""},{"path":"/reference/gartner.corn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"","code":"data(\"gartner.corn\")"},{"path":"/reference/gartner.corn.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"data frame 4949 observations following 8 variables. long longitude lat latitude mass grain mass flow per second, pounds time GPS time, seconds seconds seconds elapsed datum dist distance traveled datum, inches moist grain moisture, percent elev elevation, feet","code":""},{"path":"/reference/gartner.corn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"data collected 5 Nov 2011 corn field south Mankato, Minnesota, using combine-mounted yield monitor. https://www.google.com/maps/place/43.9237575,-93.9750632 harvested swath 12 rows wide = 360 inches. Timestamp 0 = 5 Nov 2011, 12:38:03 Central Time. Timestamp 16359 = 4.54 hours later. Yield calculated total dry weight (corrected 15.5 percent moisture), divided 56 pounds (get bushels), divided harvested area: drygrain = [massflow * seconds * (100-moisture) / (100-15.5)] / 56 harvested area = (distance * swath width) / 6272640 yield = drygrain / area","code":""},{"path":"/reference/gartner.corn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"University Minnesota Precision Agriculture Center. Retrieved 27 Aug 2015 https://web.archive.org/web/20100717003256/https://www.soils.umn.edu/academics/classes/soil4111/files/yield_a.xls Used via license: Creative Commons -SA 3.0.","code":""},{"path":"/reference/gartner.corn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"Suman Rakshit, Adrian Baddeley, Katia Stefanova, Karyn Reeves, Kefei Chen, Zhanglong Cao, Fiona Evans, Mark Gibberd (2020). Novel approach analysis spatially-varying treatment effects -farm experiments. Field Crops Research, 255, 15 September 2020, 107783. https://doi.org/10.1016/j.fcr.2020.107783","code":""},{"path":"/reference/gartner.corn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gartner.corn) dat <- gartner.corn # Calculate yield from mass & moisture dat <- transform(dat, yield=(mass*seconds*(100-moist)/(100-15.5)/56)/(dist*360/6272640)) # Delete low yield outliers dat <- subset(dat, yield >50) # Group yield into 20 bins for red-gray-blue colors medy <- median(dat$yield) ncols <- 20 wwidth <- 150 brks <- seq(from = -wwidth/2, to=wwidth/2, length=ncols-1) brks <- c(-250, brks, 250) # 250 is safe..we cleaned data outside ?(50,450)? yldbrks <- brks + medy dat <- transform(dat, yldbin = as.numeric(cut(yield, breaks= yldbrks))) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) dat$yieldcolor = redblue(ncols)[dat$yldbin] # Polygons for soil map units # Go to: https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx # Click: Lat and Long. 43.924, -93.975 # Click the little AOI rectangle icon. Drag around the field # In the AOI Properties, enter the Name: Gartner # Click the tab Soil Map to see map unit symbols, names # Click: Download Soils Data. Click: Create Download Link. # Download the zip file and find the soilmu_a_aoi files. # Read shape files libs(sf) fname <- system.file(package=\"agridat\", \"files\", \"gartner.corn.shp\") shp <- sf::st_read( fname ) # Annotate soil map units. Coordinates chosen by hand. mulabs = data.frame( name=c(\"110\",\"319\",\"319\",\"230\",\"105C\",\"110\",\"211\",\"110\",\"211\",\"230\",\"105C\"), x = c(-93.97641, -93.97787, -93.97550, -93.97693, -93.97654, -93.97480, -93.97375, -93.978284, -93.977617, -93.976715, -93.975929), y = c(43.92185, 43.92290, 43.92358, 43.92445, 43.92532, 43.92553, 43.92568, 43.922163, 43.926427, 43.926993, 43.926631) ) mulabs = st_as_sf( mulabs, coords=c(\"x\",\"y\"), crs=4326) mulabs = st_transform(mulabs, 2264) # Trim top and bottom ends of the field dat <- subset(dat, lat < 43.925850 & lat > 43.921178) # Colored points for yield dat <- st_as_sf(dat, coords=c(\"long\",\"lat\"), crs=4326) libs(ggplot2) ggplot() + geom_sf(data=dat, aes(col=yieldcolor) ) + scale_color_identity() + geom_sf_label(data=mulabs, aes(label=name), cex=2) + geom_sf(data=shp[\"MUSYM\"], fill=\"transparent\") + ggtitle(\"gartner.corn\") + theme_classic() if(0){ # Draw a 3D surface. Clearly shows the low drainage area # Re-run the steps above up, stop before the \"Colored points\" line. libs(rgl) dat <- transform(dat, x=long-min(long), y=lat-min(lat), z=elev-min(elev)) clear3d() points3d(dat$x, dat$y, dat$z/50000, col=redblue(ncols)[dat$yldbin]) axes3d() title3d(xlab='x',ylab='y',zlab='elev') close3d() } } # }"},{"path":"/reference/gathmann.bt.html","id":null,"dir":"Reference","previous_headings":"","what":"Impact of Bt corn on non-target species — gathmann.bt","title":"Impact of Bt corn on non-target species — gathmann.bt","text":"Impact Bt corn non-target species","code":""},{"path":"/reference/gathmann.bt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Impact of Bt corn on non-target species — gathmann.bt","text":"data frame 16 observations following 3 variables. gen genotype/maize, Bt ISO thysan thysan abundance aranei aranei abundance","code":""},{"path":"/reference/gathmann.bt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Impact of Bt corn on non-target species — gathmann.bt","text":"experiment involved comparing Bt maize near-isogenic control variety. Species abundances measured Thysanoptera (thrips) Araneida (spiders) 8 different plots. response probably mean across repeated measurements. Used permission Achim Gathmann.","code":""},{"path":"/reference/gathmann.bt.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Impact of Bt corn on non-target species — gathmann.bt","text":"L. . Hothorn, 2005. Evaluation Bt-Maize Field Trials Proof Safety. https://www.seedtest.org/upload/cms/user/presentation7Hothorn.pdf","code":""},{"path":"/reference/gathmann.bt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Impact of Bt corn on non-target species — gathmann.bt","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gathmann.bt) dat <- gathmann.bt # EDA suggests Bt vs ISO is significant for thysan, not for aranei libs(lattice) libs(reshape2) d2 <- melt(dat, id.var='gen') bwplot(value ~ gen|variable, d2, main=\"gathmann.bt\", ylab=\"Insect abundance\", panel=function(x,y,...){ panel.xyplot(jitter(as.numeric(x)),y,...) panel.bwplot(x,y,...) }, scales=list(relation=\"free\")) if(0){ # ----- Parametric CI. Thysan significant, aranei not significant. libs(equivalence) th0 <- with(dat, tost(thysan[1:8], thysan[9:16], alpha=.05, paired=FALSE)) lapply(th0[c(\"estimate\",\"tost.interval\")], round, 2) # 14.28-8.72=5.56, (2.51, 8.59) # match Gathmann p. 11 ar0 <- with(dat, tost(aranei[1:8], aranei[9:16], alpha=.05, epsilon=.4)) lapply(ar0[c(\"estimate\",\"tost.interval\")], round, 2) # .57-.47=.10, (-0.19, 0.40) # match Gathmann p. 11 # ----- Non-parametric exact CI. Same result. libs(coin) th1 <- wilcox_test(thysan ~ gen, data=dat, conf.int=TRUE, conf.level=0.90) lapply(confint(th1), round, 2) # 6.36, (2.8, 9.2) # Match Gathmann p. 11 ar1 <- wilcox_test(aranei ~ gen, data=dat, conf.int=TRUE, conf.level=0.90) lapply(confint(ar1), round, 2) # .05 (-.2, .4) # ----- Log-transformed exact CI. Same result. th2 <- wilcox_test(log(thysan) ~ gen, data=dat, alternative=c(\"two.sided\"), conf.int=TRUE, conf.level=0.9) lapply(confint(th2), function(x) round(exp(x),2)) # 1.66, (1.38, 2.31) # Match Gathmann p 11 # ----- Log-transform doesn't work on aranei, but asinh(x/2) does ar2 <- wilcox_test(asinh(aranei/2) ~ gen, data=dat, alternative=c(\"two.sided\"), conf.int=TRUE, conf.level=0.9) lapply(confint(ar2), function(x) round(sinh(x)*2,1)) } } # }"},{"path":"/reference/gauch.soy.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"New York soybean yields, 1977 1988, 7 genotypes, 55 environments (9 loc, 12 years), 2-3 reps.","code":""},{"path":"/reference/gauch.soy.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"data frame 1454 observations following 4 variables. yield yield, kg/ha rep repeated measurement gen genotype, 7 levels env environment, 55 levels year year, 77-88 loc location, 10 levels","code":""},{"path":"/reference/gauch.soy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"Soybean yields 13 percent moisture 7 genotypes 55 environments 4 replicates. environments 2 3 replicates. experiment RCB design, plots missing many soybean varieties experiment. replications appear random order _NOT_ define blocks. Environment names combination first letter location name last two digits year. location codes : =Aurora, C=Chazy, D=Riverhead, E=Etna, G=Geneseo, =Ithica, L=Lockport, N=Canton, R=Romulus, V=Valatie. Plots 7.6 m long, four rows wide (middle two rows harvested). data widely used (various subsets) promote benefits AMMI (Additive Main Effects Multiplicative Interactions) analyses. gen x env means Table 1 (Zobel et al 1998) least-squares means (personal communication). Retrieved Sep 2011 https://www.microcomputerpower.com/matmodel/matmodelmatmodel_sample_.html Used permission Hugh Gauch.","code":""},{"path":"/reference/gauch.soy.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"Zobel, RW Wright, MJ Gauch Jr, HG. 1998. Statistical analysis yield trial. Agronomy journal, 80, 388-393. https://doi.org/10.2134/agronj1988.00021962008000030002x","code":""},{"path":"/reference/gauch.soy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"None","code":""},{"path":"/reference/gauch.soy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gauch.soy) dat <- gauch.soy ## dat <- transform(dat, ## year = substring(env, 2), ## loc = substring(env, 1, 1)) # AMMI biplot libs(agricolae) # Figure 1 of Zobel et al 1988, means vs PC1 score dat2 <- droplevels(subset(dat, is.element(env, c(\"A77\",\"C77\",\"V77\", \"V78\",\"A79\",\"C79\",\"G79\",\"R79\",\"V79\",\"A80\",\"C80\",\"G80\",\"L80\",\"D80\", \"R80\",\"V80\",\"A81\",\"C81\",\"G81\",\"L81\",\"D81\",\"R81\",\"V81\",\"A82\",\"L82\", \"G82\",\"V82\",\"A83\",\"I83\",\"G83\",\"A84\",\"N84\",\"C84\",\"I84\",\"G84\")))) m2 <- with(dat2, AMMI(env, gen, rep, yield)) bip <- m2$biplot with(bip, plot(yield, PC1, type='n', main=\"gauch.soy -- AMMI biplot\")) with(bip, text(yield, PC1, rownames(bip), col=ifelse(bip$type==\"GEN\", \"darkgreen\", \"blue\"), cex=ifelse(bip$type==\"GEN\", 1.5, .75))) } # }"},{"path":"/reference/george.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-location/year breeding trial in California — george.wheat","title":"Multi-location/year breeding trial in California — george.wheat","text":"Multi-location/year breeding trial California","code":""},{"path":"/reference/george.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-location/year breeding trial in California — george.wheat","text":"","code":"data(\"george.wheat\")"},{"path":"/reference/george.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-location/year breeding trial in California — george.wheat","text":"data frame 13996 observations following 5 variables. gen genotype number year year loc location block block yield yield per plot","code":""},{"path":"/reference/george.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-location/year breeding trial in California — george.wheat","text":"nice example data breeding trial, check genotypes kepts whole experiment, genotypes enter leave breeding program. data highly unbalanced respect genotypes--environments. Results late-stage small-trials 211 genotypes wheat California, conducted 9 locations years 2004-2018. trial RCB 4 blocks. authors used data look GGE biplots across years concluded repeatable genotype--location patterns weak, therefore California cereal production region large, unstable, mega-environment. Data downloaded 2019-10-29 Dryad, https://doi.org/10.5061/dryad.bf8rt6b. Data public domain.","code":""},{"path":"/reference/george.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-location/year breeding trial in California — george.wheat","text":"Nicholas George Mark Lundy (2019). Quantifying Genotype x Environment Effects Long-Term Common Wheat Yield Trials Agroecologically Diverse Production Region. Crop Science, 59, 1960-1972. https://doi.org/10.2135/cropsci2019.01.0010","code":""},{"path":"/reference/george.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-location/year breeding trial in California — george.wheat","text":"None","code":""},{"path":"/reference/george.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-location/year breeding trial in California — george.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(lattice, reshape2) data(george.wheat) dat <- george.wheat dat$env <- paste0(dat$year, \".\", dat$loc) # average reps, cast to matrix mat <- reshape2::acast(dat, gen ~ env, value.var=\"yield\", fun=mean, na.rm=TRUE) lattice::levelplot(mat, aspect=\"m\", main=\"george.wheat\", xlab=\"genotype\", ylab=\"environment\", scales=list(x=list(cex=.3,rot=90),y=list(cex=.5))) } # }"},{"path":"/reference/giles.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Straw length and ear emergence for wheat genotypes. — giles.wheat","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"Straw length ear emergence wheat genotypes. Data unbalanced respect experiment year genotype.","code":""},{"path":"/reference/giles.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"","code":"data(\"giles.wheat\")"},{"path":"/reference/giles.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"data frame 247 observations following 4 variables. gen genotype. Note, numeric! env environment straw straw length emergence ear emergence, Julian date","code":""},{"path":"/reference/giles.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"Highly unbalanced data straw length ear emergence date wheat genotypes. 'genotype' column called 'Accession number' original data. genotypes chosen represent range variation trait. Julian date found preferable methods ( days sowing). Piepho (2003) fit bilinear model straw emergence data. similar Finlay-Wilkinson regression.","code":""},{"path":"/reference/giles.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"R. Giles (1990). Utilization unreplicated observations agronomic characters wheat germplasm collection. : Wheat Genetic Resources. Meeting Diverse Needs. Wiley, Chichester, U.K., pp.113-130.","code":""},{"path":"/reference/giles.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"Piepho, HP (2003). Model-based mean adjustment quantitative germplasm evaluation data. Genetic Resources Crop Evolution, 50, 281-290. https://doi.org/10.1023/:1023503900759","code":""},{"path":"/reference/giles.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(giles.wheat) dat <- giles.wheat dat <- transform(dat, gen=factor(gen)) dat_straw <- droplevels( subset(dat, !is.na(straw)) ) dat_emerg <- droplevels( subset(dat, !is.na(emergence)) ) # Traits are not related # with(dat, plot(straw~emergence)) # Show unbalancedness of data libs(lattice, reshape2) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(acast(dat_straw, env ~ gen, value.var='straw'), col.regions=redblue, scales=list(x=list(rot=90)), xlab=\"year\", ylab=\"genotype\", main=\"giles.wheat - straw length\") # ----- Analysis of straw length ----- libs(emmeans) # Mean across years. Matches Piepho Table 7 'Simple' m1 = lm(straw ~ gen, data=dat_straw) emmeans(m1, 'gen') # Simple two-way model. NOT the bi-additive model of Piepho. m2 = lm(straw ~ gen + env, data=dat_straw) emmeans(m2, 'gen') # Bi-additive model. Matches Piepho Table 6, rows (c) libs(gnm) m3 <- gnm(straw ~ env + Mult(gen,env), data=dat_straw) cbind(adjusted=round(fitted(m3),0), dat_straw) # ----- Analysis of Ear emergence ----- # Simple two-way model. m4 = lm(emergence ~ 1 + gen + env, data=dat_emerg) emmeans(m4, c('gen','env')) # Matches Piepho Table 9. rpws (c) emmeans(m4, 'gen') # Match Piepho table 10, Least Squares column } # }"},{"path":"/reference/gilmour.serpentine.html","id":null,"dir":"Reference","previous_headings":"","what":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"RCB experiment wheat South Australia, strong spatial variation serpentine row/column effects.","code":""},{"path":"/reference/gilmour.serpentine.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"data frame 330 observations following 5 variables. col column row row rep replicate factor, 3 levels gen wheat variety, 108 levels yield yield","code":""},{"path":"/reference/gilmour.serpentine.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"randomized complete block experiment. 108 varieties 3 reps. Plots 6 meters long, 0.75 meters wide, trimmed 4.2 meters lengths harvest. Trimming done spraying wheat herbicide. sprayer travelled serpentine pattern columns. trial sown serpentine manner planter seeds three rows time (Left, Middle, Right). Field width 15 columns * 6 m = 90 m Field length 22 plots * .75 m = 16.5 m Used permission Arthur Gilmour, turn permission Gil Hollamby.","code":""},{"path":"/reference/gilmour.serpentine.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"Arthur R Gilmour Brian R Cullis Arunas P Verbyla, 1997. Accounting natural extraneous variation analysis field experiments. Journal Agric Biol Env Statistics, 2, 269-293.","code":""},{"path":"/reference/gilmour.serpentine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"N. W. Galwey. 2014. Introduction Mixed Modelling: Beyond Regression Analysis Variance. Table 10.9","code":""},{"path":"/reference/gilmour.serpentine.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gilmour.serpentine) dat <- gilmour.serpentine libs(desplot) desplot(dat, yield~ col*row, num=gen, show.key=FALSE, out1=rep, aspect = 16.5/90, # true aspect main=\"gilmour.serpentine\") # Extreme field trend. Blocking insufficient--needs a spline/smoother # xyplot(yield~col, data=dat, main=\"gilmour.serpentine\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) dat <- transform(dat, rowf=factor(row), colf=factor(10*(col-8))) dat <- dat[order(dat$rowf, dat$colf), ] # Sort order needed by asreml # RCB m0 <- asreml(yield ~ gen, data=dat, random=~rep) # Add AR1 x AR1 m1 <- asreml(yield ~ gen, data=dat, resid = ~ar1(rowf):ar1(colf)) # Add spline m2 <- asreml(yield ~ gen + col, data=dat, random= ~ spl(col) + colf, resid = ~ar1(rowf):ar1(colf)) # Figure 4 shows serpentine spraying p2 <- predict(m2, data=dat, classify=\"colf\")$pvals plot(p2$predicted, type='b', xlab=\"column number\", ylab=\"BLUP\") # Define column code (due to serpentine spraying) # Rhelp doesn't like double-percent modulus symbol, so compute by hand dat <- transform(dat, colcode = factor(dat$col-floor((dat$col-1)/4)*4 -1)) m3 <- asreml(yield ~ gen + lin(colf) + colcode, data=dat, random= ~ colf + rowf + spl(colf), resid = ~ar1(rowf):ar1(colf)) # Figure 6 shows serpentine row effects p3 <- predict(m3, data=dat, classify=\"rowf\")$pvals plot(p3$predicted, type='l', xlab=\"row number\", ylab=\"BLUP\") text(1:22, p3$predicted, c('L','L','M','R','R','M','L','L', 'M','R','R','M','L','L','M','R','R','M','L','L','M','R')) # Define row code (due to serpentine planting). 1=middle, 2=left/right dat <- transform(dat, rowcode = factor(row)) levels(dat$rowcode) <- c('2','2','1','2','2','1','2','2','1', '2','2','1','2','2','1','2','2','1','2','2','1','2') m6 <- asreml(yield ~ gen + lin(colf) + colcode +rowcode, data=dat, random= ~ colf + rowf + spl(col), resid = ~ar1(rowf):ar1(colf)) plot(varioGram(m6), xlim=c(0:17), ylim=c(0,11), zlim=c(0,4000), main=\"gilmour.serpentine\") } } # }"},{"path":"/reference/gilmour.slatehall.html","id":null,"dir":"Reference","previous_headings":"","what":"Slate Hall Farm 1978 — gilmour.slatehall","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"Yields trial Slate Hall Farm 1978.","code":""},{"path":"/reference/gilmour.slatehall.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"data frame 150 observations following 5 variables. row row col column yield yield (grams/plot) gen genotype factor, 25 levels rep rep factor, 6 levels","code":""},{"path":"/reference/gilmour.slatehall.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"trial spring wheat Slate Hall Farm 1978. experiment balanced lattice 25 varieties 6 replicates. 'rep' labels arbitrary (rep labels appeared source data). row within rep incomplete block. plot size 1.5 meters 4 meters. Field width: 10 plots x 4 m = 40 m Field length: 15 plots x 1.5 meters = 22.5 m","code":""},{"path":"/reference/gilmour.slatehall.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"Arthur R Gilmour Brian R Cullis Arunas P Verbyla (1997). Accounting natural extraneous variation analysis field experiments. Journal Agricultural, Biological, Environmental Statistics, 2, 269-293. https://doi.org/10.2307/1400446","code":""},{"path":"/reference/gilmour.slatehall.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"None.","code":""},{"path":"/reference/gilmour.slatehall.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gilmour.slatehall) dat <- gilmour.slatehall libs(desplot) desplot(dat, yield ~ col * row, aspect=22.5/40, num=gen, out1=rep, cex=1, main=\"gilmour.slatehall\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # Model 4 of Gilmour et al 1997 dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf, dat$yf), ] m4 <- asreml(yield ~ gen + lin(row), data=dat, random = ~ dev(row) + dev(col), resid = ~ ar1(xf):ar1(yf)) # coef(m4)$fixed[1] # linear row # [1] 31.72252 # (sign switch due to row ordering) lucid::vc(m4) ## effect component std.error z.ratio bound ## dev(col) 2519 1959 1.3 P 0 ## dev(row) 20290 10260 2 P 0 ## xf:yf(R) 23950 4616 5.2 P 0 ## xf:yf!xf!cor 0.439 0.113 3.9 U 0 ## xf:yf!yf!cor 0.125 0.117 1.1 U 0 plot(varioGram(m4), main=\"gilmour.slatehall\") } } # }"},{"path":"/reference/gomez.fractionalfactorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","title":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","text":"Fractional factorial rice, 1/2 2^6 = 2x2x2x2x2x2. Two reps 2 blocks rep.","code":""},{"path":"/reference/gomez.fractionalfactorial.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","text":"data frame 64 observations following 6 variables. yield grain yield tons/ha rep replicate, 2 levels block block within rep, 2 levels trt treatment, levels (1) abcdef col column position field row row position field treatment, 2 levels b b treatment, 2 levels c c treatment, 2 levels d d treatment, 2 levels e e treatment, 2 levels f f treatment, 2 levels","code":""},{"path":"/reference/gomez.fractionalfactorial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","text":"Grain yield 2^6 fractional factorial experiment blocks 16 plots , two replications. Gomez inconsistencies. One example: Page 171: treatment (1) rep 1, block 2 rep 2, block 1. Page 172: treatment (1) Rep 1, block 1 rep 2, block 1. data uses layout shown page 171. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.fractionalfactorial.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 171-172.","code":""},{"path":"/reference/gomez.fractionalfactorial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.fractionalfactorial) dat <- gomez.fractionalfactorial # trt abcdef has the highest yield # Gomez, Figure 4.8 libs(desplot) desplot(dat, yield~col*row, # aspect unknown text=trt, shorten=\"none\", show.key=FALSE, cex=1, main=\"gomez.fractionalfactorial - treatment & yield\") # Ensure factors dat <- transform(dat, a=factor(a), b=factor(b), c=factor(c), d=factor(d), e=factor(e), f=factor(f) ) # Gomez table 4.24, trt SS totalled together. # Why didn't Gomez nest block within rep? m0 <- lm(yield ~ rep * block + trt, dat) anova(m0) # Gomez table 4.24, trt SS split apart m1 <- lm(yield ~ rep * block + (a+b+c+d+e+f)^3, dat) anova(m1) libs(FrF2) aliases(m1) MEPlot(m1, select=3:8, main=\"gomez.fractionalfactorial - main effects plot\") } # }"},{"path":"/reference/gomez.groupsplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Group balanced split-plot design in rice — gomez.groupsplit","title":"Group balanced split-plot design in rice — gomez.groupsplit","text":"Group balanced split-plot design rice","code":""},{"path":"/reference/gomez.groupsplit.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Group balanced split-plot design in rice — gomez.groupsplit","text":"data frame 270 observations following 7 variables. col column row row rep replicate factor, 3 levels fert fertilizer factor, 2 levels gen genotype factor, 45 levels group grouping (genotype) factor, 3 levels yield yield rice","code":""},{"path":"/reference/gomez.groupsplit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Group balanced split-plot design in rice — gomez.groupsplit","text":"Genotype group S1 less 105 days growth duration, S2 105-115 days growth duration, S3 115 days. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.groupsplit.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Group balanced split-plot design in rice — gomez.groupsplit","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 120.","code":""},{"path":"/reference/gomez.groupsplit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Group balanced split-plot design in rice — gomez.groupsplit","text":"","code":"library(agridat) data(gomez.groupsplit) dat <- gomez.groupsplit # Gomez figure 3.10. Obvious fert and group effects libs(desplot) desplot(dat, group ~ col*row, out1=rep, col=fert, text=gen, # aspect unknown main=\"gomez.groupsplit\") # Gomez table 3.19 (not partitioned by group) m1 <- aov(yield ~ fert*group + gen:group + fert:gen:group + Error(rep/fert/group), data=dat) summary(m1) #> #> Error: rep #> Df Sum Sq Mean Sq F value Pr(>F) #> Residuals 2 4.917 2.458 #> #> Error: rep:fert #> Df Sum Sq Mean Sq F value Pr(>F) #> fert 1 96.05 96.05 68.7 0.0142 * #> Residuals 2 2.80 1.40 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: rep:fert:group #> Df Sum Sq Mean Sq F value Pr(>F) #> group 2 4.259 2.1294 6.674 0.0197 * #> fert:group 2 0.628 0.3138 0.984 0.4150 #> Residuals 8 2.553 0.3191 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: Within #> Df Sum Sq Mean Sq F value Pr(>F) #> group:gen 42 20.494 0.4880 4.461 2.08e-12 *** #> fert:group:gen 42 4.093 0.0975 0.891 0.662 #> Residuals 168 18.378 0.1094 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"},{"path":"/reference/gomez.heterogeneity.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"RCB experiment rice, heterogeneity regressions","code":""},{"path":"/reference/gomez.heterogeneity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"","code":"data(\"gomez.heterogeneity\")"},{"path":"/reference/gomez.heterogeneity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"gen genotype yield yield kg/ha tillers tillers /hill","code":""},{"path":"/reference/gomez.heterogeneity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"experiment 3 genotypes examine relationship yield number tillers. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.heterogeneity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 377.","code":""},{"path":"/reference/gomez.heterogeneity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"None.","code":""},{"path":"/reference/gomez.heterogeneity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.heterogeneity) dat <- gomez.heterogeneity libs(lattice) xyplot(yield ~ tillers, dat, groups=gen, type=c(\"p\",\"r\"), main=\"gomez.heterogeneity\") } # }"},{"path":"/reference/gomez.heteroskedastic.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"RCB experiment rice, heteroskedastic varieties","code":""},{"path":"/reference/gomez.heteroskedastic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"","code":"data(\"gomez.heteroskedastic\")"},{"path":"/reference/gomez.heteroskedastic.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"data frame 105 observations following 4 variables. gen genotype group group genotypes rep replicate yield yield","code":""},{"path":"/reference/gomez.heteroskedastic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"RCB design three reps. Genotypes 1-15 hybrids, 16-32 parents, 33-35 checks. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.heteroskedastic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 310.","code":""},{"path":"/reference/gomez.heteroskedastic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"None.","code":""},{"path":"/reference/gomez.heteroskedastic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"","code":"library(agridat) data(gomez.heteroskedastic) dat <- gomez.heteroskedastic # Fix the outlier as reported by Gomez p. 311 dat[dat$gen==\"G17\" & dat$rep==\"R2\",\"yield\"] <- 7.58 libs(lattice) bwplot(gen ~ yield, dat, group=as.numeric(dat$group), ylab=\"genotype\", main=\"gomez.heterogeneous\") # Match Gomez table 7.28 m1 <- lm(yield ~ rep + gen, data=dat) anova(m1) #> Analysis of Variance Table #> #> Response: yield #> Df Sum Sq Mean Sq F value Pr(>F) #> rep 2 3.306 1.65304 5.6164 0.005528 ** #> gen 34 40.020 1.17705 3.9992 5.806e-07 *** #> Residuals 68 20.014 0.29432 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ## Response: yield ## Df Sum Sq Mean Sq F value Pr(>F) ## rep 2 3.306 1.65304 5.6164 0.005528 ** ## gen 34 40.020 1.17705 3.9992 5.806e-07 *** ## Residuals 68 20.014 0.29432"},{"path":"/reference/gomez.multilocsplitplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot","title":"Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot","text":"Grain yield measured 3 locations 2 reps per location. Within rep, main plot 6 nitrogen fertilizer treatments sub plot 2 rice varieties.","code":""},{"path":"/reference/gomez.multilocsplitplot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot","text":"data frame 108 observations following 5 variables. loc location, 3 levels nitro nitrogen kg/ha rep replicate, 2 levels gen genotype, 2 levels yield yield, kg/ha Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.multilocsplitplot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 339.","code":""},{"path":"/reference/gomez.multilocsplitplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.multilocsplitplot) dat <- gomez.multilocsplitplot dat$nf <- factor(dat$nitro) # Gomez figure 8.3 libs(lattice) xyplot(yield~nitro, dat, group=loc, type=c('p','smooth'), auto.key=TRUE, main=\"gomez.multilocsplitplot\") # AOV # Be careful to use the right stratum, 'nf' appears in both strata. # Still not quite the same as Gomez table 8.21 t1 <- terms(yield ~ loc * nf * gen + Error(loc:rep:nf), \"Error\", keep.order=TRUE) m1 <- aov(t1, data=dat) summary(m1) # F values are somewhat similar to Gomez Table 8.21 libs(lme4) m2 <- lmer(yield ~ loc*nf*gen + (1|loc/rep/nf), dat) anova(m2) ## Analysis of Variance Table ## Df Sum Sq Mean Sq F value ## loc 2 117942 58971 0.1525 ## nf 5 72841432 14568286 37.6777 ## gen 1 7557570 7557570 19.5460 ## loc:nf 10 10137188 1013719 2.6218 ## loc:gen 2 4270469 2135235 5.5223 ## nf:gen 5 1501767 300353 0.7768 ## loc:nf:gen 10 1502273 150227 0.3885 } # }"},{"path":"/reference/gomez.nitrogen.html","id":null,"dir":"Reference","previous_headings":"","what":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"Soil nitrogen three times eight fertilizer treatments","code":""},{"path":"/reference/gomez.nitrogen.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"data frame 96 observations following 4 variables. trt nitrogen treatment factor nitro soil nitrogen content, percent rep replicate stage growth stage, three periods","code":""},{"path":"/reference/gomez.nitrogen.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"Eight fertilizer treatments tested. Soil nitrogen content measured three times. P1 = 15 days post transplanting. P2 = 40 days post transplanting. P3 = panicle initiation. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.nitrogen.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 259.","code":""},{"path":"/reference/gomez.nitrogen.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"R-help mailing list, 9 May 2013. Data provided Cyril Lundrigan. Analysis method Rich Heiberger.","code":""},{"path":"/reference/gomez.nitrogen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"","code":"library(agridat) data(gomez.nitrogen) dat <- gomez.nitrogen # Note the depletion of nitrogen over time (stage) libs(HH) #> Loading required package: grid #> Loading required package: multcomp #> Loading required package: mvtnorm #> Loading required package: survival #> Loading required package: TH.data #> Loading required package: MASS #> #> Attaching package: ‘TH.data’ #> The following object is masked from ‘package:MASS’: #> #> geyser #> Loading required package: gridExtra #> #> Attaching package: ‘HH’ #> The following object is masked from ‘package:base’: #> #> is.R interaction2wt(nitro ~ rep/trt + trt*stage, data=dat, x.between=0, y.between=0, main=\"gomez.nitrogen\") # Just the fertilizer profiles with(dat, interaction.plot(stage, trt, nitro, col=1:4, lty=1:3, main=\"gomez.nitrogen\", xlab=\"Soil nitrogen at three times for each treatment\")) # Gomez table 6.16 m1 <- aov(nitro ~ Error(rep/trt) + trt*stage, data=dat) summary(m1) #> #> Error: rep #> Df Sum Sq Mean Sq F value Pr(>F) #> Residuals 3 0.8457 0.2819 #> #> Error: rep:trt #> Df Sum Sq Mean Sq F value Pr(>F) #> trt 7 1.2658 0.18083 4.935 0.00201 ** #> Residuals 21 0.7695 0.03664 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: Within #> Df Sum Sq Mean Sq F value Pr(>F) #> stage 2 52.04 26.021 715.871 < 2e-16 *** #> trt:stage 14 3.57 0.255 7.008 1.53e-07 *** #> Residuals 48 1.74 0.036 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # Gomez table 6.18 # Treatment 1 2 3 4 5 6 7 8 cont <- cbind(\"T7 vs others\" = c( 1, 1, 1, 1, 1, 1,-7, 1), \"T8 vs others\" = c( 1, 1, 1, 1, 1, 1, 0,-6), \"T2,T5 vs others\" = c(-1, 2,-1,-1, 2,-1, 0, 0), \"T2 vs T5\" = c( 0, 1, 0, 0,-1, 0, 0, 0)) contrasts(dat$trt) <- cont contrasts(dat$trt) #> T7 vs others T8 vs others T2,T5 vs others T2 vs T5 #> T1 1 1 -1 0 -3.028130e-01 #> T2 1 1 2 1 8.326673e-17 #> T3 1 1 -1 0 -2.101031e-01 #> T4 1 1 -1 0 -3.487772e-01 #> T5 1 1 2 -1 2.775558e-17 #> T6 1 1 -1 0 8.616933e-01 #> T7 -7 0 0 0 -1.387779e-17 #> T8 1 -6 0 0 0.000000e+00 #> #> T1 -6.632738e-01 -4.673031e-01 #> T2 8.326673e-17 0.000000e+00 #> T3 -1.136421e-01 8.324315e-01 #> T4 7.387109e-01 -2.875078e-01 #> T5 8.326673e-17 0.000000e+00 #> T6 3.820501e-02 -7.762061e-02 #> T7 5.551115e-17 2.775558e-17 #> T8 5.551115e-17 5.551115e-17 m2 <- aov(nitro ~ Error(rep/trt) + trt*stage, data=dat) summary(m2, expand.split=FALSE, split=list(trt=list( \"T7 vs others\"=1, \"T8 vs others\"=2, \"T2,T5 vs others\"=3, \"T2 vs T5\"=4, rest=c(5,6,7)), \"trt:stage\"=list( \"(T7 vs others):P\"=c(1,8), \"(T8 vs others):P\"=c(2,9), \"(T2,T5 vs others):P\"=c(3,10), \"(T2 vs T5):P\"=c(4,11), \"rest:P\"=c(5,6,7,12,13,14)) )) #> #> Error: rep #> Df Sum Sq Mean Sq F value Pr(>F) #> Residuals 3 0.8457 0.2819 #> #> Error: rep:trt #> Df Sum Sq Mean Sq F value Pr(>F) #> trt 7 1.2658 0.1808 4.935 0.00201 ** #> trt: T7 vs others 1 0.3511 0.3511 9.581 0.00548 ** #> trt: T8 vs others 1 0.0455 0.0455 1.242 0.27761 #> trt: T2,T5 vs others 1 0.0228 0.0228 0.621 0.43952 #> trt: T2 vs T5 1 0.1176 0.1176 3.209 0.08764 . #> trt: rest 3 0.7289 0.2430 6.630 0.00252 ** #> Residuals 21 0.7695 0.0366 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: Within #> Df Sum Sq Mean Sq F value Pr(>F) #> stage 2 52.04 26.021 715.871 < 2e-16 *** #> trt:stage 14 3.57 0.255 7.008 1.53e-07 *** #> trt:stage: (T7 vs others):P 2 2.14 1.068 29.391 4.63e-09 *** #> trt:stage: (T8 vs others):P 2 0.54 0.268 7.373 0.001613 ** #> trt:stage: (T2,T5 vs others):P 2 0.64 0.321 8.843 0.000538 *** #> trt:stage: (T2 vs T5):P 2 0.02 0.011 0.298 0.743303 #> trt:stage: rest:P 6 0.23 0.038 1.051 0.404967 #> Residuals 48 1.74 0.036 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"},{"path":"/reference/gomez.nonnormal1.html","id":null,"dir":"Reference","previous_headings":"","what":"Insecticide treatment effectiveness — gomez.nonnormal1","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"Insecticide treatment effectiveness","code":""},{"path":"/reference/gomez.nonnormal1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"","code":"data(\"gomez.nonnormal1\")"},{"path":"/reference/gomez.nonnormal1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"data frame 36 observations following 3 variables. trt insecticidal treatment rep replicate larvae number larvae","code":""},{"path":"/reference/gomez.nonnormal1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"Nine treatments (including control, T9) used four replicates. number living insect larvae recorded. data show signs non-normality, log transform used Gomez. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.nonnormal1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 300.","code":""},{"path":"/reference/gomez.nonnormal1.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"None.","code":""},{"path":"/reference/gomez.nonnormal1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"","code":"library(agridat) data(gomez.nonnormal1) dat <- gomez.nonnormal1 # Gomez figure 7.3 ## libs(dplyr) ## dat2 <- dat %>% group_by(trt) ## dat2 <- summarize(dat2, mn=mean(larvae), rng=diff(range(larvae))) ## plot(rng ~ mn, data=dat2, ## xlab=\"mean number of larvae\", ylab=\"range of number of larvae\", ## main=\"gomez.nonnormal1\") # Because some of the original values are less than 10, # the transform used is log10(x+1) instead of log10(x). dat <- transform(dat, tlarvae=log10(larvae+1)) # QQ plots for raw/transformed data libs(reshape2, lattice) qqmath( ~ value|variable, data=melt(dat), main=\"gomez.nonnormal1 - raw/transformed QQ plot\", scales=list(relation=\"free\")) #> Using trt, rep as id variables # Gomez table 7.16 m1 <- lm(tlarvae ~ rep + trt, data=dat) anova(m1) #> Analysis of Variance Table #> #> Response: tlarvae #> Df Sum Sq Mean Sq F value Pr(>F) #> rep 3 0.9567 0.31889 3.6511 0.0267223 * #> trt 8 3.9823 0.49779 5.6995 0.0004092 *** #> Residuals 24 2.0961 0.08734 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ## Response: tlarvae ## Df Sum Sq Mean Sq F value Pr(>F) ## rep 3 0.9567 0.31889 3.6511 0.0267223 * ## trt 8 3.9823 0.49779 5.6995 0.0004092 *** ## Residuals 24 2.0961 0.08734"},{"path":"/reference/gomez.nonnormal2.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"RCB experiment rice, measuring white heads","code":""},{"path":"/reference/gomez.nonnormal2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"","code":"data(\"gomez.nonnormal2\")"},{"path":"/reference/gomez.nonnormal2.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"data frame 42 observations following 3 variables. gen genotype rep replicate white percentage white heads","code":""},{"path":"/reference/gomez.nonnormal2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"data percent white heads rice variety trial 14 varieties 3 reps. many values less 10, suggested data transformation sqrt(x+.5). Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.nonnormal2.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 300.","code":""},{"path":"/reference/gomez.nonnormal2.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"None.","code":""},{"path":"/reference/gomez.nonnormal2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"","code":"library(agridat) data(gomez.nonnormal2) dat <- gomez.nonnormal2 # Gomez suggested sqrt transform dat <- transform(dat, twhite = sqrt(white+.5)) # QQ plots for raw/transformed data libs(reshape2, lattice) qqmath( ~ value|variable, data=melt(dat), main=\"gomez.nonnormal2 - raw/transformed QQ plot\", scales=list(relation=\"free\")) #> Using gen, rep as id variables # Gomez anova table 7.21 m1 <- lm(twhite ~ rep + gen, data=dat) anova(m1) #> Analysis of Variance Table #> #> Response: twhite #> Df Sum Sq Mean Sq F value Pr(>F) #> rep 2 2.401 1.2004 1.9137 0.1678 #> gen 13 48.011 3.6931 5.8877 6.366e-05 *** #> Residuals 26 16.309 0.6273 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ## Response: twhite2 ## Df Sum Sq Mean Sq F value Pr(>F) ## rep 2 2.401 1.2004 1.9137 0.1678 ## gen 13 48.011 3.6931 5.8877 6.366e-05 *** ## Residuals 26 16.309 0.6273"},{"path":"/reference/gomez.nonnormal3.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"RCB experiment rice, 12 varieties leafhopper survival","code":""},{"path":"/reference/gomez.nonnormal3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"","code":"data(\"gomez.nonnormal3\")"},{"path":"/reference/gomez.nonnormal3.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"data frame 36 observations following 3 variables. gen genotype/variety rice rep replicate hoppers percentage surviving leafhoppers","code":""},{"path":"/reference/gomez.nonnormal3.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"rice variety, 75 leafhoppers caged percentage surviving insects determined. Gomez suggest replacing 0 values 1/(4*75) replacing 100 1-1/(4*75) 75 number insects. effect, means, example, (1/4)th insect survived. data percents, Gomez suggested using arcsin transformation. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.nonnormal3.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 307.","code":""},{"path":"/reference/gomez.nonnormal3.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"None.","code":""},{"path":"/reference/gomez.nonnormal3.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"","code":"library(agridat) data(gomez.nonnormal3) dat <- gomez.nonnormal3 # First, replace 0, 100 values dat$thoppers <- dat$hoppers dat <- transform(dat, thoppers=ifelse(thoppers==0, 1/(4*75), thoppers)) dat <- transform(dat, thoppers=ifelse(thoppers==100, 100-1/(4*75), thoppers)) # Arcsin transformation of percentage p converted to degrees # is arcsin(sqrt(p))/(pi/2)*90 dat <- transform(dat, thoppers=asin(sqrt(thoppers/100))/(pi/2)*90) # QQ plots for raw/transformed data libs(reshape2, lattice) qqmath( ~ value|variable, data=melt(dat), main=\"gomez.nonnormal3 - raw/transformed QQ plot\", scales=list(relation=\"free\")) #> Using gen, rep as id variables m1 <- lm(thoppers ~ gen, data=dat) anova(m1) # Match Gomez table 7.25 #> Analysis of Variance Table #> #> Response: thoppers #> Df Sum Sq Mean Sq F value Pr(>F) #> gen 11 16838.7 1530.79 16.502 1.316e-08 *** #> Residuals 24 2226.4 92.77 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ## Response: thoppers ## Df Sum Sq Mean Sq F value Pr(>F) ## gen 11 16838.7 1530.79 16.502 1.316e-08 *** ## Residuals 24 2226.4 92.77"},{"path":"/reference/gomez.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice — gomez.rice.uniformity","title":"Uniformity trial of rice — gomez.rice.uniformity","text":"Uniformity trial rice Philippines.","code":""},{"path":"/reference/gomez.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice — gomez.rice.uniformity","text":"data frame 648 observations following 3 variables. row row col column yield grain yield, grams/m^2","code":""},{"path":"/reference/gomez.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice — gomez.rice.uniformity","text":"area 20 meters 38 meters planted rice variety IR8. harvest, 1-meter border removed around field discarded. square meter (1 meter 1 meter) harvested weighed. Field width: 18 plots x 1 m = 18 m Field length: 38 plots x 1 m = 38 m Note Gomez published paper 1969 rice uniformity data four trials conducted 1968 dry wet seasons. likely data taken one four trials. Estimated harvest year 1968. \"Estimation optimum plot size rice uniformity data\". https://www.cabidigitallibrary.org/doi/full/10.5555/19711601105 Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice — gomez.rice.uniformity","text":"Gomez, K.. Gomez, .. (1984). Statistical Procedures Agricultural Research. Wiley-Interscience. Page 481.","code":""},{"path":"/reference/gomez.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice — gomez.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.rice.uniformity) dat <- gomez.rice.uniformity libs(desplot) # Raw data plot desplot(dat, yield ~ col*row, aspect=38/18, # true aspect main=\"gomez.rice.uniformity\") libs(desplot, reshape2) # 3x3 moving average. Gomez figure 12.1 dmat <- melt(dat, id.var=c('col','row')) dmat <- acast(dmat, row~col) m0 <- dmat cx <- 2:17 rx <- 2:35 dmat3 <- (m0[rx+1,cx+1]+m0[rx+1,cx]+m0[rx+1,cx-1]+ m0[rx,cx+1]+m0[rx,cx]+m0[rx,cx-1]+ m0[rx-1,cx+1]+m0[rx-1,cx]+m0[rx-1,cx-1])/9 dat3 <- melt(dmat3) desplot(dat3, value~Var2*Var1, aspect=38/18, at=c(576,637,695,753,811,870,927), main=\"gomez.rice.uniformity smoothed\") libs(agricolae) # Gomez table 12.4 tab <- index.smith(dmat, main=\"gomez.rice.uniformity\", col=\"red\")$uniformity tab <- data.frame(tab) ## # Gomez figure 12.2 ## op <- par(mar=c(5,4,4,4)+.1) ## m1 <- nls(Vx ~ 9041/Size^b, data=tab, start=list(b=1)) ## plot(Vx ~ Size, tab, xlab=\"Plot size, m^2\") ## lines(fitted(m1) ~ tab$Size, col='red') ## axis(4, at=tab$Vx, labels=tab$CV) ## mtext(\"CV\", 4, line=2) ## par(op) } # }"},{"path":"/reference/gomez.seedrate.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of rice, 6 densities — gomez.seedrate","title":"RCB experiment of rice, 6 densities — gomez.seedrate","text":"RCB experiment rice, 6 densities","code":""},{"path":"/reference/gomez.seedrate.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of rice, 6 densities — gomez.seedrate","text":"data frame 24 observations following 3 variables. rate kg seeds per hectare rep rep (block), four levels yield yield, kg/ha","code":""},{"path":"/reference/gomez.seedrate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of rice, 6 densities — gomez.seedrate","text":"Rice yield six different densities RCB design. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.seedrate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of rice, 6 densities — gomez.seedrate","text":"Gomez, K.. Gomez, .. 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 26.","code":""},{"path":"/reference/gomez.seedrate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of rice, 6 densities — gomez.seedrate","text":"","code":"library(agridat) data(gomez.seedrate) dat <- gomez.seedrate libs(lattice) xyplot(yield ~ rate, data=dat, group=rep, type='b', main=\"gomez.seedrate\", auto.key=list(columns=4)) # Quadratic response. Use raw polynomials so we can compute optimum m1 <- lm(yield ~ rep + poly(rate,2,raw=TRUE), dat) -coef(m1)[5]/(2*coef(m1)[6]) # Optimum is at 29 #> poly(rate, 2, raw = TRUE)1 #> 29.148 # Plot the model predictions libs(latticeExtra) newdat <- expand.grid(rep=levels(dat$rep), rate=seq(25,150)) newdat$pred <- predict(m1, newdat) p1 <- aggregate(pred ~ rate, newdat, mean) # average reps xyplot(yield ~ rate, data=dat, group=rep, type='b', main=\"gomez.seedrate (with model predictions)\", auto.key=list(columns=4)) + xyplot(pred ~ rate, p1, type='l', col='black', lwd=2)"},{"path":"/reference/gomez.splitplot.subsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","title":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","text":"Split-plot experiment rice, subsamples","code":""},{"path":"/reference/gomez.splitplot.subsample.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","text":"data frame 186 observations following 5 variables. time time factor, T1-T4 manage management, M1-M6 rep rep/block, R1-R3 sample subsample, S1-S2 height plant height (cm)","code":""},{"path":"/reference/gomez.splitplot.subsample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","text":"split-plot experiment three blocks. Whole-plot 'management', sub-plot 'time' application, two subsamples. data heights, measured two single-hill sampling units plot. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.splitplot.subsample.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 481.","code":""},{"path":"/reference/gomez.splitplot.subsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.splitplot.subsample) dat <- gomez.splitplot.subsample libs(HH) interaction2wt(height ~ rep + time + manage, data=dat, x.between=0, y.between=0, main=\"gomez.splitplot.subsample - plant height\") # Management totals, Gomez table 6.8 # tapply(dat$height, dat$manage, sum) # Gomez table 6.11 analysis of variance m1 <- aov(height ~ rep + manage + time + manage:time + Error(rep/manage/time), data=dat) summary(m1) ## Error: rep ## Df Sum Sq Mean Sq ## rep 2 2632 1316 ## Error: rep:manage ## Df Sum Sq Mean Sq F value Pr(>F) ## manage 7 1482 211.77 2.239 0.0944 . ## Residuals 14 1324 94.59 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Error: rep:manage:time ## Df Sum Sq Mean Sq F value Pr(>F) ## time 3 820.8 273.61 7.945 0.000211 *** ## manage:time 21 475.3 22.63 0.657 0.851793 ## Residuals 48 1653.1 34.44 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Error: Within ## Df Sum Sq Mean Sq F value Pr(>F) ## Residuals 96 167.4 1.744 } # }"},{"path":"/reference/gomez.splitsplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-split-plot experiment of rice — gomez.splitsplit","title":"Split-split-plot experiment of rice — gomez.splitsplit","text":"Grain yield three varieties rice grown split-split plot arrangement 3 reps, nitrogen level main plot, management practice sub-plot, rice variety sub-sub plot.","code":""},{"path":"/reference/gomez.splitsplit.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-split-plot experiment of rice — gomez.splitsplit","text":"data frame 135 observations following 7 variables. rep block, 3 levels nitro nitrogen fertilizer, kilograms/hectare management plot management gen genotype/variety rice yield yield col column position field row row position field Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.splitsplit.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-split-plot experiment of rice — gomez.splitsplit","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 143.","code":""},{"path":"/reference/gomez.splitsplit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-split-plot experiment of rice — gomez.splitsplit","text":"H. P. Piepho, R. N. Edmondson. (2018). tutorial statistical analysis factorial experiments qualitative quantitative treatment factor levels. Jour Agronomy Crop Science, 8, 1-27. https://doi.org/10.1111/jac.12267","code":""},{"path":"/reference/gomez.splitsplit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-split-plot experiment of rice — gomez.splitsplit","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.splitsplit) dat <- gomez.splitsplit dat$nf <- factor(dat$nitro) libs(desplot) desplot(dat, nf ~ col*row, # aspect unknown out1=rep, col=management, num=gen, cex=1, main=\"gomez.splitsplit\") desplot(dat, yield ~ col*row, # aspect unknown out1=rep, main=\"gomez.splitsplit\") libs(HH) position(dat$nf) <- c(0,50,80,110,140) interaction2wt(yield~rep+nf+management+gen, data=dat, main=\"gomez.splitsplit\", x.between=0, y.between=0, relation=list(x=\"free\", y=\"same\"), rot=c(90,0), xlab=\"\", par.strip.text.input=list(cex=.7)) # AOV. Gomez page 144-153 m0 <- aov(yield~ nf * management * gen + Error(rep/nf/management), data=dat) summary(m0) # Similar to Gomez, p. 153. } # }"},{"path":"/reference/gomez.stripplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Strip-plot experiment of rice — gomez.stripplot","title":"Strip-plot experiment of rice — gomez.stripplot","text":"strip-plot experiment three reps, variety horizontal strip nitrogen fertilizer vertical strip.","code":""},{"path":"/reference/gomez.stripplot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Strip-plot experiment of rice — gomez.stripplot","text":"yield Grain yield kg/ha rep Rep nitro Nitrogen fertilizer kg/ha gen Rice variety col column row row","code":""},{"path":"/reference/gomez.stripplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strip-plot experiment of rice — gomez.stripplot","text":"Note, subset 'gomez.stripsplitplot' data. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.stripplot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Strip-plot experiment of rice — gomez.stripplot","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 110.","code":""},{"path":"/reference/gomez.stripplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strip-plot experiment of rice — gomez.stripplot","text":"Jan Gertheiss (2014). ANOVA Factors Ordered Levels. J Agric Biological Environmental Stat, 19, 258-277.","code":""},{"path":"/reference/gomez.stripplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strip-plot experiment of rice — gomez.stripplot","text":"","code":"library(agridat) data(gomez.stripplot) dat <- gomez.stripplot # Gomez figure 3.7 libs(desplot) desplot(dat, gen ~ col*row, # aspect unknown out1=rep, out2=nitro, num=nitro, cex=1, main=\"gomez.stripplot\") # Gertheiss figure 1 # library(lattice) # dotplot(factor(nitro) ~ yield|gen, data=dat) # Gomez table 3.12 # tapply(dat$yield, dat$rep, sum) # tapply(dat$yield, dat$gen, sum) # tapply(dat$yield, dat$nitro, sum) # Gomez table 3.15. Anova table for strip-plot dat <- transform(dat, nf=factor(nitro)) m1 <- aov(yield ~ gen * nf + Error(rep + rep:gen + rep:nf), data=dat) summary(m1) #> #> Error: rep #> Df Sum Sq Mean Sq F value Pr(>F) #> Residuals 2 9220962 4610481 #> #> Error: rep:gen #> Df Sum Sq Mean Sq F value Pr(>F) #> gen 5 57100201 11420040 7.653 0.00337 ** #> Residuals 10 14922619 1492262 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: rep:nf #> Df Sum Sq Mean Sq F value Pr(>F) #> nf 2 50676061 25338031 34.07 0.00307 ** #> Residuals 4 2974908 743727 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: Within #> Df Sum Sq Mean Sq F value Pr(>F) #> gen:nf 10 23877979 2387798 5.801 0.000427 *** #> Residuals 20 8232917 411646 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ## Error: rep ## Df Sum Sq Mean Sq F value Pr(>F) ## Residuals 2 9220962 4610481 ## Error: rep:gen ## Df Sum Sq Mean Sq F value Pr(>F) ## gen 5 57100201 11420040 7.653 0.00337 ** ## Residuals 10 14922619 1492262 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Error: rep:nf ## Df Sum Sq Mean Sq F value Pr(>F) ## nf 2 50676061 25338031 34.07 0.00307 ** ## Residuals 4 2974908 743727 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Error: Within ## Df Sum Sq Mean Sq F value Pr(>F) ## gen:nf 10 23877979 2387798 5.801 0.000427 *** ## Residuals 20 8232917 411646 # More compact view ## libs(agricolae) ## with(dat, strip.plot(rep, nf, gen, yield)) ## Analysis of Variance Table ## Response: yield ## Df Sum Sq Mean Sq F value Pr(>F) ## rep 2 9220962 4610481 11.2001 0.0005453 *** ## nf 2 50676061 25338031 34.0690 0.0030746 ** ## Ea 4 2974908 743727 1.8067 0.1671590 ## gen 5 57100201 11420040 7.6528 0.0033722 ** ## Eb 10 14922619 1492262 3.6251 0.0068604 ** ## gen:nf 10 23877979 2387798 5.8006 0.0004271 *** ## Ec 20 8232917 411646 # Mixed-model version ## libs(lme4) ## m3 <- lmer(yield ~ gen * nf + (1|rep) + (1|rep:nf) + (1|rep:gen), data=dat) ## anova(m3) ## Analysis of Variance Table ## Df Sum Sq Mean Sq F value ## gen 5 15751300 3150260 7.6528 ## nf 2 28048730 14024365 34.0690 ## gen:nf 10 23877979 2387798 5.8006"},{"path":"/reference/gomez.stripsplitplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Strip-split-plot experiment of rice — gomez.stripsplitplot","title":"Strip-split-plot experiment of rice — gomez.stripsplitplot","text":"strip-split-plot experiment three reps, genotype horizontal strip, nitrogen fertilizer vertical strip, planting method subplot factor.","code":""},{"path":"/reference/gomez.stripsplitplot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Strip-split-plot experiment of rice — gomez.stripsplitplot","text":"yield grain yield kg/ha planting planting factor, P1=broadcast, P2=transplanted rep rep, 3 levels nitro nitrogen fertilizer, kg/ha gen genotype, G1 G6 col column row row","code":""},{"path":"/reference/gomez.stripsplitplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strip-split-plot experiment of rice — gomez.stripsplitplot","text":"Note, superset 'gomez.stripplot' data. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.stripsplitplot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Strip-split-plot experiment of rice — gomez.stripsplitplot","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 155.","code":""},{"path":"/reference/gomez.stripsplitplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strip-split-plot experiment of rice — gomez.stripsplitplot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.stripsplitplot) dat <- gomez.stripsplitplot # Layout libs(desplot) desplot(dat, gen ~ col*row, out1=rep, col=nitro, text=planting, cex=1, main=\"gomez.stripsplitplot\") # Gomez table 4.19, ANOVA of strip-split-plot design dat <- transform(dat, nf=factor(nitro)) m1 <- aov(yield ~ nf * gen * planting + Error(rep + rep:nf + rep:gen + rep:nf:gen), data=dat) summary(m1) # There is a noticeable linear trend along the y coordinate which may be # an artifact that blocking will remove, or may need to be modeled. # Note the outside values in the high-nitro boxplot. libs(\"HH\") interaction2wt(yield ~ nitro + gen + planting + row, dat, x.between=0, y.between=0, x.relation=\"free\") } # }"},{"path":"/reference/gomez.wetdry.html","id":null,"dir":"Reference","previous_headings":"","what":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"Rice yield wet & dry seasons nitrogen fertilizer treatments","code":""},{"path":"/reference/gomez.wetdry.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"data frame 96 observations following 4 variables. season season = wet/dry nitrogen nitrogen fertilizer kg/ha rep replicate yield grain yield, t/ha","code":""},{"path":"/reference/gomez.wetdry.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"Five nitrogen fertilizer treatments tested 2 seasons using 3 reps. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.wetdry.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 318.","code":""},{"path":"/reference/gomez.wetdry.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"Rong-Cai Yang, Patricia Juskiw. (2011). Analysis covariance agronomy crop research. Canadian Journal Plant Science, 91:621-641. https://doi.org/10.4141/cjps2010-032","code":""},{"path":"/reference/gomez.wetdry.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.wetdry) dat <- gomez.wetdry libs(lattice) foo1 <- xyplot(yield ~ nitrogen|season, data=dat, group=rep,type='l',auto.key=list(columns=3), ylab=\"yield in each season\", main=\"gomez.wetdry raw data & model\") # Yang & Juskiw fit a quadratic model with linear and quadratic # contrasts using non-equal intervals of nitrogen levels. # This example below omits the tedious contrasts libs(latticeExtra, lme4) m1 <-lmer(yield ~ season*poly(nitrogen, 2) + (1|season:rep), data=dat) pdat <- expand.grid(season=c('dry','wet'), nitrogen=seq(from=0,to=150,by=5)) pdat$pred <- predict(m1, newdata=pdat, re.form= ~ 0) foo1 + xyplot(pred ~ nitrogen|season, data=pdat, type='l',lwd=2,col=\"black\") # m2 <-lmer(yield ~ poly(nitrogen, 2) + (1|season:rep), data=dat) # anova(m1,m2) ## m2: yield ~ poly(nitrogen, 2) + (1 | season:rep) ## m1: yield ~ season * poly(nitrogen, 2) + (1 | season:rep) ## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) ## m2 5 86.418 93.424 -38.209 76.418 ## m1 8 64.216 75.425 -24.108 48.216 28.202 3 3.295e-06 *** } # }"},{"path":"/reference/gotway.hessianfly.html","id":null,"dir":"Reference","previous_headings":"","what":"Hessian fly damage to wheat varieties — gotway.hessianfly","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"Hessian fly damage wheat varieties","code":""},{"path":"/reference/gotway.hessianfly.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"block block factor, 4 levels genotype factor, 16 wheat varieties lat latitude, numeric long longitude, numeric y number damaged plants n number total plants","code":""},{"path":"/reference/gotway.hessianfly.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"response binomial. plot square.","code":""},{"path":"/reference/gotway.hessianfly.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"C. . Gotway W. W. Stroup. Generalized Linear Model Approach Spatial Data Analysis Prediction Journal Agricultural, Biological, Environmental Statistics, 2, 157-178. https://doi.org/10.2307/1400401","code":""},{"path":"/reference/gotway.hessianfly.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"GLIMMIX procedure. https://www.ats.ucla.edu/stat/SAS/glimmix.pdf","code":""},{"path":"/reference/gotway.hessianfly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gotway.hessianfly) dat <- gotway.hessianfly dat$prop <- dat$y / dat$n libs(desplot) desplot(dat, prop~long*lat, aspect=1, # true aspect out1=block, num=gen, cex=.75, main=\"gotway.hessianfly\") # ---------------------------------------------------------------------------- # spaMM package example libs(spaMM) m1 = HLCor(cbind(y, n-y) ~ 1 + gen + (1|block) + Matern(1|long+lat), data=dat, family=binomial(), ranPars=list(nu=0.5, rho=1/.7)) summary(m1) fixef(m1) # The following line fails with \"Invalid graphics state\" # when trying to use pkgdown::build_site # filled.mapMM(m1) # ---------------------------------------------------------------------------- # Block random. See Glimmix manual, output 1.18. # Note: (Different parameterization) libs(lme4) l2 <- glmer(cbind(y, n-y) ~ gen + (1|block), data=dat, family=binomial, control=glmerControl(check.nlev.gtr.1=\"ignore\")) coef(l2) } # }"},{"path":"/reference/goulden.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of barley — goulden.barley.uniformity","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"Uniformity trial barley Canada","code":""},{"path":"/reference/goulden.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"data frame 400 observations following 3 variables. row row col column yield yield, grams per plot","code":""},{"path":"/reference/goulden.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"Yield (grams) 2304 square-yard plots barley grown field 48 yards side Dominion Rust Research Laboratory (Manitoba, Canada) 1931. field sown half density one direction, half-density perpendicular direction. letter Goulden Cochran, Goulden said: intended use yields study effect systematic arrangements also measure bias semi-Latin squares...correlation adjacent pairs plots high (0.5) difficult demonstrate bias satisfactory manner. Note: data Goulden (1939) subset 20 rows columns one corner field full dataset. Field width: 48 plots x 3 feet = 144 feet Field length: 48 plots x 3 feet = 144 feet data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/goulden.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 5.","code":""},{"path":"/reference/goulden.barley.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"C. H. Goulden, (1939). Methods statistical analysis, 1st ed. Page 18. https://archive.org/stream/methodsofstatist031744mbp Note: version 20 plots x 20 plots. Leonard, Warren Andrew Clark (1939). Field Plot Technique. Page 39. https://archive.org/stream/fieldplottechniq00leon Note: version 20 plots x 20 plots.","code":""},{"path":"/reference/goulden.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(goulden.barley.uniformity) dat <- goulden.barley.uniformity libs(desplot) desplot(dat, yield ~ col*row, aspect=48/48, # true aspect main=\"goulden.barley.uniformity\") # Left skewed distribution. See LeClerg, Leonard, Clark hist(dat$yield, main=\"goulden.barley.uniformity\", breaks=c(21,40,59,78,97,116,135,154,173,192,211,230,249,268,287)+.5) } # }"},{"path":"/reference/goulden.eggs.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample of egg weights on 24 consecutive days — goulden.eggs","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"Sample egg weights 24 consecutive days","code":""},{"path":"/reference/goulden.eggs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"","code":"data(\"goulden.eggs\")"},{"path":"/reference/goulden.eggs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"data frame 240 observations following 2 variables. day day weight weight","code":""},{"path":"/reference/goulden.eggs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"Data weights 10 eggs taken random day 24 days. Day 1 Dec 10, Day 24 Jan 2. control chart standard deviations shows 4 values beyond upper limits. data reveals single, unusually large egg days. almost surely double-yolk eggs.","code":""},{"path":"/reference/goulden.eggs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"Cyrus H. Goulden (1952). Methods Statistical Analysis, 2nd ed. Page 425.","code":""},{"path":"/reference/goulden.eggs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"None.","code":""},{"path":"/reference/goulden.eggs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(goulden.eggs) dat <- goulden.eggs libs(qicharts) # Figure 19-4 of Goulden. (Goulden uses 1/n when calculating std dev) op <- par(mfrow=c(2,1)) qic(weight, x = day, data = dat, chart = 'xbar', main = 'goulden.eggs - Xbar chart', xlab = 'Date', ylab = 'Avg egg weight' ) qic(weight, x = day, data = dat, chart = 's', main = 'goulden.eggs - S chart', xlab = 'Date', ylab = 'Std dev egg weight' ) par(op) } # }"},{"path":"/reference/goulden.latin.html","id":null,"dir":"Reference","previous_headings":"","what":"Latin square experiment for testing fungicide — goulden.latin","title":"Latin square experiment for testing fungicide — goulden.latin","text":"Latin square experiment testing fungicide","code":""},{"path":"/reference/goulden.latin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latin square experiment for testing fungicide — goulden.latin","text":"","code":"data(\"goulden.latin\")"},{"path":"/reference/goulden.latin.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Latin square experiment for testing fungicide — goulden.latin","text":"data frame 25 observations following 4 variables. trt treatment factor, 5 levels yield yield row row col column","code":""},{"path":"/reference/goulden.latin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Latin square experiment for testing fungicide — goulden.latin","text":"Five treatments tested control stem rust wheat. Treatment codes descriptions: = Dusted rains. B = Dusted rains. C = Dusted week. D = Drifting, week. E = dusted.","code":""},{"path":"/reference/goulden.latin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latin square experiment for testing fungicide — goulden.latin","text":"Cyrus H. Goulden (1952). Methods Statistical Analysis, 2nd ed. Page 216.","code":""},{"path":"/reference/goulden.latin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Latin square experiment for testing fungicide — goulden.latin","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) library(agridat) data(goulden.latin) dat <- goulden.latin libs(desplot) desplot(dat, yield ~ col*row, text=trt, cex=1, # aspect unknown main=\"goulden.latin\") # Matches Goulden. m1 <- lm(yield~ trt + factor(row) + factor(col), data=dat) anova(m1) } # }"},{"path":"/reference/goulden.splitsplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-split-plot experiment of wheat — goulden.splitsplit","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"Split-split-plot experiment wheat","code":""},{"path":"/reference/goulden.splitsplit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"","code":"data(\"goulden.splitsplit\")"},{"path":"/reference/goulden.splitsplit.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"data frame 160 observations following 9 variables. row row col column yield yield inoc inoculate trt treatment number gen genotype dry dry/wet dust application dust dust treatment block block","code":""},{"path":"/reference/goulden.splitsplit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"interesting split-split plot experiment sub-plot treatments 2*5 factorial structure. experiment conducted 1932 experimental field Dominion Rust Research Laboratory. study designed determine effect incidence root rot, variety wheat, kinds dust seed treatment, method application dust, efficacy soil inoculation root-rot organism. field 4 blocks. block 2 whole plots genotypes. whole-plot 10 sub-plots 5 different kinds dust 2 methods application. sub-plot 2 sub-sub-plots, one inoculated soil one uninoculated soil.","code":""},{"path":"/reference/goulden.splitsplit.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"C. H. Goulden, (1939). Methods statistical analysis, 1st ed. Page 18. https://archive.org/stream/methodsofstatist031744mbp","code":""},{"path":"/reference/goulden.splitsplit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"None","code":""},{"path":"/reference/goulden.splitsplit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(goulden.splitsplit) dat <- goulden.splitsplit libs(desplot) ## Experiment design. Goulden p. 152-153 ## desplot(gen ~ col*row, data=dat, ## out1=block, out2=trt, text=dust, col=inoc, cex=1, ## main=\"goulden.splitsplit\") desplot(dat, yield ~ col*row, out1=block, out2=gen, col=inoc, num=trt, cex=1, main=\"goulden.splitsplit\") # Match Goulden table 40 m1 <- aov(yield ~ gen + dust + dry + dust:dry + gen:dust + gen:dry + gen:dust:dry + inoc + inoc:gen + inoc:dust + inoc:dry + inoc:dust:dry +inoc:gen:dust + inoc:gen:dry + Error(block/(gen+gen:dust:dry+gen:inoc:dry)), data=dat) summary(m1) } # }"},{"path":"/reference/graybill.heteroskedastic.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"Wheat varieties heteroskedastic yields","code":""},{"path":"/reference/graybill.heteroskedastic.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"data frame 52 observations following 3 variables. env environment, 13 levels gen genotype, 4 levels yield yield","code":""},{"path":"/reference/graybill.heteroskedastic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"Yield 4 varieties wheat 13 locations Oklahoma, USA. data used explore variability varieties.","code":""},{"path":"/reference/graybill.heteroskedastic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"F. . Graybill, 1954. Variance heterogeneity randomized block design, Biometrics, 10, 516-520.","code":""},{"path":"/reference/graybill.heteroskedastic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"Hans-Pieter Piepho, 1994. Missing observations analysis stability. Heredity, 72, 141–145. https://doi.org/10.1038/hdy.1994.20","code":""},{"path":"/reference/graybill.heteroskedastic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(graybill.heteroskedastic) dat <- graybill.heteroskedastic # Genotypes are obviously not homoscedastic boxplot(yield ~ gen, dat, main=\"graybill.heteroskedastic\") # Shukla stability variance of each genotype, same as Grubbs' estimate # Matches Piepho 1994 page 143. # Do not do this! Nowadays, use mixed models instead. libs(\"reshape2\") datm <- acast(dat, gen~env) w <- datm w <- sweep(w, 1, rowMeans(datm)) w <- sweep(w, 2, colMeans(datm)) w <- w + mean(datm) w <- rowSums(w^2) k=4; n=13 sig2 <- k*w/((k-2)*(n-1)) - sum(w)/((k-1)*(k-2)*(n-1)) ## sig2 ## G1 G2 G3 G4 ## 145.98 -14.14 75.15 18.25 var.shukla <- function(x,N){ # Estimate variance of shukla stability statistics # Piepho 1994 equation (5) K <- length(x) # num genotypes S <- outer(x,x) S1 <- diag(S) S2 <- rowSums(S) - S1 S[!upper.tri(S)] <- 0 # Make S upper triangular # The ith element of S3 is the sum of the upper triangular elements of S, # excluding the ith row and ith column S3 <- sum(S) - rowSums(S) - colSums(S) var.si2 <- 2*S1/(N-1) + 4/( (N-1)*(K-1)^2 ) * ( S2 + S3/(K-2)^2 ) return(var.si2) } # Set negative estimates to zero sig2[sig2<0] <- 0 # Variance of shukla stat. Match Piepho 1994, table 5, example 1 var.shukla(sig2,13) ## G1 G2 G3 G4 ## 4069.3296 138.9424 1423.0797 306.5270 } # }"},{"path":"/reference/gregory.cotton.html","id":null,"dir":"Reference","previous_headings":"","what":"Factorial experiment of cotton in Sudan. — gregory.cotton","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"Factorial experiment cotton Sudan.","code":""},{"path":"/reference/gregory.cotton.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"","code":"data(\"gregory.cotton\")"},{"path":"/reference/gregory.cotton.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"data frame 144 observations following 6 variables. yield yield year year nitrogen nitrogen level date sowing date water irrigation amount spacing spacing plants","code":""},{"path":"/reference/gregory.cotton.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"Experiment conducted Sudan Gezira Research Farm 1929-1930 1930-1931. effects yield four factors studied possible combinations. Sowing dates 1929: D1 = Jul 24, D2 = Aug 11, D3 = Sep 2, D4 = Sep 25. Spacing: S1 = 25 cm holes, S2 = 50 cm, S3 = 75 cm. usual spacing 50-70 cm. Irrigation: I1 = Light, I2 = Medium, I3 = Heavy. Nitrogen: N0 = None/Control, N1 = 600 rotls/feddan. year 4*3*2*2=72 treatments, replicated four times. means given . Gregory (1932) two interesting graphics: 1. radial bar plot 2. photographs 3D model treatment means.","code":""},{"path":"/reference/gregory.cotton.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"Gregory, FG Crowther, F Lambert, AR (1932). interrelation factors controlling production cotton irrigation Sudan. Journal Agricultural Science, 22, 617-638. Table 1, 10. https://doi.org/10.1017/S0021859600054137","code":""},{"path":"/reference/gregory.cotton.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"Paterson, D. Statistical Technique Agricultural Research, p. 211.","code":""},{"path":"/reference/gregory.cotton.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gregory.cotton) dat <- gregory.cotton # Main effect means, Gregory table 2 ## libs(dplyr) ## dat ## dat ## dat ## dat # Figure 2 of Gregory. Not recommended, but an interesting exercise. # https://stackoverflow.com/questions/13887365 if(FALSE){ libs(ggplot2) d1 <- subset(dat, year==\"Y1\") d1 <- transform(d1, grp=factor(paste(date,nitrogen,water,spacing))) d1 <- d1[order(d1$grp),] # for angles # Rotate labels on the left half 180 deg. First 18, last 18 labels d1$ang <- 90+seq(from=(360/nrow(d1))/1.5, to=(1.5*(360/nrow(d1)))-360, length.out=nrow(d1))+80 d1$ang[1:18] <- d1$ang[1:18] + 180 d1$ang[55:72] <- d1$ang[55:72] + 180 # Lables on left half to right-adjusted d1$hjust <- 0 d1$hjust[1:18] <- d1$hjust[55:72] <- 1 gg <- ggplot(d1, aes(x=grp,y=yield,fill=factor(spacing))) + geom_col() + guides(fill=FALSE) + # no legend for 'spacing' coord_polar(start=-pi/2) + # default is to start at top labs(title=\"gregory.cotton 1929\",x=\"\",y=\"\",label=\"\") + # The bar columns are centered on 1:72, subtract 0.5 to add radial axes geom_vline(xintercept = seq(1, 72, by=3)-0.5, color=\"gray\", size=.25) + geom_vline(xintercept = seq(1, 72, by=18)-0.5, size=1) + geom_vline(xintercept = seq(1, 72, by=9)-0.5, size=.5) + geom_hline(yintercept=c(1,2,3)) + geom_text(data=d1, aes(x=grp, y=max(yield), label=grp, angle=ang, hjust=hjust), size=2) + theme(panel.background=element_blank(), axis.title=element_blank(), panel.grid=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks=element_blank() ) print(gg) } } # }"},{"path":"/reference/grover.diallel.html","id":null,"dir":"Reference","previous_headings":"","what":"Diallel 6x6 — grover.diallel","title":"Diallel 6x6 — grover.diallel","text":"Diallel 6x6 4 blocks.","code":""},{"path":"/reference/grover.diallel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diallel 6x6 — grover.diallel","text":"","code":"data(\"grover.diallel\")"},{"path":"/reference/grover.diallel.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diallel 6x6 — grover.diallel","text":"data frame 144 observations following 5 variables. yield yield value rep character vector parent1 character vector parent2 character vector cross character vector","code":""},{"path":"/reference/grover.diallel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diallel 6x6 — grover.diallel","text":"Yield 6x6 diallel 4 reps. Note: mean 2x2 cross slightly different Grover p. 252. appears unknown error one 4 reps data page 250.","code":""},{"path":"/reference/grover.diallel.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Diallel 6x6 — grover.diallel","text":"Grover, Deepak & Lajpat Rai (2010). Experimental Designing Data Analysis Agriculture Biology. Agrotech Publishing Academy. Page 85. https://archive.org/details/expldesnanddatanalinagblg00023","code":""},{"path":"/reference/grover.diallel.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diallel 6x6 — grover.diallel","text":"None","code":""},{"path":"/reference/grover.diallel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diallel 6x6 — grover.diallel","text":"","code":"if (FALSE) { # \\dontrun{ data(grover.diallel) dat <- grover.diallel anova(aov(yield ~ rep + cross, data=dat)) # These effects match the GCA and SCA values in Grover table 3, page 253. libs(lmDiallel) m2 <- lm.diallel(yield ~ parent1 + parent2, Block=rep, data=dat, fct=\"GRIFFING1\") library(multcomp) summary( glht(linfct=diallel.eff(m2), test=adjusted(type=\"none\")) ) ## Linear Hypotheses: ## Estimate Std. Error t value Pr(>|t|) ## Intercept == 0 93.0774 0.9050 102.851 <0.01 *** ## g_P1 == 0 1.4851 1.4309 1.038 1.0000 ## g_P2 == 0 -0.9911 1.4309 -0.693 1.0000 ## g_P3 == 0 2.2631 1.4309 1.582 0.9748 ## g_P4 == 0 5.4247 1.4309 3.791 0.0302 * ## g_P5 == 0 -4.2490 1.4309 -2.969 0.1972 ## g_P6 == 0 -3.9328 1.4309 -2.748 0.3008 ## ts_P1:P1 == 0 -10.4026 4.5249 -2.299 0.6014 ## ts_P1:P2 == 0 -9.7214 3.2629 -2.979 0.1933 ## ts_P1:P3 == 0 -0.4581 3.2629 -0.140 1.0000 ## ts_P1:P4 == 0 17.0428 3.2629 5.223 <0.01 *** ## ts_P1:P5 == 0 25.4765 3.2629 7.808 <0.01 *** ## ts_P1:P6 == 0 -21.9372 3.2629 -6.723 <0.01 *** ## ts_P2:P1 == 0 -9.7214 3.2629 -2.979 0.1928 ## ts_P2:P2 == 0 7.0899 4.5249 1.567 0.9773 } # }"},{"path":"/reference/grover.rcb.subsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Rice RCB with subsamples — grover.rcb.subsample","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"experiment rice 9 fertilizer treatments 4 blocks, 4 hills per plot.","code":""},{"path":"/reference/grover.rcb.subsample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"","code":"data(\"grover.rcb.subsample\")"},{"path":"/reference/grover.rcb.subsample.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"data frame 144 observations following 4 variables. tiller number tillers trt treatment factor block block factor unit subsample unit","code":""},{"path":"/reference/grover.rcb.subsample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"experiment rice 9 fertilizer treatments 4 blocks, 4 hills per plot. response variable tiller count (per hill). hills sampling units.","code":""},{"path":"/reference/grover.rcb.subsample.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"Grover, Deepak & Lajpat Rai (2010). Experimental Designing Data Analysis Agriculture Biology. Agrotech Publishing Academy. Page 85. https://archive.org/details/expldesnanddatanalinagblg00023","code":""},{"path":"/reference/grover.rcb.subsample.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"None.","code":""},{"path":"/reference/grover.rcb.subsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"","code":"if (FALSE) { # \\dontrun{ data(grover.rcb.subsample) # Fixed-effects ANOVA. Matches Grover page 86. anova(aov(tiller ~ block + trt + block:trt, data=grover.rcb.subsample)) ## Response: tiller ## Df Sum Sq Mean Sq F value Pr(>F) ## block 3 930 310.01 3.6918 0.01415 * ## trt 8 11816 1477.00 17.5891 < 2e-16 *** ## block:trt 24 4721 196.71 2.3425 0.00158 ** ## Residuals 108 9069 83.97 } # }"},{"path":"/reference/gumpertz.pepper.html","id":null,"dir":"Reference","previous_headings":"","what":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","title":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","text":"Phytophtera disease incidence pepper field","code":""},{"path":"/reference/gumpertz.pepper.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","text":"data frame 800 observations following 6 variables. field field factor, 2 levels row x ordinate quadrat y ordinate disease presence (Y) absence (N) disease water soil moisture percent leaf leaf assay count","code":""},{"path":"/reference/gumpertz.pepper.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","text":"field 20 rows 20 quadrates, 2 3 bell pepper plants per plot. plant wilted, dead, lesions, Phytophthora disease considered present plot. soil pathogen load assayed number leaf disks colonized five. field 2, pattern disease presence appears follow soil water content. field 1, obvious trends present. Gumpertz et al. model presence disease using soil moisture leaf assay covariates, using disease presence neighboring plots covariates autologistic model. Used permission Marcia Gumpertz. Research funded USDA.","code":""},{"path":"/reference/gumpertz.pepper.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","text":"Marcia L. Gumpertz; Jonathan M. Graham; Jean B. Ristaino (1997). Autologistic Model Spatial Pattern Phytophthora Epidemic Bell Pepper: Effects Soil Variables Disease Presence. Journal Agricultural, Biological, Environmental Statistics, Vol. 2, . 2., pp. 131-156.","code":""},{"path":"/reference/gumpertz.pepper.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gumpertz.pepper) dat <- gumpertz.pepper # Gumpertz deletes two outliers dat[ dat$field ==\"F1\" & dat$row==20 & dat$quadrat==10, 'water'] <- NA dat[ dat$field ==\"F2\" & dat$row==5 & dat$quadrat==4, 'water'] <- NA # Horizontal flip dat <- transform(dat, row=21-row) # Disease presence. Gumpertz fig 1a, 2a. libs(desplot) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) desplot(dat, disease ~ row*quadrat|field, col.regions=c('white','black'), aspect=1, # uncertain aspect main=\"gumpertz.pepper disease presence\", ) # Soil water. Gumpertz fig 1b, 2b desplot(dat, water ~ row*quadrat|field, col.regions=grays(5), aspect=1, # uncertain aspect at=c(5,7.5,10,12.5,15,18), main=\"gumpertz.pepper soil moisture\") # Leaf assay. Gumpertz fig 1c, 2c desplot(dat, leaf ~ row*quadrat|field, col.regions=grays(6), at=c(0,1,2,3,4,5,6)-.5, aspect=1, # uncertain aspect main=\"gumpertz.pepper leaf assay\", ) # Use the inner 16x16 grid of plots in field 2 dat2 <- droplevels(subset(dat, field==\"F2\" & !is.na(water) & row > 2 & row < 19 & quadrat > 2 & quadrat < 19)) m21 <- glm(disease ~ water + leaf, data=dat2, family=binomial) coef(m21) # These match Gumpertz et al table 4, model 1 ## (Intercept) water leaf ## -9.1019623 0.7059993 0.4603931 dat2$res21 <- resid(m21) if(0){ libs(desplot) desplot(dat2, res21 ~ row*quadrat, main=\"gumpertz.pepper field 2, model 1 residuals\") # Still shows obvious trends. Gumpertz et al add spatial covariates for # neighboring plots, but with only minor improvement in misclassification } } # }"},{"path":"/reference/hadasch.lettuce.html","id":null,"dir":"Reference","previous_headings":"","what":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"Lettuce resistance downy mildew resistance (marker data).","code":""},{"path":"/reference/hadasch.lettuce.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"","code":"data(\"hadasch.lettuce\")"},{"path":"/reference/hadasch.lettuce.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"data frame 703 observations following 4 variables. loc locations gen genotype rep replicate dmr downy mildew resistance","code":""},{"path":"/reference/hadasch.lettuce.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"biparental cross 95 recombinant inbred lines \"Salinas 88\" (susceptible) \"La Brillante\" (highly resistant downy mildew). 89 RILs evaluated field experiments performed 2010 2011 near Salinas, California. loc 2 3 rep RCB design. approximately 30 plants per plot. Plots scored 0 (disease) 5 (severe disease). authors used following model first-stage analysis compute adjusted means genotype: y = loc + gen + gen:loc + block:loc + error gen fixed terms random. adjusted means used response second stage: mn = 1 + Zu + error Z design matrix marker effects. error term fixed covariance matrix R first stage. Genotyping performed 95 SNPs 205 amplified fragment length polymporphism markers marker matrix M (89×300) provided. biallelic marker M(iw) ith genotype wth marker alleles A1 (.e. reference allele) A2 coded 1 A1,A1, -1 A2,A2 0 A1,A2 A2,A2. electronic version lettuce data licensed CC-4 downloaded 20 Feb 2021. https://figshare.com/articles/dataset/Lettuce_trial_phenotypic_and_marker_data_/8299493","code":""},{"path":"/reference/hadasch.lettuce.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"Hadasch, S., . Simko, R. J. Hayes, J. O. Ogutu, H.P. Piepho (2016). Comparing predictive abilities phenotypic marker-assisted selection methods biparental lettuce population. Plant Genome 9. https://doi.org/10.3835/plantgenome2015.03.0014","code":""},{"path":"/reference/hadasch.lettuce.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"Hayes, R. J., Galeano, C. H., Luo, Y., Antonise, R., & Simko, . (2014). Inheritance Decay Fresh-cut Lettuce Recombinant Inbred Line Population \"Salinas 88\" × \"La Brillante\". J. Amer. Soc. Hort. Sci., 139(4), 388-398. https://doi.org/10.21273/JASHS.139.4.388","code":""},{"path":"/reference/hadasch.lettuce.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hadasch.lettuce) data(hadasch.lettuce.markers) dat <- hadasch.lettuce datm <- hadasch.lettuce.markers libs(agridat) # loc 1 has 2 reps, loc 3 has higher dmr dotplot(dmr ~ factor(gen)|factor(loc), dat, group=rep, layout=c(1,3), main=\"hadasch.lettuce\") # kinship matrix # head( tcrossprod(as.matrix(datm[,-1])) ) if(require(\"asreml\", quietly=TRUE)){ libs(asreml) dat <- transform(dat, loc=factor(loc), gen=factor(gen), rep=factor(rep)) m1 <- asreml(dmr ~ 1 + gen, data=dat, random = ~ loc + gen:loc + rep:loc) p1 <- predict(m1, classify=\"gen\")$pvals } libs(sommer) m2 <- mmer(dmr ~ 0 + gen, data=dat, random = ~ loc + gen:loc + rep:loc) p2 <- coef(m2) head(p1) head(p2) } # }"},{"path":"/reference/hanks.sprinkler.html","id":null,"dir":"Reference","previous_headings":"","what":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"Three wheat varieties planted 3 blocks, line sprinkler crossing whole plots.","code":""},{"path":"/reference/hanks.sprinkler.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"data frame 108 observations following 7 variables. block block row row subplot column gen genotype, 3 levels yield yield (tons/ha) irr irrigation level, 1..6 dir direction sprinkler, N/S","code":""},{"path":"/reference/hanks.sprinkler.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"line-source sprinkler placed middle experiment (subplots 6 7). Subplots closest sprinkler receive irrigation. Subplots far sprinkler (near edges) lowest yields. One data value modified original (following example authors).","code":""},{"path":"/reference/hanks.sprinkler.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"Hanks, R.J., Sisson, D.V., Hurst, R.L, Hubbard K.G. (1980). Statistical Analysis Results Irrigation Experiments Using Line-Source Sprinkler System. Soil Science Society America Journal, 44, 886-888. https://doi.org/10.2136/sssaj1980.03615995004400040048x","code":""},{"path":"/reference/hanks.sprinkler.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"Johnson, D. E., Chaudhuri, U. N., Kanemasu, E. T. (1983). Statistical Analysis Line-Source Sprinkler Irrigation Experiments Nonrandomized Experiments Using Multivariate Methods. Soil Science Society American Journal, 47, 309-312. Stroup, W. W. (1989). Use Mixed Model Procedure Analyze Spatially Correlated Data: Example Applied Line-Source Sprinkler Irrigation Experiment. Applications Mixed Models Agriculture Related Disciplines, Southern Cooperative Series Bulletin . 343, 104-122. SAS Stat User's Guide. https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_mixed_sect038.htm","code":""},{"path":"/reference/hanks.sprinkler.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hanks.sprinkler) dat <- hanks.sprinkler # The line sprinkler is vertical between subplots 6 & 7 libs(desplot) desplot(dat, yield~subplot*row, out1=block, out2=irr, cex=1, # aspect unknown num=gen, main=\"hanks.sprinkler\") libs(lattice) xyplot(yield~subplot|block, dat, type=c('b'), group=gen, layout=c(1,3), auto.key=TRUE, main=\"hanks.sprinkler\", panel=function(x,y,...){ panel.xyplot(x,y,...) panel.abline(v=6.5, col='wheat') }) ## This is the model from the SAS documentation ## proc mixed; ## class block gen dir irr; ## model yield = gen|dir|irr@2; ## random block block*dir block*irr; ## repeated / type=toep(4) sub=block*gen r; if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) dat <- transform(dat, subf=factor(subplot), irrf=factor(irr)) dat <- dat[order(dat$block, dat$gen, dat$subplot),] # In asreml3, we can specify corb(subf, 3) # In asreml4, only corb(subf, 1) runs. corb(subf, 3) says: # Correlation structure is not positive definite m1 <- asreml(yield ~ gen + dir + irrf + gen:dir + gen:irrf + dir:irrf, data=dat, random= ~ block + block:dir + block:irrf, resid = ~ block:gen:corb(subf, 3)) lucid::vc(m1) ## effect component std.error z.ratio bound ## block 0.2195 0.2378 0.92 P 0.5 ## block:dir 0.01769 0.03156 0.56 P 0 ## block:irrf 0.03539 0.0362 0.98 P 0.1 ## block:gen:subf!R 0.2851 0.05088 5.6 P 0 ## block:gen:subf!subf!cor1 0.02829 0.1142 0.25 U 0.9 ## block:gen:subf!subf!cor2 0.004997 0.1278 0.039 U 9.5 ## block:gen:subf!subf!cor3 -0.3245 0.09044 -3.6 U 0.1 } } # }"},{"path":"/reference/hanover.whitepine.html","id":null,"dir":"Reference","previous_headings":"","what":"Mating crosses of white pine trees — hanover.whitepine","title":"Mating crosses of white pine trees — hanover.whitepine","text":"Mating crosses white pine trees","code":""},{"path":"/reference/hanover.whitepine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mating crosses of white pine trees — hanover.whitepine","text":"","code":"data(\"hanover.whitepine\")"},{"path":"/reference/hanover.whitepine.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mating crosses of white pine trees — hanover.whitepine","text":"data frame 112 observations following 4 variables. rep replicate female female parent male male parent length epicotyl length, cm","code":""},{"path":"/reference/hanover.whitepine.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mating crosses of white pine trees — hanover.whitepine","text":"Four male (pollen parent) White Pine trees mated seven female trees 2654 progeny grown four replications, one plot per mating replication. Parent trees sourced Idaho, USA. data plot means epicotyl length. Becker (1984) used data demonstrate calculation heritability.","code":""},{"path":"/reference/hanover.whitepine.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mating crosses of white pine trees — hanover.whitepine","text":"Hanover, James W Barnes, Burton V. (1962). Heritability height growth year-old western white pine. Proc Forest Genet Workshop. 22, 71–76. Walter . Becker (1984). Manual Quantitative Genetics, 4th ed. Page 83.","code":""},{"path":"/reference/hanover.whitepine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mating crosses of white pine trees — hanover.whitepine","text":"None","code":""},{"path":"/reference/hanover.whitepine.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mating crosses of white pine trees — hanover.whitepine","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hanover.whitepine) dat <- hanover.whitepine libs(lattice) # Relatively high male-female interaction in growth comared # to additive gene action. Response is more consistent within # male progeny than female progeny. # with(dat, interaction.plot(female, male, length)) # with(dat, interaction.plot(male, female, length)) bwplot(length ~ male|female, data=dat, main=\"hanover.whitepine - length for male:female crosses\", xlab=\"Male parent\", ylab=\"Epicotyl length\") # Progeny sums match Becker p 83 sum(dat$length) # 380.58 aggregate(length ~ female + male, data=dat, FUN=sum) # Sum of squares matches Becker p 85 m1 <- aov(length ~ rep + male + female + male:female, data=dat) anova(m1) # Variance components match Becker p. 85 libs(lme4) libs(lucid) m2 <- lmer(length ~ (1|rep) + (1|male) + (1|female) + (1|male:female), data=dat) #as.data.frame(lme4::VarCorr(m2)) vc(m2) ## grp var1 var2 vcov sdcor ## male:female (Intercept) 0.1369 0.3699 ## female (Intercept) 0.02094 0.1447 ## male (Intercept) 0.1204 0.3469 ## rep (Intercept) 0.01453 0.1205 ## Residual 0.2004 0.4477 # Becker used this value for variability between individuals, within plot s2w <- 1.109 # Calculating heritability for individual trees s2m <- .120 s2f <- .0209 s2mf <- .137 vp <- s2m + s2f + s2mf + s2w # variability of phenotypes = 1.3869 4*s2m / vp # heritability male 0.346 4*s2f / vp # heritability female 0.06 2*(s2m+s2f)/vp # heritability male+female .203 # As shown in the boxplot, heritability is stronger through the # males than through the females. } # }"},{"path":"/reference/hansen.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"Multi-year uniformity trial Denmark","code":""},{"path":"/reference/hansen.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"","code":"data(\"hansen.multi.uniformity\")"},{"path":"/reference/hansen.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"data frame 662 observations following 6 variables. field field name year year crop crop yield yield (percent mean) row row col column","code":""},{"path":"/reference/hansen.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"Uniformity trials carried 1906 1911 two fields Aarslev, Denmark. yield values expressed percent mean yield year. scale map Hansen shows \"Alen\" scale. See https://en.wikipedia.org/wiki/Alen_(unit_of_length) Danish alen = 62.77 cm. Field A2: Based map, field approximately 60 alen x 70 alen (38 m x 44 m), orientation field clear. Plots probably circa 7.4 m side. Divided 30 plots – 6 strips 5. crops grown : 1907 oats, 1908 rye, 1909 barley, 1910 mangolds, 1911 barley. Sanders said: appeared two printer errors paper. field A2 yields given 1908 add 3010 instead 3000: reference Fig. 6 given seemed indicate excess lay row 3 eventually decided reduce plots 3c 96 3f 84. Field E2: Field approximately 120 alen x 200 alen (76m x 125m). Plots probably circa 8-9m side. Divided 128 plots: 16 strips 8. Crops grown: 1906 oats, 1907 barley, 1908 seeds, 1909 rye. Sanders said, remarkable oscillation fertility across field E2 one direction, 1st, 3rd, ... 15th strips (columns) consistently giving much higher yields 2nd, 4th, ... 16th strips (columns). fact four years odd numbered strips gave total yield 27,817, compared 23,383 even numbered strips. oscillation apparently arose legacy old practice ploughing high ridges: tops ridges exhibited greater fertility borders furrows, soil worked former latter field leveled . meant site old furrows good depth rich soil, whilst shallow ridges . strips arranged cover site furrow ridge alternately, result noted . Sanders: order escape variation, table condensed taking 2 strips together (new strips included whole one old \"lands\") making 8 8 square. Sanders said: field E2 1908, column 10 sums 791 instead 786 shown: reference Fig. 13 indicated yield plot 10g probably 92 instead 97. version data package uses changes suggested Sanders. Data typed K.Wright.","code":""},{"path":"/reference/hansen.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"Hansen, Niels Anton (1914). Prøvedyrkning paa Forsøgsstationen ved Aarslev. Page 557 field A2. Page 562 field E2. https://dca.au.dk/publikationer/historiske/planteavl","code":""},{"path":"/reference/hansen.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"Eden, T. E. J. Maskell. (1928). influence soil heterogeneity growth yield successive crops. Journal Agricultural Science, 18, 163-185. https://archive.org/stream/.ernet.dli.2015.25895/2015.25895.Journal--Agricultural-Science-Vol-xviii-1928#page/n175 Sanders, H. G. 1930. note value uniformity trials subsequent experiments. Journal Agricultural Science. 20, 63-73. https://dx.doi.org/10.1017/S0021859600088626 https://repository.rothamsted.ac.uk/item/97039/-note---value--uniformity-trials--subsequent-experiments","code":""},{"path":"/reference/hansen.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hansen.multi.uniformity) dat <- hansen.multi.uniformity # Field A2: Average across years libs(dplyr,reshape2) #dat # Field E2: Match column totals #dat # Heatmaps. Aspect ratio is an educated guess libs(dplyr, desplot) dat <- dat dat dat # Look at correlation of experimental unit plots across years libs(dplyr, reshape2, lattice) dat <- mutate(dat, plot=paste(row,col)) mat1 <- filter(dat, field==\"A2\") splom(mat1, main=\"hansen.multi.uniformity field A2\") mat2 <- filter(dat, field==\"E2\") splom(mat2, main=\"hansen.multi.uniformity field A2\") } # }"},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"Uniformity trial sugar beet Russia.","code":""},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"","code":"data(\"haritonenko.sugarbeet.uniformity\")"},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"data frame 416 observations following 3 variables. row Row ordinate col Column ordinate yield Yield pfund per plot","code":""},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"Roemer (1920) says: Haritonenko (36), experiment Ivanovskoye Agricultural Experimental Station, Novgorod Governorate. test area 5.68 ha 416 sections (plots) 136.5 square meters. Row 1 significantly less soil three rows. Based heatmap, 'Row 1' left column. Roemer p. 63 says: Table 4: Root yield pfund 30 quadratfaden (1.33 x 22.5). use 1 faden = 7 feet, : (1.33 faden * 7 feet) * (22.5 faden * 7 feet) * 416 plots = 609991 sq feet = 5.68 hectares, matches experiment description. 'pfund' (Germany pound) today defined 500g, 1920 might different, perhaps 467g??? Field width: 4 plots * (22.5 faden * 7 feet/faden) = 630 feet. Field length: 104 plots * (1.33 faden * 7 feet/faden) = 968 feet. Note: Cochran says plots 8 x 135 ft. seems based 1 faden = 6 feet, match total area 5.68 ha. Note: name Haritonenko sometimes translated English : Pavel Kharitonenko. data typed K.Wright Roemer (1920), table 4, p. 63.","code":""},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"Haritonenko, Pavlo. Neue Prazisionsmethoden auf den Versuchsfeldern. Arbeiten der landw. Versuchsstation Iwanowskoje 1904-06, S. 159. Russian German summary.","code":""},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"Neyman, J., & Iwaszkiewicz, K. (1935). Statistical problems agricultural experimentation. Supplement Journal Royal Statistical Society, 2(2), 107-180. Roemer, T. (1920). Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ","code":""},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(haritonenko.sugarbeet.uniformity) dat <- haritonenko.sugarbeet.uniformity mean(dat$yield) # 615.68. # Roemer page 37 says 617 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(104*1.33*7)/(4*22.5*7), ticks=TRUE, main=\"haritonenko.sugarbeet.uniformity\") } # }"},{"path":"/reference/harris.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","title":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","text":"Uniformity trials multiple crops, Huntley Field Station, Montana, 1911-1925.","code":""},{"path":"/reference/harris.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","text":"data frame 1058 observations following 5 variables. series series (field coordinate) plot plot number (field ordinate) year year, 1911-1925 crop crop yield yield per plot (pounds)","code":""},{"path":"/reference/harris.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","text":"yields given Harris (1920) (Practical universality...) given quarter-plots. yields given Harris (1920) (Permanence ...) yields given Harris (1928) given single plots. Field width: 2 plots * 317 ft + 5 feet alley = 639 feet Field length: 23 plots * 23.3 feet = 536 feet yields given pound per plot. original data Harris (1920) 1911 sugarbeet yields tons/ac, (Harris 1920, table 3 footnote), converted pounds/plot purpose dataset. Harris (1928) shows map location page 16. Harris (1920): 1911: spring 1911 field laid 46 plots, measuring 23.5 317 feet containing 0.17 acre, arranged two parallel series 23 plots . two series plots separated merely temporary irrigation ditch. 1911 planted sugar beets. 1912: spring 1912 seeded alfalfa, one cutting harvested year. stand remained ground 1913 1914, entire field fall-plowed. 1913: Three cuttings made, third cutting lost heavy wind scattered mixed crop weighings various plots made. first cutting, designated alfalfa , made plots one-half original size. second cutting harvested plots one-quarter original size. 1914: first second cuttings 1914 weighed plots one-quarter original size–, 0.0425-acre plots– third cutting recorded plots one-third original size. furnish data alfalfa , II, III 1914. Total yields first second cuttings 1913 1914 first, second, third cuttings 1914 also considered. 1915: Ear corn. 1916: Ear corn. 1917: fields planted oats, records made grain, straw, total yield. 1918: Silage corn grown. 1919: land produced crop barley. 1920: Silage corn 1921 Alfalfa 1922 Alfalfa, cutting 3 1923 Alfalfa, cutting 1 3 1914 Alfalfa, cutting 2 3 Harris (1928): southeast corner Series II, east series, 80 feet main canal, southwest corner Series III 50 feet Ouster Coulee. main project canal carries normally irrigation season 400 second-feet water. water surface canal 4 feet high corner field. evident surface conditions, well borings made canal field, extensive seepage canal subsoil field. volume seepage larger recent years earlier years cropping experiments, probably canal bank worn away internal erosion, exposing stratum sandy subsoil underlies canal part field. Whereas earlier crops Series II better alfalfa, Series III better alfalfa later period. writers feel inclined suggest earlier experiments height water table harmful effect upon deep-rooted crop alfalfa. quite possible drier periods higher water table actually favored alfalfa growth Series II. higher water tables recent years probably deleterious influence, especially marked Series II, water apparently comes nearer surface Series III.","code":""},{"path":"/reference/harris.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","text":"Harris, J Arthur Scofield, CS. (1920). Permanence differences plats experimental field. Jour. Agr. Res, 20, 335-356. https://naldc.nal.usda.gov/catalog/IND43966236 https://www.google.com/books/edition/Journal_of_the_American_Society_of_Agron/Zwz0AAAAMAAJ?hl=en&gbpv=1&pg=PA257 data 1911-1919. Harris, J Arthur Scofield, CS. (1928). studies permanence differences plots experimental field. Jour. Agr. Res, 36, 15–40. https://naldc.nal.usda.gov/catalog/IND43967538 data 1920-1925.","code":""},{"path":"/reference/harris.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(harris.multi.uniformity) dat <- harris.multi.uniformity # Combine year/crop into 'harvest' dat <- transform(dat, harv = factor(paste0(year,\".\",crop))) # Average yields. Harris 1928, table 2. aggregate(yield~harv, dat, mean) # Corrgram libs(reshape2,corrgram) mat <- acast(dat, series+plot~harv, value.var='yield') corrgram(mat, main=\"harris.multi.uniformity - correlation of crop yields\") # Compare to Harris 1928, table 4. More positive than negative correlations. # densityplot(as.vector(cor(mat)), xlab=\"correlations\", # main=\"harris.multi.uniformity\") # Standardize yields for each year mats <- scale(mat) # Melt and re-name columns so we can make field maps. Obvious spatial # patterns that persist over years d2 <- melt(mats) names(d2) <- c('ord','harv','yield') d2$series <- as.numeric(substring(d2$ord,1,1)) d2$plot <- as.numeric(substring(d2$ord,3)) # Series 2 is on the east side, so switch 2 and 3 for correct plotting d2$xord <- 5 - dat$series # Note that for alfalfa, higher-yielding plots in 1912-1914 were # lower-yielding in 1922-1923. # Heatmaps for individual year/harvest combinations libs(desplot) desplot(d2, yield ~ xord*plot|harv, aspect=536/639, flip=TRUE, # true aspect main=\"harris.multi.uniformity\") # Crude fertility map by averaging across years shows probable # sub-surface water effects agg <- aggregate(yield ~ xord + plot, data=d2, mean) desplot(agg, yield ~ xord + plot, aspect=536/639, # true aspect main=\"harris.multi.uniformity fertility\") } # }"},{"path":"/reference/harris.wateruse.html","id":null,"dir":"Reference","previous_headings":"","what":"Water use by horticultural trees — harris.wateruse","title":"Water use by horticultural trees — harris.wateruse","text":"Water use horticultural trees","code":""},{"path":"/reference/harris.wateruse.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Water use by horticultural trees — harris.wateruse","text":"data frame 1040 observations following 6 variables. species species factor, 2 levels age age factor, 2 levels tree tree factor, 40 (non-consecutive) levels day day, numeric water water use, numeric","code":""},{"path":"/reference/harris.wateruse.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Water use by horticultural trees — harris.wateruse","text":"Ten trees four groups (two species, two ages) assessed water usage, approximately every five days. Missing values included benefit asreml, needs 'balanced' data set due kronecker-like syntax R matrix. Used permission Roger Harris Virginia Polytechnic.","code":""},{"path":"/reference/harris.wateruse.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Water use by horticultural trees — harris.wateruse","text":"Schabenberger, Oliver Francis J. Pierce. 2002. Contemporary Statistical Models Plant Soil Sciences. CRC Press. Page 512.","code":""},{"path":"/reference/harris.wateruse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Water use by horticultural trees — harris.wateruse","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(harris.wateruse) dat <- harris.wateruse # Compare to Schabenberger & Pierce, fig 7.23 libs(latticeExtra) useOuterStrips(xyplot(water ~ day|species*age,dat, as.table=TRUE, group=tree, type=c('p','smooth'), main=\"harris.wateruse 2 species, 2 ages (10 trees each)\")) # Note that measurements on day 268 are all below the trend line and # thus considered outliers. Delete them. dat <- subset(dat, day!=268) # Schabenberger figure 7.24 xyplot(water ~ day|tree,dat, subset=age==\"A2\" & species==\"S2\", as.table=TRUE, type=c('p','smooth'), ylab=\"Water use profiles of individual trees\", main=\"harris.wateruse (Age 2, Species 2)\") # Rescale day for nicer output, and convergence issues, add quadratic term dat <- transform(dat, ti=day/100) dat <- transform(dat, ti2=ti*ti) # Start with a subgroup: age 2, species 2 d22 <- droplevels(subset(dat, age==\"A2\" & species==\"S2\")) # ----- Model 1, for subgroup A2,S2 # First, a fixed quadratic that is common to all trees, plus # a random quadratic deviation for each tree. ## Schabenberger, Output 7.26 ## proc mixed; ## class tree; ## model water = ti ti*ti / s; ## random intercept ti ti*ti/subject=tree; libs(nlme,lucid) ## We use pdDiag() to get uncorrelated random effects m1n <- lme(water ~ 1 + ti + ti2, data=d22, na.action=na.omit, random = list(tree=pdDiag(~1+ti+ti2))) # lucid::vc(m1n) ## effect variance stddev ## (Intercept) 0.2691 0.5188 ## ti 0 0.0000144 ## ti2 0 0.0000039 ## Residual 0.1472 0.3837 # Various other models with lme4 & asreml libs(lme4, lucid) m1l <- lmer(water ~ 1 + ti + ti2 + (1|tree) + (0+ti|tree) + (0+ti2|tree), data=d22) # lucid::vc(m1l) ## grp var1 var2 vcov sdcor ## tree (Intercept) 0.2691 0.5188 ## tree.1 ti 0 0 ## tree.2 ti2 0 0 ## Residual 0.1472 0.3837 # Once the overall quadratic trend has been removed, there is not # too much evidence for consecutive observations being correlated ## d22r <- subset(d22, !is.na(water)) ## d22r$res <- resid(m1n) ## xyplot(res ~ day|tree,d22r, ## as.table=TRUE, type=c('p','smooth'), ## ylab=\"residual\", ## main=\"harris.wateruse - Residuals of individual trees\") ## op <- par(mfrow=c(4,3)) ## tapply(d22r$res, d22r$tree, acf) ## par(op) # ----- Model 2, add correlation of consecutive measurements ## Schabenberger (page 516) adds correlation. ## Note how the fixed quadratic model is on the \"ti = day/100\" scale ## and the correlated observations are on the \"day\" scale. The ## only impact this has on the fitted model is to increase the ## correlation parameter by a factor of 100, which was likely ## done to get better convergence. ## proc mixed data=age2sp2; ## class tree; ## model water = ti ti*ti / s ; ## random intercept /subject=tree s; ## repeated /subject=tree type=sp(exp)(day); ## Same as SAS, use ti for quadratic, day for correlation m2l <- lme(water ~ 1 + ti + ti2, data=d22, random = ~ 1|tree, cor = corExp(form=~ day|tree), na.action=na.omit) m2l # Match output 7.27. Same fixef, ranef, variances, exp corr # lucid::vc(m2l) ## effect variance stddev ## (Intercept) 0.2656 0.5154 ## Residual 0.1541 0.3926 # --- ## Now use asreml. When I tried rcov=~tree:exp(ti), ## the estimated parameter value was on the 'boundary', i.e. 0. ## Changing rcov to the 'day' scale produced a sensible estimate ## that matched SAS. ## Note: SAS and asreml use different parameterizations for the correlation ## SAS uses exp(-d/phi) and asreml uses phi^d. ## SAS reports 3.79, asreml reports 0.77, and exp(-1/3.7945) = 0.7683274 ## Note: normally a quadratic would be included as 'pol(day,2)' if(require(\"asreml\", quietly=TRUE)){ libs(asreml) d22 <- d22[order(d22$tree, d22$day),] m2a <- asreml(water ~ 1 + ti + ti2, data=d22, random = ~ tree, residual=~tree:exp(day)) lucid::vc(m2a) ## effect component std.error z.ratio constr ## tree!tree.var 0.2656 0.1301 2 pos ## R!variance 0.1541 0.01611 9.6 pos ## R!day.pow 0.7683 0.04191 18 uncon } # ----- Model 3. Full model for all species/ages. Schabenberger p. 518 ## /* Continuous AR(1) autocorrelations included */ ## proc mixed data=wateruse; ## class age species tree; ## model water = age*species age*species*ti age*species*ti*ti / noint s; ## random intercept ti / subject=age*species*tree s; ## repeated / subject=age*species*tree type=sp(exp)(day); m3l <- lme(water ~ 0 + age:species + age:species:ti + age:species:ti2, data=dat, na.action=na.omit, random = list(tree=pdDiag(~1+ti)), cor = corExp(form=~ day|tree) ) m3l # Match Schabenberger output 7.27. Same fixef, ranef, variances, exp corr # lucid::vc(m3l) ## effect variance stddev ## (Intercept) 0.1549 0.3936 ## ti 0.02785 0.1669 ## Residual 0.16 0.4 # --- asreml if(require(\"asreml\", quietly=TRUE)){ dat <- dat[order(dat$tree,dat$day),] m3a <- asreml(water ~ 0 + age:species + age:species:ti + age:species:ti2, data=dat, random = ~ age:species:tree + age:species:tree:ti, residual = ~ tree:exp(day) ) # lucid::vc(m3a) # Note: day.pow = .8091 = exp(-1/4.7217) ## effect component std.error z.ratio constr ## age:species:tree!age.var 0.1549 0.07192 2.2 pos ## age:species:tree:ti!age.var 0.02785 0.01343 2.1 pos ## R!variance 0.16 0.008917 18 pos ## R!day.pow 0.8091 0.01581 51 uncon } } # }"},{"path":"/reference/harrison.priors.html","id":null,"dir":"Reference","previous_headings":"","what":"Ranges of analytes in soybean from other authors — harrison.priors","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"Ranges analytes soybean authors","code":""},{"path":"/reference/harrison.priors.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"data frame 80 observations following 5 variables. source Source document substance Analyte substance min minimum amount (numeric) max maximum analyte amount (numeric) number number substances","code":""},{"path":"/reference/harrison.priors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"Harrison et al. show construct informative Bayesian prior previously-published ranges concentration several analytes. units daidzein, genistein, glycitein micrograms per gram. raffinose stachyose units converted common 'percent' scale. author names 'source' variable shortened forms citations supplemental information Harrison et al.","code":""},{"path":"/reference/harrison.priors.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"Jay M. Harrison, Matthew L. Breeze, Kristina H. Berman, George G. Harrigan. 2013. Bayesian statistical approaches compositional analyses transgenic crops 2. Application validation informative prior distributions. Regulatory Toxicology Pharmacology, 65, 251-258. https://doi.org/10.1016/j.yrtph.2012.12.002 Data retrieved Supplemental Information source.","code":""},{"path":"/reference/harrison.priors.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"Jay M. Harrison, Derek Culp, George G. Harrigan. 2013. Bayesian MCMC analyses regulatory assessments safety food composition Proceedings 24th Conference Applied Statistics Agriculture (2012).","code":""},{"path":"/reference/harrison.priors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(harrison.priors) dat <- harrison.priors d1 <- subset(dat, substance==\"daidzein\") # Stack the data to 'tall' format and calculate empirical cdf d1t <- with(d1, data.frame(xx = c(min, max), yy=c(1/(number+1), number/(number+1)))) # Harrison 2012 Example 4: Common prior distribution # Harrison uses the minimum and maximum levels of daidzein from previous # studies as the first and last order statistics of a lognormal # distribution, and finds the best-fit lognormal distribution. m0 <- mean(log(d1t$xx)) # 6.37 s0 <- sd(log(d1t$xx)) # .833 mod <- nls(yy ~ plnorm(xx, meanlog, sdlog), data=d1t, start=list(meanlog=m0, sdlog=s0)) coef(mod) # Matches Harrison 2012 ## meanlog sdlog ## 6.4187829 0.6081558 plot(yy~xx, data=d1t, xlim=c(0,2000), ylim=c(0,1), main=\"harrison.priors - Common prior\", xlab=\"daidzein level\", ylab=\"CDF\") mlog <- coef(mod)[1] # 6.4 slog <- coef(mod)[2] # .61 xvals <- seq(0, 2000, length=100) lines(xvals, plnorm(xvals, meanlog=mlog, sdlog=slog)) d1a <- d1 d1a$source <- as.character(d1a$source) d1a[19,'source'] <- \"(All)\" # Add a blank row for the densitystrip d1 libs(latticeExtra) # Plot the range for each source, a density curve (with arbitary # vertical scale) for the common prior distribution, and a density # strip by stacking the individual bands and using transparency segplot(factor(source) ~ min+max, d1a, main=\"harrison.priors\",xlab=\"daidzein level\",ylab=\"source\") + xyplot(5000*dlnorm(xvals, mlog, slog)~xvals, type='l') + segplot(factor(rep(1,18)) ~ min+max, d1, 4, level=d1$number, col.regions=\"gray20\", alpha=.1) } # }"},{"path":"/reference/hartman.tomato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of tomato — hartman.tomato.uniformity","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"Uniformity trial tomato Indiana","code":""},{"path":"/reference/hartman.tomato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"","code":"data(\"hartman.tomato.uniformity\")"},{"path":"/reference/hartman.tomato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"data frame 384 observations following 3 variables. row row col column yield yield, pounds per plot","code":""},{"path":"/reference/hartman.tomato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"Grown Indiana 1941. column ordinates R package dataset quite exactly field due presence roads. Plants spaced 3 feet apart rows 6 feet apart, 330 feet long. row divided 3 sections 34 plants sparated strips 12 feet long provide roadways vehicles. row divided 4-plant plots, 8 plots section row one plant left guard end section. 49 plants missing 3072 total plants, ignored. Note, data given Table 1 Hartman 8-plant plots! Field width: 3 sections (34 plants * 3 feet) + 2 roads * 12 feet = 330 feet. Field length: 32 rows * 6 feet = 192 feet oriented page, plots , average, 330/12=27.5. feet wide, 6 feet tall. Discussion notes Hartman. Total yield 26001 pounds. Hartman says yield field 10.24 tons per acre, can verify: 26001 lb/field * (1/384 field/plot) * (1/(24*6) plot/ft2) * (43560 ft2/acre) * (1/2000 tons/lb) = 10.24 tons/acre rows top/bottom (north/south) intended guard rows, yields similar rows, suggesting competition rows exist. comparing varieties, 96*6 foot plots work well.","code":""},{"path":"/reference/hartman.tomato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"J. D. Hartman E. C. Stair (1942). Field Plot Technique Tomatoes. Proceedings American Society Horticultural Science, 41, 315-320. https://archive.org/details/.ernet.dli.2015.240678","code":""},{"path":"/reference/hartman.tomato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"None","code":""},{"path":"/reference/hartman.tomato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hartman.tomato.uniformity) libs(desplot) desplot(hartman.tomato.uniformity, yield ~ col*row, flip=TRUE, tick=TRUE, aspect=192/330, # true aspect main=\"hartman.tomato.uniformity\") } # }"},{"path":"/reference/harvey.lsmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"Average daily gain 65 steers 3 lines, 9 sires.","code":""},{"path":"/reference/harvey.lsmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"","code":"data(\"harvey.lsmeans\")"},{"path":"/reference/harvey.lsmeans.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"data frame 65 observations following 7 variables. line line dam sire sire damage age class dam calf calf number weanage calf age weaning weight calf weight start feeding adg average daily gain","code":""},{"path":"/reference/harvey.lsmeans.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"average daily gain 'adg' 65 Hereford steers. calf age weaning initial weight beginning test feeding also given. steers fed length time feed lot. assumed calf unique dam twins repeat matings. Harvey (1960) one earliest papers presenting least squares means (lsmeans).","code":""},{"path":"/reference/harvey.lsmeans.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"Harvey, Walter R. (1960). Least-squares Analysis Data Unequal Subclass Numbers. Technical Report ARS 20-8. USDA, Agricultural Research Service. Page 101-102. Reprinted ARS H-4, 1975. https://archive.org/details/leastsquaresanal04harv","code":""},{"path":"/reference/harvey.lsmeans.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"Also appears 'dmm' package 'harv101.df' See package vignette complete analysis data.","code":""},{"path":"/reference/harvey.lsmeans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(harvey.lsmeans) dat = harvey.lsmeans libs(lattice) dotplot(adg ~ sire|line,dat, main=\"harvey.lsmeans\", xlab=\"sire\", ylab=\"average daily gain\") # Model suggested by Harvey on page 103 m0 <- lm(adg ~ 1 + line + sire + damage + line:damage + weanage + weight, data=dat) # Due to contrast settings, it can be hard to compare model coefficients to Harvey, # but note the slopes of the continuous covariates match Harvey p. 107, where his # b is weanage, d is weight # coef(m0) # weanage weight # -0.008154879 0.001970446 # A quick attempt to reproduce table 4 of Harvey, p. 109. Not right. # libs(emmeans) # emmeans(m0,c('line','sire','damage')) } # }"},{"path":"/reference/harville.lamb.html","id":null,"dir":"Reference","previous_headings":"","what":"Birth weight of lambs from different lines/sires — harville.lamb","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"Birth weight lambs different lines/sires","code":""},{"path":"/reference/harville.lamb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"","code":"data(\"harville.lamb\")"},{"path":"/reference/harville.lamb.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"data frame 62 observations following 4 variables. line genotype line number sire sire number damage dam age, class 1,2,3 weight lamb birth weight","code":""},{"path":"/reference/harville.lamb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"Weight birth 62 lambs. 5 distinct lines. sires multiple lambs. dam one lamb. age dam category: 1 (1-2 years), 2 (2-3 years) 3 (3 years). Note: Jiang, gives data table 1.2, small error. Jiang weight 9.0 sire 31, line 3, age 3. correct value 9.5.","code":""},{"path":"/reference/harville.lamb.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"David . Harville Alan P. Fenech (1985). Confidence Intervals Variance Ratio, Heritability, Unbalanced Mixed Linear Model. Biometrics, 41, 137-152. https://doi.org/10.2307/2530650","code":""},{"path":"/reference/harville.lamb.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"Jiming Jiang, Linear Generalized Linear Mixed Models Applications. Table 1.2. Andre . Khuri, Linear Model Methodology. Table 11.5. Page 368. https://books.google.com/books?id=UfDvCAAAQBAJ&pg=PA164 Daniel Gianola, Keith Hammond. Advances Statistical Methods Genetic Improvement Livestock. Table 8.1, page 165.","code":""},{"path":"/reference/harville.lamb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(harville.lamb) dat <- harville.lamb dat <- transform(dat, line=factor(line), sire=factor(sire), damage=factor(damage)) library(lattice) bwplot(weight ~ line, dat, main=\"harville.lamb\", xlab=\"line\", ylab=\"birth weights\") if(0){ libs(lme4, lucid) m1 <- lmer(weight ~ -1 + line + damage + (1|sire), data=dat) summary(m1) vc(m1) # Khuri reports variances 0.5171, 2.9616 ## grp var1 var2 vcov sdcor ## sire (Intercept) 0.5171 0.7191 ## Residual 2.962 1.721 } } # }"},{"path":"/reference/hayman.tobacco.html","id":null,"dir":"Reference","previous_headings":"","what":"Diallel cross of Aztec tobacco — hayman.tobacco","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"Diallel cross Aztec tobacco 2 reps","code":""},{"path":"/reference/hayman.tobacco.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"year year block block factor, 2 levels male male parent, 8 levels female female parent day mean flowering time (days)","code":""},{"path":"/reference/hayman.tobacco.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"Data collected 1951 (Hayman 1954a) 1952 (Hayman 1954b). year 8 varieties Aztec tobacco, Nicotiana rustica L.. cross/self represented 10 progeny, two plots 5 plants . data mean flowering time per plot. Note, 1951 data published Hayman (1954a) Table 5 contain \"10 times mean flowering time\". data divided 10 comparable 1952 data. Hayman (1954b) says \"Table 2 lists...three characters diallel cross Nicotiana rustica varieties repeated three years.\" seems indicate varieties 1951 1952. Calculating GCA effects separately 1951 1952 comparing estimates shows highly correlated.","code":""},{"path":"/reference/hayman.tobacco.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"B. . Hayman (1954a). Analysis Variance Diallel Tables. Biometrics, 10, 235-244. Table 5, page 241. https://doi.org/10.2307/3001877 Hayman, B.. (1954b). theory analysis diallel crosses. Genetics, 39, 789-809. Table 3, page 805. https://www.genetics.org/content/39/6/789.full.pdf","code":""},{"path":"/reference/hayman.tobacco.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"# 1951 data Mohring, Melchinger, Piepho. (2011). REML-Based Diallel Analysis. Crop Science, 51, 470-478. # 1952 data C. Clark Cockerham B. S. Weir. (1977). Quadratic analyses reciprocal crosses. Biometrics, 33, 187-203. Appendix C. Andrea Onofri, Niccolo Terzaroli, Luigi Russi (2020). Linear models diallel crosses: review R functions. Theoretical Applied Genetics. https://doi.org/10.1007/s00122-020-03716-8","code":""},{"path":"/reference/hayman.tobacco.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) # 1951 data. Fit the first REML model of Mohring 2011 Supplement. data(hayman.tobacco) dat1 <- subset(hayman.tobacco, year==1951) # Hayman's model # dat1 <- subset(hayman.tobacco, year==1951) # libs(lmDiallel) # m1 <- lm.diallel(day ~ male+female, Block=block, data=dat1, fct=\"HAYMAN2\") # anova(m1) # Similar to table 7 of Hayman 1954a ## Response: day ## Df Sum Sq Mean Sq F value Pr(>F) ## Block 1 1.42 1.42 0.3416 0.56100 ## Mean Dom. Dev. 1 307.97 307.97 73.8840 3.259e-12 *** ## GCA 7 2777.17 396.74 95.1805 < 2.2e-16 *** ## Dom. Dev. 7 341.53 48.79 11.7050 1.957e-09 *** ## SCA 20 372.89 18.64 4.4729 2.560e-06 *** ## RGCA 7 67.39 9.63 2.3097 0.03671 * ## RSCA 21 123.73 5.89 1.4135 0.14668 ## Residuals 63 262.60 # Griffing's model # https://www.statforbiology.com/2021/stat_met_diallel_griffing/ # dat1 <- subset(hayman.tobacco, year==1951) # libs(lmDiallel) # contrasts(dat1$block) <- \"contr.sum\" # dmod1 and dmod2 are the same model with different syntax # dmod1 <- lm(day ~ block + GCA(male, female) + tSCA(male, female) + # REC(male, female) , data = dat1) # dmod2 <- lm.diallel(day ~ male + female, Block=block, # data = dat1, fct = \"GRIFFING1\") # anova(dmod1) # anova(dmod2) ## Response: day ## Df Sum Sq Mean Sq F value Pr(>F) ## Block 1 1.42 1.42 0.3416 0.56100 ## GCA 7 2777.17 396.74 95.1805 < 2.2e-16 *** ## SCA 28 1022.38 36.51 8.7599 6.656e-13 *** ## Reciprocals 28 191.12 6.83 1.6375 0.05369 . ## Residuals 63 262.60 # Make a factor 'comb' in which G1xG2 is the same cross as G2xG1 dat1 <- transform(dat1, comb = ifelse(as.character(male) < as.character(female), paste0(male,female), paste0(female,male))) # 'dr' is the direction of the cross, 0 for self dat1$dr <- 1 dat1 <- transform(dat1, dr = ifelse(as.character(male) < as.character(female), -1, dr)) dat1 <- transform(dat1, dr = ifelse(as.character(male) == as.character(female), 0, dr)) # asreml r version 3 & 4 code for Mixed Griffing. # Mohring Table 2, column 2 (after dividing by 10^2) gives variances: # GCA 12.77, SCA 11.09, RSCA .65, Error 4.23. # Mohring Supplement ASREML code part1 model is: # y ~ mu r !r mother and(father) combination combination.dr # Note that the levels of 'male' and 'female' are the same, so the # and(female) term tells asreml to use the same levels (or, equivalently, # fix the correlation of the male/female levels to be 1. # The block effect is minimial and therefore ignored. ## libs(asreml, lucid) ## m1 <- asreml(day~1, data=dat1, ## random = ~ male + and(female) + comb + comb:dr) ## vc(m1) ## effect component std.error z.ratio con ## male!male.var 12.77 7.502 1.7 Positive ## comb!comb.var 11.11 3.353 3.3 Positive ## comb:dr!comb.var 0.6603 0.4926 1.3 Positive ## R!variance 4.185 0.7449 5.6 Positive # ---------- # 1952 data. Reproduce table 3 and figure 2 of Hayman 1954b. dat2 <- subset(hayman.tobacco, year==1952) # Does flowering date follow a gamma distn? Maybe. libs(lattice) densityplot(~day, data=dat2, main=\"hayman.tobacco\", xlab=\"flowering date\") d1 <- subset(dat2, block=='B1') d2 <- subset(dat2, block=='B2') libs(reshape2) m1 <- acast(d1, male~female, value.var='day') m2 <- acast(d2, male~female, value.var='day') mn1 <- (m1+t(m1))/2 mn2 <- (m2+t(m2))/2 # Variance and covariance of 'rth' offspring vr1 <- apply(mn1, 1, var) vr2 <- apply(mn2, 1, var) wr1 <- apply(mn1, 1, cov, diag(mn1)) wr2 <- apply(mn2, 1, cov, diag(mn2)) # Remove row names to prevent a mild warning rownames(mn1) <- rownames(mn2) <- NULL summ <- data.frame(rbind(mn1,mn2)) summ$block <- rep(c('B1','B2'), each=8) summ$vr <- c(vr1,vr2) summ$wr <- c(wr1,wr2) summ$male <- rep(1:8,2) # Vr and Wr match Hayman table 3 with(summ, plot(wr~vr, type='n', main=\"hayman.tobacco\")) with(summ, text(vr, wr, male)) # Match Hayman figure 2 abline(0,1,col=\"gray\") # Hayman notes that 1 and 3 do not lie along the line, # so modifies them and re-analyzes. } # }"},{"path":"/reference/hazell.vegetables.html","id":null,"dir":"Reference","previous_headings":"","what":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"Gross profit 4 vegetable crops 6 years","code":""},{"path":"/reference/hazell.vegetables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"","code":"data(\"hazell.vegetables\")"},{"path":"/reference/hazell.vegetables.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"data frame 6 observations following 5 variables. year year factor, 6 levels carrot Carrot profit, dollars/acre celery Celery profit, dollars/acre cucumber Cucumber profit, dollars/acre pepper Pepper profit, dollars/acre","code":""},{"path":"/reference/hazell.vegetables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"values table gross profits (loss) dollars per acre. criteria example (1) total acres < 200, (2) total labor < 10000, (3) crop rotation. example shows use linear programming maximize expected profit.","code":""},{"path":"/reference/hazell.vegetables.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"P.B.R. Hazell, (1971). linear alternative quadratic semivariance programming farm planning uncertainty. . J. Agric. Econ., 53, 53-62. https://doi.org/10.2307/3180297","code":""},{"path":"/reference/hazell.vegetables.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"Carlos Romero, Tahir Rehman. (2003). Multiple Criteria Analysis Agricultural Decisions. Elsevier.","code":""},{"path":"/reference/hazell.vegetables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hazell.vegetables) dat <- hazell.vegetables libs(lattice) xyplot(carrot+celery+cucumber+pepper ~ year,dat, ylab=\"yearly profit by crop\", type='b', auto.key=list(columns=4), panel.hline=0) # optimal strategy for planting crops (calculated below) dat2 <- apply(dat[,-1], 1, function(x) x*c(0, 27.5, 100, 72.5))/1000 colnames(dat2) <- rownames(dat) barplot(dat2, legend.text=c(\" 0 carrot\", \"27.5 celery\", \" 100 cucumber\", \"72.5 pepper\"), xlim=c(0,7), ylim=c(-5,120), col=c('orange','green','forestgreen','red'), xlab=\"year\", ylab=\"Gross profit, $1000\", main=\"hazell.vegetables - retrospective profit from optimal strategy\", args.legend=list(title=\"acres, crop\")) libs(linprog) # colMeans(dat[ , -1]) # 252.8333 442.6667 283.8333 515.8333 # cvec = avg across-years profit per acre for each crop cvec <- c(253, 443, 284, 516) # Maximize c'x for Ax=b A <- rbind(c(1,1,1,1), c(25,36,27,87), c(-1,1,-1,1)) colnames(A) <- names(cvec) <- c(\"carrot\",\"celery\",\"cucumber\",\"pepper\") rownames(A) <- c('land','labor','rotation') # bvec criteria = (1) total acres < 200, (2) total labor < 10000, # (3) crop rotation. bvec <- c(200,10000,0) const.dir <- c(\"<=\",\"<=\",\"<=\") m1 <- solveLP(cvec, bvec, A, maximum=TRUE, const.dir=const.dir, lpSolve=TRUE) # m1$solution # optimal number of acres for each crop # carrot celery cucumber pepper # 0.00000 27.45098 100.00000 72.54902 # Average income for this plan ## sum(cvec * m1$solution) ## [1] 77996.08 # Year-to-year income for this plan ## as.matrix(dat[,-1]) ## [,1] ## [1,] 80492.16 ## [2,] 80431.37 ## [3,] 81884.31 ## [4,] 106868.63 ## [5,] 37558.82 ## [6,] 80513.73 # optimum allocation that minimizes year-to-year income variability. # brute-force search # For generality, assume we have unequal probabilities for each year. probs <- c(.15, .20, .20, .15, .15, .15) # Randomly allocate crops to 200 acres, 100,000 times #set.seed(1) mat <- matrix(runif(4*100000), ncol=4) mat <- 200*sweep(mat, 1, rowSums(mat), \"/\") # each row is one strategy, showing profit for each of the six years # profit <- mat profit <- tcrossprod(mat, as.matrix(dat[,-1])) # Each row is profit, columns are years # calculate weighted variance using year probabilities wtvar <- apply(profit, 1, function(x) cov.wt(as.data.frame(x), wt=probs)$cov) # five best planting allocations that minimizes the weighted variance ix <- order(wtvar)[1:5] mat[ix,] ## carrot celery cucumber pepper ## [,1] [,2] [,3] [,4] ## [1,] 71.26439 28.09259 85.04644 15.59657 ## [2,] 72.04428 27.53299 84.29760 16.12512 ## [3,] 72.16332 27.35147 84.16669 16.31853 ## [4,] 72.14622 29.24590 84.12452 14.48335 ## [5,] 68.95226 27.39246 88.61828 15.03700 } # }"},{"path":"/reference/heady.fertilizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"Yield corn, alfalfa, clover two fertilizers","code":""},{"path":"/reference/heady.fertilizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"","code":"data(\"heady.fertilizer\")"},{"path":"/reference/heady.fertilizer.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"data frame 81 observations following 3 variables. crop crop rep replicate (block) P phosphorous, pounds/acre K potassium, pounds/acre N nitrogen, pounds/acre yield yield","code":""},{"path":"/reference/heady.fertilizer.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"Heady et al. fit two-variable semi-polynomial response surfaces crop. Clover alfalfa yields tons/acre. clover alfalfa experiments grown 1952. Corn yields given bu/acre. corn experiments grown 1952 1953. test plots used 1953 1952, fertilizer applied 1953–response yield due residual fertilizer 1952. experiments used incomplete factorial design. treatment combinations present.","code":""},{"path":"/reference/heady.fertilizer.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"Earl O. Heady, John T. Pesek, William G. Brown. (1955). Crop Response Surfaces Economic Optima Fertilizer Use. Agricultural Experiment Station, Iowa State College. Research bulletin 424. Pages 330-332. https://lib.dr.iastate.edu/cgi/viewcontent.cgi?filename=12&article=1032&context=ag_researchbulletins&type=additional","code":""},{"path":"/reference/heady.fertilizer.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"Pesek, John Heady, Earl O. 1956. two nutrient-response function determination economic optima rate grade fertilizer alfalfa. Soil Science Society America Journal, 20, 240-246. https://doi.org/10.2136/sssaj1956.03615995002000020025x","code":""},{"path":"/reference/heady.fertilizer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(heady.fertilizer) dat <- heady.fertilizer libs(lattice) xyplot(yield ~ P|crop, data=dat, scales=list(relation=\"free\"), groups=factor(paste(dat$N,dat$K)), auto.key=list(columns=5), main=\"heady.fertilizer\", xlab=\"Phosphorous\") # Corn. Matches Heady, p. 292 d1 <- subset(dat, crop==\"corn\") m1 <- lm(yield ~ N + P + sqrt(N) + sqrt(P) + sqrt(N*P), data=d1) summary(m1) # Alfalfa. Matches Heady, p. 292. Also Pesek equation 3, p. 241 d2 <- subset(dat, crop==\"alfalfa\") m2 <- lm(yield ~ K + P + sqrt(K) + sqrt(P) + sqrt(K*P), data=d2) summary(m2) ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1.8735521 0.1222501 15.326 < 2e-16 *** ## K -0.0013943 0.0007371 -1.891 0.061237 . ## P -0.0050195 0.0007371 -6.810 5.74e-10 *** ## sqrt(K) 0.0617458 0.0160142 3.856 0.000196 *** ## sqrt(P) 0.1735383 0.0160142 10.837 < 2e-16 *** ## sqrt(K * P) -0.0014402 0.0007109 -2.026 0.045237 * # Clover. Matches Heady, p. 292. d3 <- subset(dat, crop==\"clover\") m3 <- lm(yield ~ P + sqrt(K) + sqrt(P) + sqrt(K*P), data=d3) summary(m3) # Corn with residual fertilizer. Matches Heady eq 56, p. 322. d4 <- subset(dat, crop==\"corn2\") m4 <- lm(yield ~ N + P + sqrt(N) + sqrt(P) + sqrt(N*P), data=d4) summary(m4) libs(rgl) with(d1, plot3d(N,P,yield)) with(d2, plot3d(K,P,yield)) with(d3, plot3d(K,P,yield)) with(d4, plot3d(N,P,yield)) # Mostly linear in both N and P close3d() } # }"},{"path":"/reference/heath.cabbage.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cabbage. — heath.cabbage.uniformity","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"Uniformity trial cabbage.","code":""},{"path":"/reference/heath.cabbage.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"","code":"data(\"heath.cabbage.uniformity\")"},{"path":"/reference/heath.cabbage.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"data frame 48 observations following 3 variables. yield pounds per plot col column row row","code":""},{"path":"/reference/heath.cabbage.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"Heath says plot .011 acres. acre 43560 sq ft, plot 479.16 sq feet, rounds 480 sq feet. Heath Figure 3-1 correctly shaped, plot approximately 12 feet x 40 feet = 480 sq ft. plot \"350\" plants. Harvested 1958.","code":""},{"path":"/reference/heath.cabbage.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"O.V.S. Heath (1970). Investigation Experiment. Fig. 3-1, p. 50. https://archive.org/details/investigationbye0000heat","code":""},{"path":"/reference/heath.cabbage.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"None.","code":""},{"path":"/reference/heath.cabbage.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(heath.cabbage.uniformity) dat <- heath.cabbage.uniformity # Heath Fig 3-1, p. 50 libs(desplot) desplot(dat, yield ~ col*row, aspect=(8*12)/(6*40), main=\"heath.cabbage.uniformity\") } # }"},{"path":"/reference/heath.raddish.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of radish — heath.radish.uniformity","title":"Uniformity trial of radish — heath.radish.uniformity","text":"Uniformity trial radish four containers.","code":""},{"path":"/reference/heath.raddish.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of radish — heath.radish.uniformity","text":"","code":"data(\"heath.radish.uniformity\")"},{"path":"/reference/heath.raddish.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of radish — heath.radish.uniformity","text":"data frame 400 observations following 4 variables. row row col column block block yield weight per plant, grams","code":""},{"path":"/reference/heath.raddish.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of radish — heath.radish.uniformity","text":"Weight 399 radish plants grown 1 inch x 1 inch spacing four plastic basins. Seed wetted 1968-02-15, planted 1968-02-17, harvested 1968-03-26. Heath said, large plants round edges...one important source variation might competition light.","code":""},{"path":"/reference/heath.raddish.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of radish — heath.radish.uniformity","text":"O.V.S. Heath (1970). Investigation Experiment. Table 1, p 24-25. https://archive.org/details/investigationbye0000heat","code":""},{"path":"/reference/heath.raddish.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of radish — heath.radish.uniformity","text":"None","code":""},{"path":"/reference/heath.raddish.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of radish — heath.radish.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ require(agridat) data(heath.radish.uniformity) dat <- heath.radish.uniformity libs(desplot, dplyr) desplot(dat, yield ~ col*row|block, aspect=1, main=\"heath.radish.uniformity\") # Indicator for border/interior plants dat <- mutate(dat, inner = row > 1 & row < 10 & col > 1 & col < 10) # Heath has 5.80 and 9.63 (we assume this is a typo of 9.36) dat <- group_by(dat, inner) summarize(dat, mean=mean(yield, na.rm=TRUE)) # Interior plots are significantly lower yielding anova(aov(yield ~ block + inner, dat)) # lattice::bwplot(yield ~ inner, dat, horiz=0) # similar to Heath fig 2-2 # lattice::histogram( ~ yield|inner, dat, layout=c(1,2), n=20) } # }"},{"path":"/reference/henderson.milkfat.html","id":null,"dir":"Reference","previous_headings":"","what":"Milk fat yields for a single cow — henderson.milkfat","title":"Milk fat yields for a single cow — henderson.milkfat","text":"Average daily fat yields (kg/day) milk single cow 35 weeks.","code":""},{"path":"/reference/henderson.milkfat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Milk fat yields for a single cow — henderson.milkfat","text":"data frame 35 observations following 2 variables. week week, numeric yield yield, kg/day","code":""},{"path":"/reference/henderson.milkfat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Milk fat yields for a single cow — henderson.milkfat","text":"Charles McCulloch. Workshop Generalized Linear Mixed Models. Used permission Charles McCulloch Harold Henderson.","code":""},{"path":"/reference/henderson.milkfat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Milk fat yields for a single cow — henderson.milkfat","text":"None.","code":""},{"path":"/reference/henderson.milkfat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Milk fat yields for a single cow — henderson.milkfat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(henderson.milkfat) dat <- henderson.milkfat plot(yield~week, data=dat, cex = 0.8, ylim=c(0,.9), main=\"henderson.milkfat\", xlab = \"Week\", ylab = \"Fat yield (kg/day)\") # Yield ~ a * t^b * exp(g*t) # where t is time m1 <- nls(yield ~ alpha * week^beta * exp(gamma * week), data=dat, start=list(alpha=.1, beta=.1, gamma=.1)) # Or, take logs and fit a linear model # log(yield) ~ log(alpha) + beta*log(t) + gamma*t m2 <- lm(log(yield) ~ 1 + log(week) + week, dat) # Or, use glm and a link to do the transform m3 <- glm(yield ~ 1 + log(week) + week, quasi(link = \"log\"), dat) # Note: m2 has E[log(y)] = log(alpha) + beta*log(t) + gamma*t # and m3 has log(E[y]) = log(alpha) + beta*log(t) + gamma*t # Generalized additive models libs(\"mgcv\") m4 <- gam(log(yield) ~ s(week), gaussian, dat) m5 <- gam(yield ~ s(week), quasi(link = \"log\"), dat) # Model predictions pdat <- data.frame(week = seq(1, 35, by = 0.1)) pdat <- transform(pdat, p1 = predict(m1, pdat), p2 = exp(predict(m2, pdat)), # back transform p3 = predict(m3, pdat, type=\"resp\"), # response scale p4 = exp(predict(m4, pdat)), p5 = predict(m5, pdat, type=\"response\")) # Compare fits with(pdat, { lines(week, p1) lines(week, p2, col = \"red\", lty=\"dotted\") lines(week, p3, col = \"red\", lty=\"dashed\") lines(week, p4, col = \"blue\", lty = \"dashed\") lines(week, p5, col = \"blue\") }) legend(\"topright\", c(\"obs\", \"lm, log-transformed\", \"glm, log-link\", \"gam, log-transformed\", \"gam, log-link\"), lty = c(\"solid\", \"dotted\", \"dashed\", \"dashed\", \"solid\"), col = c(\"black\", \"red\", \"red\", \"blue\", \"blue\"), cex = 0.8, bty = \"n\") } # }"},{"path":"/reference/hernandez.nitrogen.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","title":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","text":"Corn response nitrogen fertilizer 5 sites.","code":""},{"path":"/reference/hernandez.nitrogen.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","text":"data frame 136 observations following 5 variables. site site factor, 5 levels loc location name rep rep, 4 levels nitro nitrogen, kg/ha yield yield, Mg/ha","code":""},{"path":"/reference/hernandez.nitrogen.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","text":"Experiment conducted 2006 5 sites Minnesota.","code":""},{"path":"/reference/hernandez.nitrogen.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","text":"Hernandez, J.. Mulla, D.J. 2008. Estimating uncertainty economically optimum fertilizer rates, Agronomy Journal, 100, 1221-1229. https://doi.org/10.2134/agronj2007.0273 Electronic data kindly supplied Jose Hernandez.","code":""},{"path":"/reference/hernandez.nitrogen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hernandez.nitrogen) dat <- hernandez.nitrogen cprice <- 118.1 # $118.1/Mg or $3/bu nprice <- 0.6615 # $0.66/kg N or $0.30/lb N # Hernandez optimized yield with a constraint on the ratio of the prices. # Simpler to just calculate the income and optimize that. dat <- transform(dat, inc = yield * cprice - nitro * nprice) libs(lattice) xyplot(inc ~ nitro|site, dat, groups=rep, auto.key=list(columns=4), xlab=\"nitrogen\", ylab=\"income\", main=\"hernandez.nitrogen\") # Site 5 only dat1 <- subset(dat, site=='S5') # When we optimize on income, a simple quadratic model works just fine, # and matches the results of the nls model below. # Note, 'poly(nitro)' gives weird coefs lm1 <- lm(inc ~ 1 + nitro + I(nitro^2), data=dat1) c1 <- coef(lm1) -c1[2] / (2*c1[3]) ## nitro ## 191.7198 # Optimum nitrogen is 192 for site 5 # Use the delta method to get a conf int libs(\"car\") del1 <- deltaMethod(lm1, \"-b1/(2*b2)\", parameterNames= paste(\"b\", 0:2, sep=\"\")) # Simple Wald-type conf int for optimum del1$Est + c(-1,1) * del1$SE * qt(1-.1/2, nrow(dat1)-length(coef(lm1))) ## 118.9329 264.5067 # Nonlinear regression # Reparameterize b0 + b1*x + b2*x^2 using th2 = -b1/2b2 so that th2 is optimum nls1 <- nls(inc ~ th11- (2*th2*th12)*nitro + th12*nitro^2, data = dat1, start = list(th11 = 5, th2 = 150, th12 =-0.1),) summary(nls1) # Wald conf int wald <- function(object, alpha=0.1){ nobs <- length(resid(object)) npar <- length(coef(object)) est <- coef(object) stderr <- summary(object)$parameters[,2] tval <- qt(1-alpha/2, nobs-npar) ci <- cbind(est - tval * stderr, est + tval * stderr) colnames(ci) <- paste(round(100*c(alpha/2, 1-alpha/2), 1), \"pct\", sep= \"\") return(ci) } round(wald(nls1),2) ## 5 ## th11 936.44 1081.93 ## th2 118.93 264.51 # th2 is the optimum ## th12 -0.03 -0.01 # Likelihood conf int libs(MASS) round(confint(nls1, \"th2\", level = 0.9),2) ## 5 ## 147.96 401.65 # Bootstrap conf int libs(boot) dat1$fit <- fitted(nls1) bootfun <- function(rs, i) { # bootstrap the residuals dat1$y <- dat1$fit + rs[i] coef(nls(y ~ th11- (2*th2*th12)*nitro + th12*nitro^2, dat1, start = coef(nls1) )) } res1 <- scale(resid(nls1), scale = FALSE) # remove the mean. Why? It is close to 0. set.seed(5) # Sometime the bootstrap fails, but this seed works boot1 <- boot(res1, bootfun, R = 500) boot.ci(boot1, index = 2, type = c(\"perc\"), conf = 0.9) ## Level Percentile ## 90 } # }"},{"path":"/reference/hessling.argentina.html","id":null,"dir":"Reference","previous_headings":"","what":"Relation between wheat yield and weather in Argentina — hessling.argentina","title":"Relation between wheat yield and weather in Argentina — hessling.argentina","text":"Relation wheat yield weather Argentina","code":""},{"path":"/reference/hessling.argentina.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Relation between wheat yield and weather in Argentina — hessling.argentina","text":"data frame 30 observations following 15 variables. yield average yield, kg/ha year year p05 precipitation (mm) May p06 precip June p07 precip July p08 precip August p09 precip Septempber p10 precip October p11 precip November p12 precip December t06 june temperature deviation normal, deg Celsius t07 july temp deviation t08 august temp deviation t09 september temp deviation t10 october temp deviation t11 november temp deviation","code":""},{"path":"/reference/hessling.argentina.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Relation between wheat yield and weather in Argentina — hessling.argentina","text":"Argentina wheat typically sown May August. Harvest begins November December.","code":""},{"path":"/reference/hessling.argentina.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Relation between wheat yield and weather in Argentina — hessling.argentina","text":"N. . Hessling, 1922. Relations weather yield wheat Argentine republic, Monthly Weather Review, 50, 302-308. https://doi.org/10.1175/1520-0493(1922)50<302:RBTWAT>2.0.CO;2","code":""},{"path":"/reference/hessling.argentina.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Relation between wheat yield and weather in Argentina — hessling.argentina","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hessling.argentina) dat <- hessling.argentina # Fig 1 of Hessling. Use avg Aug-Nov temp to predict yield dat <- transform(dat, avetmp=(t08+t09+t10+t11)/4) # Avg temp m0 <- lm(yield ~ avetmp, dat) plot(yield~year, dat, ylim=c(100,1500), type='l', main=\"hessling.argentina: observed (black) and predicted yield (blue)\") lines(fitted(m0)~year, dat, col=\"blue\") # A modern, PLS approach libs(pls) yld <- dat[,\"yield\",drop=FALSE] yld <- as.matrix(sweep(yld, 2, colMeans(yld))) cov <- dat[,c(\"p06\",\"p07\",\"p08\",\"p09\",\"p10\",\"p11\", \"t08\",\"t09\",\"t10\",\"t11\")] cov <- as.matrix(scale(cov)) m2 <- plsr(yld~cov) # biplot(m2, which=\"x\", var.axes=TRUE, main=\"hessling.argentina\") libs(corrgram) corrgram(dat, main=\"hessling.argentina - correlations of yield and covariates\") } # }"},{"path":"/reference/hildebrand.systems.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"Maize yields four cropping systems 14 -farm trials.","code":""},{"path":"/reference/hildebrand.systems.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"data frame 56 observations following 4 variables. village village, 2 levels farm farm, 14 levels system cropping system yield yield, t/ha","code":""},{"path":"/reference/hildebrand.systems.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"Yields 14 -farm trials Phalombe Project region south-eastern Malawi. farms located near two different villages. farm, four different cropping systems tested. systems : LM = Local Maize, LMF = Local Maize Fertilizer, CCA = Improved Composite, CCAF = Improved Composite Fertilizer.","code":""},{"path":"/reference/hildebrand.systems.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"P. E. Hildebrand, 1984. Modified Stability Analysis Farmer Managed, -Farm Trials. Agronomy Journal, 76, 271–274. https://doi.org/10.2134/agronj1984.00021962007600020023x","code":""},{"path":"/reference/hildebrand.systems.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"H. P. Piepho, 1998. Methods Comparing Yield Stability Cropping Systems. Journal Agronomy Crop Science, 180, 193–213. https://doi.org/10.1111/j.1439-037X.1998.tb00526.x","code":""},{"path":"/reference/hildebrand.systems.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hildebrand.systems) dat <- hildebrand.systems # Piepho 1998 Fig 1 libs(lattice) dotplot(yield ~ system, dat, groups=village, auto.key=TRUE, main=\"hildebrand.systems\", xlab=\"cropping system by village\") # Plot of risk of 'failure' of System 2 vs System 1 s11 = .30; s22 <- .92; s12 = .34 mu1 = 1.35; mu2 = 2.70 lambda <- seq(from=0, to=5, length=20) system1 <- pnorm((lambda-mu1)/sqrt(s11)) system2 <- pnorm((lambda-mu2)/sqrt(s22)) # A simpler view plot(lambda, system1, type=\"l\", xlim=c(0,5), ylim=c(0,1), xlab=\"Yield level\", ylab=\"Prob(yield < level)\", main=\"hildebrand.systems - risk of failure for each system\") lines(lambda, system2, col=\"red\") # Prob of system 1 outperforming system 2. Table 8 pnorm((mu1-mu2)/sqrt(s11+s22-2*s12)) # .0331 # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Environmental variance model, unstructured correlations dat <- dat[order(dat$system, dat$farm),] m1 <- asreml(yield ~ system, data=dat, resid = ~us(system):farm) # Means, table 5 ## predict(m1, data=dat, classify=\"system\")$pvals ## system pred.value std.error est.stat ## CCA 1.164 0.2816 Estimable ## CCAF 2.657 0.3747 Estimable ## LM 1.35 0.1463 Estimable ## LMF 2.7 0.2561 Estimable # Variances, table 5 # lucid::vc(m1)[c(2,4,7,11),] ## effect component std.error z.ratio constr ## R!system.CCA:CCA 1.11 0.4354 2.5 pos ## R!system.CCAF:CCAF 1.966 0.771 2.5 pos ## R!system.LM:LM 0.2996 0.1175 2.5 pos ## R!system.LMF:LMF 0.9185 0.3603 2.5 pos # Stability variance model m2 <- asreml(yield ~ system, data=dat, random = ~ farm, resid = ~ dsum( ~ units|system)) m2 <- update(m2) # predict(m2, data=dat, classify=\"system\")$pvals ## # Variances, table 6 # lucid::vc(m2) ## effect component std.error z.ratio bound ## farm 0.2998 0.1187 2.5 P 0 ## system_CCA!R 0.4133 0.1699 2.4 P 0 ## system_CCAF!R 1.265 0.5152 2.5 P 0 ## system_LM!R 0.0003805 0.05538 0.0069 P 1.5 ## system_LMF!R 0.5294 0.2295 2.3 P 0 } } # }"},{"path":"/reference/holland.arthropods.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"Counts arthropods grid-sampled wheat field","code":""},{"path":"/reference/holland.arthropods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"","code":"data(\"holland.arthropods\")"},{"path":"/reference/holland.arthropods.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"data frame 63 observations following 8 variables. row row col column n.brevicollis species counts linyphiidae species counts collembola species counts carabidae species counts lycosidae species counts weedcover percent weed cover","code":""},{"path":"/reference/holland.arthropods.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"Arthropods sampled 30m x 30m grid wheat field near Wimborne, Dorest, UK 6 dates Jun/Jul 1996. Arthropod counts aggregated across 6 dates. Holland et al. used SADIE (Spatial Analysis Distance Indices) look spatial patterns. Significant patterns found N. brevicollis, Carabidae, Lycosidae. Lycosidae counts also significantly associated weed cover. Used permission John Holland.","code":""},{"path":"/reference/holland.arthropods.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"Holland J. M., Perry J. N., Winder, L. (1999). within-field spatial temporal distribution arthropods within winter wheat. Bulletin Entomological Research, 89: 499-513. Figure 3 (large grid 1996). https://doi.org/10.1017/S0007485399000656","code":""},{"path":"/reference/holland.arthropods.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(holland.arthropods) dat <- holland.arthropods # use log count to make it possible to have same scale for insects libs(reshape2, lattice) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) dat2 <- melt(dat, id.var=c('row','col')) contourplot(log(value) ~ col*row|variable, dat2, col.regions=grays(7), region=TRUE, main=\"holland.arthropods - log counts in winter wheat\") if(0){ # individual species libs(lattice) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) contourplot(linyphiidae ~ col*row, dat, at=c(0,40,80,120,160,200), region=TRUE, col.regions=grays(5), main=\"holland.arthropods - linyphiidae counts in winter wheat\") contourplot(n.brevicollis ~ col*row, dat, region=TRUE) contourplot(linyphiidae~ col*row, dat, region=TRUE) contourplot(collembola ~ col*row, dat, region=TRUE) contourplot(carabidae ~ col*row, dat, region=TRUE) contourplot(lycosidae ~ col*row, dat, region=TRUE) contourplot(weedcover ~ col*row, dat, region=TRUE) } } # }"},{"path":"/reference/holshouser.splitstrip.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-strip-plot of soybeans — holshouser.splitstrip","title":"Split-strip-plot of soybeans — holshouser.splitstrip","text":"Split-strip-plot soybeans","code":""},{"path":"/reference/holshouser.splitstrip.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-strip-plot of soybeans — holshouser.splitstrip","text":"data frame 160 observations following 8 variables. block block factor, 4 levels plot plot number cultivar cultivar factor, 4 levels spacing row spacing pop population (thousand per acre) yield yield row row col column","code":""},{"path":"/reference/holshouser.splitstrip.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-strip-plot of soybeans — holshouser.splitstrip","text":"Within block, cultivars whole plots. Withing whole plots, spacing applied strips vertically, population applied strips horizontally. Used permission David Holshouser Virginia Polytechnic.","code":""},{"path":"/reference/holshouser.splitstrip.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-strip-plot of soybeans — holshouser.splitstrip","text":"Schabenberger, Oliver Francis J. Pierce. 2002. Contemporary Statistical Models Plant Soil Sciences CRC Press, Boca Raton, FL. Page 493.","code":""},{"path":"/reference/holshouser.splitstrip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-strip-plot of soybeans — holshouser.splitstrip","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(holshouser.splitstrip) dat <- holshouser.splitstrip dat$spacing <- factor(dat$spacing) dat$pop <- factor(dat$pop) # Experiment layout and field trends libs(desplot) desplot(dat, yield ~ col*row, out1=block, # unknown aspect main=\"holshouser.splitstrip\") desplot(dat, spacing ~ col*row, out1=block, out2=cultivar, # unknown aspect col=cultivar, text=pop, cex=.8, shorten='none', col.regions=c('wheat','white'), main=\"holshouser.splitstrip experiment design\") # Overall main effects and interactions libs(HH) interaction2wt(yield~cultivar*spacing*pop, dat, x.between=0, y.between=0, main=\"holshouser.splitstrip\") ## Schabenberger's SAS model, page 497 ## proc mixed data=splitstripplot; ## class block cultivar pop spacing; ## model yield = cultivar spacing spacing*cultivar pop pop*cultivar ## spacing*pop spacing*pop*cultivar / ddfm=satterth; ## random block block*cultivar block*cultivar*spacing block*cultivar*pop; ## run; ## Now lme4. This design has five error terms--four are explicitly given. libs(lme4) libs(lucid) m1 <- lmer(yield ~ cultivar * spacing * pop + (1|block) + (1|block:cultivar) + (1|block:cultivar:spacing) + (1|block:cultivar:pop), data=dat) vc(m1) ## Variances match Schabenberger, page 498. ## grp var1 var2 vcov sdcor ## block:cultivar:pop (Intercept) 2.421 1.556 ## block:cultivar:spacing (Intercept) 1.244 1.116 ## block:cultivar (Intercept) 0.4523 0.6725 ## block (Intercept) 3.037 1.743 ## Residual 3.928 1.982 } # }"},{"path":"/reference/holtsmark.timothy.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of timothy — holtsmark.timothy.uniformity","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"Uniformity trial timothy hay circa 1905","code":""},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"","code":"data(\"holtsmark.timothy.uniformity\")"},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"data frame 240 observations following 3 variables. row row col column yield yield per plot, kg","code":""},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"Field width: 40 plots * 5 m = 200 m. Field length: 6 plots * 5 m = 30 m Holtsmark & Larsen used trial compare standard deviations different sized plots (combined smaller plots).","code":""},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"Holtsmark, G Larsen, BR (1905). Om Muligheder indskraenke de Fejl, som ved Markforsog betinges af Jordens Uensartethed. Tidsskrift Landbrugets Planteavl. 12, 330-351. (Danish) Data page 347. https://books.google.com/books?id=MdM0AQAAMAAJ&pg=PA330 https://dca.au.dk/publikationer/historiske/planteavl/ Uber die Fehler, welche bei Feldversuchen, durch die Ungleichartigkeit des Bodens bedingt werden. Die Landwirtschaftlichen Versuchs-Stationen, 65, 1–22. (German) https://books.google.com/books?id=eXA2AQAAMAAJ&pg=PA1","code":""},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"Theodor Roemer (1920). Der Feldversuch. Page 67, table 11.","code":""},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(holtsmark.timothy.uniformity) dat <- holtsmark.timothy.uniformity # Define diagonal 'check' plots like Holtsmark does dat <- transform(dat, check = ifelse(floor((row+col)/3)==(row+col)/3, \"C\", \"\")) libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, text=check, show.key=FALSE, aspect=30/200, # true aspect main=\"holtsmark.timothy.uniformity\") # sd(dat$yield) # 2.92 matches Holtsmark p. 348 } # }"},{"path":"/reference/huehn.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"Multi-environment trial illustrate stability statistics","code":""},{"path":"/reference/huehn.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"","code":"data(\"huehn.wheat\")"},{"path":"/reference/huehn.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"data frame 200 observations following 3 variables. gen genotype env environment yield yield dt/ha","code":""},{"path":"/reference/huehn.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"Yields winter-wheat trial 20 genotypes 10 environments. Note: Huehn 1979 use genotype-centered data calculating stability statistics.","code":""},{"path":"/reference/huehn.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"Manfred Huehn (1979). Beitrage zur Erfassung der phanotypischen Stabilitat . Vorschlag einiger auf Ranginformationen beruhenden Stabilitatsparameter. EDV Medizin und Biologie, 10 (4), 112-117. Table 1. https://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-145979","code":""},{"path":"/reference/huehn.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"Nassar, R Huehn, M. (1987). Studies estimation phenotypic stability: Tests significance nonparametric measures phenotypic stability. Biometrics, 43, 45-53.","code":""},{"path":"/reference/huehn.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(huehn.wheat) dat <- huehn.wheat # Nassar & Huehn, p. 51 \"there is no evidence for differences in stability # among the 20 varieties\". libs(gge) m1 <- gge(dat, yield ~ gen*env) biplot(m1, main=\"huehn.wheat\") libs(reshape2) datm <- acast(dat, gen~env, value.var='yield') apply(datm,1,mean) # Gen means match Huehn 1979 table 1 apply(datm,2,mean) # Env means apply(datm, 2, rank) # Ranks match Huehn table 1 # Huehn 1979 did not use genotype-centered data, and his definition # of S2 is different from later papers. # I'm not sure where 'huehn' function is found # apply(huehn(datm, corrected=FALSE), 2, round,2) # S1 matches Huehn ## MeanRank S1 ## Jubilar 6.70 3.62 ## Diplomat 8.35 5.61 ## Caribo 11.20 6.07 ## Cbc710 13.65 6.70 # Very close match to Nassar & Huehn 1987 table 4. # apply(huehn(datm, corrected=TRUE), 2, round,2) ## MeanRank S1 Z1 S2 Z2 ## Jubilar 10.2 4.00 5.51 11.29 4.29 ## Diplomat 11.0 6.31 0.09 27.78 0.27 ## Caribo 10.6 6.98 0.08 34.49 0.01 ## Cbc710 10.9 8.16 1.78 47.21 1.73 } # }"},{"path":"/reference/hughes.grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of grape, disease incidence — hughes.grapes","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"Disease incidence grape leaves RCB experiment 6 different treatments.","code":""},{"path":"/reference/hughes.grapes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"data frame 270 observations following 6 variables. block Block factor, 1-3 trt Treatment factor, 1-6 vine Vine factor, 1-3 shoot Shoot factor, 1-5 diseased Number diseased leaves per shoot total Number total leaves per shoot","code":""},{"path":"/reference/hughes.grapes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"data come study downy mildew grapes. experiment conducted Wooster, Ohio, experimental farm Ohio Agricultural Research Development Center, Ohio State University. 3 blocks 6 treatments. Treatment 1 unsprayed control. 30 Sep 1990, disease incidence measured. plot, 5 randomly chosen shoots 3 vines observed. canopy closed shoots intertwined. shoot, total number leaves number infected leaves recorded. Used permission Larry Madden.","code":""},{"path":"/reference/hughes.grapes.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"Hughes, G. Madden, LV. 1995. methods allowing aggregated patterns disease incidence analysis data designed experiments. Plant Pathology, 44, 927–943. https://doi.org/10.1111/j.1365-3059.1995.tb02651.x","code":""},{"path":"/reference/hughes.grapes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"Hans-Pieter Piepho. 1999. Analysing disease incidence data designed experiments generalized linear mixed models. Plant Pathology, 48, 668–684. https://doi.org/10.1046/j.1365-3059.1999.00383.x","code":""},{"path":"/reference/hughes.grapes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hughes.grapes) dat <- hughes.grapes dat <- transform(dat, rate = diseased/total, plot=trt:block) # Trt 1 has higher rate, more variable, Trt 3 lower rate, less variable libs(lattice) foo <- bwplot(rate ~ vine|block*trt, dat, main=\"hughes.grapes\", xlab=\"vine\") libs(latticeExtra) useOuterStrips(foo) # Table 1 of Piepho 1999 tapply(dat$rate, dat$trt, mean) # trt 1 does not match Piepho tapply(dat$rate, dat$trt, max) # Piepho model 3. Binomial data. May not be exactly the same model # Use the binomial count data with lme4 libs(lme4) m1 <- glmer(cbind(diseased, total-diseased) ~ trt + block + (1|plot/vine), data=dat, family=binomial) m1 # Switch from binomial counts to bernoulli data libs(aod) bdat <- splitbin(cbind(diseased, total-diseased) ~ block+trt+plot+vine+shoot, data=dat)$tab names(bdat)[2] <- 'y' # Using lme4 m2 <- glmer(y ~ trt + block + (1|plot/vine), data=bdat, family=binomial) m2 # Now using MASS:::glmmPQL libs(MASS) m3 <- glmmPQL(y ~ trt + block, data=bdat, random=~1|plot/vine, family=binomial) m3 } # }"},{"path":"/reference/hunter.corn.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"Corn yield response nitrogen","code":""},{"path":"/reference/hunter.corn.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"data frame 54 observations following 4 variables. nitro nitrogen fertilizer, pound/acre year year loc location yield yield, bu/ac","code":""},{"path":"/reference/hunter.corn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"Experiments conducted eastern Oregon years 1950-1952. Planting rates varied 15,000 21,000 planter per acre.","code":""},{"path":"/reference/hunter.corn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"Albert S. Hunter, John . Yungen (1955). Influence Variations Fertility Levels Upon Yield Protein Content Field Corn Eastern Oregon. Soil Science Society America Journal, 19, 214-218. https://doi.org/10.2136/sssaj1955.03615995001900020027x","code":""},{"path":"/reference/hunter.corn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"James Leo Paschal, Burton Leroy French (1956). method economic analysis applied nitrogen fertilizer rate experiments irrigated corn. Tech Bull 1141. United States Dept Agriculture. books.google.com/books?id=gAdZtsEziCcC&pg=PP1","code":""},{"path":"/reference/hunter.corn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hunter.corn) dat <- hunter.corn dat <- transform(dat, env=factor(paste(loc,year))) libs(lattice) xyplot(yield~nitro|env, dat, type='b', main=\"hunter.corn - nitrogen response curves\") } # }"},{"path":"/reference/hutchinson.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton — hutchinson.cotton.uniformity","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"Uniformity trial cotton harvested 1941","code":""},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"","code":"data(\"hutchinson.cotton.uniformity\")"},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"data frame 2000 observations following 3 variables. row row ordinate col column ordinate yield yield per plant, grams","code":""},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"data lint yield single plants cotton uniformity trial St. Vincent 1940-41. experiment planted 50 rows 40 plants row. spacing 1.5 feet within rows 4 feet rows. Field length: 40 plants * 1.5 feet = 60 feet Field width: 50 columns * 4 feet = 200 feet data made available special help staff Rothamsted Research Library. Rothamsted library scanned paper documents pdf. K.Wright used pdf manually type values Excel file immediately checked hand-typed values. Plants marked \"Dead\" PDF left blank. 6 numbers illegible PDF. also left blank.","code":""},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 2.","code":""},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":". C. Brewer R. Mead (1986). Continuous Second Order Models Spatial Variation Application Efficiency Field Crop Experiments. Journal Royal Statistical Society. Series (General), 149(4), 314–348. See page 325. http://doi.org/10.2307/2981720","code":""},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hutchinson.cotton.uniformity) dat <- hutchinson.cotton.uniformity require(desplot) desplot(dat, yield ~ col*row, tick=TRUE, flip=TRUE, aspect=(40*1.5)/(50*4), # true aspect main=\"hutchinson.cotton.uniformity\") } # }"},{"path":"/reference/igue.sugarcane.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"Uniformity trial sugarcane Brazil, 1982.","code":""},{"path":"/reference/igue.sugarcane.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"","code":"data(\"igue.sugarcane.uniformity\")"},{"path":"/reference/igue.sugarcane.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"data frame 1512 observations following 3 variables. row row col column yield yield, kg/plot","code":""},{"path":"/reference/igue.sugarcane.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"uniformity trial sugarcane state Sao Paulo, Brazil, 1982. field 40 rows, 90 m long, 1.5 m rows. Field width: 36 plots * 1.5 m = 54 m Field length: 42 plots * 2 m = 84 m","code":""},{"path":"/reference/igue.sugarcane.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"Toshio Igue, Ademar Espironelo, Heitor Cantarella, Erseni Joao Nelli. (1991). Tamanho e forma de parcela experimental para cana-de-acucar (Plot size shape sugar cane experiments). Bragantia, 50, 163-180. Appendix, page 169-170. https://dx.doi.org/10.1590/S0006-87051991000100016","code":""},{"path":"/reference/igue.sugarcane.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"None","code":""},{"path":"/reference/igue.sugarcane.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(igue.sugarcane.uniformity) dat <- igue.sugarcane.uniformity # match Igue CV top row of page 171 sd(dat$yield)/mean(dat$yield) # 16.4 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, tick=TRUE, aspect=(42*2)/(36*1.5), main=\"igue.sugarcane.uniformity\") } # }"},{"path":"/reference/ilri.sheep.html","id":null,"dir":"Reference","previous_headings":"","what":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"Birth weight weaning weight 882 lambs partial diallel cross Dorper Red Maasi breeds.","code":""},{"path":"/reference/ilri.sheep.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"data frame 882 observations following 12 variables. year year lamb birth, 1991-1996 lamb lamb id sex sex lamb, M=Male/F=Female gen genotype, DD, DR, RD, RR birthwt weight lamb birth, kg weanwt weight lamb weaning, kg weanage age lamb weaning, days ewe ewe id ewegen ewe genotype: D, R damage ewe (dam) age years ram ram id ramgen ram genotype: D, R","code":""},{"path":"/reference/ilri.sheep.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"Red Maasai sheep East Africa perceived resistant certain parasites. ILRI decided 1990 investigate degree resistance exhibited Red Maasai breed initiated study Kenya. susceptible breed, Dorper, chosen provide direct comparison Red Maasai. Dorper well-adapted area also larger Red Maasai, makes sheep attractive farmers. Throughout six years 1991 1996 Dorper (D), Red Maasai (R) Red Maasai x Dorper crossed ewes mated Red Maasai Dorper rams produce number different lamb genotypes. purposes example, following four offspring genotypes considered (Sire x Dam): D x D, D x R, R x D R x R. Records missing 182 lambs, mostly earlier death.","code":""},{"path":"/reference/ilri.sheep.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"Mixed model analysis estimation components genetic variation lamb weaning weight. International Livestock Research Institute. Permanent link: https://hdl.handle.net/10568/10364 https://biometrics.ilri.org/CS/case Retrieved Dec 2011. Used via license: Creative Commons -NC-SA 3.0.","code":""},{"path":"/reference/ilri.sheep.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"Baker, RL Nagda, S. Rodriguez-Zas, SL Southey, BR Audho, JO Aduda, EO Thorpe, W. (2003). Resistance resilience gastro-intestinal nematode parasites relationships productivity Red Maasai, Dorper Red Maasai x Dorper crossbred lambs sub-humid tropics. Animal Science, 76, 119-136. https://doi.org/10.1017/S1357729800053388 Gota Morota, Hao Cheng, Dianne Cook, Emi Tanaka (2021). ASAS-NANP SYMPOSIUM: prospects interactive dynamic graphics era data-rich animal science. Journal Animal Science, Volume 99, Issue 2, February 2021, skaa402. https://doi.org/10.1093/jas/skaa402","code":""},{"path":"/reference/ilri.sheep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ilri.sheep) dat <- ilri.sheep dat <- transform(dat, lamb=factor(lamb), ewe=factor(ewe), ram=factor(ram), year=factor(year)) # dl is linear covariate, same as damage, but truncated to [2,8] dat <- within(dat, { dl <- damage dl <- ifelse(dl < 3, 2, dl) dl <- ifelse(dl > 7, 8, dl) dq <- dl^2 }) dat <- subset(dat, !is.na(weanage)) # EDA libs(lattice) ## bwplot(weanwt ~ year, dat, main=\"ilri.sheep\", xlab=\"year\", ylab=\"Wean weight\", ## panel=panel.violin) # Year effect bwplot(weanwt ~ factor(dl), dat, main=\"ilri.sheep\", xlab=\"Dam age\", ylab=\"Wean weight\") # Dam age effect # bwplot(weanwt ~ gen, dat, # main=\"ilri.sheep\", xlab=\"Genotype\", ylab=\"Wean weight\") # Genotype differences xyplot(weanwt ~ weanage, dat, type=c('p','smooth'), main=\"ilri.sheep\", xlab=\"Wean age\", ylab=\"Wean weight\") # Age covariate # case study page 4.18 lm1 <- lm(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen, data=dat) summary(lm1) anova(lm1) # ---------- libs(lme4) lme1 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen + (1|ewe) + (1|ram), data=dat) print(lme1, corr=FALSE) lme2 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen + (1|ewe), data=dat) lme3 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen + (1|ram), data=dat) anova(lme1, lme2, lme3) # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # case study page 4.20 m1 <- asreml(weanwt ~ year + sex + weanage + dl + dq + ramgen + ewegen, data=dat) # wald(m1) # case study page 4.26 m2 <- asreml(weanwt ~ year + sex + weanage + dl + dq + ramgen + ewegen, random = ~ ram + ewe, data=dat) # wald(m2) # case study page 4.37, year means # predict(m2, data=dat, classify=\"year\") ## year predicted.value standard.error est.status ## 1 91 12.638564 0.2363652 Estimable ## 2 92 11.067659 0.2285252 Estimable ## 3 93 11.561932 0.1809891 Estimable ## 4 94 9.636058 0.2505478 Estimable ## 5 95 9.350247 0.2346849 Estimable ## 6 96 10.188482 0.2755387 Estimable } } # }"},{"path":"/reference/immer.sugarbeet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","title":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","text":"Uniformity trial sugarbeets, Minnesota, 1930, measurements yield, sugar, purity.","code":""},{"path":"/reference/immer.sugarbeet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","text":"data frame 600 observations following 5 variables. year year experiment row row col column yield yield, pounds per plot sugar sugar percentage purity apparent purity","code":""},{"path":"/reference/immer.sugarbeet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","text":"1930 Experiment Beets planted rows 22 inches apart, thinned 1 plant per row. harvest, rows marked segments 33 feet long 2 foot alleys ends plots. harvested area 60 rows 350 feet long. Field width: 10 plots * 33 feet + 9 alleys * 2 feet = 348 feet Field length: 60 plots/rows * 22 /row / 12 /feet = 110 feet Planted 1930. Field conditions uniform. Beets planted rows 22 inches apart. thinning, one beet left 12-inch unit. harvest, field marked plot 33 feet long, 2-foot alley plots minimize carryover harvester. sample 10 beets taken uniformly (approximately every third beet) measured sugar percentage apparent purity. beets counted weighing time yields calculated basis 33 beets per plot. Immer found aggregating data one row two resulted dramatic reduction standard error (yield). ———- 1931 Experiment Planted 13 May 1931. Field layout previous year. Unclear land used. Field width: 10 plots * 33 feet + 9 alleys * 2 feet = 348 feet Field length: 60 plots * 22 inches/row / 12 /feet = 110 feet data experiment published Immer (1933), deposited Rothamsted. data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/immer.sugarbeet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","text":"Immer, F. R. (1932). Size shape plot relation field experiments sugar beets. Journal Agricultural Research, 44, 649-668. https://naldc.nal.usda.gov/download/IND43968078/PDF Immer, F. R. S. M. Raleigh (1933). studies size shape plot relation field experiments sugar beets. Journal Agricultural Research, 47, 591-598. https://naldc.nal.usda.gov/download/IND43968370/PDF Rothamsted Research Library, Box STATS17 WG Cochran, Folder 5.","code":""},{"path":"/reference/immer.sugarbeet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","text":"","code":"library(agridat) data(immer.sugarbeet.uniformity) dat <- immer.sugarbeet.uniformity # Immer numbers rows from the top libs(desplot) # Similar to Immer (1932) figure 2 desplot(dat, yield~col*row, subset=year==1930, aspect=110/348, tick=TRUE, flip=TRUE, # true aspect main=\"immer.sugarbeet.uniformity - 1930 yield\") # Similar to Immer (1932) figure 3 desplot(dat, sugar~col*row, subset=year==1930, aspect=110/348, tick=TRUE, flip=TRUE, main=\"immer.sugarbeet.uniformity - 1930 sugar\") # Similar to Immer (1932) figure 4 desplot(dat, purity~col*row, subset=year==1930, aspect=110/348, tick=TRUE, flip=TRUE, main=\"immer.sugarbeet.uniformity - 1930 purity\") pairs(dat[,c('yield','sugar','purity')], main=\"immer.sugarbeet.uniformity\") # Similar to Immer (1933) figure 1 desplot(dat, yield~col*row, subset=year==1931, aspect=110/348, tick=TRUE, flip=TRUE, # true aspect main=\"immer.sugarbeet.uniformity - 1931 yield\")"},{"path":"/reference/ivins.herbs.html","id":null,"dir":"Reference","previous_headings":"","what":"Percent ground cover of herbage species and nettles. — ivins.herbs","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"Percent ground cover herbage species nettles.","code":""},{"path":"/reference/ivins.herbs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"data frame 78 observations following 4 variables. block block, 6 levels gen genotype, 13 levels nettle percent ground cover nettles herb percent ground cover herbage species","code":""},{"path":"/reference/ivins.herbs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"University Nottingham farm, 13 different strains species herbage plants sown 4 acres RCB design. grass species sown together white clover seed. establishment herbage plants, became apparent Urtica dioica (nettle) became established according particular herbage plant plot. particular, nettle became established plots sown leguminous species two grass species. graminaceous plots less nettles. data percentage ground cover nettle herbage plants September 1951. Note, percent ground cover amounts originally reported 'trace'. arbitrarily set 0.1 data.","code":""},{"path":"/reference/ivins.herbs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"Ivins, JD. (1952). Concerning Ecology Urtica Dioica L., Journal Ecology, 40, 380-382. https://doi.org/10.2307/2256806","code":""},{"path":"/reference/ivins.herbs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"Ivins, JD (1950). Weeds relation establishment Ley. Grass Forage Science, 5, 237–242. https://doi.org/10.1111/j.1365-2494.1950.tb01287.x O'Gorman, T.W. (2001). comparison F-test, Friedman's test, several aligned rank tests analysis randomized complete blocks. Journal agricultural, biological, environmental statistics, 6, 367–378. https://doi.org/10.1198/108571101317096578","code":""},{"path":"/reference/ivins.herbs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ivins.herbs) dat <- ivins.herbs # Nettle is primarily established in legumes. libs(lattice) xyplot(herb~nettle|gen, dat, main=\"ivins.herbs - herb yield vs weeds\", xlab=\"Percent groundcover in nettles\", ylab=\"Percent groundcover in herbs\") # O'Brien used first 7 species to test gen differences dat7 <- droplevels(subset(dat, is.element(gen, c('G01','G02','G03','G04','G05','G06','G07')))) m1 <- lm(herb ~ gen + block, data=dat7) anova(m1) # gen p-value is .041 ## Response: herb ## Df Sum Sq Mean Sq F value Pr(>F) ## gen 6 1083.24 180.540 2.5518 0.04072 * ## block 5 590.69 118.138 1.6698 0.17236 ## Residuals 30 2122.48 70.749 friedman.test(herb ~ gen|block, dat7) # gen p-value .056 } # }"},{"path":"/reference/iyer.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of wheat in India — iyer.wheat.uniformity","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"Uniformity trials wheat India.","code":""},{"path":"/reference/iyer.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"","code":"data(\"iyer.wheat.uniformity\")"},{"path":"/reference/iyer.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"data frame 2000 observations following 3 variables. row row col column yield yield, ounces per plot","code":""},{"path":"/reference/iyer.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"Data collected Agricultural Sub-station Karnal, India, April 1978. net area 400 ft x 125 ft harvested dividing 80x25 units 5 ft x 5 ft eliminating minimum border 3.5 ft around net area. Field width: 80 plots * 5 feet = 400 feet Field length: 25 rows * 5 feet = 125 feet second paper, Iyer used data compare random vs. balanced arrangements treatments plots, conclusion \"difficult say [method] better. However, tendency randomized arrangements give accurate results.\"","code":""},{"path":"/reference/iyer.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"P. V. Krishna Iyer (1942). Studies wheat uniformity trial data. . Size shape experimental plots relative efficiency different layouts. Indian Journal Agricultural Science, 12, 240-262. Page 259-262. https://archive.org/stream/.ernet.dli.2015.7638/2015.7638.-Indian-Journal--Agricultural-Science-Vol-xii-1942#page/n267/mode/2up","code":""},{"path":"/reference/iyer.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"None.","code":""},{"path":"/reference/iyer.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(iyer.wheat.uniformity) dat <- iyer.wheat.uniformity libs(desplot) desplot(dat, yield ~ col*row, main=\"iyer.wheat.uniformity\", tick=TRUE, aspect=(25*5)/(80*5)) # true aspect # not exactly the same as Iyer table 1, p. 241 var(subset(dat, col <= 20)$yield) var(subset(dat, col > 20 & col <= 40)$yield) var(subset(dat, col > 40 & col <= 60)$yield) var(subset(dat, col > 60)$yield) # cv for 1x1 whole-field # sd(dat$yield)/mean(dat$yield) # 18.3 } # }"},{"path":"/reference/jansen.apple.html","id":null,"dir":"Reference","previous_headings":"","what":"Infestation of apple shoots by apple canker. — jansen.apple","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"Infestation apple shoots apple canker.","code":""},{"path":"/reference/jansen.apple.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"","code":"data(\"jansen.apple\")"},{"path":"/reference/jansen.apple.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"data frame 36 observations following 5 variables. inoculum inoculum level gen genotype/variety block block y number inoculations developing canker n number inoculations","code":""},{"path":"/reference/jansen.apple.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"Shoots apple trees infected fungus Nectria galligena, may cause apple canker. incoulum density treatment 3 levels, measured macroconidia per ml. 4 blocks. Used permission J. Jansen. Electronic version supplied Miroslav Zoric.","code":""},{"path":"/reference/jansen.apple.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"J. Jansen & J.. Hoekstra (1993). analysis proportions agricultural experiments generalized linear mixed model. Statistica Neerlandica, 47(3), 161-174. https://doi.org/10.1111/j.1467-9574.1993.tb01414.x","code":""},{"path":"/reference/jansen.apple.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"None.","code":""},{"path":"/reference/jansen.apple.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jansen.apple) dat <- jansen.apple libs(lattice) xyplot(inoculum ~ y/n|gen, data=dat, group=block, layout=c(3,1), main=\"jansen.apple\", xlab=\"Proportion infected per block/inoculum\", ylab=\"Inoculum level\") ## libs(lme4) ## # Tentative model. Needs improvement. ## m1 <- glmer(cbind(y,n-y) ~ gen + factor(inoculum) + (1|block), ## data=dat, family=binomial) ## summary(m1) } # }"},{"path":"/reference/jansen.carrot.html","id":null,"dir":"Reference","previous_headings":"","what":"Infestation of carrots by fly larvae — jansen.carrot","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"Infestation 16 carrot genotypes fly larvae, comparing 2 treatments 16 blocks.","code":""},{"path":"/reference/jansen.carrot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"","code":"data(\"jansen.carrot\")"},{"path":"/reference/jansen.carrot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"data frame 96 observations following 5 variables. trt treatment gen genotype block block n number carrots sampled per plot y number carrots infested per plot","code":""},{"path":"/reference/jansen.carrot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"experiment designed compare different genotypes carrots respect resistance infestation larvae carrotfly. 16 genotypes, 2 levels pest-control treatments, conducted 3 randomized complete blocks. 50 carrots sampled plot evaluated. data show number carrots number infested fly larvae. Used permission J. Jansen. Electronic version supplied Miroslav Zoric.","code":""},{"path":"/reference/jansen.carrot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"J. Jansen & J.. Hoekstra (1993). analysis proportions agricultural experiments generalized linear mixed model. Statistica Neerlandica, 47(3), 161-174. https://doi.org/10.1111/j.1467-9574.1993.tb01414.x","code":""},{"path":"/reference/jansen.carrot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"None.","code":""},{"path":"/reference/jansen.carrot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jansen.carrot) dat <- jansen.carrot libs(lattice) dotplot(gen ~ y/n, data=dat, group=trt, auto.key=TRUE, main=\"jansen.carrot\", xlab=\"Proportion of carrots infected per block\", ylab=\"Genotype\") # Not run because CRAN wants < 5 seconds per example. This is close. libs(lme4) # Tentative model. Needs improvement. m1 <- glmer(cbind(y,n-y) ~ gen*trt + (1|block), data=dat, family=binomial) summary(m1) # Todo: Why are these results different from Jansen? # Maybe he used ungrouped bernoulli data? Too slow with 4700 obs } # }"},{"path":"/reference/jansen.strawberry.html","id":null,"dir":"Reference","previous_headings":"","what":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"Ordered disease ratings strawberry crosses.","code":""},{"path":"/reference/jansen.strawberry.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"","code":"data(\"jansen.strawberry\")"},{"path":"/reference/jansen.strawberry.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"data frame 144 observations following 5 variables. male male parent female female parent block block category disease damage, C1 < C2 < C3 count number plants category","code":""},{"path":"/reference/jansen.strawberry.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"strawberries, red core disease caused fungus, Phytophtora fragariae. experiment evaluated different populations damage caused red core disease. 3 male strawberry plants 4 DIFFERENT female strawberry plants crossed create 12 populations. Note: Jansen labeled male parents 1,2,3 female parents 1,2,3,4. reduce confusion, data labels female parents 5,6,7,8. experiment four blocks 12 plots (one population). Plots usually 10 plants, plots 9 plants. plant assessed damage fungus rated belonging category C1, C2, C3 (increasing damage). Used permission Hans Jansen.","code":""},{"path":"/reference/jansen.strawberry.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"J. Jansen, 1990. statistical analysis ordinal data extravariation present. Applied Statistics, 39, 75-84, Table 1. https://doi.org/10.2307/2347813","code":""},{"path":"/reference/jansen.strawberry.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jansen.strawberry) dat <- jansen.strawberry dat <- transform(dat, category=ordered(category, levels=c('C1','C2','C3'))) dtab <- xtabs(count ~ male + female + category, data=dat) ftable(dtab) mosaicplot(dtab, color=c(\"lemonchiffon1\",\"lightsalmon1\",\"indianred\"), main=\"jansen.strawberry disease ratings\", xlab=\"Male parent\", ylab=\"Female parent\") libs(MASS,vcd) # Friendly suggests a minimal model is [MF][C] # m1 <- loglm( ~ 1*2 + 3, dtab) # Fails, only with devtools # mosaic(m1) } # }"},{"path":"/reference/jayaraman.bamboo.html","id":null,"dir":"Reference","previous_headings":"","what":"Bamboo progeny trial — jayaraman.bamboo","title":"Bamboo progeny trial — jayaraman.bamboo","text":"Bamboo progeny trial 2 locations, 3 blocks","code":""},{"path":"/reference/jayaraman.bamboo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bamboo progeny trial — jayaraman.bamboo","text":"","code":"data(\"jayaraman.bamboo\")"},{"path":"/reference/jayaraman.bamboo.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Bamboo progeny trial — jayaraman.bamboo","text":"data frame 216 observations following 5 variables. loc location factor block block factor tree tree factor family family factor height height, cm","code":""},{"path":"/reference/jayaraman.bamboo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bamboo progeny trial — jayaraman.bamboo","text":"Data replicated trial bamboo two locations Kerala, India. location 3 blocks. block 6 families, 6 trees family.","code":""},{"path":"/reference/jayaraman.bamboo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bamboo progeny trial — jayaraman.bamboo","text":"K. Jayaraman (1999). \"Statistical Manual Forestry Research\". Forestry Research Support Programme Asia Pacific. Page 170.","code":""},{"path":"/reference/jayaraman.bamboo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bamboo progeny trial — jayaraman.bamboo","text":"None","code":""},{"path":"/reference/jayaraman.bamboo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bamboo progeny trial — jayaraman.bamboo","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jayaraman.bamboo) dat <- jayaraman.bamboo # very surprising differences between locations libs(lattice) bwplot(height ~ family|loc, dat, main=\"jayaraman.bamboo\") # match Jayarman's anova table 6.3, page 173 # m1 <- aov(height ~ loc+loc:block + family + family:loc + # family:loc:block, data=dat) # anova(m1) # more modern approach with mixed model, match variance components needed # for equation 6.9, heritability of the half-sib averages as m2 <- lme4::lmer(height ~ 1 + (1|loc/block) + (1|family/loc/block), data=dat) lucid::vc(m2) } # }"},{"path":"/reference/jegorow.oats.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"Uniformity trial oats Russia","code":""},{"path":"/reference/jegorow.oats.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"","code":"data(\"jegorow.oats.uniformity\")"},{"path":"/reference/jegorow.oats.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"data frame 240 observations following 3 variables. row row ordinate col column ordinate yield yield per plot, kg","code":""},{"path":"/reference/jegorow.oats.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"Sumskaya (Ssumy?) agricultural experimental station (Kharkov Governorate), field planted April 1908 harvested summer plots 1 sazhen sqauare. 'sazhen' 7 feet. Field width: 8 plots * 1 sazhen Field length: 30 plots * 1 sazhen Data typed K.Wright Roemer (1920), table 10.","code":""},{"path":"/reference/jegorow.oats.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"Jegorow, M. (1909). Zur Methodik des feldversuches. Russian Journ Expt Agric, 10, 502-520. uniformity trial oats. https://www.google.com/books/edition/Journal_de_l_agriculture_experimentale/510jAQAAIAAJ?hl=en","code":""},{"path":"/reference/jegorow.oats.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"Neyman, J., & Iwaszkiewicz, K. (1935). Statistical problems agricultural experimentation. Supplement Journal Royal Statistical Society, 2(2), 107-180. Roemer, T. (1920). Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ","code":""},{"path":"/reference/jegorow.oats.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jegorow.oats.uniformity) dat <- jegorow.oats.uniformity mean(dat$yield) # Jegorow reports 2.03 libs(desplot) desplot(dat, yield~col*row, aspect=10/24, flip=TRUE, tick=TRUE, main=\"jegorow.oats.uniformity\") } # }"},{"path":"/reference/jenkyn.mildew.html","id":null,"dir":"Reference","previous_headings":"","what":"Yields from treatment for mildew control — jenkyn.mildew","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"Yields treatment mildew control","code":""},{"path":"/reference/jenkyn.mildew.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"data frame 38 observations following 4 variables. plot plot number trt treatment factor, 4 levels block block factor, 9 levels yield grain yield, tons/ha","code":""},{"path":"/reference/jenkyn.mildew.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"four spray treatments: 0 (none), 1 (early), 2 (late), R (repeated). treatment occurs 9 ordered pairs treatments. first last plot assigned block.","code":""},{"path":"/reference/jenkyn.mildew.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"Norman Draper Irwin Guttman (1980). Incorporating Overlap Effects Neighboring Units Response Surface Models. Appl Statist, 29, 128–134. https://doi.org/10.2307/2986297","code":""},{"path":"/reference/jenkyn.mildew.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"Maria Durban, Christine Hackett, Iain Currie. Blocks, Trend Interference Field Trials.","code":""},{"path":"/reference/jenkyn.mildew.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jenkyn.mildew) dat <- jenkyn.mildew libs(lattice) bwplot(yield ~ trt, dat, main=\"jenkyn.mildew\", xlab=\"Treatment\") # Residuals from treatment model show obvious spatial trends m0 <- lm(yield ~ trt, dat) xyplot(resid(m0)~plot, dat, ylab=\"Residual\", main=\"jenkyn.mildew - treatment model\") # The blocks explain most of the variation m1 <- lm(yield ~ trt + block, dat) xyplot(resid(m1)~plot, dat, ylab=\"Residual\", main=\"jenkyn.mildew - block model\") } # }"},{"path":"/reference/john.alpha.html","id":null,"dir":"Reference","previous_headings":"","what":"Alpha lattice design of spring oats — john.alpha","title":"Alpha lattice design of spring oats — john.alpha","text":"Alpha lattice design spring oats","code":""},{"path":"/reference/john.alpha.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Alpha lattice design of spring oats — john.alpha","text":"data frame 72 observations following 5 variables. plot plot number rep replicate block incomplete block gen genotype (variety) yield dry matter yield (tonnes/ha) row Row ordinate col Column ordinate","code":""},{"path":"/reference/john.alpha.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Alpha lattice design of spring oats — john.alpha","text":"spring oats trial grown Craibstone, near Aberdeen. 24 varieties 3 replicates, consisting 6 incomplete blocks 4 plots. Planted resolvable alpha design. Caution: Note table page 146 John & Williams (1995) physical layout. plots laid single line.","code":""},{"path":"/reference/john.alpha.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Alpha lattice design of spring oats — john.alpha","text":"J. . John & E. R. Williams (1995). Cyclic computer generated designs. Chapman Hall, London. Page 146.","code":""},{"path":"/reference/john.alpha.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Alpha lattice design of spring oats — john.alpha","text":"Piepho, H.P. Mohring, J. (2007), Computing heritability selection response unbalanced plant breeding trials. Genetics, 177, 1881-1888. https://doi.org/10.1534/genetics.107.074229 Paul Schmidt, Jens Hartung, Jörn Bennewitz, Hans-Peter Piepho (2019). Heritability Plant Breeding Genotype-Difference Basis. Genetics, 212, 991-1008. https://doi.org/10.1534/genetics.119.302134","code":""},{"path":"/reference/john.alpha.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Alpha lattice design of spring oats — john.alpha","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(john.alpha) dat <- john.alpha # RCB (no incomplete block) m0 <- lm(yield ~ 0 + gen + rep, data=dat) # Block fixed (intra-block analysis) (bottom of table 7.4 in John) m1 <- lm(yield ~ 0 + gen + rep + rep:block, dat) anova(m1) # Block random (combined inter-intra block analysis) libs(lme4, lucid) m2 <- lmer(yield ~ 0 + gen + rep + (1|rep:block), dat) anova(m2) ## Analysis of Variance Table ## Df Sum Sq Mean Sq F value ## gen 24 380.43 15.8513 185.9942 ## rep 2 1.57 0.7851 9.2123 vc(m2) ## grp var1 var2 vcov sdcor ## rep:block (Intercept) 0.06194 0.2489 ## Residual 0.08523 0.2919 # Variety means. John and Williams table 7.5. Slight, constant # difference for each method as compared to John and Williams. means <- data.frame(rcb=coef(m0)[1:24], ib=coef(m1)[1:24], intra=fixef(m2)[1:24]) head(means) ## rcb ib intra ## genG01 5.201233 5.268742 5.146433 ## genG02 4.552933 4.665389 4.517265 ## genG03 3.381800 3.803790 3.537934 ## genG04 4.439400 4.728175 4.528828 ## genG05 5.103100 5.225708 5.075944 ## genG06 4.749067 4.618234 4.575394 libs(lattice) splom(means, main=\"john.alpha - means for RCB, IB, Intra-block\") # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Heritability calculation of Piepho & Mohring, Example 1 m3 <- asreml(yield ~ 1 + rep, data=dat, random=~ gen + rep:block) sg2 <- summary(m3)$varcomp['gen','component'] # .142902 # Average variance of a difference of two adjusted means (BLUP) p3 <- predict(m3, data=dat, classify=\"gen\", sed=TRUE) # Matrix of pair-wise SED values, squared vdiff <- p3$sed^2 # Average variance of two DIFFERENT means (using lower triangular of vdiff) vblup <- mean(vdiff[lower.tri(vdiff)]) # .05455038 # Note that without sed=TRUE, asreml reports square root of the average variance # of a difference between the variety means, so the following gives the same value # predict(m3, data=dat, classify=\"gen\")$avsed ^ 2 # .05455038 # Average variance of a difference of two adjusted means (BLUE) m4 <- asreml(yield ~ 1 + gen + rep, data=dat, random = ~ rep:block) p4 <- predict(m4, data=dat, classify=\"gen\", sed=TRUE) vdiff <- p4$sed^2 vblue <- mean(vdiff[lower.tri(vdiff)]) # .07010875 # Again, could use predict(m4, data=dat, classify=\"gen\")$avsed ^ 2 # H^2 Ad-hoc measure of heritability sg2 / (sg2 + vblue/2) # .803 # H^2c Similar measure proposed by Cullis. 1-(vblup / (2*sg2)) # .809 } # ---------- # lme4 to calculate Cullis H2 # https://stackoverflow.com/questions/38697477 libs(lme4) cov2sed <- function(x){ # Convert var-cov matrix to SED matrix # sed[i,j] = sqrt( x[i,i] + x[j,j]- 2*x[i,j] ) n <- nrow(x) vars <- diag(x) sed <- sqrt( matrix(vars, n, n, byrow=TRUE) + matrix(vars, n, n, byrow=FALSE) - 2*x ) diag(sed) <- 0 return(sed) } # Same as asreml model m4. Note 'gen' must be first term m5blue <- lmer(yield ~ 0 + gen + rep + (1|rep:block), dat) libs(emmeans) ls5blue <- emmeans(m5blue, \"gen\") con <- ls5blue@linfct[,1:24] # contrast matrix for genotypes # The 'con' matrix is identity diagonal, so we don't need to multiply, # but do so for a generic approach # sed5blue <- cov2sed(con tmp <- tcrossprod( crossprod(t(con), vcov(m5blue)[1:24,1:24]), con) sed5blue <- cov2sed(tmp) # vblue Average variance of difference between genotypes vblue <- mean(sed5blue[upper.tri(sed5blue)]^2) vblue # .07010875 matches 'vblue' from asreml # Now blups m5blup <- lmer(yield ~ 0 + (1|gen) + rep + (1|rep:block), dat) # Need lme4::ranef in case ordinal is loaded re5 <- lme4::ranef(m5blup,condVar=TRUE) vv1 <- attr(re5$gen,\"postVar\") vblup <- 2*mean(vv1) # .0577 not exactly same as 'vblup' above vblup # H^2 Ad-hoc measure of heritability sg2 <- c(lme4::VarCorr(m5blup)[[\"gen\"]]) # 0.142902 sg2 / (sg2 + vblue/2) # .803 matches asreml # H^2c Similar measure proposed by Cullis. 1-(vblup / 2 / sg2) # .809 from asreml, .800 from lme4 # ---------- # Sommer to calculate Cullis H2 libs(sommer) m2.ran <- mmer(fixed = yield ~ rep, random = ~ gen + rep:block, data = dat) vc_g <- m2.ran$sigma$gen # genetic variance component n_g <- n_distinct(dat$gen) # number of genotypes C22_g <- m2.ran$PevU$gen$yield # Prediction error variance matrix for genotypic BLUPs trC22_g <- sum(diag(C22_g)) # trace # Mean variance of a difference between genotypic BLUPs. Smith eqn 26 # I do not see the algebraic reason for this...2 av2 <- 2/n_g * (trC22_g - (sum(C22_g)-trC22_g) / (n_g-1)) ### H2 Cullis 1-(av2 / (2 * vc_g)) #0.8091 } # }"},{"path":"/reference/johnson.blight.html","id":null,"dir":"Reference","previous_headings":"","what":"Potato blight due to weather in Prosser, Washington — johnson.blight","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"Potato blight due weather Prosser, Washington","code":""},{"path":"/reference/johnson.blight.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"data frame 25 observations following 6 variables. year year area area affected, hectares blight blight detected, 0/1 numeric rain.number rainy days April May rain.ja number rainy days July August precip.m precipitation May temp > 5C, milimeters","code":""},{"path":"/reference/johnson.blight.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"variable 'blight detected' 1 'area' > 0.","code":""},{"path":"/reference/johnson.blight.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"Johnson, D.. Alldredge, J.R. Vakoch, D.L. (1996). Potato late blight forecasting models semiarid environment south-central Washington. Phytopathology, 86, 480–484. https://doi.org/10.1094/Phyto-86-480","code":""},{"path":"/reference/johnson.blight.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"Vinayanand Kandala, Logistic Regression","code":""},{"path":"/reference/johnson.blight.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(johnson.blight) dat <- johnson.blight # Define indicator for blight in previous year dat$blight.prev[2:25] <- dat$blight[1:24] dat$blight.prev[1] <- 0 # Need this to match the results of Johnson dat$blight.prev <- factor(dat$blight.prev) dat$blight <- factor(dat$blight) # Johnson et al developed two logistic models to predict outbreak of blight m1 <- glm(blight ~ blight.prev + rain.am + rain.ja, data=dat, family=binomial) summary(m1) ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -11.4699 5.5976 -2.049 0.0405 * ## blight.prev1 3.8796 1.8066 2.148 0.0318 * ## rain.am 0.7162 0.3665 1.954 0.0507 . ## rain.ja 0.2587 0.2468 1.048 0.2945 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## (Dispersion parameter for binomial family taken to be 1) ## Null deviance: 34.617 on 24 degrees of freedom ## Residual deviance: 13.703 on 21 degrees of freedom ## AIC: 21.703 m2 <- glm(blight ~ blight.prev + rain.am + precip.m, data=dat, family=binomial) summary(m2) ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -7.5483 3.8070 -1.983 0.0474 * ## blight.prev1 3.5526 1.6061 2.212 0.0270 * ## rain.am 0.6290 0.2763 2.276 0.0228 * ## precip.m -0.0904 0.1144 -0.790 0.4295 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## (Dispersion parameter for binomial family taken to be 1) ## Null deviance: 34.617 on 24 degrees of freedom ## Residual deviance: 14.078 on 21 degrees of freedom ## AIC: 22.078 libs(lattice) splom(dat[,c('blight','rain.am','rain.ja','precip.m')], main=\"johnson.blight - indicator of blight\") } # }"},{"path":"/reference/johnson.douglasfir.html","id":null,"dir":"Reference","previous_headings":"","what":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"study small-plots old-growth Douglas Fir Oregon.","code":""},{"path":"/reference/johnson.douglasfir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"","code":"data(\"johnson.douglasfir\")"},{"path":"/reference/johnson.douglasfir.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"data frame 1600 observations following 3 variables. row row col column volume volume per plot","code":""},{"path":"/reference/johnson.douglasfir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"study 40 acres old-growth Douglas-Fir near Eugene, Oregon. area divided 40--40 grid plots, 1/40 acre. volume represents total timber volume (Scribner Decimal C) 1/40 acre plot. authors conclude 1-chain 3-chain 3/10 acre rectangle efficient intensive cruise work. convert plot volume total volume per acre, multiply 40 ( plot 1/40 acre) multiply 10 (correction Scribner scale).","code":""},{"path":"/reference/johnson.douglasfir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"Floyd . Johnson, Homer J. Hixon. (1952). efficient size shape plot use cruising old-growth Douglas-fir timber. Jour. Forestry, 50, 17-20. https://doi.org/10.1093/jof/50.1.17","code":""},{"path":"/reference/johnson.douglasfir.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"None","code":""},{"path":"/reference/johnson.douglasfir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"","code":"library(agridat) data(johnson.douglasfir) dat <- johnson.douglasfir # Average volume per acre. Johnson & Hixon give 91000. # Transcription may have some errors...the pdf was blurry. mean(dat$volume) * 400 #> [1] 91124.25 # 91124 libs(lattice) levelplot(volume ~ col*row, dat, main=\"johnson.douglasfir\", aspect=1) histogram( ~ volume, data=dat, main=\"johnson.douglasfir\")"},{"path":"/reference/jones.corn.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of corn. — jones.corn.uniformity","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"Uniformity trial corn Iowa 2016.","code":""},{"path":"/reference/jones.corn.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"","code":"data(\"jones.corn.uniformity\")"},{"path":"/reference/jones.corn.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"data frame 144 observations following 3 variables. col column ordinate row row ordinate yield yield, bu/ac","code":""},{"path":"/reference/jones.corn.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"data corresponds field \"ISU.SE\" paper Jones. Field width: 12 columns, 4.6 meters . Field length: 12 rows, 3 meters . Electronic version provided online supplement. \"row\" \"col\" variables supplement swapped presentation data order consistent figures paper. electronic supplemental data bu/ac, paper uses kg/ha. Used permission Marcus Jones.","code":""},{"path":"/reference/jones.corn.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"Jones, M., Harbur, M., & Moore, K. J. (2021). Automating Uniformity Trials Optimize Precision Agronomic Field Trials. Agronomy, 11(6), 1254. https://doi.org/10.3390/agronomy11061254","code":""},{"path":"/reference/jones.corn.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"None","code":""},{"path":"/reference/jones.corn.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jones.corn.uniformity) dat <- jones.corn.uniformity library(desplot) # Compare to figure 5 of Jones et al. desplot(dat, yield ~ col*row, aspect=(12*4.6)/(12*3), main=\"jones.corn.uniformity\") } # }"},{"path":"/reference/jurowski.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"Uniformity trial wheat Russia","code":""},{"path":"/reference/jurowski.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"","code":"data(\"jurowski.wheat.uniformity\")"},{"path":"/reference/jurowski.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"data frame 480 observations following 3 variables. row row ordinate col column ordinate yield yield, Pud per plot","code":""},{"path":"/reference/jurowski.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"experiment conducted Russia Ofrossimowka. word \"Ofrossimowka\" appeared German text Sapehin, otherwise extremely difficult find. may alternate ways actual Russian name translated German/English. Likewise, name \"Jurowski\" difficult find may transliterations. Sapehin gives original source : Arbeiten d. Vers.-St. d. Russ. Ges. f. Zuckerind. 1913. may expand Arbeiten der Versuchsstationen der Russ. Ges. f. Zuckerindustrie. 1913. Sepehin appendix says plot size \"4 x 12 m^2\". clear plot dimension 4 m 12 m. 4m wide 12m tall, field 48m wide x 480m long. 4m tall 12m wide, field 144m wide x 160m long. seems much likely. Sapehin says std dev \"21.8 pud\". \"pud\" Russian unit weight equal 16.38 kilograms. Data converted OCR Sapehin hand-checked K.Wright.","code":""},{"path":"/reference/jurowski.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"Sapehin, . . (1927). Beitrage zur Methodik des Feldversuches. Die Landwirtschaflichen Versuchsstationen, 105, 243-259. https://www.google.com/books/edition/Die_Landwirthschaftlichen_Versuchs_Stati/cLZGAAAAYAAJ?hl=en&pg=PA243","code":""},{"path":"/reference/jurowski.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"None","code":""},{"path":"/reference/jurowski.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jurowski.wheat.uniformity) dat <- jurowski.wheat.uniformity sd(dat$yield) libs(desplot) desplot(dat, yield~col*row, aspect=(40*4)/(12*12), flip=TRUE, tick=TRUE, main=\"jurowski.wheat.uniformity\") } # }"},{"path":"/reference/kadam.millet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of millet — kadam.millet.uniformity","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"Uniformity trial millet India 2 years","code":""},{"path":"/reference/kadam.millet.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"","code":"data(\"kadam.millet.uniformity\")"},{"path":"/reference/kadam.millet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"data frame 240 observations following 4 variables. year year row row col column yield yield, ounces","code":""},{"path":"/reference/kadam.millet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"Uniformity trials conducted kharip (monsoon) seasons 1933 1934 Kundewadi, Niphad, district Nasik, India. Bajari (pearl millet) strain 54 used. 1933: Field width: 8 plots * 16.5 feet Field length: 10 plots * 33 feet 1934: Field width: 8 plots * 16.5 feet Field length: 20 plots * 16.5 feet","code":""},{"path":"/reference/kadam.millet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"B. S. Kadam S. M. Patel. (1937). Studies Field-Plot Technique P. Typhoideum Rich. Empire Journal Experimental Agriculture, 5, 219-230. https://archive.org/details/.ernet.dli.2015.25282","code":""},{"path":"/reference/kadam.millet.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"None.","code":""},{"path":"/reference/kadam.millet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kadam.millet.uniformity) dat <- kadam.millet.uniformity # similar to Kadam fig 1 libs(desplot) desplot(dat, yield ~ col*row, subset=year==1933, flip=TRUE, aspect=(10*33)/(8*16.5), # true aspect main=\"kadam.millet.uniformity 1933\") desplot(dat, yield ~ col*row, subset=year==1934, flip=TRUE, aspect=(20*16.5)/(8*16.5), # true aspect main=\"kadam.millet.uniformity 1934\") } # }"},{"path":"/reference/kalamkar.potato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of potatoes — kalamkar.potato.uniformity","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"Uniformity trial potatoes Saskatchewan, Canada, 1929.","code":""},{"path":"/reference/kalamkar.potato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"","code":"data(\"kalamkar.potato.uniformity\")"},{"path":"/reference/kalamkar.potato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"data frame 576 observations following 3 variables. row row col column yield yield, pounds per plot","code":""},{"path":"/reference/kalamkar.potato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"data potato yields 96 rows, 132 feet long, 3 feet rows. row harvested six plots, 22 feet long. hill one seed piece. Hills spaced 2 feet apart row. Field width: 6 plots * 22 feet = 132 feet Field length: 96 rows * 3 feet = 288 feet Units yield given. experiment, 22 plants per plot. Today potato plants yield 3-5 pounds. assume experiment yield 2 pound per plant, 22 pounds per plot, similar data values. Also, Kirk 1929 mentions \"200 bushels per acre\", 22 pounds per plot x (43560/66) divided (60 pounds per bushel) = 242, seems reasonable. Also `kirk.potato` data author recorded pounds per plot.","code":""},{"path":"/reference/kalamkar.potato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"Kalamkar, R.J. (1932). Experimental Error Field-Plot Technique Potatoes. Journal Agricultural Science, 22, 373-385. https://doi.org/10.1017/S0021859600053697","code":""},{"path":"/reference/kalamkar.potato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"Kirk, L. E. (1929) Field plot technique potatoes special reference Latin square. Scientific Agriculture, 9, 719. https://cdnsciencepub.com/doi/10.4141/sa-1929-0067 https://doi.org/10.4141/sa-1929-0067 https://www.google.com/books/edition/Revue_Agronomique_Canadien/-gMkAQAAMAAJ","code":""},{"path":"/reference/kalamkar.potato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kalamkar.potato.uniformity) dat <- kalamkar.potato.uniformity # Similar to figure 1 of Kalamkar libs(desplot) desplot(dat, yield~col*row, flip=TRUE, tick=TRUE, aspect=288/132, # true aspect main=\"kalamkar.potato.uniformity\") } # }"},{"path":"/reference/kalamkar.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — kalamkar.wheat.uniformity","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"Uniformity trial wheat Rothamsted, UK 1931.","code":""},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"","code":"data(\"kalamkar.wheat.uniformity\")"},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"data frame 1280 observations following 4 variables. row row col column yield yield, grams/half-meter ears ears per half-meter","code":""},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"Kalamkar's paper published 1932. Estimated crop year 1931. Plot 18 Four Course Rotation Experiment, Great Hoos, Rothamsted, UK used. Sown Yeoman II wheat. Field width = 16 segments * 0.5 meters = 8 meters. Field length: 80 rows * 6 inches apart = 40 feet. grain yield number ears half-meter length recorded. quite small field, 1/40 acre size. Edge rows higher yields. Row-end units higher yields interior units. border effects significant. Variation rows greater variation within rows. Negative correlation rows may indicate competition effects. ears, Kalamkar discarded 4 rows side 3 half-meter lengths end. Kalamkar suggested using four parallel half-meter rows sampling unit. Note, Rothamsted report 1931, page 57, says: year three workers (F. R. Immer, S. H. Justensen R. J. Kalamkar) taken question efficient use land experiments edge row must discarded...","code":""},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"Kalamkar, R. J (1932). Study Sampling Technique Wheat. Journal Agricultural Science, Vol.22(4), pp.783-796. https://doi.org/10.1017/S0021859600054599","code":""},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"None.","code":""},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kalamkar.wheat.uniformity) dat <- kalamkar.wheat.uniformity plot(yield ~ ears, dat, main=\"kalamkar.wheat.uniformity\") # totals match Kalamkar # sum(dat$yield) # 24112.5 # sum(dat$ears) # 25850 libs(desplot) desplot(dat, ears ~ col*row, flip=TRUE, aspect=(80*0.5)/(16*1.64042), # true aspect main=\"kalamkar.wheat.uniformity - ears\") desplot(dat, yield ~ col*row, flip=TRUE, aspect=(80*0.5)/(16*1.64042), # true aspect main=\"kalamkar.wheat.uniformity - yield\") # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Show the negative correlation between rows dat <- transform(dat, rowf=factor(row), colf=factor(col)) dat <- dat[order(dat$rowf, dat$colf),] m1 = asreml(yield ~ 1, data=dat, resid= ~ ar1(rowf):ar1(colf)) lucid::vc(m1) ## effect component std.error z.ratio bound pctch ## rowf:colf!R 81.53 3.525 23 P 0 ## rowf:colf!rowf!cor -0.09464 0.0277 -3.4 U 0.1 ## rowf:colf!colf!cor 0.2976 0.02629 11 U 0.1 } } # }"},{"path":"/reference/kang.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"Maize yields 4 locs 3 years Louisianna.","code":""},{"path":"/reference/kang.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"","code":"data(\"kang.maize\")"},{"path":"/reference/kang.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"gen genotype, 17 levels env environment, 12 levels yield yield, tonnes/ha environment environment, 13 levels year year, 85-87 loc location, 4 levels","code":""},{"path":"/reference/kang.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"Yield trials conducted four locations (Alexandria, Baton Rouge, Bossier City, St. Joseph) Louisiana 1985 1987. loc planted RCB design 4 reps. Mean yields given data. Used permission Dan Gorman.","code":""},{"path":"/reference/kang.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"Kang, MS Gorman, DP. (1989). Genotype x environment interaction maize. Agronomy Journal, 81, 662-664. Table 2.","code":""},{"path":"/reference/kang.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kang.maize) dat <- kang.maize # Sweep out loc means, then show interaction plot. libs(reshape2) mat <- acast(dat, gen~env, value.var='yield') mat <- sweep(mat, 2, colMeans(mat)) dat2 <- melt(mat) names(dat2) <- c('gen','env','yield') libs(lattice) xyplot(yield~env|gen, data=dat2, type='l', group=gen, panel=function(x,y,...){ panel.abline(h=0,col=\"gray70\") panel.xyplot(x,y,...) }, ylab=\"Environment-centered yield\", main=\"kang.maize - maize hybrid yields\", scales=list(x=list(rot=90))) # Weather covariates for each environment. covs <- data.frame(env=c(\"AL85\",\"AL86\",\"AL87\", \"BR85\",\"BR86\",\"BR87\", \"BC85\",\"BC86\",\"BC87\", \"SJ85\",\"SJ86\",\"SJ87\"), maxt=c(30.7,30.2,29.7,31.5,29.4,28.5, 31.9, 30.4,31.7, 32,29.6,28.9), mint=c(18.7,19.3,18.5, 19.7,18,17.2, 19.1,20.4,20.3, 20.4,19.1,17.5), rain=c(.2,.34,.22, .28,.36,.61, .2,.43,.2, .36,.41,.22), humid=c(82.8,91.1,85.4, 88.1,90.9,88.6, 95.4,90.4,86.7, 95.6,89.5,85)) } # }"},{"path":"/reference/kang.peanut.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"Peanut yields 10 genotypes 15 environments","code":""},{"path":"/reference/kang.peanut.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"","code":"data(\"kang.peanut\")"},{"path":"/reference/kang.peanut.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"data frame 590 observations following 4 variables. gen genotype factor, 10 levels rep replicate factor, 4 levels yield yield env environment factor, 15 levels","code":""},{"path":"/reference/kang.peanut.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"Florman, Tegua, mf484, mf485, mf487, mf489 long crop cycle. others short crop cycle. data also likely used Casanoves et al 2005, \"Evaluation Multienvironment Trials Peanut Cultivars\", appears slightly smaller subset (10 genotypes, perhaps years 96,97,98,99). Based d.f. table 5, appears environment E13 grown 1998. (5 loc * (4-1) = 15, table 14, 98-99 3 reps instead 4 reps.) Data National Institute Agricultural Technology, Argentina.","code":""},{"path":"/reference/kang.peanut.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"M. S. Kang, M. Balzarini, J. L. L. Guerra (2004). Genotype--environment interaction\". : . Saxton (2004). \"Genetic Analysis Complex Traits Using SAS\".","code":""},{"path":"/reference/kang.peanut.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"Johannes Forkman, Julie Josse, Hans-Peter Piepho (2019). Hypothesis Tests Principal Component Analysis Variables Standardized. JABES https://doi.org/10.1007/s13253-019-00355-5","code":""},{"path":"/reference/kang.peanut.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kang.peanut) dat <- kang.peanut # Table 5.1 of Kang et al. (Chapter 5 of Saxton) libs(reshape2) Y0 <- acast(dat, env~gen, value.var='yield', fun=mean) round(Y0,2) # GGE biplot of Kang, p. 82. libs(gge) m1 <- gge(dat, yield~gen*env, scale=FALSE) biplot(m1, flip=c(1,1), main=\"kang.peanut - GGE biplot\") # Forkman 2019, fig 2 # m2 <- gge(dat, yield~gen*env, scale=TRUE) # biplot(m2, main=\"kang.peanut - GGE biplot\") # biplot(m2, comps=3:4, main=\"kang.peanut - GGE biplot\") } # }"},{"path":"/reference/karcher.turfgrass.html","id":null,"dir":"Reference","previous_headings":"","what":"Turfgrass ratings for different treatments — karcher.turfgrass","title":"Turfgrass ratings for different treatments — karcher.turfgrass","text":"Turfgrass ratings different treatments","code":""},{"path":"/reference/karcher.turfgrass.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Turfgrass ratings for different treatments — karcher.turfgrass","text":"data frame 128 observations following 6 variables. week week number rep blocking factor manage management factor, 4 levels nitro nitrogen factor, 2 levels rating turfgrass rating, 4 ordered levels count number samples given rating","code":""},{"path":"/reference/karcher.turfgrass.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Turfgrass ratings for different treatments — karcher.turfgrass","text":"Turf color assessed scale Poor, Average, Good, Excellent. data number times combination management style nitrogen level received particular rating across four replicates four sampling weeks. eight treatments completely randomized design. Nitrogen level 1 2.5 g/m^2, level 2 5 g/m^2. Management 1 = N applied supplemental water injection. M2 = surface applied supplemental water injection. M3 = nitrogen injected 7.6 cm deep M4 = nitrogen injected 12.7 cm deep.","code":""},{"path":"/reference/karcher.turfgrass.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Turfgrass ratings for different treatments — karcher.turfgrass","text":"Schabenberger, Oliver Francis J. Pierce. 2002. Contemporary Statistical Models Plant Soil Sciences. CRC Press. Page 380.","code":""},{"path":"/reference/karcher.turfgrass.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turfgrass ratings for different treatments — karcher.turfgrass","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(karcher.turfgrass) dat <- karcher.turfgrass dat$rating <- ordered(dat$rating, levels=c('Poor','Average', 'Good','Excellent')) ftable(xtabs(~manage+nitro+rating, dat)) # Table 6.19 of Schabenberger # Probably would choose management M3, nitro N2 mosaicplot(xtabs(count ~ manage + rating + nitro, dat), shade=TRUE, dir=c('h','v','h'), main=\"karcher.turfgrass - turfgrass ratings\") # Multinomial logistic model. Probit Ordered Logistic Regression. libs(MASS) m1 <- polr(rating ~ nitro*manage + week, dat, weights=count, Hess=TRUE, method='logistic') summary(m1) # Try to match the \"predicted marginal probability distribution\" of # Schabenberger table 6.20. He doesn't define \"marginal\". # Are the interaction terms included before aggregation? # Are 'margins' calculated before/after back-transforming? # At what level is the covariate 'week' included? # Here is what Schabenberger presents: ## M1 M2 M3 M4 | N1 N2 ## Poor .668 .827 .001 .004 | .279 .020 ## Avg .330 .172 .297 .525 | .712 .826 ## Good .002 .001 .695 .008 | .008 .153 ## Exc .000 .000 .007 .003 | .001 .001 ## We use week=3.5, include interactions, then average newd <- expand.grid(manage=levels(dat$manage), nitro=levels(dat$nitro), week=3.5) newd <- cbind(newd, predict(m1, newdata=newd, type='probs')) # probs) print(aggregate( . ~ manage, data=newd, mean), digits=2) ## manage nitro week Poor Average Good Excellent ## 1 M1 1.5 3.5 0.67 0.33 0.0011 0.0000023 ## 2 M2 1.5 3.5 0.76 0.24 0.00059 0.0000012 ## 3 M3 1.5 3.5 0.0023 0.48 0.52 0.0042 ## 4 M4 1.5 3.5 0.0086 0.57 0.42 0.0035 } # }"},{"path":"/reference/kayad.alfalfa.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"Yield monitor data 4 cuttings alfalfa Saudi Arabia.","code":""},{"path":"/reference/kayad.alfalfa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"","code":"data(\"kayad.alfalfa\")"},{"path":"/reference/kayad.alfalfa.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"data frame 8628 observations following 4 variables. harvest harvest number lat latitude long longitude yield yield, tons/ha","code":""},{"path":"/reference/kayad.alfalfa.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"Data collected 23.5 ha field alfalfa Saudia Arabia. field harvested four consecutive times (H8 = 5 Dec 2013, H9 = 16 Feb 2014, H10 = 2 Apr 2014, H11 = 6 May 2014). Data collected using geo-referenced yield monitor. Supporting information contains yield monitor data 4 hay harvests center-pivot field. # TODO: Normalize yields harvest, average together # create productivity map. Two ways normalize: # Normalize 0-100: ((mapValue - min) * 100) / (max - min) # Standardize: ((mapValue - mean) / stdev) * 100","code":""},{"path":"/reference/kayad.alfalfa.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"Ahmed G. Kayad, et al. (2016). Assessing Spatial Variability Alfalfa Yield Using Satellite Imagery Ground-Based Data. PLOS One, 11(6). https://doi.org/10.1371/journal.pone.0157166","code":""},{"path":"/reference/kayad.alfalfa.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"None","code":""},{"path":"/reference/kayad.alfalfa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"","code":"library(agridat) data(kayad.alfalfa) dat <- kayad.alfalfa # match Kayad table 1 stats libs(dplyr) #> #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:gridExtra’: #> #> combine #> The following object is masked from ‘package:MASS’: #> #> select #> The following object is masked from ‘package:nlme’: #> #> collapse #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union dat <- group_by(dat, harvest) summarize(dat, min=min(yield), max=max(yield), mean=mean(yield), stdev=sd(yield), var=var(yield)) #> # A tibble: 4 × 6 #> harvest min max mean stdev var #> #> 1 H10 0 6.68 2.86 1.24 1.55 #> 2 H11 0 9.96 4.01 1.69 2.85 #> 3 H8 0 5.86 2.32 1.01 1.03 #> 4 H9 0.191 5.97 2.45 1.07 1.14 # Figure 4 of Kayad libs(latticeExtra) catcols <- c(\"#cccccc\",\"#ff0000\",\"#ffff00\",\"#55ff00\",\"#0070ff\",\"#c500ff\",\"#73004c\") levelplot(yield ~ long*lat |harvest, dat, aspect=1, at = c(0,2,3,4,5,6,7,10), col.regions=catcols, main=\"kayad.alfalfa\", prepanel=prepanel.default.xyplot, panel=panel.levelplot.points) # Similar to Kayad fig 5. ## levelplot(yield ~ long*lat |harvest, dat, ## prepanel=prepanel.default.xyplot, ## panel=panel.levelplot.points, ## col.regions=pals::brewer.reds)"},{"path":"/reference/keen.potatodamage.html","id":null,"dir":"Reference","previous_headings":"","what":"Damage to potato tubers from lifting rods. — keen.potatodamage","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"Damage potato tubers lifting rods.","code":""},{"path":"/reference/keen.potatodamage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"","code":"data(\"keen.potatodamage\")"},{"path":"/reference/keen.potatodamage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"data frame 1152 observations following 6 variables. energy energy factor weight weight class gen genotype/variety factor rod rod factor damage damage category count count tubers combination categories","code":""},{"path":"/reference/keen.potatodamage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"Experiments performed Wageningen, Netherlands. Potatoes can damaged lifter. experiment, eight types lifting rod compared. Two energy levels, six genotypes/varieties three weight classes used. combinations treatments involved 20 potato tubers. Tubers rated undamaged (D1) severely damaged (D4). main interest differences rods, interactions. factors (besides rod) introduced create variety experimental conditions interest. Keen Engle estimated following rod effects. # Rod: 1 2 3 4 5 6 7 8 # Effect: 0 -1.26 -0.42 0.55 -1.50 -1.85 -1.76 -2.09 Used permission Bas Engel.","code":""},{"path":"/reference/keen.potatodamage.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":". Keen B. Engel. Analysis mixed model ordinal data iterative re-weighted REML. Statistica Neerlandica, 51, 129–144. Table 2. https://doi.org/10.1111/1467-9574.00044","code":""},{"path":"/reference/keen.potatodamage.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"R. Larsson & Jesper Ryden (2021). Applications discrete factor analysis. Communications Statistics - Simulation Computation. https://doi.org/10.1080/03610918.2021.1964528","code":""},{"path":"/reference/keen.potatodamage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(keen.potatodamage) dat <- keen.potatodamage # Energy E1, Rod R4, Weight W1 have higher proportions of severe damage # Rod 8 has the least damage d2 <- xtabs(count~energy+rod+gen+weight+damage, data=dat) mosaicplot(d2, color=c(\"lemonchiffon1\",\"moccasin\",\"lightsalmon1\",\"indianred\"), xlab=\"Energy / Genotype\", ylab=\"Rod / Weight\", main=\"keen.potatodamage\") # Not run because CRAN prefers examples less than 5 seconds. libs(ordinal) # Note, the clmm2 function can have only 1 random term. Results are # similar to Keen & Engle, but necessarily different (they had multiple # random terms). m1 <- clmm2(damage ~ rod + energy + gen + weight, data=dat, weights=count, random=rod:energy, link='probit') round(coef(m1)[4:10],2) ## rodR2 rodR3 rodR4 rodR5 rodR6 rodR7 rodR8 ## -1.19 -0.41 0.50 -1.46 -1.73 -1.67 -1.99 # Alternative # m2 <- clmm(damage ~ rod + energy + gen + weight + # (1|rod:energy), data=dat, weights=count, link='probit') } # }"},{"path":"/reference/kempton.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of barley — kempton.barley.uniformity","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"Uniformity trial barley Cambridge, England, 1978.","code":""},{"path":"/reference/kempton.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"data frame 196 observations following 3 variables. row row col column yield grain yield, kg","code":""},{"path":"/reference/kempton.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"uniformity trial spring barley planted 1978. Conducted Plant Breeding Institute Cambridge, England. plot 5 feet wide, 14 feet long. Field width: 7 plots * 14 feet = 98 feet Field length: 28 plots * 5 feet = 140 feet","code":""},{"path":"/reference/kempton.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"R. . Kempton C. W. Howes (1981). use neighbouring plot values analysis variety trials. Applied Statistics, 30, 59–70. https://doi.org/10.2307/2346657","code":""},{"path":"/reference/kempton.barley.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science. 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667","code":""},{"path":"/reference/kempton.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kempton.barley.uniformity) dat <- kempton.barley.uniformity libs(desplot) desplot(dat, yield~col*row, aspect=140/98, tick=TRUE, # true aspect main=\"kempton.barley.uniformity\") # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91 dat <- transform(dat, xf = factor(col), yf=factor(row)) # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) dat <- transform(dat, xf = factor(col), yf=factor(row)) m1 <- asreml(yield ~ 1, data=dat, resid = ~ar1(xf):ar1(yf)) # lucid::vc(m1) ## effect component std.error z.ratio bound ## xf:yf!R 0.1044 0.02197 4.7 P 0 ## xf:yf!xf!cor 0.2458 0.07484 3.3 U 0 ## xf:yf!yf!cor 0.8186 0.03821 21 U 0 # asreml estimates auto-regression correlations of 0.25, 0.82 # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91 } # ---------- if(0){ # Kempton defines 4 blocks, randomly assigns variety codes 1-49 in each block, fits # RCB model, computes mean squares for variety and residual. Repeat 40 times. # Kempton's estimate: variety = 1032, residual = 1013 # Our estimate: variety = 825, residual = 1080 fitfun <- function(dat){ dat <- transform(dat, block=factor(ceiling(row/7)), gen=factor(c(sample(1:49),sample(1:49),sample(1:49),sample(1:49)))) m2 <- lm(yield*100 ~ block + gen, dat) anova(m2)[2:3,'Mean Sq'] } set.seed(251) out <- replicate(50, fitfun(dat)) rowMeans(out) # 826 1079 } } # }"},{"path":"/reference/kempton.competition.html","id":null,"dir":"Reference","previous_headings":"","what":"Sugar beet trial with competition effects — kempton.competition","title":"Sugar beet trial with competition effects — kempton.competition","text":"Yield sugar beets 36 varieties 3-rep RCB experiment. Competition effects present.","code":""},{"path":"/reference/kempton.competition.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sugar beet trial with competition effects — kempton.competition","text":"data frame 108 observations following 5 variables. gen genotype, 36 levels rep rep, 3 levels row row col column yield yield, kg/plot","code":""},{"path":"/reference/kempton.competition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sugar beet trial with competition effects — kempton.competition","text":"Entries grown 12m rows, 0.5m apart. Guard rows grown alongside replicate boundaries, yields plots included.","code":""},{"path":"/reference/kempton.competition.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sugar beet trial with competition effects — kempton.competition","text":"R Kempton, 1982. Adjustment competition varieties plant breeding trials, Journal Agricultural Science, 98, 599-611. https://doi.org/10.1017/S0021859600054381","code":""},{"path":"/reference/kempton.competition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sugar beet trial with competition effects — kempton.competition","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kempton.competition) dat <- kempton.competition # Raw means in Kempton table 2 round(tapply(dat$yield, dat$gen, mean),2) # Fixed genotype effects, random rep effects, # Autocorrelation of neighboring plots within the same rep, phi = -0.22 libs(nlme) m1 <- lme(yield ~ -1+gen, random=~1|rep, data=dat, corr=corAR1(form=~col|rep)) # Lag 1 autocorrelation is negative--evidence of competition plot(ACF(m1), alpha=.05, grid=TRUE, main=\"kempton.competition\", ylab=\"Autocorrelation between neighborning plots\") # Genotype effects round(fixef(m1),2) # Variance of yield increases with yield plot(m1, main=\"kempton.competition\") } # }"},{"path":"/reference/kempton.rowcol.html","id":null,"dir":"Reference","previous_headings":"","what":"Row-column experiment of wheat — kempton.rowcol","title":"Row-column experiment of wheat — kempton.rowcol","text":"Row-column experiment wheat, 35 genotypes, 2 reps.","code":""},{"path":"/reference/kempton.rowcol.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Row-column experiment of wheat — kempton.rowcol","text":"data frame 68 observations following 5 variables. rep replicate factor, 2 levels row row col column gen genotype factor, 35 levels yield yield","code":""},{"path":"/reference/kempton.rowcol.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Row-column experiment of wheat — kempton.rowcol","text":"Included illustrate REML analysis row-column design.","code":""},{"path":"/reference/kempton.rowcol.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Row-column experiment of wheat — kempton.rowcol","text":"R Kempton P N Fox, Statistical Methods Plant Variety Evaluation, Chapman Hall, 1997.","code":""},{"path":"/reference/kempton.rowcol.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row-column experiment of wheat — kempton.rowcol","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kempton.rowcol) dat <- kempton.rowcol dat <- transform(dat, rowf=factor(row), colf=factor(col)) libs(desplot) desplot(dat, yield~col*row|rep, num=gen, out1=rep, # unknown aspect main=\"kempton.rowcol\") # Model with rep, row, col as random. Kempton, page 62. # Use \"-1\" so that the vcov matrix doesn't include intercept libs(lme4) m1 <- lmer(yield ~ -1 + gen + rep + (1|rep:rowf) + (1|rep:colf), data=dat) # Variance components match Kempton. print(m1, corr=FALSE) # Standard error of difference for genotypes. Kempton page 62, bottom. covs <- as.matrix(vcov(m1)[1:35, 1:35]) vars <- diag(covs) vdiff <- outer(vars, vars, \"+\") - 2 * covs sed <- sqrt(vdiff[upper.tri(vdiff)]) min(sed) # Minimum SED mean(sed) # Average SED max(sed) # Maximum SED } # }"},{"path":"/reference/kempton.slatehall.html","id":null,"dir":"Reference","previous_headings":"","what":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"Yields Slate Hall Farm 1976 spring wheat trial.","code":""},{"path":"/reference/kempton.slatehall.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"data frame 150 observations following 5 variables. rep rep, 6 levels row row col column gen genotype, 25 levels yield yield (grams/plot)","code":""},{"path":"/reference/kempton.slatehall.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"trial balanced lattice 25 varieties 6 replicates, 10 ranges 15 columns. plot size 1.5 meters 4 meters. row within rep (incomplete) block. Field width: 15 columns * 1.5m = 22.5m Field length: 10 ranges * 4m = 40m","code":""},{"path":"/reference/kempton.slatehall.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"R Kempton P N Fox. (1997). Statistical Methods Plant Variety Evaluation, Chapman Hall. Page 84. Julian Besag David Higdon. 1993. Bayesian Inference Agricultural Field Experiments. Bull. Int. Statist. Table 4.1.","code":""},{"path":"/reference/kempton.slatehall.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"Gilmour, Arthur R Robin Thompson Brian R Cullis. (1994). Average Information REML: Efficient Algorithm Variance Parameter Estimation Linear Mixed Models, Biometrics, 51, 1440-1450.","code":""},{"path":"/reference/kempton.slatehall.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kempton.slatehall) dat <- kempton.slatehall # Besag 1993 figure 4.1 (left panel) libs(desplot) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) desplot(dat, yield ~ col * row, aspect=40/22.5, # true aspect num=gen, out1=rep, col.regions=grays, # unknown aspect main=\"kempton.slatehall - spring wheat yields\") # ---------- # Incomplete block model of Gilmour et al 1995 libs(lme4, lucid) dat <- transform(dat, xf=factor(col), yf=factor(row)) m1 <- lmer(yield ~ gen + (1|rep) + (1|rep:yf) + (1|rep:xf), data=dat) vc(m1) ## groups name variance stddev ## rep:xf (Intercept) 14810 121.7 ## rep:yf (Intercept) 15600 124.9 ## rep (Intercept) 4262 65.29 ## Residual 8062 89.79 # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Incomplete block model of Gilmour et al 1995 dat <- transform(dat, xf=factor(col), yf=factor(row)) m2 <- asreml(yield ~ gen, random = ~ rep/(xf+yf), data=dat) lucid::vc(m2) ## effect component std.error z.ratio constr ## rep!rep.var 4262 6890 0.62 pos ## rep:xf!rep.var 14810 4865 3 pos ## rep:yf!rep.var 15600 5091 3.1 pos ## R!variance 8062 1340 6 pos # Table 4 # asreml4 # predict(m2, data=dat, classify=\"gen\")$pvals } } # }"},{"path":"/reference/kenward.cattle.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"Repeated measurements weights calves trial control intestinal parasites.","code":""},{"path":"/reference/kenward.cattle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"","code":"data(\"kenward.cattle\")"},{"path":"/reference/kenward.cattle.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"data frame 660 observations following 4 variables. animal animal factor trt treatment factor, B day day, numberic, 0-133 weight bodyweight, kg","code":""},{"path":"/reference/kenward.cattle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"Grazing cattle can ingest larvae, deprives host animal nutrients weakens immune system, affecting growth animal. Two treatments B applied randomly 60 animals (30 two groups) control disease. animal weighed 11 times two-week intervals (one week final two measurements). difference treatments, difference first become manifest?","code":""},{"path":"/reference/kenward.cattle.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"Kenward, Michael G. (1987). Method Comparing Profiles Repeated Measurements. Applied Statistics, 36, 296-308. Table 1. https://doi.org/10.2307/2347788","code":""},{"path":"/reference/kenward.cattle.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"W. Zhang, C. Leng C. Y. Tang (2015). joint modelling approach longitudinal studies J. R. Statist. Soc. B, 77 (2015), 219–238. https://doi.org/10.1111/rssb.12065","code":""},{"path":"/reference/kenward.cattle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kenward.cattle) dat <- kenward.cattle # Profile plots libs(lattice) foo1 <- xyplot(weight~day|trt, data=dat, type='l', group=animal, xlab=\"Day\", ylab=\"Animal weight\", main=\"kenward.cattle\") print(foo1) # ---------- # lme4. Fixed treatment intercepts, treatment polynomial trend. # Random deviation for each animal libs(lme4) m1a <-lmer(weight ~ trt*poly(day, 4) + (1|animal), data=dat, REML = FALSE) # Change separate polynomials into common polynomial m1b <-lmer(weight ~ trt + poly(day, 4) + (1|animal), data=dat, REML = FALSE) # Drop treatment differences m1c <-lmer(weight ~ poly(day, 4) + (1|animal), data=dat, REML = FALSE) anova(m1a, m1b, m1c) # Significant differences between trt polynomials # Overlay polynomial predictions on plot libs(latticeExtra) dat$pred <- predict(m1a, re.form=NA) foo1 + xyplot(pred ~ day|trt, data=dat, lwd=2, col=\"black\", type='l') # A Kenward-Roger Approximation and Parametric Bootstrap # libs(pbkrtest) # KRmodcomp(m1b, m1c) # Non-signif # Model comparison of nested models using parametric bootstrap methods # PBmodcomp(m1b, m1c, nsim=500) ## Parametric bootstrap test; time: 13.20 sec; samples: 500 extremes: 326; ## large : weight ~ trt + poly(day, 4) + (1 | animal) ## small : weight ~ poly(day, 4) + (1 | animal) ## stat df p.value ## LRT 0.2047 1 0.6509 ## PBtest 0.2047 0.6527 # ----------- # ASREML approach to model. Not final by any means. # Maybe a spline curve for each treatment, plus random deviations for each time if(require(\"asreml\", quietly=TRUE)){ libs(asreml) m1 <- asreml(weight ~ 1 + lin(day) + # overall line trt + trt:lin(day), # different line for each treatment data=dat, random = ~ spl(day) + # overall spline trt:spl(day) + # different spline for each treatment dev(day) + trt:dev(day) ) # non-spline deviation at each time*trt p1 <- predict(m1, data=dat, classify=\"trt:day\") p1 <- p1$pvals foo2 <- xyplot(predicted.value ~ day|trt, p1, type='l', lwd=2, lty=1, col=\"black\") libs(latticeExtra) print(foo1 + foo2) # Not much evidence for treatment differences # wald(m1) ## Df Sum of Sq Wald statistic Pr(Chisq) ## (Intercept) 1 37128459 139060 <2e-16 *** ## trt 1 455 2 0.1917 ## lin(day) 1 570798 2138 <2e-16 *** ## trt:lin(day) 1 283 1 0.3031 ## residual (MS) 267 # lucid::vc(m1) ## effect component std.error z.ratio constr ## spl(day) 25.29 24.09 1 pos ## dev(day) 1.902 4.923 0.39 pos ## trt:spl(day)!trt.var 0.00003 0.000002 18 bnd ## trt:dev(day)!trt.var 0.00003 0.000002 18 bnd ## R!variance 267 14.84 18 pos } } # }"},{"path":"/reference/kerr.sugarcane.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"Uniformity trials sugarcane, 4 fields","code":""},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"","code":"data(\"kerr.sugarcane.uniformity\")"},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"data frame 564 observations following 4 variables. row row col column yield yield, pounds per plot trial trial number","code":""},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"Experiment conducted Sugar Experiment Station, Brisbane, Queensland, Australia 1937. Four trials harvested, 12 plots 12 plots, plot 19 feet 19 feet (one field used 18-foot plots). Trial 1 plant cane. Trial 2 ratoon cane. Trial 3 plant cane, irrigated. Trial 4 ratoon cane, irrigated. Field length: 12 plots * 19 feet = 228 feet. Field width: 12 plots * 19 feet = 228 feet.","code":""},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"H. W. Kerr (1939). Notes plot technique. Proc. Internat. Soc. Sugarcane Technol. 6, 764–778.","code":""},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"None","code":""},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kerr.sugarcane.uniformity) dat <- kerr.sugarcane.uniformity # match Kerr figure 4 libs(desplot) desplot(dat, yield ~ col*row|trial, flip=TRUE, aspect=1, # true aspect main=\"kerr.sugarcane.uniformity\") # CV matches Kerr table 2, page 768 # aggregate(yield ~ trial, dat, FUN= function(x) round(100*sd(x)/mean(x),2)) ## trial yield ## 1 T1 7.95 ## 2 T2 9.30 ## 3 T3 10.37 ## 4 T4 13.76 } # }"},{"path":"/reference/khan.brassica.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of brassica. — khan.brassica.uniformity","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"Uniformity trial brassica India.","code":""},{"path":"/reference/khan.brassica.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"","code":"data(\"khan.brassica.uniformity\")"},{"path":"/reference/khan.brassica.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"data frame 648 observations following 4 variables. field Field, F1 F2 row row ordinate col column ordinate yield yield, 1/8 ounce","code":""},{"path":"/reference/khan.brassica.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"Two different fields used, representing average type soil Lyallpur. area 90 ft 90 ft marked harvested individual plots 5 feet per side. data copied pdf hand-corrected.","code":""},{"path":"/reference/khan.brassica.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"Khan, Abdur Rashid Jage Ram Dalal (1943). Optimum Size Shape Plots Brassica Experiments Punjab. Sankhyā: Indian Journal Statistics ,6, 3. Proceedings Indian Statistical Conference 1942 (1943), pp. 317-320. https://www.jstor.org/stable/25047782","code":""},{"path":"/reference/khan.brassica.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"None.","code":""},{"path":"/reference/khan.brassica.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(khan.brassica.uniformity) dat <- khan.brassica.uniformity # Slightly different results than Khan Table 1. ## dat ## mutate(yield=yield/8) ## group_by(field) ## summarize(mn=mean(yield), sd=sd(yield)) libs(desplot) desplot(dat, yield ~ col*row | field, flip=TRUE, aspect=1, main=\"khan.brassica.uniformity\") } # }"},{"path":"/reference/khin.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice — khin.rice.uniformity","title":"Uniformity trial of rice — khin.rice.uniformity","text":"Uniformity trial rice Burma, 1948.","code":""},{"path":"/reference/khin.rice.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of rice — khin.rice.uniformity","text":"","code":"data(\"khin.rice.uniformity\")"},{"path":"/reference/khin.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice — khin.rice.uniformity","text":"data frame 1080 observations following 3 variables. row row col column yield yield, oz/plot","code":""},{"path":"/reference/khin.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice — khin.rice.uniformity","text":"uniformity trial rice. Conducted Mudon Agricultural Station, Burma, 1947-48. Basic plots 3 feet square. Field width: 30 plots * 3 feet. Field length: 36 plots * 3 feet. Data typed K.Wright.","code":""},{"path":"/reference/khin.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice — khin.rice.uniformity","text":"Khin, San. 1950. Investigation relative costs rice experiments based efficiency designs. Dissertation: Imperial College Tropical Agriculture (ICTA). Appendix XV. https://hdl.handle.net/2139/42422","code":""},{"path":"/reference/khin.rice.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of rice — khin.rice.uniformity","text":"None.","code":""},{"path":"/reference/khin.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice — khin.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(khin.rice.uniformity) dat <- khin.rice.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, main=\"khin.rice.uniformity\", aspect=(36*3)/(30*3)) # true aspect } # }"},{"path":"/reference/kiesselbach.oats.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of oats — kiesselbach.oats.uniformity","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"Uniformity trial oats Nebraska 1916.","code":""},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"","code":"data(\"kiesselbach.oats.uniformity\")"},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"data frame 207 observations following 3 variables. row row col column yield yield bu/ac","code":""},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"Experiment conducted 1916. Crop Kerson oats. plot covered 1/30th acre. Oats drilled plats 66 inches wide 16 rods long. drill 66 inches wide. Plats separated space 16 inches outside drill rows. source document includes three photographs field. 1 acre = 43560 sq feet 1/30 acre = 1452 sq feet = 16 rods * 16.5 ft/rod * 5.5 ft Field width: 3 plats * 16 rods/plat * 16.5 ft/rod = 792 feet Field length: 69 plats * 5.5 ft + 68 gaps * 1.33 feet = 469 feet","code":""},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"Kiesselbach, Theodore . (1917). Studies Concerning Elimination Experimental Error Comparative Crop Tests. University Nebraska Agricultural Experiment Station Research Bulletin . 13. Pages 51-72. https://archive.org/details/StudiesConcerningTheEliminationOfExperimentalErrorInComparativeCrop https://digitalcommons.unl.edu/extensionhist/430/","code":""},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"None.","code":""},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kiesselbach.oats.uniformity) dat <- kiesselbach.oats.uniformity range(dat$yield) # 56.7 92.8 match Kiesselbach p 64. libs(desplot) desplot(dat, yield ~ col*row, tick=TRUE, flip=TRUE, aspect=792/469, # true aspect main=\"kiesselbach.oats.uniformity\") } # }"},{"path":"/reference/kirk.potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Variety trial of potatoes, highly replicated — kirk.potato","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"Variety trial potatoes, highly replicated","code":""},{"path":"/reference/kirk.potato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"","code":"data(\"kirk.potato\")"},{"path":"/reference/kirk.potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"data frame 380 observations following 5 variables. row row ordinate col column ordinate rep replicate (block) gen genotype (variety) yield yield, pounds per plot","code":""},{"path":"/reference/kirk.potato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"highly-replicated variety trial potatoes planted 1924 check plots every 5th row. Entries randomized. rod rows planted series across field, rows spaced five links apart (nearly 3.5 feet) 3.5 foot passes series. replicates sometimes dis-jointed, really blocks.","code":""},{"path":"/reference/kirk.potato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"Kirk, L. E. C. H. Goulden (1925) statistical observations yield test potato varieties. Scientific Agriculture, 6, 89-97. https://doi.org/10.4141/sa-1925-0088 (paywall) https://www.google.com/books/edition/Canadian_Journal_of_Agriculture_Science/TgIkAQAAMAAJ","code":""},{"path":"/reference/kirk.potato.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"None","code":""},{"path":"/reference/kirk.potato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kirk.potato) dat <- kirk.potato libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=1, main=\"kirk.potato\") # Match means in Table I libs(dplyr) dat } # }"},{"path":"/reference/kling.augmented.html","id":null,"dir":"Reference","previous_headings":"","what":"Augmented design of meadowfoam — kling.augmented","title":"Augmented design of meadowfoam — kling.augmented","text":"Augmented design meadowfoam","code":""},{"path":"/reference/kling.augmented.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augmented design of meadowfoam — kling.augmented","text":"","code":"data(\"kling.augmented\")"},{"path":"/reference/kling.augmented.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Augmented design of meadowfoam — kling.augmented","text":"data frame 68 observations following 7 variables. plot Plot number gen Genotype / Entry name Genotype name block Block, text tsw Thousand seed weight row Row ordinate col Column ordinate","code":""},{"path":"/reference/kling.augmented.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Augmented design of meadowfoam — kling.augmented","text":"experiment meadowfoam. Blocks one direction, serpentine layout. 50 new genotypes 3 checks (C1=Ross, C2=OMF183, C3=Starlight). New genotypes 1 rep, checks 6 reps. response variable thousand seed weight.","code":""},{"path":"/reference/kling.augmented.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Augmented design of meadowfoam — kling.augmented","text":"Jennifer Kling, \"Introduction Augmented Experimental Design\" https://plant-breeding-genomics.extension.org/introduction--augmented-experimental-design/ Accessed May 2022.","code":""},{"path":"/reference/kling.augmented.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Augmented design of meadowfoam — kling.augmented","text":"None","code":""},{"path":"/reference/kling.augmented.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Augmented design of meadowfoam — kling.augmented","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kling.augmented) dat <- kling.augmented libs(desplot,lattice,lme4) # Layout and yields desplot(dat, tsw ~ col*row, text=name, cex=1.5) # Mixed model, fixed blocks, random genotypes m1 <- lmer(tsw ~ block + (1|name), data=dat) ran1 <- ranef(m1, condVar=TRUE) ran1 dotplot(ran1) # Caterpillar plot } # }"},{"path":"/reference/kreusler.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"Growth maize plants Germany 1875-1878.","code":""},{"path":"/reference/kreusler.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"","code":"data(\"kreusler.maize\")"},{"path":"/reference/kreusler.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"data frame 165 observations following 17 variables. gen genotype year year date calendar date raindays number days rain per week (zahl der regenstage) rain rain amount (mm) temp temperature mean (deg C) (temperatur mittel) parentseed weight parent seed (g) (alte korner) roots weight roots (g) (wurzel) leaves weight leaves (g) (blatter) stem weight stem (g) (stengel) tassel weight tassel (g) (blutenstande) grain weight grain (korner) plantweight weight entire plant (ganze pflanze) plantheight plant height (cm) (mittlere hohe der pflanzen) leafcount number leaves (anzahl der blatter) leafarea leaf area (cm^2) (flachenmaass der blatter)","code":""},{"path":"/reference/kreusler.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"Experiments performed Poppelsdorf, Germany (near Bonn) years 1875 1878. Observations collected weekly throughout growing season. Five varieties grown 1875. Two 1876, one 1877 1878. plants selected eye representative, number plants chosen decreasing growing season. example, dry-weight data based following number plants: 1875 number sampled began 20 dropped 10. 1876 number sampled began 45 dropped 24. 1877 number sampled began 90 dropped 36. 1878 number sampled began 120 dropped 40. observations included fresh weight dry weight entire plants, along leaf area, date inflorescence, fertilization, kernel development. data Hornberger 71 Kreusler/Hornberger, complete. temperature data originally given degrees Reaumur 1875 1876, degrees Celsius 1877 1878. temperatures data degrees Celsius. Note: deg C = 1.25 deg R. Briggs, Kidd & West (1920) give temperature Celsius.","code":""},{"path":"/reference/kreusler.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"1875-1876 data : . Prehn & G. Becker. (1878) Jahresbericht fur Agrikultur-chemie, Vol 20, p. 216-220. https://books.google.com/books?id=ZfxNAAAAYAAJ&pg=216 1877 data : . Kreusler, . Prehn, Hornberger. (1880) Jahresbericht fur Agrikultur-Chemie, Vol 21, p 248. https://books.google.com/books?id=U3IYAQAAIAAJ&pg=248 1878 data : U. Kreusler, . Prehn, R. Hornberger. (1880). Jahresbericht fur Agrikultur-Chemie, Vol 22, p. 211. https://books.google.com/books?id=9HIYAQAAIAAJ&pg=211 Dry plant weight leaf area genotypes years repeated : G. E. Briggs, Franklin Kidd, Cyril West. (1920). Quantitative Analysis Plant Growth. Part . Annals Applied Biology, 7, 103-123. G. E. Briggs, Franklin Kidd, Cyril West. (1920). Quantitative Analysis Plant Growth. Part II. Annals Applied Biology, 7, 202-223.","code":""},{"path":"/reference/kreusler.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"Roderick Hunt, G. Clifford Evans. 1980. Classical Data Growth Maize: Curve Fitting Statistical Analysis. New Phytol, 86, 155-180.","code":""},{"path":"/reference/kreusler.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"","code":"if (FALSE) { # \\dontrun{ data(kreusler.maize) dat <- kreusler.maize dat$date2 <- as.Date(dat$date,\"%d %b %Y\") dat$doy <- as.numeric(strftime(dat$date2, format=\"%j\")) # Hunt & Evans Fig 2a libs(lattice) xyplot(log10(plantweight)~doy|factor(year), data=dat, group=gen, type=c('p','smooth'), span=.4, as.table=TRUE, xlab=\"Day of year\", main=\"kreusler.maize - growth of maize\", auto.key=list(columns=5)) # Hunt & Evans Fig 2b xyplot(log10(plantweight)~doy|gen, data=dat, group=factor(year), type=c('p','smooth'), span=.5, as.table=TRUE, xlab=\"Day of year\", auto.key=list(columns=4)) # Hunt & Evans Fig 3a xyplot(log10(leafarea)~doy|factor(year), data=dat, group=gen, type=c('p','smooth'), span=.5, as.table=TRUE, xlab=\"Day of year\", auto.key=list(columns=5)) # Hunt & Evans Fig 3a xyplot(log10(leafarea)~doy|gen, data=dat, group=factor(year), type=c('p','smooth'), span=.5, as.table=TRUE, xlab=\"Day of year\", auto.key=list(columns=4)) # All traits xyplot(raindays~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(rain~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(temp~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(parentseed~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(roots~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(leaves~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(stem~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(grain~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(plantweight~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(plantheight~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(leafcount~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(leafarea~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(tassel~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) } # }"},{"path":"/reference/kristensen.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of barley — kristensen.barley.uniformity","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"Uniformity trial barley conducted Denmark, 1905.","code":""},{"path":"/reference/kristensen.barley.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"","code":"data(\"kristensen.barley.uniformity\")"},{"path":"/reference/kristensen.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"data frame 718 observations following 3 variables. row row col column yield yield, hectograms/plot","code":""},{"path":"/reference/kristensen.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"Experiment conducted 1905 Askov, Denmark. Harvested plot size 10 x 14 'alen', 6.24 x 8.79 meters. soil uniform, attack mildew spread adjacent field. Yield measured hectograms/plot straw grain together. (Page 468). Orientation plots dimensions clear text, aspect used example aligns well Kristensen figure 1. Field width: 22 plots * 8.79 m Field length: 11 plots * 6.24 m Notes Kristensen: Fig 5 3x3 moving average, Fig 6 deviation trend, Fig 7 field average added deviation. Fig 13 another uniformity trial barley 1924, Fig 14 uniformity trial oats 1924.","code":""},{"path":"/reference/kristensen.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"R. K. Kristensen (1925). Anlaeg og Opgoerelse af Markforsoeg. Tidsskrift landbrugets planteavl, Vol 31, 464-494. Fig 1, pg. 467. https://dca.au.dk/publikationer/historiske/planteavl/","code":""},{"path":"/reference/kristensen.barley.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"J. Neyman, K. Iwaszkiewicz, St. Kolodziejczyk. (1935). Statistical Problems Agricultural Experimentation. Supplement Journal Royal Statistical Society, Vol. 2, . 2 (1935), pp. 107-180. https://doi.org/10.2307/2983637","code":""},{"path":"/reference/kristensen.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kristensen.barley.uniformity) dat <- kristensen.barley.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(11*6.24)/(22*8.79), main=\"kristensen.barley.uniformity\") } # }"},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"Uniformity trial sorghum India, 3 years plots 1930-1932.","code":""},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"","code":"data(\"kulkarni.sorghum.uniformity\")"},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"data frame 480 observations following 4 variables. row row col column yield grain yield, tolas per plot year year","code":""},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"experiment conducted Sholapur district India three consecutive years 1930-1932. One acre land (290 ft x 150 ft) chosen midst bigger area (plot 13 Mohol Plot) sowing sorghum. harvested plots 1/160 acre (72 ft 6 x 3 ft 9 ) containing three rows plants 15 . apart. 160 plots arranged forty rows four columns, yields measured tolas. plot division kept intact three years, yields 160 plots available three consecutive harvests. original data given Appendix . Field width: 4 plots * 72.5 feet = 290 feet Field length: 40 plots * 3.75 feet = 150 feet Conclusions: \"Thus, highly narrow strips plots (length much greater breadth) lead greater precision plots area much wider narrow.\" Correlation plots year years low.","code":""},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"Kulkarni, R. K., Bose, S. S., Mahalanobis, P. C. (1936). influence shape size plots effective precision field experiments sorghum. Indian J. Agric. Sci., 6, 460-474. Appendix 1, page 172. https://archive.org/details/.ernet.dli.2015.271737","code":""},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"None.","code":""},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kulkarni.sorghum.uniformity) dat <- kulkarni.sorghum.uniformity # match means on page 462 # tapply(dat$yield, dat$year, mean) # 1930 1931 1932 # 116.2875 67.2250 126.3688 libs(reshape2) libs(lattice) dmat <- acast(dat, row+col ~ year, value.var=\"yield\") splom(dmat, main=\"kulkarni.sorghum.uniformity\") cor(dmat) libs(desplot) desplot(dat, yield ~ col*row|year, flip=TRUE, aspect=150/290, main=\"kulkarni.sorghum.uniformity\") } # }"},{"path":"/reference/lambert.soiltemp.html","id":null,"dir":"Reference","previous_headings":"","what":"Average monthly soil temperature near Zurich — lambert.soiltemp","title":"Average monthly soil temperature near Zurich — lambert.soiltemp","text":"Average monthly soil temperature near Zurich, seven depths, averaged four years.","code":""},{"path":"/reference/lambert.soiltemp.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Average monthly soil temperature near Zurich — lambert.soiltemp","text":"data frame 84 observations following 3 variables. month month depth depth soil (feet) temp temperature (units \"du Crest\")","code":""},{"path":"/reference/lambert.soiltemp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Average monthly soil temperature near Zurich — lambert.soiltemp","text":"one earliest time series scientific literature. data show monthly soil temperature near Zurich, averaged four years (beginning 1762), 7 different depths. temperature measurements related 'du Crest' scale. (measurements seem exactly according du Crest scale. can read German, use Google books link see can figure .) Even scale Lambert's graph match data. Greater depths show less variation greater lag temperature responsiveness air temperature. data also appears Pedometrics, issue 23, December 2007. , formula converting temperature make sense data Table 1 directly match corresponding figure.","code":""},{"path":"/reference/lambert.soiltemp.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Average monthly soil temperature near Zurich — lambert.soiltemp","text":"Johann Heinrich Lambert (1779), Pyrometrie. Page 358. https://books.google.com/books?id=G5I_AAAAcAAJ&pg=PA358 Graph: https://www.fisme.science.uu.nl/wiskrant/artikelen/hist_grafieken/begin/images/pyrometrie.gif","code":""},{"path":"/reference/lambert.soiltemp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average monthly soil temperature near Zurich — lambert.soiltemp","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) # Reproduce Lambert figure 39. data(lambert.soiltemp) dat <- lambert.soiltemp # Make 3 cycles of the data so that the loess line bends back up at # month 1 and month 12 dat <- rbind(dat, transform(dat, month=month-12), transform(dat, month=month+12)) libs(lattice) xyplot(temp ~ month, dat, group=depth, type=c('p','smooth'), main=\"lambert.soiltemp\", xlim=c(-3,15), ylab=\"Soil temperature (du Crest) at depth (feet)\", span=.2, auto.key=list(columns=4)) # To do: Find a good model for this data } # }"},{"path":"/reference/lander.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"Uniformity trials wheat chari, 4 years land, India.","code":""},{"path":"/reference/lander.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"","code":"data(\"lander.multi.uniformity\")"},{"path":"/reference/lander.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"data frame 780 observations following 5 variables. row row col column yield yield, maunds per plot year year crop crop","code":""},{"path":"/reference/lander.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"Note, \"chari\" paper Andropogon Sorghum, \"wheat\" Triticum vulgare. Uniformity trials carried Rawalpindi, India. area consisted 5 fields (D4,D5,D6,D7,D8), 5 acres size. 5 fields divided three sub-divisions , B, C, means two strong bunds 5 feet wide. 3 sub-divisions divided 5 blocks, consisting 13 experimental plots 14 non-experiment strips 5 feet wide separating plots . dimensions plot 207 ft 5 19 ft 1 . land used 4 consecutive crops. first crop wheat, followed chari (sorghum), followed wheat 2 times. Field width: 207.42 * 5 plots = 1037.1 feet Field length: (19.08+5)*39 rows = 939.12 feet Conclusions: evident, therefore, soil heterogenity revealed one crop true index subsequent behavior area respect crops. Even crop raised different seasons shown constancy regards soil heterogeneity.","code":""},{"path":"/reference/lander.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"Lander, P. E. et al. (1938). Soil Uniformity Trials Punjab . Ind. J. Agr. Sci. 8:271-307.","code":""},{"path":"/reference/lander.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"None","code":""},{"path":"/reference/lander.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lander.multi.uniformity) dat <- lander.multi.uniformity # Yearly means, similar to Lander table 7 ## filter(dat) ## 1 1929 18.1 ## 2 1930 58.3 ## 3 1931 22.8 ## 4 1932 14.1 # heatmaps for all years libs(desplot) dat$year <- factor(dat$year) desplot(dat, yield ~ col*row|year, flip=TRUE, aspect=(1037.1/939.12), main=\"lander.multi.uniformity\") } # }"},{"path":"/reference/lasrosas.corn.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"Yield monitor data corn field Argentina variable nitrogen.","code":""},{"path":"/reference/lasrosas.corn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"","code":"data(\"lasrosas.corn\")"},{"path":"/reference/lasrosas.corn.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"data frame 3443 observations following 8 variables. year year, 1999 2001 lat latitude long longitude yield yield, quintals/ha nitro nitrogen fertilizer, kg/ha topo topographic factor bv brightness value (proxy low organic matter content) rep rep factor nf nitrogen factor, N0-N4","code":""},{"path":"/reference/lasrosas.corn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"Corn yield nitrogen fertilizer treatment field characteristics Las Rosas farm, Rio Cuarto, Cordoba, Argentina. Data 6 nitro treatments, 3 reps, strips. Data collected using yield monitor, harvests 1999 2001. points within long strip averaged distance points _within_ strip distance _between_ strips (9.8 meters). topographic factor factor levels W = West slope, HT = Hilltop, E = East slope, LO = Low East. 'rep' factor data added hand appear original data. Slightly different levels nitrogen used two years, nitrogen factor 'nf' created common levels across years. Published descriptions data describe experiment design randomized nitrogen treatments. nitrogen treatments randomized within one rep, randomization used two reps. Anselin et al. used corn grain price $6.85/quintal nitrogen cost $0.4348/kg. corners field 1999 : https://www.google.com/maps/place/-33.0501258,-63.8488636 https://www.google.com/maps/place/-33.05229635,-63.84181819 Anselin et al. found significant response nitrogen slope. However, Bongiovanni Lowenberg-DeBoer (2002) found slope position significant 2001. Used permission ASU GeoDa Center.","code":""},{"path":"/reference/lasrosas.corn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"Las Rosas data files obtained https://geodacenter.asu.edu/sdata converted ESRI shape files flat data.frame.","code":""},{"path":"/reference/lasrosas.corn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"Bongiovanni Lowenberg-DeBoer (2000). Nitrogen management corn spatial regression model. Proceedings Fifth International Conference Precision Agriculture. Anselin, L., R. Bongiovanni, J. Lowenberg-DeBoer (2004). spatial econometric approach economics site-specific nitrogen management corn production. American Journal Agricultural Economics, 86, 675–687. https://doi.org/10.1111/j.0002-9092.2004.00610.x Lambert, Lowenberg-Deboer, Bongiovanni (2004). Comparison Four Spatial Regression Models Yield Monitor Data: Case Study Argentina. Precision Agriculture, 5, 579-600. https://doi.org/10.1007/s11119-004-6344-3 Suman Rakshit, Adrian Baddeley, Katia Stefanova, Karyn Reeves, Kefei Chen, Zhanglong Cao, Fiona Evans, Mark Gibberd (2020). Novel approach analysis spatially-varying treatment effects -farm experiments. Field Crops Research, 255, 15 September 2020, 107783. https://doi.org/10.1016/j.fcr.2020.107783","code":""},{"path":"/reference/lasrosas.corn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lasrosas.corn) dat <- lasrosas.corn # yield map libs(lattice,latticeExtra) # for panel.levelplot.points redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(yield ~ long*lat|factor(year), data=dat, main=\"lasrosas.corn grain yield\", xlab=\"Longitude\", ylab=\"Latitude\", scales=list(alternating=FALSE), prepanel = prepanel.default.xyplot, panel = panel.levelplot.points, type = c(\"p\", \"g\"), aspect = \"iso\", col.regions=redblue) d1 <- subset(dat, year==1999) # Experiment design xyplot(lat~long, data=d1, col=as.numeric(as.factor(d1$nitro)), pch=d1$topo, main=\"lasrosas.corn experiment layout 1999\") # A quadratic response to nitrogen is suggested xyplot(yield~nitro|topo, data=d1, type=c('p','smooth'), layout=c(4,1), main=\"lasrosas.corn yield by topographic zone 1999\") # Full-field quadratic response to nitrogen. Similar to Bongiovanni 2000, # table 1. m1 <- lm(yield ~ 1 + nitro + I(nitro^2), data=d1, subset=year==1999) coef(m1) } # }"},{"path":"/reference/lavoranti.eucalyptus.html","id":null,"dir":"Reference","previous_headings":"","what":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"Height Eucalyptus trees southern Brazil","code":""},{"path":"/reference/lavoranti.eucalyptus.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"data frame 490 observations following 4 variables. gen genotype (progeny) factor origin origin progeny loc location height height, meters","code":""},{"path":"/reference/lavoranti.eucalyptus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"genotypes originated three different locations Queensland, Australia, tested southern Brazil. experiment conducted randomized complete block design 6 plants per plot 10 blocks. Mean tree height reported. testing locations described following table: Arciniegas-Alarcon (2010) used 'Ravenshoe' subset data illustrate imputation missing values.","code":""},{"path":"/reference/lavoranti.eucalyptus.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"O J Lavoranti (2003). Estabilidade e adaptabilidade fenotipica atraves da reamostragem bootstrap modelo AMMI, PhD thesis, University Sao Paulo, Brazil.","code":""},{"path":"/reference/lavoranti.eucalyptus.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"Arciniegas-Alarcon, S. Garcia-Pena, M. dos Santos Dias, C.T. Krzanowski, W.J. (2010). alternative methodology imputing missing data trials genotype--environment interaction, Biometrical Letters, 47, 1-14. https://doi.org/10.2478/bile-2014-0006","code":""},{"path":"/reference/lavoranti.eucalyptus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"","code":"if (FALSE) { # \\dontrun{ # Arciniegas-Alarcon et al use SVD and regression to estimate missing values. # Partition the matrix X as a missing value xm, row vector xr1, column # vector xc1, and submatrix X11 # X = [ xm xr1 ] # [ xc1 X11 ] and let X11 = UDV'. # Estimate the missing value xm = xr1 V D^{-1} U' xc1 data(lavoranti.eucalyptus) dat <- lavoranti.eucalyptus libs(lattice) levelplot(height~loc*gen, dat, main=\"lavoranti.eucalyptus - GxE heatmap\") dat <- droplevels(subset(dat, origin==\"Ravenshoe\")) libs(reshape2) dat <- acast(dat, gen~loc, value.var='height') dat[1,1] <- NA x11 <- dat[-1,][,-1] X11.svd <- svd(x11) xc1 <- dat[-1,][,1] xr1 <- dat[,-1][1,] xm <- xr1 xm # = 18.29, Original value was 17.4 } # }"},{"path":"/reference/laycock.tea.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of tea — laycock.tea.uniformity","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"Uniformity trials tea","code":""},{"path":"/reference/laycock.tea.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"","code":"data(\"laycock.tea.uniformity\")"},{"path":"/reference/laycock.tea.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"data frame 54 observations following 4 variables. loc location, L1 L2 row row col column yield yield (pounds)","code":""},{"path":"/reference/laycock.tea.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"Actual physical dimensions tea shrubs given, use estimate four feet square shrub (similar eden.tea.uniformity experiment). Location 1 (Laycock, page 108) Research Station, Nyasaland. Plots 10 15 bushes, harvested 23 times 1942. Field length: 8 plots * 10 bushes * 4 feet = 320 feet. Field width: 4 plots * 15 bushes * 4 feet = 240 feet. Location 2 (Laycock page 110) Mianga Estate, Nyasaland. Plots 9 11 bushes, harvested 18 times 1951/52. Field length: 9 plots * 9 bushes * 4 feet = 324 feet. Field width: 6 plots * 11 bushes * 4 feet = 264 feet.","code":""},{"path":"/reference/laycock.tea.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"Laycock, D. H. (1955). effect plot shape reducing errors tea experiments. Tropical Agriculture, 32, 107-114.","code":""},{"path":"/reference/laycock.tea.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"Zimmerman, Dale L., David . Harville. (1991). random field approach analysis field-plot experiments spatial experiments. Biometrics, 47, 223-239.","code":""},{"path":"/reference/laycock.tea.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(laycock.tea.uniformity) dat <- laycock.tea.uniformity libs(desplot) desplot(dat, yield ~ col*row|loc, flip=TRUE, aspect=322/252, # average of 2 locs main=\"laycock.tea.uniformity\") } # }"},{"path":"/reference/lee.potatoblight.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeated measurements of resistance to potato blight — lee.potatoblight","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"Repeated measurements resistance potato blight.","code":""},{"path":"/reference/lee.potatoblight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"","code":"data(\"lee.potatoblight\")"},{"path":"/reference/lee.potatoblight.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"data frame 14570 observations following 7 variables. year planting year gen genotype / cultivar factor col column row row rep replicate block (numeric) date date data collection y score 1-9 blight resistance","code":""},{"path":"/reference/lee.potatoblight.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"data werre collected biennial screening trials conducted New Zealand Institute Crop Food Research Pukekohe Field Station. trials evaluate resistance potato cultivars late blight caused fungus Phytophthora infestans. trial, damage necrotic tissue rated 1-9 scale multiple time points growing season. Lee (2009) used Bayesian model extends ordinal regression McCullagh include spatial variation sigmoid logistic curves model time dependence repeated measurements plot. Data 1989 included due different trial setup used. trials laid latinized row-column designs 4 5 reps. plot consisted four seed tubers planted two Ilam Hardy spread plants single row 2 meters long 76 centimeter spacing rows. 1997, 18 plots lost due flooding. 2001, end season plants nearly dead. Note, plant-breeding, common use \"breeder code\" genotype, several years testing changed registered commercial variety name. R package, Potato Pedigree Database, https://www.plantbreeding.wur.nl/potatopedigree/reverselookup.php, used change breeder codes (early testing) variety names used later testing. example, among changes made following: Used permission Arier Chi-Lun Lee John Anderson. Data retrieved https://researchspace.auckland.ac.nz/handle/2292/5240. Licensed via Open Database License 1.0. (allows sub-licensing). See: https://opendatacommons.org/licenses/dbcl/1.0/","code":""},{"path":"/reference/lee.potatoblight.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"Lee, Arier Chi-Lun (2009). Random effects models ordinal data. Ph.D. thesis, University Auckland. https://researchspace.auckland.ac.nz/handle/2292/4544.","code":""},{"path":"/reference/lee.potatoblight.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lee.potatoblight) dat <- lee.potatoblight # Common cultivars across years. # Based on code from here: https://stackoverflow.com/questions/20709808 gg <- tapply(dat$gen, dat$year, function(x) as.character(unique(x))) tab <- outer(1:11, 1:11, Vectorize(function(a, b) length(Reduce(intersect, gg[c(a, b)])))) head(tab) # Matches Lee page 27. ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] ## [1,] 20 10 7 5 3 2 3 2 3 3 2 ## [2,] 10 30 17 5 4 3 4 4 5 4 2 ## [3,] 7 17 35 9 6 3 4 5 6 4 3 ## [4,] 5 5 9 35 16 8 9 14 15 13 11 ## [5,] 3 4 6 16 40 12 11 18 18 16 14 # Note the progression to lower scores as time passes in each year skp <- c(rep(0,10), rep(0,7),1,1,1, rep(0,8),1,1, rep(0,6),1,1,1,1, rep(0,5),1,1,1,1,1, rep(0,5),1,1,1,1,1, rep(0,6),1,1,1,1, rep(0,5),1,1,1,1,1, rep(0,5),1,1,1,1,1, rep(0,5),1,1,1,1,1) libs(desplot) desplot(dat, y ~ col*row|date, ylab=\"Year of testing\", # unknown aspect layout=c(10,11),skip=as.logical(skp), main=\"lee.potatoblight - maps of blight resistance over time\") # 1983 only. I.Hardy succumbs to blight quickly libs(lattice) xyplot(y ~ date|gen, dat, subset=year==1983, group=rep, xlab=\"Date\", ylab=\"Blight resistance score\", main=\"lee.potatoblight 1983\", as.table=TRUE, auto.key=list(columns=5), scales=list(alternating=FALSE, x=list(rot=90, cex=.7))) } # }"},{"path":"/reference/lehmann.millet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of millet in India — lehmann.millet.uniformity","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"Uniformity trial millet India, 3 years land.","code":""},{"path":"/reference/lehmann.millet.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"","code":"data(\"lehmann.millet.uniformity\")"},{"path":"/reference/lehmann.millet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"data frame 396 observations following 5 variables. year year plot plot (row) range range (column) yield grain yield (pounds) total total crop yield (pounds)","code":""},{"path":"/reference/lehmann.millet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"Experiment farm near Bangalore. plots 1/10 acre, 50 links wide 200 links long. [6th report, p. 2]. middle part field occupied buildings. 6th report: Map (partially scanned pdf). \"part dry lands nearest tank, quite uniform remainder, already excluded experimental ground proper\". 7th report: P. 12 (pdf page 233) grain/straw yield 1905. 9th report: P. 1-10 comments. P. 36-39 data: Table 1 grain yield, table 2 total yield grain straw. Columns , left-right, -F. Rows , top-bottom, 1-22. season 1906 abnormally wet compared 1905 1907. [9th report] Field width: 6 plots * 200 links Field length: 22 plots * 50 links","code":""},{"path":"/reference/lehmann.millet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"Lehmann, . Ninth Annual Report Agricultural Chemist Year 1907-08. Department Agriculture, Mysore State. [2nd-9th] Annual Report Agricultural Chemist. https://books.google.com/books?id=u_dHAAAAYAAJ","code":""},{"path":"/reference/lehmann.millet.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"Theodor Roemer (1920). Der Feldversuch. Page 69, table 13.","code":""},{"path":"/reference/lehmann.millet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lehmann.millet.uniformity) dat <- lehmann.millet.uniformity libs(desplot) dat$year = factor(dat$year) desplot(dat, yield ~ range*plot|year, aspect=(22*50)/(6*200), main=\"lehmann.millet.uniformity\", flip=TRUE, tick=TRUE) desplot(dat, total ~ range*plot|year, aspect=(22*50)/(6*200), main=\"lehmann.millet.uniformity\", flip=TRUE, tick=TRUE) # libs(dplyr) # group_by(dat, year) } # }"},{"path":"/reference/lehner.soybeanmold.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"Yield, white mold, sclerotia soybeans Brazil","code":""},{"path":"/reference/lehner.soybeanmold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"","code":"data(\"lehner.soybeanmold\")"},{"path":"/reference/lehner.soybeanmold.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"data frame 382 observations following 9 variables. study study number year year harvest loc location name elev elevation region region trt treatment number yield crop yield, kg/ha mold white mold incidence, percent sclerotia weight sclerotia g/ha","code":""},{"path":"/reference/lehner.soybeanmold.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"Data mean 4 reps. Original source (Portuguese) https://ainfo.cnptia.embrapa.br/digital/bitstream/item/101371/1/Ensaios-cooperativos-de-controle-quimico-de-mofo-branco-na-cultura-da-soja-safras-2009--2012.pdf Data included via GPL3 license.","code":""},{"path":"/reference/lehner.soybeanmold.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"Lehner, M. S., Pethybridge, S. J., Meyer, M. C., & Del Ponte, E. M. (2016). Meta-analytic modelling incidence-yield incidence-sclerotial production relationships soybean white mould epidemics. Plant Pathology. doi:10.1111/ppa.12590","code":""},{"path":"/reference/lehner.soybeanmold.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"Full commented code analysis https://emdelponte.github.io/paper-white-mold-meta-analysis/","code":""},{"path":"/reference/lehner.soybeanmold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lehner.soybeanmold) dat <- lehner.soybeanmold if(0){ op <- par(mfrow=c(2,2)) hist(dat$mold, main=\"White mold incidence\") hist(dat$yield, main=\"Yield\") hist(dat$sclerotia, main=\"Sclerotia weight\") par(op) } libs(lattice) xyplot(yield ~ mold|study, dat, type=c('p','r'), main=\"lehner.soybeanmold\") # xyplot(sclerotia ~ mold|study, dat, type=c('p','r')) # meta-analysis. Could use metafor package to construct the forest plot, # but latticeExtra is easy; ggplot is slow/clumsy libs(latticeExtra, metafor) # calculate correlation & confidence for each loc cors <- split(dat, dat$study) cors <- sapply(cors, FUN=function(X){ res <- cor.test(X$yield, X$mold) c(res$estimate, res$parameter[1], conf.low=res$conf.int[1], conf.high=res$conf.int[2]) }) cors <- as.data.frame(t(as.matrix(cors))) cors$study <- rownames(cors) # Fisher Z transform cors <- transform(cors, ri = cor) cors <- transform(cors, ni = df + 2) cors <- transform(cors, yi = 1/2 * log((1 + ri)/(1 - ri)), vi = 1/(ni - 3)) # Overall correlation across studies overall <- rma.uni(yi, vi, method=\"ML\", data=cors) # metafor package # back transform overall <- predict(overall, transf=transf.ztor) # weight and size for forest plot wi <- 1/sqrt(cors$vi) size <- 0.5 + 3.0 * (wi - min(wi))/(max(wi) - min(wi)) # now the forest plot # must use latticeExtra::layer in case ggplot2 is also loaded segplot(factor(study) ~ conf.low+conf.high, data=cors, draw.bands=FALSE, level=size, centers=ri, cex=size, col.regions=colorRampPalette(c(\"gray85\", \"dodgerblue4\")), main=\"White mold vs. soybean yield\", xlab=paste(\"Study correlation, confidence, and study weight (blues)\\n\", \"Overall (black)\"), ylab=\"Study ID\") + latticeExtra::layer(panel.abline(v=overall$pred, lwd=2)) + latticeExtra::layer(panel.abline(v=c(overall$cr.lb, overall$cr.ub), lty=2, col=\"gray\")) # Meta-analyses are typically used when the original data is not available. # Since the original data is available, a mixed model is probably better. libs(lme4) m1 <- lmer(yield ~ mold # overall slope + (1+mold |study), # random intercept & slope per study data=dat) summary(m1) } # }"},{"path":"/reference/lessman.sorghum.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sorghum — lessman.sorghum.uniformity","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"Uniformity trial sorghum Ames, Iowa, 1959.","code":""},{"path":"/reference/lessman.sorghum.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"","code":"data(\"lessman.sorghum.uniformity\")"},{"path":"/reference/lessman.sorghum.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"data frame 2640 observations following 3 variables. row row col column yield yield, ounces","code":""},{"path":"/reference/lessman.sorghum.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"uniformity trial conducted Agronomy Farm Ames, Iowa, 1959. field planted grain sorghum rows spaces 40 inches apart, thinned stand three inches plants. entire field 48 rows (40 inches apart), 300 feet long harvested 5-foot lengths. Threshed grain dried 8-10 percent moisture weighing. Weights ounces. Average yield field 95.3 bu/ac. Field width: 48 rows * 40 inches / 12in/ft = 160 feet Field length: 60 plots * 5 feet = 300 feet Plot yields two outer rows side field omitted analysis. CV values data quite match Lessman's value. first page Table 17 manually checked correctness problems optical character recognition ( obvious errors like 0/o).","code":""},{"path":"/reference/lessman.sorghum.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"Lessman, Koert James (1962). Comparisons methods testing grain yield sorghum. Iowa State University. Retrospective Theses Dissertations. Paper 2063. Appendix Table 17. https://lib.dr.iastate.edu/rtd/2063","code":""},{"path":"/reference/lessman.sorghum.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"None.","code":""},{"path":"/reference/lessman.sorghum.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lessman.sorghum.uniformity) dat <- lessman.sorghum.uniformity libs(desplot) desplot(dat, yield ~ col*row, aspect=300/160, tick=TRUE, flip=TRUE, # true aspect main=\"lessman.sorghum.uniformity\") # Omit outer two columns (called 'rows' by Lessman) dat <- subset(dat, col > 2 & col < 47) nrow(dat) var(dat$yield) # 9.09 sd(dat$yield)/mean(dat$yield) # CV 9.2 libs(reshape2) libs(agricolae) dmat <- acast(dat, row~col, value.var='yield') index.smith(dmat, main=\"lessman.sorghum.uniformity\", col=\"red\") # Similar to Lessman Table 1 # Lessman said that varying the width of plots did not have an appreciable # effect on CV, and optimal row length was 3.2 basic plots, about 15-20 } # }"},{"path":"/reference/li.millet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of millet — li.millet.uniformity","title":"Uniformity trial of millet — li.millet.uniformity","text":"Uniformity trial millet China 1934.","code":""},{"path":"/reference/li.millet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of millet — li.millet.uniformity","text":"data frame 600 observations following 3 variables. row row col column yield yield (grams)","code":""},{"path":"/reference/li.millet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of millet — li.millet.uniformity","text":"Crop date estimated 1934. Field 100 ft x 100 ft. Plots 15 feet long 1 foot wide. Field width: 100 plots * 1 foot = 100 feet Field length: 6 plots * 15 feet = 100 feet Li found efficient use land obtained plats 15 feet long two rowss wide. Also satisfactory one row 30 feet long.","code":""},{"path":"/reference/li.millet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of millet — li.millet.uniformity","text":"Li, HW Meng, CJ Liu, TN. 1936. Field Results Millet Breeding Experiment. Agronomy Journal, 28, 1-15. Table 1. https://doi.org/10.2134/agronj1936.00021962002800010001x","code":""},{"path":"/reference/li.millet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of millet — li.millet.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(li.millet.uniformity) dat <- li.millet.uniformity mean(dat$yield) # matches Li et al. libs(desplot) desplot(dat, yield~col*row, aspect=100/100, # true aspect main=\"li.millet.uniformity\") } # }"},{"path":"/reference/libs.html","id":null,"dir":"Reference","previous_headings":"","what":"Load multiple packages and install if needed — libs","title":"Load multiple packages and install if needed — libs","text":"Install load packages \"fly\".","code":""},{"path":"/reference/libs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load multiple packages and install if needed — libs","text":"","code":"libs(...)"},{"path":"/reference/libs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load multiple packages and install if needed — libs","text":"... Comma-separated unquoted package names","code":""},{"path":"/reference/libs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load multiple packages and install if needed — libs","text":"None","code":""},{"path":"/reference/libs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Load multiple packages and install if needed — libs","text":"'agridat' package uses dozens packages examples dataset. 'libs' function provides simple way load multiple packages , can install missing packages --fly. similar `pacman::p_load` function.","code":""},{"path":"/reference/libs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Load multiple packages and install if needed — libs","text":"None","code":""},{"path":"/reference/libs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Load multiple packages and install if needed — libs","text":"Kevin Wright","code":""},{"path":"/reference/libs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load multiple packages and install if needed — libs","text":"","code":"if (FALSE) { # \\dontrun{ libs(dplyr,reshape2) } # }"},{"path":"/reference/lillemo.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"Resistance wheat powdery mildew","code":""},{"path":"/reference/lillemo.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"","code":"data(\"lillemo.wheat\")"},{"path":"/reference/lillemo.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"data frame 408 observations following 4 variables. gen genotype, 24 levels env environrment, 13 levels score score scale scale used score","code":""},{"path":"/reference/lillemo.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"data means across reps original scores. Lower scores indicate better resistance mildew. location used one four different measurement scales scoring resistance powdery mildew: 0-5 scale, 1-9 scale, 0-9 scale, percent. Environment codes consist two letters location name two digits year testing. Location names: CA=Cruz Alta, Brazil. Ba= Bawburgh, UK. Aa=, Norway. Ha=Hamar, Norway. Ch=Choryn, Poland. Ce=Cerekwica, Poland. Ma=Martonvasar, Hungary. Kh=Kharkiv, Ukraine. BT=Bila Tserkva, Ukraine. Gl=Glevakha, Ukraine. Bj=Beijing, China. Note, Lillemo et al. remove genotype effects customary calculating Huehn's non-parametric stability statistics. examples , results quite match results Lillemo. easily result original data table rounded 1 decimal place. example, environment 'Aa03' 3 reps mean genotype 1 probably 16.333, 16.3. Used permission Morten Lillemo. Electronic data supplied Miroslav Zoric.","code":""},{"path":"/reference/lillemo.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"Morten Lillemo, Ravi Sing, Maarten van Ginkel. (2011). Identification Stable Resistance Powdery Mildew Wheat Based Parametric Nonparametric Methods Crop Sci. 50:478-485. https://doi.org/10.2135/cropsci2009.03.0116","code":""},{"path":"/reference/lillemo.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"None.","code":""},{"path":"/reference/lillemo.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lillemo.wheat) dat <- lillemo.wheat # Change factor levels to match Lillemo dat$env <- as.character(dat$env) dat$env <- factor(dat$env, levels=c(\"Bj03\",\"Bj05\",\"CA03\",\"Ba04\",\"Ma04\", \"Kh06\",\"Gl05\",\"BT06\",\"Ch04\",\"Ce04\", \"Ha03\",\"Ha04\",\"Ha05\",\"Ha07\",\"Aa03\",\"Aa04\",\"Aa05\")) # Interesting look at different measurement scales by environment libs(lattice) qqmath(~score|env, dat, group=scale, as.table=TRUE, scales=list(y=list(relation=\"free\")), auto.key=list(columns=4), main=\"lillemo.wheat - QQ plots by environment\") # Change data to matrix format libs(reshape2) datm <- acast(dat, gen~env, value.var='score') # Environment means. Matches Lillemo Table 3 apply(datm, 2, mean) # Two different transforms within envts to approximate 0-9 scale datt <- datm datt[,\"CA03\"] <- 1.8 * datt[,\"CA03\"] ix <- c(\"Ba04\",\"Kh06\",\"Gl05\",\"BT06\",\"Ha03\",\"Ha04\",\"Ha05\",\"Ha07\",\"Aa03\",\"Aa04\",\"Aa05\") datt[,ix] <- apply(datt[,ix],2,sqrt) # Genotype means of transformed data. Matches Lillemo table 3. round(rowMeans(datt),2) # Biplot of transformed data like Lillemo Fig 2 libs(gge) biplot(gge(datt, scale=FALSE), main=\"lillemo.wheat\") # Median polish of transformed table m1 <- medpolish(datt) # Half-normal prob plot like Fig 1 # libs(faraway) # halfnorm(abs(as.vector(m1$resid))) # Nonparametric stability statistics. Lillemo Table 4. huehn <- function(mat){ # Gen in rows, Env in cols nenv <- ncol(mat) # Corrected yield. Remove genotype effects # Remove the following line to match Table 4 of Lillemo mat <- sweep(mat, 1, rowMeans(mat)) + mean(mat) # Ranks in each environment rmat <- apply(mat, 2, rank) # Mean genotype rank across envts MeanRank <- apply(rmat, 1, mean) # Huehn S1 gfun <- function(x){ oo <- outer(x,x,\"-\") sum(abs(oo)) # sum of all absolute pairwise differences } S1 <- apply(rmat, 1, gfun)/(nenv*(nenv-1)) # Huehn S2 S2 <- apply((rmat-MeanRank)^2,1,sum)/(nenv-1) out <- data.frame(MeanRank,S1,S2) rownames(out) <- rownames(mat) return(out) } round(huehn(datm),2) # Matches table 4 # I do not think phenability package gives correct values for S1 # libs(phenability) # nahu(datm) } # }"},{"path":"/reference/lin.superiority.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"Multi-environment trial 33 barley genotypes 12 locations","code":""},{"path":"/reference/lin.superiority.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"","code":"data(\"lin.superiority\")"},{"path":"/reference/lin.superiority.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"data frame 396 observations following 4 variables. gen genotype/cultivar region region loc location yield yield (kg/ha)","code":""},{"path":"/reference/lin.superiority.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"Yield six-row barley 1983 annual report Eastern Cooperative Test Canada. named cultivars Bruce, Conquest, Laurier, Leger checks, cultivars tests.","code":""},{"path":"/reference/lin.superiority.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"C. S. Lin, M. R. Binns (1985). Procedural approach assessing cultivar-location data: Pairwise genotype-environment interactions test cultivars checks Canadian Journal Plant Science, 1985, 65(4): 1065-1071. Table 1. https://doi.org/10.4141/cjps85-136","code":""},{"path":"/reference/lin.superiority.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"C. S. Lin, M. R. Binns (1988). Superiority Measure Cultivar Performance Cultivar x Location Data. Canadian Journal Plant Science, 68, 193-198. https://doi.org/10.4141/cjps88-018 Mohammed Ali Hussein, Asmund Bjornstad, . H. Aastveit (2000). SASG x ESTAB: SAS Program Computing Genotype x Environment Stability Statistics. Agronomy Journal, 92; 454-459. https://doi.org/10.2134/agronj2000.923454x","code":""},{"path":"/reference/lin.superiority.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lin.superiority) dat <- lin.superiority libs(latticeExtra) libs(reshape2) # calculate the superiority measure of Lin & Binns 1988 dat2 <- acast(dat, gen ~ loc, value.var=\"yield\") locmean <- apply(dat2, 2, mean) locmax <- apply(dat2, 2, max) P <- apply(dat2, 1, function(x) { sum((x-locmax)^2)/(2*length(x)) })/1000 P <- sort(P) round(P) # match Lin & Binns 1988 table 2, column Pi # atlantic & quebec regions overlap # libs(gge) # m1 <- gge(dat, yield ~ gen*loc, env.group=region, # main=\"lin.superiority\") # biplot(m1) # create a figure similar to Lin & Binns 1988 # add P, locmean, locmax back into the data dat$locmean <- locmean[match(dat$loc, names(locmean))] dat$locmax <- locmax[match(dat$loc, names(locmax))] dat$P <- P[match(dat$gen, names(P))] dat$gen <- reorder(dat$gen, dat$P) xyplot(locmax ~ locmean|gen, data=dat, type=c('p','r'), as.table=TRUE, col=\"gray\", main=\"lin.superiority - Superiority index\", xlab=\"Location Mean\", ylab=\"Yield of single cultivars (blue) & Maximum (gray)\") + xyplot(yield ~ locmean|gen, data=dat, type=c('p','r'), as.table=TRUE, pch=19) } # }"},{"path":"/reference/lin.unbalanced.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"Multi-environment trial 33 barley genotypes 18 locations","code":""},{"path":"/reference/lin.unbalanced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"","code":"data(\"lin.unbalanced\")"},{"path":"/reference/lin.unbalanced.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"data frame 405 observations following 4 variables. gen genotype/cultivar loc location yield yield (kg/ha) region region","code":""},{"path":"/reference/lin.unbalanced.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"Yield six-row barley 1986 Eastern Cooperative trial named cultivars Bruce, Laurier, Leger checks, cultivars tests. Cultivar names use following codes: \"\" Atlantic-Quebec. \"O\" \"Ontario\". \"S\" second-year. \"T\" third-year.","code":""},{"path":"/reference/lin.unbalanced.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"C. S. Lin, M. R. Binns (1988). Method Assessing Regional Trial Data Test Cultivars Unbalanced Respect Locations. Canadian Journal Plant Science, 68(4): 1103-1110. https://doi.org/10.4141/cjps88-130","code":""},{"path":"/reference/lin.unbalanced.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"None","code":""},{"path":"/reference/lin.unbalanced.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lin.unbalanced) dat <- lin.unbalanced # location maximum, Lin & Binns table 1 # aggregate(yield ~ loc, data=dat, FUN=max) # location mean/index, Lin & Binns, table 1 dat2 <- subset(dat, is.element(dat$gen, c('Bruce','Laurier','Leger','S1','S2', 'S3','S4','S5','S6','S7','T1','T2'))) aggregate(yield ~ loc, data=dat2, FUN=mean) libs(reshape2) dat3 <- acast(dat, gen ~ loc, value.var=\"yield\") libs(lattice) lattice::levelplot(t(scale(dat3)), main=\"lin.unbalanced\", xlab=\"loc\", ylab=\"genotype\") # calculate the superiority measure of Lin & Binns 1988. # lower is better locmax <- apply(dat3, 2, max, na.rm=TRUE) P <- apply(dat3, 1, function(x) { sum((x-locmax)^2, na.rm=TRUE)/(2*length(na.omit(x))) })/1000 P <- sort(P) round(P) # match Lin & Binns 1988 table 2, column P } # }"},{"path":"/reference/linder.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat in Switzerland — linder.wheat","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"Multi-environment trial wheat Switzerland","code":""},{"path":"/reference/linder.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"","code":"data(\"linder.wheat\")"},{"path":"/reference/linder.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"data frame 252 observations following 4 variables. env environment block block gen genotype yield yield, 10 kg/ha","code":""},{"path":"/reference/linder.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"experiment 9 varieties wheat 7 localities Switzerland 1960, RCB design.","code":""},{"path":"/reference/linder.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"Arthur Linder (1960). Design Analysis Experiments, notes lectures held fall semester 1963 Statistics Department, University North Carolina, page 160. https://www.stat.ncsu.edu/information/library/mimeo.archive/ISMS_1964_398-.pdf","code":""},{"path":"/reference/linder.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"None.","code":""},{"path":"/reference/linder.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"","code":"library(agridat) data(linder.wheat) dat <- linder.wheat libs(gge) dat <- transform(dat, eb=paste0(env,block)) m1 <- gge(dat, yield~gen*eb, env.group=env) biplot(m1, main=\"linder.wheat\")"},{"path":"/reference/little.splitblock.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-block experiment of sugar beets — little.splitblock","title":"Split-block experiment of sugar beets — little.splitblock","text":"Split-block experiment sugar beets.","code":""},{"path":"/reference/little.splitblock.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split-block experiment of sugar beets — little.splitblock","text":"","code":"data(\"little.splitblock\")"},{"path":"/reference/little.splitblock.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-block experiment of sugar beets — little.splitblock","text":"data frame 80 observations following 6 variables. row row col column yield sugar beet yield, tons/acre harvest harvest date, weeks planting nitro nitrogen, pounds/acre block block","code":""},{"path":"/reference/little.splitblock.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-block experiment of sugar beets — little.splitblock","text":"Four rates nitrogen, laid 4x4 Latin-square experiment. Within column block, sub-plots strips (across 4 rows) 5 different harvest dates. use sub-plots s strips necessitates care determining error terms ANOVA table. Note, Little yield value 22.3 row 3, column -H3. data uses 23.3 order match marginal totals given Little.","code":""},{"path":"/reference/little.splitblock.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-block experiment of sugar beets — little.splitblock","text":"Thomas M. Little, F. Jackson Hills. (1978) Agricultural Experimentation","code":""},{"path":"/reference/little.splitblock.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-block experiment of sugar beets — little.splitblock","text":"None.","code":""},{"path":"/reference/little.splitblock.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-block experiment of sugar beets — little.splitblock","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(little.splitblock) dat <- little.splitblock # Match marginal totals given by Little. ## sum(dat$yield) ## with(dat, tapply(yield,col,sum)) ## with(dat, tapply(yield,row,sum)) # Layout shown by Little figure 10.2 libs(desplot) desplot(dat, yield ~ col*row, out1=block, out2=col, col=nitro, cex=1, num=harvest, main=\"little.splitblock\") # Convert continuous traits to factors dat <- transform(dat, R=factor(row), C=factor(block), H=factor(harvest), N=factor(nitro)) if(0){ libs(lattice) xyplot(yield ~ nitro|H,dat) xyplot(yield ~ harvest|N,dat) } # Anova table matches Little, table 10.3 m1 <- aov(yield ~ R + C + N + H + N:H + Error(R:C:N + C:H + C:N:H), data=dat) summary(m1) } # }"},{"path":"/reference/loesell.bean.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of white pea beans — loesell.bean.uniformity","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"Uniformity trial white pea beans","code":""},{"path":"/reference/loesell.bean.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"","code":"data(\"loesell.bean.uniformity\")"},{"path":"/reference/loesell.bean.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"data frame 1890 observations following 3 variables. row row ordinate col column ordinate yield yield, grams per plot","code":""},{"path":"/reference/loesell.bean.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"Trial conducted Michigan Agricultural Experiment Station, 1.75 acres. Beans planted rows 28 inches apart 15 Jun 1932. Plants spaced 1 2 inches apart. planting, area 210 ft x 210 feet. area divided 21 columns, 10 foot wide, containing90 rows. Field length: 90 rows * 28 inches = 210 feet. Field width: 21 series * 10 feet = 210 feet. Author's conclusion: Increasing size plot increasing length efficient increasing width. Note, missing values dataset result PDF scan omitting corners table.","code":""},{"path":"/reference/loesell.bean.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"Loesell, Clarence (1936). Size plot & number replications necessary varietal trials white pea beans. PhD Thesis, Michigan State. Table 3, p. 9-10. https://d.lib.msu.edu/etd/5271","code":""},{"path":"/reference/loesell.bean.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"None","code":""},{"path":"/reference/loesell.bean.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ require(agridat) data(loesell.bean.uniformity) dat <- loesell.bean.uniformity require(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=1, tick=TRUE, main=\"loesell.bean.uniformity\") } # }"},{"path":"/reference/lonnquist.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize, half diallel — lonnquist.maize","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"Half diallel maize","code":""},{"path":"/reference/lonnquist.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"","code":"data(\"lonnquist.maize\")"},{"path":"/reference/lonnquist.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"data frame 78 observations following 3 variables. p1 parent 1 factor p2 parent 2 factor yield yield","code":""},{"path":"/reference/lonnquist.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"Twelve hybrids selfed/crossed half-diallel design. data means adjusted block effects. Original experiment 3 reps 2 locations 2 years.","code":""},{"path":"/reference/lonnquist.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"J. H. Lonnquist, C. O. Gardner. (1961) Heterosis Intervarietal Crosses Maize Implication Breeding Procedures. Crop Science, 1, 179-183. Table 1.","code":""},{"path":"/reference/lonnquist.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"Mohring, Melchinger, Piepho. (2011). REML-Based Diallel Analysis. Crop Science, 51, 470-478. https://doi.org/10.2135/cropsci2010.05.0272 C. O. Gardner S. . Eberhart. 1966. Analysis Interpretation Variety Cross Diallel Related Populations. Biometrics, 22, 439-452. https://doi.org/10.2307/2528181","code":""},{"path":"/reference/lonnquist.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lonnquist.maize) dat <- lonnquist.maize dat <- transform(dat, p1=factor(p1, levels=c(\"C\",\"L\",\"M\",\"H\",\"G\",\"P\",\"B\",\"RM\",\"N\",\"K\",\"R2\",\"K2\")), p2=factor(p2, levels=c(\"C\",\"L\",\"M\",\"H\",\"G\",\"P\",\"B\",\"RM\",\"N\",\"K\",\"R2\",\"K2\"))) libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(yield ~ p1*p2, dat, col.regions=redblue, main=\"lonnquist.maize - yield of diallel cross\") # Calculate the F1 means in Lonnquist, table 1 # libs(reshape2) # mat <- acast(dat, p1~p2) # mat[upper.tri(mat)] <- t(mat)[upper.tri(mat)] # make symmetric # diag(mat) <- NA # round(rowMeans(mat, na.rm=TRUE),1) ## C L M H G P B RM N K R2 K2 ## 94.8 89.2 95.0 96.4 95.3 95.2 97.3 93.7 95.0 94.0 98.9 102.4 # Griffings method # https://www.statforbiology.com/2021/stat_met_diallel_griffing/ # libs(lmDiallel) # dat2 <- lonnquist.maize # dat2 <- subset(dat2, # is.element(p1, c(\"M\",\"H\",\"G\",\"B\",\"K\",\"K2\")) & # is.element(p2, c(\"M\",\"H\",\"G\",\"B\",\"K\",\"K2\"))) # dat2 <- droplevels(dat2) # dmod1 <- lm(yield ~ GCA(p1, p2) + tSCA(p1, p2), # data = dat2) # dmod2 <- lm.diallel(yield ~ p1 + p2, # data = dat2, fct = \"GRIFFING2\") # anova.diallel(dmod1, MSE=7.1, dfr=60) ## Response: yield ## Df Sum Sq Mean Sq F value Pr(>F) ## GCA(p1, p2) 5 234.23 46.846 6.5980 5.923e-05 *** ## tSCA(p1, p2) 15 238.94 15.929 2.2436 0.01411 * ## Residuals 60 7.100 # ---------- if(require(\"asreml\", quietly=TRUE)){ # Mohring 2011 used 6 varieties to calculate GCA & SCA # Matches Table 3, column 2 d2 <- subset(dat, is.element(p1, c(\"M\",\"H\",\"G\",\"B\",\"K\",\"K2\")) & is.element(p2, c(\"M\",\"H\",\"G\",\"B\",\"K\",\"K2\"))) d2 <- droplevels(d2) libs(asreml,lucid) m2 <- asreml(yield~ 1, data=d2, random = ~ p1 + and(p2)) lucid::vc(m2) ## effect component std.error z.ratio con ## p1!p1.var 3.865 3.774 1 Positive ## R!variance 15.93 5.817 2.7 Positive # Calculate GCA effects m3 <- asreml(yield~ p1 + and(p2), data=d2) coef(m3)$fixed-1.462 # Matches Gardner 1966, Table 5, Griffing method } } # }"},{"path":"/reference/lord.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice — lord.rice.uniformity","title":"Uniformity trial of rice — lord.rice.uniformity","text":"Uniformity trial rice Ceylon, 1929.","code":""},{"path":"/reference/lord.rice.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of rice — lord.rice.uniformity","text":"","code":"data(\"lord.rice.uniformity\")"},{"path":"/reference/lord.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice — lord.rice.uniformity","text":"data frame 560 observations following 5 variables. field field row row col column grain grain weight, pounds per plot straw straw weight, pounds per plot","code":""},{"path":"/reference/lord.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice — lord.rice.uniformity","text":"1929, eight fields 1/5 acre size broadcast seeded rice Anuradhapura Experiment Station northern dry zone Ceylon. broadcast, fields marked 10 ft 10 ft squares. harvest, weights grain straw recorded. Fields 10-14 one side drain, fields 26-28 side. field surrounded bund. Plots next bunds higher yields. Field width: 5 plots * 10 feet = 50 feet Field length: 14 plots * 10 feet = 140 feet Conclusions: \"appear plots 1/87 acre effective.\"","code":""},{"path":"/reference/lord.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice — lord.rice.uniformity","text":"Lord, L. (1931). Uniformity Trial Irrigated Broadcast Rice. Journal Agricultural Science, 21(1), 178-188. https://doi.org/10.1017/S0021859600008029","code":""},{"path":"/reference/lord.rice.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of rice — lord.rice.uniformity","text":"None","code":""},{"path":"/reference/lord.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice — lord.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lord.rice.uniformity) dat <- lord.rice.uniformity # match table on page 180 ## libs(dplyr) ## dat ## field grain straw ## ## 1 10 590 732 ## 2 11 502 600 ## 3 12 315 488 ## 4 13 291 538 ## 5 14 489 670 ## 6 26 441 560 ## 7 27 451 629 ## 8 28 530 718 # There are consistently high yields along all edges of the field # libs(lattice) # bwplot(grain ~ factor(col)|field,dat) # bwplot(grain ~ factor(col)|field,dat) # Heatmaps libs(desplot) desplot(dat, grain ~ col*row|field, flip=TRUE, aspect=140/50, main=\"lord.rice.uniformity\") # bivariate scatterplots # xyplot(grain ~ straw|field, dat) } # }"},{"path":"/reference/love.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton — love.cotton.uniformity","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"Uniformity trial cotton","code":""},{"path":"/reference/love.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"","code":"data(\"love.cotton.uniformity\")"},{"path":"/reference/love.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"data frame 170 observations following 3 variables. row row col column yield yield, unknown units","code":""},{"path":"/reference/love.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"Within 100-foot row, first 20 feet harvested single plot, rest row harvested 5-foot lengths. Field width: 17 plots. First plot 20 foot segment, remaining 5 foot segments. Field length: 10 plots. distance rows given. Crop location certain. However, Love & Reisner (2012) mentions cotton \"blank test\" 200 plots Nanking 1929-1930. Neither document mentions weight unit. Possibly information collected papers Harry Love Cornell: https://rmc.library.cornell.edu/EAD/htmldocs/RMA00890.html Cotton - Plot Technic Study 1930-1932. Box 3, Folder 34 However, turned hand-written manuscript Shiao .k.. Siao, contained trial data ","code":""},{"path":"/reference/love.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"Harry Love (1937). Application Statistical Methods Agricultural Research. Commercial Press, Shanghai. Page 411. https://archive.org/details/.ernet.dli.2015.233346/page/n421","code":""},{"path":"/reference/love.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"Harry Houser Love & John Henry Reisner (2012). Cornell-Nanking Story. Internet-First University Press. https://ecommons.cornell.edu/bitstream/1813/29080/2/Cornell-Nanking_15Jun12_PROOF.pdf","code":""},{"path":"/reference/love.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(love.cotton.uniformity) # omit first column which has 20-foot plots dat <- subset(love.cotton.uniformity, col > 1) libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=20/80, # just a guess main=\"love.cotton.uniformity\") } # }"},{"path":"/reference/lu.stability.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"Multi-environment trial illustrate stability statistics","code":""},{"path":"/reference/lu.stability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"","code":"data(\"lu.stability\")"},{"path":"/reference/lu.stability.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"data frame 120 observations following 4 variables. yield yield gen genotype factor, 5 levels env environment factor, 6 levels block block factor, 4 levels","code":""},{"path":"/reference/lu.stability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"Data 5 maize genotypes 2 years x 3 sites = 6 environments.","code":""},{"path":"/reference/lu.stability.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"H.Y. Lu C. T. Tien. (1993) Studies nonparametric method phenotypic stability: II. Selection stability agroeconomic concept. J. Agric. Assoc. China 164:1-17.","code":""},{"path":"/reference/lu.stability.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"Hsiu Ying Lu. 1995. PC-SAS Program Estimating Huehn's Nonparametric Stability Statistics. Agron J. 87:888-891. Kae-Kang Hwu Li-yu D Liu. (2013) Stability Analysis Using Multiple Environment Trials Data Linear Regression. (Chinese) Crop, Environment & Bioinformatics 10:131-142.","code":""},{"path":"/reference/lu.stability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lu.stability) dat <- lu.stability # GxE means. Match Lu 1995 table 1 libs(reshape2) datm <- acast(dat, gen~env, fun=mean, value.var='yield') round(datm, 2) # Gen/Env means. Match Lu 1995 table 3 apply(datm, 1, mean) apply(datm, 2, mean) # Traditional ANOVA. Match Hwu table 2 # F value for gen,env m1 = aov(yield~env+gen+Error(block:env+env:gen), data=dat) summary(m1) # F value for gen:env, block:env m2 <- aov(yield ~ gen + env + gen:env + block:env, data=dat) summary(m2) # Finlay Wilkinson regression coefficients # First, calculate env mean, merge in libs(dplyr) dat2 <- group_by(dat, env) dat2 <- mutate(dat2, locmn=mean(yield)) m4 <- lm(yield ~ gen -1 + gen:locmn, data=dat2) coef(m4) # Match Hwu table 4 # Table 6: Shukla's heterogeneity test dat2$ge = paste0(dat2$gen, dat2$env) # Create a separate ge interaction term m6 <- lm(yield ~ gen + env + ge + ge:locmn, data=dat2) m6b <- lm( yield ~ gen + env + ge + locmn, data=dat2) anova(m6, m6b) # Non-significant difference # Table 7 - Shukla stability # First, environment means emn <- group_by(dat2, env) emn <- summarize(emn, ymn=mean(yield)) # Regress GxE terms on envt means getab = (model.tables(m2,\"effects\")$tables)$'gen:env' getab for (ll in 1:nrow(getab)){ m7l <- lm(getab[ll, ] ~ emn$ymn) cat(\"\\n\\n*************** Gen \",ll,\" ***************\\n\") cat(\"Regression coefficient: \",round(coefficients(m7l)[2],5),\"\\n\") print(anova(m7l)) } # Match Hwu table 7. } # } # dontrun"},{"path":"/reference/lucas.switchback.html","id":null,"dir":"Reference","previous_headings":"","what":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"Switchback experiment dairy cattle, milk yield 3 treatments","code":""},{"path":"/reference/lucas.switchback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"","code":"data(\"lucas.switchback\")"},{"path":"/reference/lucas.switchback.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"data frame 36 observations following 5 variables. cow cow factor, 12 levels trt treatment factor, 3 levels period period factor, 3 levels yield yield (FCM = fat corrected milk), pounds/day block block factor","code":""},{"path":"/reference/lucas.switchback.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"Lucas says \"data feeding trials employing present designs yet available, uniformity data used\". Six cows started together block 1, three cows block 2 three cows block 3.","code":""},{"path":"/reference/lucas.switchback.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"Lucas, HL. 1956. Switchback trials two treatments. Journal Dairy Science, 39, 146-154. https://doi.org/10.3168/jds.S0022-0302(56)94721-X","code":""},{"path":"/reference/lucas.switchback.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"Sanders, WL Gaynor, PJ. 1987. Analysis Switchback Data Using Statistical Analysis System. Journal Dairy Science, 70, 2186-2191. https://doi.org/10.3168/jds.S0022-0302(87)80273-4","code":""},{"path":"/reference/lucas.switchback.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lucas.switchback) dat <- lucas.switchback # Create a numeric period variable dat$per <- as.numeric(substring(dat$period,2)) libs(lattice) xyplot(yield ~ period|block, data=dat, group=cow, type=c('l','r'), auto.key=list(columns=6), main=\"lucas.switchback - (actually uniformity data)\") # Need to use 'terms' to preserve the order of the model terms # Really, cow(block), per:cow(block), period(block) m1 <- aov(terms(yield ~ block + cow:block + per:cow:block + period:block + trt, keep.order=TRUE), data=dat) anova(m1) # Match Sanders & Gaynor table 3 ## Analysis of Variance Table ## Df Sum Sq Mean Sq F value Pr(>F) ## block 2 30.93 15.464 55.345 5.132e-05 *** ## block:cow 9 1700.97 188.997 676.426 1.907e-09 *** ## block:cow:per 12 120.47 10.040 35.932 4.137e-05 *** ## block:period 3 14.85 4.950 17.717 0.001194 ** ## trt 2 1.58 0.789 2.825 0.126048 ## Residuals 7 1.96 0.279 coef(m1) # trtT2 and trtT3 match Sanders table 3 trt diffs } # }"},{"path":"/reference/lyon.potato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of potatoes — lyon.potato.uniformity","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"Uniformity trial potatoes Nebraska Experiment Station, 1909.","code":""},{"path":"/reference/lyon.potato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"data frame 204 observations following 3 variables. row row col column, section yield yield, pounds","code":""},{"path":"/reference/lyon.potato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"1909, potatoes harvested uniform land Nebraska Experiment Station. 34 rows, 34 inches apart. Lyon, page 97 says \"harvested row six sections, seventy-two feet seven inches long.\" clear SECTION 72 feet long, ROW 72 feet long. Yield potato roughly 0.5 0.8 pounds per square foot, seems plausible entire row 72 feet long (see calculations ). Field width: 6 plots = 72 feet Field length: 34 rows * 34 / 12in/ft = 96 ft","code":""},{"path":"/reference/lyon.potato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"Lyon, T.L. (1911). experiments estimate errors field plat tests. Proc. Amer. Soc. Agron, 3, 89-114. Table III. https://doi.org/10.2134/agronj1911.00021962000300010016x","code":""},{"path":"/reference/lyon.potato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"None.","code":""},{"path":"/reference/lyon.potato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lyon.potato.uniformity) dat <- lyon.potato.uniformity # Yield per square foot, assuming 72 foot rows sum(dat$yield)/(72*96) # 0.67 # seems about right # Yield per square foot, assuming 72 foot plots sum(dat$yield)/(6*72*96) # 0.11 libs(desplot) desplot(dat, yield ~ col*row, tick=TRUE, flip=TRUE, aspect=96/72, # true aspect main=\"lyon.potato.uniformity\") } # }"},{"path":"/reference/lyons.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"Yield winter wheat 12 sites 4 years.","code":""},{"path":"/reference/lyons.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"data frame 48 observations following 3 variables. loc location, 12 levels year year, numeric yield yield (kg)","code":""},{"path":"/reference/lyons.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"Krzanowski uses briefly multi-dimensional scaling.","code":""},{"path":"/reference/lyons.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"R. Lyons (1980). review multidimensional scaling. Unpublished M.Sc. dissertation, University Reading.","code":""},{"path":"/reference/lyons.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"Krzanowski, W.J. (1988) Principles multivariate analysis. Oxford University Press.","code":""},{"path":"/reference/lyons.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lyons.wheat) dat <- lyons.wheat libs(lattice) xyplot(yield~factor(year), dat, group=loc, main=\"lyons.wheat\", auto.key=list(columns=4), type=c('p','l')) } # }"},{"path":"/reference/magistad.pineapple.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of pineapple — magistad.pineapple.uniformity","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"Uniformity trial pineapple Hawaii 1932","code":""},{"path":"/reference/magistad.pineapple.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"","code":"data(\"magistad.pineapple.uniformity\")"},{"path":"/reference/magistad.pineapple.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"data frame 137 observations following 6 variables. field field number plat plat number row row col column number number fruits weight weight fruits, grams","code":""},{"path":"/reference/magistad.pineapple.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"Field 19. Kunia. Harvested 1932. \"field, harvested 1932, four rows per bed. 300-foot bed divided four equal parts form plats 1, 2, 3, 4. third [sic, second] bed similarly divided form plats 5 8, inclusive. manner plats 9 24 formed. way 24 plats 75 feet long 1 bed wide formed.\" Page 635: \"smallest plats 75 6.5 feet\". Field length: 4 plats * 75 feet = 300 feet Field width: 6 plats * 6.5 feet = 39 feet Field 82. Pearl City. \"Eight beds, separated two beds, selected harvested. Beds 8 feet center center. bed divided three plats 76 feet long.\" columns data bed 1, 4, 7, 10, 13, 16, 19, 22 Note: Layout plats rows/columns assumes pattern field 19. Field length: 3 plats * 76 feet = 228 feet Field width: 22 plats * 8 feet = 176 feet. Field 21. Kahuku. \"field 21, Kahuku, experimental plan Latin square type, five beds five plats . beds 7.5 feet center center. plat approximately 60 feet long third bed selected harvested.\" Note: Layout plats rows/columns assumes pattern field 19. Field lenght: 5 plats * 60 feet = 300 feet Field width: 13 plats * 7.5 feet = 97.5 feet Field 1. Kunia. \"experiment another Latin square test eight plats column eight plats row. harvested 1930. plat consisted two beds 150 feet long. Beds 6 feet center center consisted three rows . entire experimental area occupied 2.85 acres.\" Field length: 8 plats * 150 feet = 1200 feet Field width: 8 plats * 2 beds * 6 feet = 96 feet Total area: 1200*96/43560=2.64 acres","code":""},{"path":"/reference/magistad.pineapple.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"O. C. Magistad & C. . Farden (1934). Experimental Error Field Experiments Pineapples. Journal American Society Agronomy, 26, 631–643. https://doi.org/10.2134/agronj1934.00021962002600080001x","code":""},{"path":"/reference/magistad.pineapple.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"None","code":""},{"path":"/reference/magistad.pineapple.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(magistad.pineapple.uniformity) dat <- magistad.pineapple.uniformity # match table page 641 ## dat ## summarize(number=mean(number), ## weight=mean(weight)) ## field number weight ## 1 1 596.4062 2499.922 ## 2 19 171.1667 2100.250 ## 3 21 171.1600 2056.800 ## 4 82 220.7500 1264.500 libs(desplot) desplot(dat, weight ~ col*row, subset=field==19, aspect=300/39, main=\"magistad.pineapple.uniformity - field 19\") desplot(dat, weight ~ col*row, subset=field==82, aspect=228/176, main=\"magistad.pineapple.uniformity - field 82\") desplot(dat, weight ~ col*row, subset=field==21, aspect=300/97.5, main=\"magistad.pineapple.uniformity - field 21\") desplot(dat, weight ~ col*row, subset=field==1, aspect=1200/96, main=\"magistad.pineapple.uniformity - field 1\") } # }"},{"path":"/reference/masood.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice — masood.rice.uniformity","title":"Uniformity trial of rice — masood.rice.uniformity","text":"Uniformity trial rice Lahore, Punjab, circa 2011.","code":""},{"path":"/reference/masood.rice.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of rice — masood.rice.uniformity","text":"","code":"data(\"masood.rice.uniformity\")"},{"path":"/reference/masood.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice — masood.rice.uniformity","text":"data frame 288 observations following 3 variables. row row col column yield yield, kg/m^2","code":""},{"path":"/reference/masood.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice — masood.rice.uniformity","text":"Data collected Rice Research Institute paddy yield trial. single variety rice harvested area 12m x 24 m. Yield kilograms measured square meter. Masood et al report low degree similarity neighboring plots. Note, Smith index calculations match results Pakistan Journal Agricultural Research, match results American-Eurasian Journal, seems paper seems refer data. results may simply differ scaling factor. yield values Masood labeled \"gm^2\" (gram per sq meter), extremely low. Probably \"kgm^2\". Field length: 24 plots x 1m = 24m. Field width: 12 plots x 1m = 12m. Used permission Asif Masood.","code":""},{"path":"/reference/masood.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice — masood.rice.uniformity","text":"Masood, M Asif Raza, Irum. 2012. Estimation optimum field plot size shape paddy yield trial. Pakistan J. Agric. Res., Vol. 25 . 4, 2012","code":""},{"path":"/reference/masood.rice.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of rice — masood.rice.uniformity","text":"Masood, M Asif Raza, Irum. 2012. Estimation optimum field plot size shape paddy yield trial. American-Eurasian Journal Scientific Research, 7, 264-269. Table 1. https://doi.org/10.5829/idosi.aejsr.2012.7.6.1926","code":""},{"path":"/reference/masood.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice — masood.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(masood.rice.uniformity) dat <- masood.rice.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, tick=TRUE, aspect=24/12, # true aspect main=\"masood.rice.uniformity - yield heatmap\") libs(agricolae) libs(reshape2) dmat <- acast(dat, row~col, value.var='yield') index.smith(dmat, main=\"masood.rice.uniformity\", col=\"red\") # CVs match Table 3 } # }"},{"path":"/reference/mcclelland.corn.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of corn — mcclelland.corn.uniformity","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"Uniformity trial corn Arkansas Experiment Station, 1925.","code":""},{"path":"/reference/mcclelland.corn.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"","code":"data(\"mcclelland.corn.uniformity\")"},{"path":"/reference/mcclelland.corn.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"data frame 438 observations following 3 variables. row row col column yield yield","code":""},{"path":"/reference/mcclelland.corn.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"uniformity trial corn 1925 Arkansas Experimental Station. Unit measure given. Field width = 66ft * 2 = 132 feet. Field length = 219 rows * 44 inches / 12 inches/ft = 803 ft. Note: source document, table 2, first 'west' column second--last row (page 822), value 1.40 assumed typographical error changed 14.0 data. source document give unit measure plot yields. yield bu/ac, value 12 bu/ac low. hand, value 12 pounds per plot * 180 plots per acre / 56 pounds per bushel = 39 bu/ac reasonable yield corn 1925, whereas 12 kg per plot unlikely high. Also, 1925, pound likely kilogram.","code":""},{"path":"/reference/mcclelland.corn.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"McClelland, Chalmer Kirk (1926). determinations plat variability. Agronomy Journal, 18, 819-823. https://doi.org/10.2134/agronj1926.00021962001800090009x","code":""},{"path":"/reference/mcclelland.corn.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"None","code":""},{"path":"/reference/mcclelland.corn.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mcclelland.corn.uniformity) dat <- mcclelland.corn.uniformity # McClelland table 3, first row, gives 11.2 # Probable error = 0.67449 * sd(). Relative to mean. # 0.67449 * sd(dat$yield)/mean(dat$yield) # 11.2 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(219*44/12)/132, # true aspect, 219 rows * 44 inches x 132 feet main=\"mcclelland.corn.uniformity\") } # }"},{"path":"/reference/mcconway.turnip.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of turnips — mcconway.turnip","title":"RCB experiment of turnips — mcconway.turnip","text":"RCB experiment turnips, 2 treatments planting date density","code":""},{"path":"/reference/mcconway.turnip.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of turnips — mcconway.turnip","text":"data frame 64 observations following 6 variables. gen genotype date planting date, levels 21Aug1990 28Aug1990 density planting density, 1, 2, 4, 8 kg/ha block block, 4 levels yield yield","code":""},{"path":"/reference/mcconway.turnip.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of turnips — mcconway.turnip","text":"randomized block experiment 16 treatments allocated random four blocks. 16 treatments combinations two varieties, two planting dates, four densities. Lee et al (2008) proposed analysis using mixed models changing treatment variances. Piepho (2009) proposed ordinary ANOVA using transformed data. Used permission Kevin McConway.","code":""},{"path":"/reference/mcconway.turnip.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of turnips — mcconway.turnip","text":"K. J. McConway, M. C. Jones, P. C. Taylor. Statistical Modelling Using Genstat.","code":""},{"path":"/reference/mcconway.turnip.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of turnips — mcconway.turnip","text":"Michael Berthold, D. J. Hand. Intelligent data analysis: introduction, 1998. Pages 75–82. Lee, C.J. O Donnell, M. O Neill, M. (2008). Statistical analysis field trials changing treatment variance. Agronomy Journal, 100, 484–489. Piepho, H.P. (2009), Data transformation statistical analysis field trials changing treatment variance. Agronomy Journal, 101, 865–869. https://doi.org/10.2134/agronj2008.0226x","code":""},{"path":"/reference/mcconway.turnip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of turnips — mcconway.turnip","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mcconway.turnip) dat <- mcconway.turnip dat$densf <- factor(dat$density) # Table 2 of Lee et al. m0 <- aov( yield ~ gen * densf * date + block, dat ) summary(m0) ## Df Sum Sq Mean Sq F value Pr(>F) ## gen 1 84.0 83.95 8.753 0.00491 ** ## densf 3 470.4 156.79 16.347 2.51e-07 *** ## date 1 233.7 233.71 24.367 1.14e-05 *** ## block 3 163.7 54.58 5.690 0.00216 ** ## gen:densf 3 8.6 2.88 0.301 0.82485 ## gen:date 1 36.5 36.45 3.800 0.05749 . ## densf:date 3 154.8 51.60 5.380 0.00299 ** ## gen:densf:date 3 18.0 6.00 0.626 0.60224 ## Residuals 45 431.6 9.59 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # Boxplots suggest heteroskedasticity for date, density libs(\"HH\") interaction2wt(yield ~ gen + date + densf +block, dat, x.between=0, y.between=0, main=\"mcconway.turnip - yield\") libs(nlme) # Random block model m1 <- lme(yield ~ gen * date * densf, random= ~1|block, data=dat) summary(m1) anova(m1) # Multiplicative variance model over densities and dates m2 <- update(m1, weights=varComb(varIdent(form=~1|densf), varIdent(form=~1|date))) summary(m2) anova(m2) # Unstructured variance model over densities and dates m3 <- update(m1, weights=varIdent(form=~1|densf*date)) summary(m3) anova(m3) # Table 3 of Piepho, using transformation m4 <- aov( yield^.235 ~ gen * date * densf + block, dat ) summary(m4) } # }"},{"path":"/reference/mckinstry.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"Uniformity trial cotton South Rhodesia","code":""},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"","code":"data(\"mckinstry.cotton.uniformity\")"},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"data frame 480 observations following 3 variables. row row ordinate col column ordinate yield yield per plot, ounces","code":""},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"uniformity trial cotton experiment Gatooma, South Rhodesia. Conducted Empire Cotton Growing Corporation. Planted Nov 1934. Harvested Jun 1935. Field length: 20 rows x 25 feet. Field width: 24 columns x 3.5 feet. Crop History: season good peak flowering - good growth, heavy flowering - 5 weeks drought critical period crop, aggravated exceptionally heavy aphis attack heavy boll-worm attack accounts. Lay-: harvest, block 24 rows x 500 ft, row marked 20 lengths 25 ft , giving 480 small plots. use made data advisable ignore row 1 row 20, bordering roads. data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 5.","code":""},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"None","code":""},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"","code":"library(agridat) data(mckinstry.cotton.uniformity) dat <- mckinstry.cotton.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, tick=TRUE, aspect=(20*25)/(24*3.5), main=\"mckinstry.cotton.uniformity\")"},{"path":"/reference/mcleod.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"Yield yield components barley different seeding rates.","code":""},{"path":"/reference/mcleod.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"data frame 40 observations following 10 variables. year year, numeric site site factor rate rate, numeric plants plants per sq meter tillers tillers per plant heads heads per plant surviving percent surviving tillers grains grains per head weight weight 1000 grains yield yield tons/hectare","code":""},{"path":"/reference/mcleod.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"Trials conducted 5 sites, 3 years South Canterbury. (sites every year). Values average 6 blocks. 1974 severe drought. years favorable growing conditions.","code":""},{"path":"/reference/mcleod.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"C. C. McLeod (1982). Effects rates seeding barley sown grain. New Zealand Journal Experimental Agriculture, 10, 133-136. https://doi.org/10.1080/03015521.1982.10427857.","code":""},{"path":"/reference/mcleod.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"Maindonald (1992).","code":""},{"path":"/reference/mcleod.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mcleod.barley) dat <- mcleod.barley # Table 3 of McLeod. Across-environment means by planting rate d1 <- aggregate(cbind(plants, tillers, heads, surviving, grains, weight, yield) ~ rate, dat, FUN=mean) # Calculate income based on seed cost of $280/ton, grain $140/ton. d1 <- transform(d1, income=140*yield-280*rate/1000) signif(d1,3) ## rate plants tillers heads surviving grains weight yield ## 50 112.12 5.22 4.36 83.95 21.25 46.11 3.97 ## 75 162.75 4.04 3.26 80.89 19.95 45.10 4.26 ## 100 202.62 3.69 2.73 74.29 19.16 44.66 4.38 ## 125 239.00 3.28 2.33 71.86 18.45 43.45 4.41 ## 150 293.62 2.90 2.00 69.54 17.94 42.77 4.47 # Even though tillers/plant, heads/plant, surviving tillers, # grains/head, weight/1000 grains are all decreasing as planting # rate increases, the total yield is still increasing. # But, income peaks around seed rate of 100. libs(lattice) xyplot(yield +income +surviving +grains +weight +plants +tillers +heads ~ rate, data=d1, outer=TRUE, type=c('p','l'), scales=list(y=list(relation=\"free\")), xlab=\"Nitrogen rate\", ylab=\"Trait value\", main=\"mcleod.barley - nitrogen response curves\" ) } # }"},{"path":"/reference/mead.cauliflower.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaves for cauliflower plants at different times — mead.cauliflower","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"Leaves cauliflower plants different times two years.","code":""},{"path":"/reference/mead.cauliflower.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"data frame 14 observations following 4 variables. year year factor degdays degree days 32F leaves number leaves","code":""},{"path":"/reference/mead.cauliflower.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"Numbers leaves 10 cauliflower plants two years, temperature degree-days 32F, divided 100. year 1956-57 1957-58. data range shown, number leaves increasing linearly. Extrapolating backwards shows linear model inappropriate, glm used.","code":""},{"path":"/reference/mead.cauliflower.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"Roger Mead, Robert N Curnow, Anne M Hasted. 2002. Statistical Methods Agriculture Experimental Biology, 3rd ed. Chapman Hall. Page 251.","code":""},{"path":"/reference/mead.cauliflower.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"Mick O'Neill. Regression & Generalized Linear (Mixed) Models. Statistical Advisory & Training Service Pty Ltd.","code":""},{"path":"/reference/mead.cauliflower.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.cauliflower) dat <- mead.cauliflower dat <- transform(dat, year=factor(year)) m1 <- glm(leaves ~ degdays + year, data=dat, family=poisson) coef(m1) ## (Intercept) degdays year1957 ## 3.49492453 0.08512651 0.21688760 dat$pred <- predict(m1, type=\"response\") libs(lattice) libs(latticeExtra) xyplot(leaves~degdays, data=dat, groups=year, type=c('p'), auto.key=list(columns=2), main=\"mead.cauliflower - observed (symbol) & fitted (line)\", xlab=\"degree days\", ylab=\"Number of leaves\", ) + xyplot(pred~degdays, data=dat, groups=year, type=c('l'), col=\"black\") } # }"},{"path":"/reference/mead.cowpea.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"Intercropping experiment maize/cowpea, multiple nitrogen treatments.","code":""},{"path":"/reference/mead.cowpea.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"data frame 72 observations following 6 variables. block block, 3 levels nitro nitrogen, 4 levels cowpea cowpea variety, 2 levels maize maize variety, 3 levels cyield cowpea yield, kg/ha myield maize yield, kg/ha","code":""},{"path":"/reference/mead.cowpea.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"intercropping experiment conducted Nigeria. four nitrogen treatments 0, 40, 80, 120 kg/ha.","code":""},{"path":"/reference/mead.cowpea.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"Roger Mead. 1990. Review Methodology Analysis Intercropping Experiments. Training Working Document . 6. CIMMYT. https://repository.cimmyt.org/xmlui/handle/10883/868","code":""},{"path":"/reference/mead.cowpea.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"Roger Mead, Robert N Curnow, Anne M Hasted. 2002. Statistical Methods Agriculture Experimental Biology, 3rd ed. Chapman Hall. Page 390.","code":""},{"path":"/reference/mead.cowpea.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.cowpea.maize) dat <- mead.cowpea.maize # Cowpea and maize yields are clearly in competition libs(\"latticeExtra\") useOuterStrips(xyplot(myield ~ cyield|maize*cowpea, dat, group=nitro, main=\"mead.cowpea.maize - intercropping\", xlab=\"cowpea yield\", ylab=\"maize yield\", auto.key=list(columns=4))) # Mead Table 2 Cowpea yield anova...strongly affected by maize variety. anova(aov(cyield ~ block + maize + cowpea + nitro + maize:cowpea + maize:nitro + cowpea:nitro + maize:cowpea:nitro, dat)) # Cowpea mean yields for nitro*cowpea aggregate(cyield ~ nitro+cowpea, dat, FUN=mean) # Cowpea mean yields for each maize variety aggregate(cyield ~ maize, dat, FUN=mean) # Bivariate analysis aov.c <- anova(aov(cyield/1000 ~ block + maize + cowpea + nitro + maize:cowpea + maize:nitro + cowpea:nitro + maize:cowpea:nitro, dat)) aov.m <- anova(aov(myield/1000 ~ block + maize + cowpea + nitro + maize:cowpea + maize:nitro + cowpea:nitro + maize:cowpea:nitro, dat)) aov.cm <- anova(aov(cyield/1000 + myield/1000 ~ block + maize + cowpea + nitro + maize:cowpea + maize:nitro + cowpea:nitro + maize:cowpea:nitro, dat)) biv <- cbind(aov.m[,1:2], aov.c[,2], aov.cm[,2]) names(biv) <- c('df','maize ss','cowpea ss','ss for sum') biv$'sum of prod' <- (biv[,4] - biv[,2] - biv[,3] ) /2 biv$cor <- biv[,5]/(sqrt(biv[,2] * biv[,3])) signif(biv,2) ## df maize ss cowpea ss ss for sum sum of prod cor ## block 2 0.290 0.0730 0.250 -0.058 -0.400 ## maize 2 18.000 0.4100 13.000 -2.600 -0.980 ## cowpea 1 0.027 0.0060 0.058 0.013 1.000 ## nitro 3 29.000 0.1100 25.000 -1.800 -0.980 ## maize:cowpea 2 1.100 0.0099 0.920 -0.099 -0.950 ## maize:nitro 6 1.300 0.0680 0.920 -0.200 -0.680 ## cowpea:nitro 3 0.240 0.1700 0.150 -0.130 -0.640 ## maize:cowpea:nitro 6 1.300 0.1400 1.300 -0.033 -0.079 ## Residuals 46 16.000 0.6000 14.000 -1.400 -0.460 } # }"},{"path":"/reference/mead.germination.html","id":null,"dir":"Reference","previous_headings":"","what":"Seed germination with different temperatures/concentrations — mead.germination","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"Seed germination different temperatures/concentrations","code":""},{"path":"/reference/mead.germination.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"data frame 64 observations following 5 variables. temp temperature regimen rep replication factor (blocking) conc chemical concentration germ number seeds germinating seeds number seeds tested = 50","code":""},{"path":"/reference/mead.germination.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"rep factor blocking factor. Used permission Roger Mead, Robert Curnow, Anne Hasted.","code":""},{"path":"/reference/mead.germination.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"Roger Mead, Robert N Curnow, Anne M Hasted. 2002. Statistical Methods Agriculture Experimental Biology, 3rd ed. Chapman Hall. Page 350-351.","code":""},{"path":"/reference/mead.germination.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"Schabenberger, O. Pierce, F.J., 2002. Contemporary statistical models plant soil sciences. CRC.","code":""},{"path":"/reference/mead.germination.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.germination) dat <- mead.germination dat <- transform(dat, concf=factor(conc)) libs(lattice) xyplot(germ~log(conc+.01)|temp, dat, layout=c(4,1), main=\"mead.germination\", ylab=\"number of seeds germinating\") m1 <- glm(cbind(germ, seeds-germ) ~ 1, dat, family=binomial) m2 <- glm(cbind(germ, seeds-germ) ~ temp, dat, family=binomial) m3 <- glm(cbind(germ, seeds-germ) ~ concf, dat, family=binomial) m4 <- glm(cbind(germ, seeds-germ) ~ temp + concf, dat, family=binomial) m5 <- glm(cbind(germ, seeds-germ) ~ temp * concf, dat, family=binomial) anova(m1,m2,m3,m4,m5) ## Resid. Df Resid. Dev Df Deviance ## 1 63 1193.80 ## 2 60 430.11 3 763.69 ## 3 60 980.10 0 -549.98 ## 4 57 148.11 3 831.99 ## 5 48 55.64 9 92.46 # Show logit and fitted values. T2 has highest germination subset(cbind(dat, predict(m5), fitted(m5)), rep==\"R1\") } # }"},{"path":"/reference/mead.lamb.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"Number lambs born 3 breeds 3 farms","code":""},{"path":"/reference/mead.lamb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"","code":"data(\"mead.lamb\")"},{"path":"/reference/mead.lamb.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"data frame 36 observations following 4 variables. farm farm: F1, F2, F3 breed breed: B1, B2, B3 lambclass lambing class: L0, L1, L2, L3 y count ewes class","code":""},{"path":"/reference/mead.lamb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"data 'y' counts ewes different lambing classes. classes number live lambs per birth 0, 1, 2, 3+ lambs.","code":""},{"path":"/reference/mead.lamb.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"Roger Mead, Robert N Curnow, Anne M Hasted. 2002. Statistical Methods Agriculture Experimental Biology, 3rd ed. Chapman Hall. Page 359.","code":""},{"path":"/reference/mead.lamb.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"None","code":""},{"path":"/reference/mead.lamb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.lamb) dat <- mead.lamb # farm 1 has more ewes in lambclass 3 d2 <- xtabs(y ~ farm+breed+lambclass, data=dat) mosaicplot(d2, color=c(\"lemonchiffon1\",\"moccasin\",\"lightsalmon1\",\"indianred\"), xlab=\"farm/lambclass\", ylab=\"breed\", main=\"mead.lamb\") names(dat) <- c('F','B','L','y') # for compactness # Match totals in Mead example 14.6 libs(dplyr) dat <- group_by(dat, F,B) summarize(dat, y=sum(y)) ## F B y ## ## 1 F1 A 150 ## 2 F1 B 46 ## 3 F1 C 78 ## 4 F2 A 72 ## 5 F2 B 79 ## 6 F2 C 28 ## 7 F3 A 224 ## 8 F3 B 129 ## 9 F3 C 34 # Models m1 <- glm(y ~ F + B + F:B, data=dat, family=poisson(link=log)) m2 <- update(m1, y ~ F + B + F:B + L) m3 <- update(m1, y ~ F + B + F:B + L + B:L) m4 <- update(m1, y ~ F + B + F:B + L + F:L) m5 <- update(m1, y ~ F + B + F:B + L + B:L + F:L) AIC(m1, m2, m3, m4, m5) # Model 4 has best AIC ## df AIC ## m1 9 852.9800 ## m2 12 306.5457 ## m3 18 303.5781 ## m4 18 206.1520 ## m5 24 213.8873 # Change contrasts for Miroslav m4 <- update(m4, contrasts=list(F=contr.sum,B=contr.sum,L=contr.sum)) summary(m4) # Match deviance table from Mead libs(broom) all <- do.call(rbind, lapply(list(m1, m2, m3, m4, m5), broom::glance)) all$model <- unlist(lapply(list(m1, m2, m3, m4, m5), function(x) as.character(formula(x)[3]))) all[,c('model','deviance','df.residual')] ## model deviance df.residual ## 1 F + B + F:B 683.67257 27 ## 2 F + B + L + F:B 131.23828 24 ## 3 F + B + L + F:B + B:L 116.27069 18 ## 4 F + B + L + F:B + F:L 18.84460 18 ## 5 F + B + L + F:B + B:L + F:L 14.57987 12 if(0){ # Using MASS::loglm libs(MASS) # Note: without 'fitted=TRUE', devtools::run_examples has an error m4b <- MASS::loglm(y ~ F + B + F:B + L + F:L, data = dat, fitted=TRUE) # Table of farm * class interactions. Match Mead p. 360 round(coef(m4b)$F.L,2) fitted(m4b) resid(m4b) # libs(vcd) # mosaic(m4b, shade=TRUE, # formula = ~ F + B + F:B + L + F:L, # residual_type=\"rstandard\", keep_aspect=FALSE) } } # }"},{"path":"/reference/mead.strawberry.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of strawberry — mead.strawberry","title":"RCB experiment of strawberry — mead.strawberry","text":"RCB experiment strawberry","code":""},{"path":"/reference/mead.strawberry.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of strawberry — mead.strawberry","text":"data frame 32 observations following 5 variables. row row col column block block, 4 levels gen genotype, 8 levels yield yield, pounds","code":""},{"path":"/reference/mead.strawberry.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of strawberry — mead.strawberry","text":"hedge along right side (column 8) caused shading lower yields. R. Mead said (discussion Besag & Higdon paper), \"blocks defined (given experimenter) entire horizontal rows...design trial actually (unrecognized also) checker-board eight half-blocks two groups split-plot varieties\". two sub-groups genotypes G, V, R1, F Re, M, E, P.","code":""},{"path":"/reference/mead.strawberry.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of strawberry — mead.strawberry","text":"Unknown, prior 1968 according Besag. Probably via R. Mead.","code":""},{"path":"/reference/mead.strawberry.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of strawberry — mead.strawberry","text":"R. Mead, 1990, Design Experiments. Julian Besag D Higdon, 1999. Bayesian Analysis Agricultural Field Experiments, Journal Royal Statistical Society: Series B (Statistical Methodology),61, 691–746. Table 4.","code":""},{"path":"/reference/mead.strawberry.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of strawberry — mead.strawberry","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.strawberry) dat <- mead.strawberry dat$sub <- ifelse(is.element(dat$gen, c('G', 'V', 'R1', 'F')), \"S1\",\"S2\") libs(desplot) desplot(dat, yield~col*row, text=gen, cex=1, out1=block, out2=sub, # unknown aspect main=\"mead.strawberry\") } # }"},{"path":"/reference/mead.turnip.html","id":null,"dir":"Reference","previous_headings":"","what":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"Density/spacing experiment turnips 3 blocks.","code":""},{"path":"/reference/mead.turnip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"","code":"data(\"mead.turnip\")"},{"path":"/reference/mead.turnip.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"data frame 60 observations following 4 variables. yield log yield (pounds/plot) block block spacing row spacing, inches density density seeds, pounds/acre","code":""},{"path":"/reference/mead.turnip.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"experiment turnips, 3 blocks, 20 treatments factorial arrangement 5 seeding rates (density) 4 widths (spacing).","code":""},{"path":"/reference/mead.turnip.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"Roger Mead. (1988). Design Experiments: Statistical Principles Practical Applications. Example 12.3. Page 323.","code":""},{"path":"/reference/mead.turnip.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"H. P. Piepho, R. N. Edmondson. (2018). tutorial statistical analysis factorial experiments qualitative quantitative treatment factor levels. Jour Agronomy Crop Science, 8, 1-27. https://doi.org/10.1111/jac.12267","code":""},{"path":"/reference/mead.turnip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.turnip) dat <- mead.turnip dat$ratef <- factor(dat$density) dat$widthf <- factor(dat$spacing) m1 <- aov(yield ~ block + ratef + widthf + ratef:widthf, data=dat) anova(m1) # table 12.10 in Mead # Similar to Piepho fig 10 libs(lattice) xyplot(yield ~ log(spacing)|ratef, data=dat, auto.key=list(columns=5), main=\"mead.turnip - log(yield) for each density\", group=ratef) } # }"},{"path":"/reference/mercer.mangold.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of mangolds — mercer.mangold.uniformity","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"Uniformity trial mangolds Rothamsted Experiment Station, England, 1910.","code":""},{"path":"/reference/mercer.mangold.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"","code":"data(\"mercer.mangold.uniformity\")"},{"path":"/reference/mercer.mangold.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"data frame 200 observations following 4 variables. row row col column roots root yields, pounds leaves leaf yields, pounds","code":""},{"path":"/reference/mercer.mangold.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"Grown 1910. plot 3 drills, drill 2.4 feet wide. Plots 1/200 acres, 7.2 feet 30.25 feet long \"length plots runs horizontal lines figures [ Table ], also direction drills across field.\" Field width: 10 plots * 30.25ft = 302.5 feet Field length: 20 plots * 7.25 ft = 145 feet","code":""},{"path":"/reference/mercer.mangold.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"Mercer, WB Hall, AD, 1911. experimental error field trials Journal Agricultural Science, 4, 107-132. Table 1. https://doi.org/10.1017/S002185960000160X","code":""},{"path":"/reference/mercer.mangold.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667 Theodor Roemer (1920). Der Feldversuch. Page 64, table 5.","code":""},{"path":"/reference/mercer.mangold.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mercer.mangold.uniformity) dat <- mercer.mangold.uniformity libs(desplot) desplot(dat, leaves~col*row, aspect=145/302, # true aspect main=\"mercer.mangold.uniformity - leaves\") libs(desplot) desplot(dat, roots~col*row, aspect=145/302, # true aspect main=\"mercer.mangold.uniformity - roots\") libs(lattice) xyplot(roots~leaves, data=dat) } # }"},{"path":"/reference/mercer.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — mercer.wheat.uniformity","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"Uniformity trial wheat Rothamsted Experiment Station, England, 1910.","code":""},{"path":"/reference/mercer.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"data frame 500 observations following 4 variables. row row col column grain grain yield, pounds straw straw yield, pounds","code":""},{"path":"/reference/mercer.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"wheat crop grown summer 1910 Rothamsted Experiment Station (Harpenden, Hertfordshire, England). Great Knott, seemingly uniform area 1 acre harvested separate plots, 1/500th acre size. grain straw plot weighed separately. McCullagh gives information plot size. Field width: 25 plots * 8 ft = 200 ft Field length: 20 plots * 10.82 ft = 216 ft D. G. Rossiter (2014) uses data extensive data analysis tutorial.","code":""},{"path":"/reference/mercer.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"Mercer, WB Hall, AD, (1911). experimental error field trials Journal Agricultural Science, 4, 107-132. Table 5. https://doi.org/10.1017/S002185960000160X","code":""},{"path":"/reference/mercer.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667 Theodor Roemer (1920). Der Feldversuch. Page 65, table 6. D. G. Rossiter (2014). Tutorial: Using R Environment Statistical Computing example Mercer & Hall wheat yield dataset. G. . Baker (1941). Fundamental Distribution Errors Agricultural Field Trials. National Mathematics Magazine, 16, 7-19. https://doi.org/10.2307/3028105 'spdep' package includes grain yields () spatial positions plot centres example dataset 'wheat'. Note, checked '4.03' values data match original document.","code":""},{"path":"/reference/mercer.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mercer.wheat.uniformity) dat <- mercer.wheat.uniformity libs(desplot) desplot(dat, grain ~ col*row, aspect=216/200, # true aspect main=\"mercer.wheat.uniformity - grain yield\") libs(lattice) xyplot(straw ~ grain, data=dat, type=c('p','r'), main=\"mercer.wheat.uniformity - regression\") libs(hexbin) hexbinplot(straw ~ grain, data=dat) libs(sp, gstat) plot.wid <- 2.5 plot.len <- 3.2 nr <- length(unique(dat$row)) nc <- length(unique(dat$col)) xy <- expand.grid(x = seq(plot.wid/2, by=plot.wid, length=nc), y = seq(plot.len/2, by=plot.len, length=nr)) dat.sp <- dat coordinates(dat.sp) <- xy # heatmap spplot(dat.sp, zcol = \"grain\", cuts=8, cex = 1.6, col.regions = bpy.colors(8), main = \"Grain yield\", key.space = \"right\") # variogram # Need gstat::variogram to get the right method vg <- gstat::variogram(grain ~ 1, dat.sp, cutoff = plot.wid * 10, width = plot.wid) plot(vg, plot.numbers = TRUE, main=\"mercer.wheat.uniformity - variogram\") } # }"},{"path":"/reference/miguez.biomass.html","id":null,"dir":"Reference","previous_headings":"","what":"Biomass of 3 crops in Greece — miguez.biomass","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"Biomass 3 crops Greece","code":""},{"path":"/reference/miguez.biomass.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"","code":"data(\"miguez.biomass\")"},{"path":"/reference/miguez.biomass.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"data frame 212 observations following 5 variables. doy day year block block, 1-4 input management input, Lo/Hi crop crop type yield yield tons/ha","code":""},{"path":"/reference/miguez.biomass.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"Experiment conducted Greece 2009. Yield values destructive Measurements -ground biomass fiber sorghum, maize, sweet sorghum. Hi management refers weekly irrigation high nitrogen applications. Lo management refers bi-weekly irrigation low nitrogen. experiment 4 blocks. Crops planted DOY 141 0 yield.","code":""},{"path":"/reference/miguez.biomass.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"Fernando E. Miguez. R package nlraa. https://github.com/femiguez/nlraa","code":""},{"path":"/reference/miguez.biomass.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"Sotirios V. Archontoulis Fernando E. Miguez (2013). Nonlinear Regression Models Applications Agricultural Research. Agron. Journal, 105:1-13. https://doi.org/10.2134/agronj2012.0506 Hamze Dokoohaki. https://www.rpubs.com/Para2x/100378 https://rstudio-pubs-static.s3.amazonaws.com/100440_26eb9108524c4cc99071b0db8e648e7d.html","code":""},{"path":"/reference/miguez.biomass.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(miguez.biomass) dat <- miguez.biomass dat <- subset(dat, doy > 141) libs(lattice) xyplot(yield ~ doy | crop*input, data = dat, main=\"miguez.biomass\", groups = crop, type=c('p','smooth'), auto.key=TRUE) # ---------- # Archontoulis et al fit some nonlinear models. # Here is a simple example which does NOT account for crop/input # Slow, so dont run if(0){ dat2 <- transform(dat, eu = paste(block, input, crop)) dat2 <- groupedData(yield ~ doy | eu, data = dat2) fit.lis <- nlsList(yield ~ SSfpl(doy, A, B, xmid, scal), data = dat2, control=nls.control(maxiter=100)) print(plot(intervals(fit.lis))) libs(nlme) # use all data to get initial values inits <- getInitial(yield ~ SSfpl(doy, A, B, xmid, scal), data = dat2) inits xvals <- 150:325 y1 <- with(as.list(inits), SSfpl(xvals, A, B, xmid, scal)) plot(yield ~ doy, dat2) lines(xvals,y1) # must have groupedData object to use augPred dat2 <- groupedData(yield ~ doy|eu, data=dat2) plot(dat2) # without 'random', all effects are included in 'random' m1 <- nlme(yield ~ SSfpl(doy, A, B, xmid,scale), data= dat2, fixed= A + B + xmid + scale ~ 1, # random = B ~ 1|eu, # to make only B random random = A + B + xmid + scale ~ 1|eu, start=inits) fixef(m1) summary(m1) plot(augPred(m1, level=0:1), main=\"miguez.biomass - observed/predicted data\") # only works with groupedData object } } # }"},{"path":"/reference/minnesota.barley.weather.html","id":null,"dir":"Reference","previous_headings":"","what":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"monthly weather summaries 6 sites barley yield trials conducted.","code":""},{"path":"/reference/minnesota.barley.weather.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"data frame 719 observations following 8 variables. site site, 6 levels year year, 1927-1936 mo month, 1-12, numeric cdd monthly cooling degree days, Fahrenheit hdd monthly heating degree days, Fahrenheit precip monthly precipitation, inches min monthly average daily minimum temp, Fahrenheit max monthly average daily maximum temp, Fahrenheit","code":""},{"path":"/reference/minnesota.barley.weather.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"weather data extracted National Climate Data Center, following weather stations chosen, based availability weather data given time frame (1927-1936) proximity town (site) barley data. 'cdd' cooling degree days, number degree days temperature _above_ 65 Fahrenheit. 'hdd' heating degree days, _below_ 65 Fahrenheit. data available Duluth Dec, 1931.","code":""},{"path":"/reference/minnesota.barley.weather.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"National Climate Data Center, https://www.ncdc.noaa.gov/.","code":""},{"path":"/reference/minnesota.barley.weather.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"Kevin Wright. 2013. Revisiting Immer's Barley Data. American Statistitician, 67, 129-133. https://doi.org/10.1080/00031305.2013.801783","code":""},{"path":"/reference/minnesota.barley.weather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(minnesota.barley.yield) dat <- minnesota.barley.yield data( minnesota.barley.weather) datw <- minnesota.barley.weather # Weather trends over time libs(latticeExtra) useOuterStrips(xyplot(cdd~mo|year*site, datw, groups=year, main=\"minnesota.barley\", xlab=\"month\", ylab=\"Cooling degree days\", subset=(mo > 3 & mo < 10), scales=list(alternating=FALSE), type='l', auto.key=list(columns=5))) # Total cooling/heating/precip in Apr-Aug for each site/yr ww <- subset(datw, mo>=4 & mo<=8) ww <- aggregate(cbind(cdd,hdd,precip)~site+year, data=ww, sum) # Average yield per each site/env yy <- aggregate(yield~site+year, dat, mean) minn <- merge(ww, yy) # Higher yields generally associated with cooler temps, more precip libs(reshape2) me <- melt(minn, id.var=c('site','year')) mey <- subset(me, variable==\"yield\") mey <- mey[,c('site','year','value')] names(mey) <- c('site','year','y') mec <- subset(me, variable!=\"yield\") names(mec) <- c('site','year','covar','x') mecy <- merge(mec, mey) mecy$yr <- factor(mecy$year) foo <- xyplot(y~x|covar*site, data=mecy, groups=yr, cex=1, ylim=c(5,65), par.settings=list(superpose.symbol=list(pch=substring(levels(mecy$yr),4))), xlab=\"\", ylab=\"yield\", main=\"minnesota.barley\", panel=function(x,y,...) { panel.lmline(x,y,..., col=\"gray\") panel.superpose(x,y,...) }, scales=list(x=list(relation=\"free\"))) libs(latticeExtra) foo <- useOuterStrips(foo, strip.left = strip.custom(par.strip.text=list(cex=.7))) combineLimits(foo, margin.x=2L) # Use a common x axis for all rows } # }"},{"path":"/reference/minnesota.barley.yield.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"data come barley breeding experiments conducted Minnesota years 1893-1942. early years, experiments conducted StPaul. late 1920s, experiments expanded 6 sites across state.","code":""},{"path":"/reference/minnesota.barley.yield.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"data frame 647 observations following 4 variables. site site factor, 6 levels gen_name genotype name gen genotype (CI cereal introduction ID) year year yield yield bu/ac","code":""},{"path":"/reference/minnesota.barley.yield.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"lattice package contains smaller version data years 1931 1932. expanded version barley data often used illustrate dot plots. following comments reference mentioned source documents. —– Notes Immer (1934) —– University Farm location Saint Paul. source provides yield data three blocks location 1931 1932. following registration numbers names given: —– Notes Harlan et al (1925) —– data early tests accurate stations, may problems stations. (p. 14). Identification many varieties inadequate...chance incorrectly identified small...Officials StPaul station expressed desire conclusions drawn yields limitations earlier experiments taken full consideration. (p. 72) Chevalier Hanna varieties well adapted StPaul (p. 73). —– Notes Harlan et al (1929) —– —– Notes Harlan et al (1935) —– 1931 yields match average values Immer (1934). Minnesota 474 475 cultivars 'Svanhals x Lion' crosses. yields reported Crookston 1928 crop failure. (Page 20) Also, report North Dakota says \"zero yields Williston, ND 1931 caused drought\". (Page 31) —– Notes Wiebe et al (1935) —– —– Notes Wiebe et al (1940) —– 1932 data generally match average values Immer (1934) following notes. data Glabron St Paul 1932 missing, given 36.8 Immer (1934). value treated missing R dataset. data Svansota Morris 1932 missing, given 35.0 Immer (1934). value treated missing R dataset. yield 'Wisconsin 38' St Paul 1932 shown 3.80, 38 Immer (1934). latter value used R dataset. yields No475 1932 reported Wiebe (1940), reported Immer (1934). yields reported Morris 1933 1934, crop failure owing drought. —– Notes Hayes (1942) —– source gives block-level yield data 5 cultivars 4 sites 1932 1935. Cultivar 'Barbless' 'Wisconsin No38'.","code":""},{"path":"/reference/minnesota.barley.yield.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"Harry V. Harlan Mary L. Martini Merrit N. Pope (1925). Tests barley varieties America. United States Department Agriculture, Department Bulletin 1334. https://archive.org/details/testsofbarleyvar1334harl H. V. Harlan L. H. Newman Mary L. Martini (1929). Yields barley United States Canada 1922-1926. United States Department Agriculture, Technical Bulletin 96. https://handle.nal.usda.gov/10113/CAT86200091 Harlan, H. V. Philip Russell Cowan Lucille Reinbach. (1935). Yields barley United States Canada 1927-1931. United States Dept Agriculture, Technical Bulletin 446. https://naldc.nal.usda.gov/download/CAT86200440/PDF Wiebe, Gustav . Philip Russell Cowan, Lucille Reinbach-Welch. (1940). Yields barley varieties United States Canada 1932-36. United States Dept Agriculture, Technical Bulletin 735. https://books.google.com/books?id=OUfxLocnpKkC&pg=PA19 Wiebe, Gustav . Philip Russell Cowan, Lucille Reinbach-Welch. (1944). Yields barley varieties United States Canada, 1937-41. United States Dept Agriculture, Technical Bulletin 881. https://handle.nal.usda.gov/10113/CAT86200873","code":""},{"path":"/reference/minnesota.barley.yield.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"Immer, R. F. H. K. Hayes LeRoy Powers. (1934). Statistical Determination Barley Varietal Adaptation. Journal American Society Agronomy, 26, 403-419. https://doi.org/10.2134/agronj1934.00021962002600050008x Hayes, H.K. Immer, F.R. (1942). Methods plant breeding. McGraw Hill. Kevin Wright. (2013). Revisiting Immer's Barley Data. American Statistitician, 67, 129-133. https://doi.org/10.1080/00031305.2013.801783","code":""},{"path":"/reference/minnesota.barley.yield.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(minnesota.barley.yield) dat <- minnesota.barley.yield dat$yr <- factor(dat$year) # Drop Dryland, Jeans, CompCross, MechMixture because they have less than 5 # year-loc values dat <- subset(dat, !is.element(gen_name, c(\"CompCross\",\"Dryland\",\"Jeans\",\"MechMixture\"))) dat <- subset(dat, year >= 1927 & year <= 1936) dat <- droplevels(dat) # 1934 has huge swings from one loc to the next libs(lattice) dotplot(gen_name~yield|site, dat, groups=yr, main=\"minnesota.barley.yield\", auto.key=list(columns=5), scales=list(y=list(cex=.5))) } # }"},{"path":"/reference/montgomery.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"Uniformity trial wheat Nebraska Experiment Station, 1909 & 1911.","code":""},{"path":"/reference/montgomery.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"","code":"data(\"montgomery.wheat.uniformity\")"},{"path":"/reference/montgomery.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"data frame 448 observations following 3 variables. year year col column row row yield yield, grams","code":""},{"path":"/reference/montgomery.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"Experiments conducted Nebraska Experiment Station. field sown Turkey winter wheat fall 1908 harvested 1909. drill, 5.5 feet wide, driven across first series 14 blocks, boundaries blocks later established. series sown way, space allowed blocks. block 5.5 ft square. experiment done 3 times harvests 1909, 1910, 1911. simple heatmap 3 years' yields shown Montgomery (1912), figure 3, p. 178. 1909 data given Montgomery (1913), figure 10, page 37. NOTE: North right side diagram (determined comparing yield values fertility map Montgomery 1912, p. 178). 1910 data available. 1911 data given Montgomery (1912), figure 1, page 165. NOTE: North top diagram. Field width: 14 plots * 5.5 feet Field length: 16 blocks * 5.5 feet Surface & Pearl (1916) give simple method adjusting yield due fertility effects using 1909 data.","code":""},{"path":"/reference/montgomery.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"E. G. Montgomery (1912). Variation Yield Methods Arranging Plats Secure Comparative Results. Twenty-Fifth Annual Report Agricultural Experiment Station Nebraska, 164-180. https://books.google.com/books?id=M-5BAQAAMAAJ&pg=RA4-PA164 E. G. Montgomery (1913). Experiments Wheat Breeding: Experimental Error Nursery Variation Nitrogen Yield. U.S. Dept Agriculture, Bureau Plant Industry, Bulletin 269. Figure 10, page 37. https://doi.org/10.5962/bhl.title.43602","code":""},{"path":"/reference/montgomery.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"Surface & Pearl, (1916). method correcting soil heterogeneity variety tests. Journal Agricultural Research, 5, 22, 1039-1050. Figure 2. https://books.google.com/books?id=BVNyoZXFVSkC&pg=PA1039","code":""},{"path":"/reference/montgomery.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(montgomery.wheat.uniformity) dat <- montgomery.wheat.uniformity dat09 <- subset(dat, year==1909) dat11 <- subset(dat, year==1911) # Match the figures of Montgomery 1912 Fig 3, p. 178 libs(desplot) desplot(dat09, yield ~ col*row, aspect=1, # true aspect main=\"montgomery.wheat.uniformity - 1909 yield\") desplot(dat, yield ~ col*row, subset= year==1911, aspect=1, # true aspect main=\"montgomery.wheat.uniformity - 1911 yield\") # Surface & Pearl adjust 1909 yield for fertility effects. # They calculate smoothed yield as (row sum)*(column sum)/(total) # and subtract this from the overall mean to get 'deviation'. # We can do something similar with a linear model with rows and columns # as factors, then predict yield to get the smooth trend. # Corrected yield = observed - deviation = observed - (smooth-mean) m1 <- lm(yield ~ factor(col) + factor(row), data=dat09) dev1 <- predict(m1) - mean(dat09$yield) # Corrected. Similar (but not exact) to Surface, fig 2. dat09$correct <- round(dat09$yield - dev1,0) libs(desplot) desplot(dat09, yield ~ col*row, shorten=\"none\", text=yield, main=\"montgomery.wheat.uniformity 1909 observed\") desplot(dat09, correct ~ col*row, text=correct, cex=0.8, shorten=\"none\", main=\"montgomery.wheat.uniformity 1909 corrected\") # Corrected yields are slightly shrunk toward overall mean plot(correct~yield,dat09, xlim=c(350,1000), ylim=c(350,1000)) abline(0,1) } # }"},{"path":"/reference/moore.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"Uniformity trials pole beans, bush beans, sweet corn, carrots, spring fall cauliflower Washington, 1952-1955.","code":""},{"path":"/reference/moore.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"data frame following columns minimum. datasets additional trait column. row row col column yield yield (pounds)","code":""},{"path":"/reference/moore.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"trials grown sandy loam soil Puyallup valley Washington. experiments gradient soil fertility evident. Moore & Darroch appear assigned 4 treatments plots used residual variation calculate CV. examples 'raw' CV calculated always higher CV given Moore & Darroch. Blue Lake Pole Beans. Conducted 1952. Seven pickings made 5-day intervals. Table 26. Field width: 12 rows x 5 feet = 60 feet. Field length: 12 ranges x 10 feet = 120 feet. Bush Beans. Conducted 1955. Two harvests. Table 27. Field width: 24 rows x 3 feet = 72 feet. Field length: 24 ranges x 5 feet = 120 feet. Sweet Corn. Conducted 1952. Table 28-29. Field width: 24 rows x 3 feet = 72 feet. Field length: 12 ranges x 10 feet = 120 feet. Carrot. Conducted 1952. Table 30. Field width: 24 rows * 1.5 feet = 36 feet. Field length: 12 ranges * 5 feet = 60 feet. Spring Cauliflower. Conducted spring 1951. Five harvests. Table 31-32. Field width: 12 rows x 3 feet = 36 feet. Field length: 10 plants * 1.5 feet * 20 ranges = 300 feet. Fall Cauliflower. Conducted fall 1951. Five harvests. Table 33-34. Field width: 12 rows x 3 feet = 36 feet. Field length: 10 plants * 1.5 feet * 20 ranges = 300 feet.","code":""},{"path":"/reference/moore.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"Moore, John F Darroch, JG. (1956). Field plot technique Blue Lake pole beans, bush beans, carrots, sweet corn, spring fall cauliflower, page 25-30. Washington Agricultural Experiment Stations, Institute Agricultural Sciences, State College Washington. https://babel.hathitrust.org/cgi/pt?id=uiug.30112019919072&view=1up&seq=33&skin=2021","code":""},{"path":"/reference/moore.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"None.","code":""},{"path":"/reference/moore.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) cv <- function(x) sd(x)/mean(x) libs(desplot) # Pole Bean data(moore.polebean.uniformity) cv(moore.polebean.uniformity$yield) # 8.00. Moore says 6.73. desplot(moore.polebean.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=120/60, # true aspect main=\"moore.polebean.uniformity - yield\") # Bush bean data(moore.bushbean.uniformity) cv(moore.bushbean.uniformity$yield) # 12.1. Moore says 10.8 desplot(moore.bushbean.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=120/72, # true aspect main=\"moore.bushbean.uniformity - yield\") # Sweet corn data(moore.sweetcorn.uniformity) cv(moore.sweetcorn.uniformity$yield) # 17.5. Moore says 13.6 desplot(moore.sweetcorn.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=120/72, # true aspect main=\"moore.sweetcorn.uniformity - yield\") ## desplot(moore.sweetcorn.uniformity, ears~col*row, ## flip=TRUE, tick=TRUE, aspect=120/72, # true aspect ## main=\"moore.sweetcorn.uniformity - ears\") ## libs(lattice) ## xyplot(yield ~ ears, moore.sweetcorn.uniformity) libs(desplot) # Carrot data(moore.carrot.uniformity) cv(moore.carrot.uniformity$yield) # 33.4. Moore says 27.6 desplot(moore.carrot.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=60/36, # true aspect main=\"moore.carrot.uniformity - yield\") libs(desplot) # Spring cauliflower data(moore.springcauliflower.uniformity) cv(moore.springcauliflower.uniformity$yield) # 21. Moore says 19.5 desplot(moore.springcauliflower.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=300/36, # true aspect main=\"moore.springcauliflower.uniformity - yield\") ## desplot(moore.springcauliflower.uniformity, heads~col*row, ## flip=TRUE, tick=TRUE, aspect=300/36, # true aspect ## main=\"moore.springcauliflower.uniformity - heads\") ## libs(lattice) ## xyplot(yield ~ heads, moore.springcauliflower.uniformity) libs(desplot) # Fall cauliflower data(moore.fallcauliflower.uniformity) cv(moore.fallcauliflower.uniformity$yield) # 17.7. Moore says 17.0 desplot(moore.fallcauliflower.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=300/36, # true aspect main=\"moore.fallcauliflower.uniformity - yield\") ## desplot(moore.fallcauliflower.uniformity, heads~col*row, ## flip=TRUE, tick=TRUE, aspect=300/36, # true aspect ## main=\"moore.fallcauliflower.uniformity - heads\") ## libs(lattice) ## xyplot(yield ~ heads, moore.fallcauliflower.uniformity) } # }"},{"path":"/reference/nagai.strawberry.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of strawberry — nagai.strawberry.uniformity","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"Uniformity trial strawberry Brazil.","code":""},{"path":"/reference/nagai.strawberry.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"","code":"data(\"nagai.strawberry.uniformity\")"},{"path":"/reference/nagai.strawberry.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"data frame 432 observations following 3 variables. row row col column yield yield, grams/plot","code":""},{"path":"/reference/nagai.strawberry.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"uniformity trial strawberry, Jundiai, Brazil, April 1976. spacing plants rows 0.3 m. Test area 233.34 m^2. 18 rows 144 plants. plat consisted 6 consecutive plants. 432 plats, 0.54 m^2. Field length: 18 rows * 0.3 m = 5.4 m. Field width: 24 columns * 6 plants * 0.3 m = 43.2 m.","code":""},{"path":"/reference/nagai.strawberry.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"Violeta Nagai (1978). Tamanho da parcela e numero de repeticoes em experimentos com morangueiro (Plot size number repetitions experiments strawberry). Bragantia, 37, 71-81. Table 2, page 75. https://dx.doi.org/10.1590/S0006-87051978000100009","code":""},{"path":"/reference/nagai.strawberry.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"None","code":""},{"path":"/reference/nagai.strawberry.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(nagai.strawberry.uniformity) dat <- nagai.strawberry.uniformity # CV matches Nagai # with(dat, sd(yield)/mean(yield)) # 23.42 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(5.4)/(43.2), # true aspect main=\"nagai.strawberry.uniformity\") } # }"},{"path":"/reference/nair.turmeric.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of turmeric. — nair.turmeric.uniformity","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"Uniformity trial turmeric India, 1984.","code":""},{"path":"/reference/nair.turmeric.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"","code":"data(\"nair.turmeric.uniformity\")"},{"path":"/reference/nair.turmeric.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"data frame 864 observations following 3 variables. row row ordinate col column ordinate yield yield, grams per plot","code":""},{"path":"/reference/nair.turmeric.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"experiment conducted College Horticulture, Vellanikkara, India, 1984. crop grown raised beds. gross experimental area 74.2 m long x 15.2 m wide. Small elevated beds 0.6 m x 1.5 m raised providing channels 0.4 m around bed. One row beds around experiment discarded eliminate border effects. discarding borders, 432 beds experiment. time harvest, bed divided equal plots size .6 m x .75 m, yield plot recorded. Field map page 64 Nair. Nair focused mostly statistical methods discuss actual experimental results much detail. excess number plots 0 yield. Field length: 14 plots * .6 m + 13 alleys * .4 m = 13.6 m Field width: 72 plots * .75 m + 35 alleys * .4 m = 68 m Data found appendix.","code":""},{"path":"/reference/nair.turmeric.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"Nair, B. Gopakumaran (1984). Optimum plot size field experiments turmeric. Thesis, Kerala Agriculture University. http://hdl.handle.net/123456789/7829","code":""},{"path":"/reference/nair.turmeric.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"None.","code":""},{"path":"/reference/nair.turmeric.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(nair.turmeric.uniformity) dat <- nair.turmeric.uniformity libs(lattice) qqmath( ~ yield, dat) libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=13.6/68, main=\"nair.turmeric.uniformity\") } # }"},{"path":"/reference/narain.sorghum.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sorghum — narain.sorghum.uniformity","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"Uniformity trial sorghum Pakistan, 1936.","code":""},{"path":"/reference/narain.sorghum.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"","code":"data(\"narain.sorghum.uniformity\")"},{"path":"/reference/narain.sorghum.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"data frame 160 observations following 3 variables. row row col column yield yield, maunds per 1/40 acre","code":""},{"path":"/reference/narain.sorghum.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"uniformity trial chari (sorghum) Rawalpindi Agricultural Station (Pakistan) kharif (monsoon season) 1936. plot 36 feet 30.25 feet. source document describe orientation plots, fertility map shown Narain figure 1 shows plots taller wide. Field width: 10 plots * 30.25 feet Field length: 16 plots * 36 feet","code":""},{"path":"/reference/narain.sorghum.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"R. Narain . Singh, (1940). Note Shape Blocks Field Experiments. Ind. J. Agr. Sci., 10, 844-853. Page 845. https://archive.org/stream/.ernet.dli.2015.271745","code":""},{"path":"/reference/narain.sorghum.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"None","code":""},{"path":"/reference/narain.sorghum.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(narain.sorghum.uniformity) dat <- narain.sorghum.uniformity # Narain figure 1 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(16*36)/(10*30.25), main=\"narain.sorghum.uniformity\") } # }"},{"path":"/reference/nass.corn.html","id":null,"dir":"Reference","previous_headings":"","what":"U.S. historical crop yields by state — nass.corn","title":"U.S. historical crop yields by state — nass.corn","text":"Yields acres harvested state major agricultural crops United States, approximately 1900 2011. Crops include: barley, corn, cotton, hay, rice, sorghum, soybeans, wheat.","code":""},{"path":"/reference/nass.corn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"U.S. historical crop yields by state — nass.corn","text":"","code":"nass.barley nass.corn nass.cotton nass.hay nass.sorghum nass.wheat nass.rice nass.soybean"},{"path":"/reference/nass.corn.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"U.S. historical crop yields by state — nass.corn","text":"year year state state factor acres acres harvested yield average yield","code":""},{"path":"/reference/nass.corn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"U.S. historical crop yields by state — nass.corn","text":"cautious yield values states small acres harvested. Yields bushels/acre, except: cotton pounds/acre, hay tons/acre, rice pounds/acre. crop separate dataset: nass.barley, nass.corn, nass.cotton, nass.hay, nass.sorghum, nass.wheat, nass.rice, nass.soybean.","code":""},{"path":"/reference/nass.corn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"U.S. historical crop yields by state — nass.corn","text":"United States Department Agriculture, National Agricultural Statistics Service. https://quickstats.nass.usda.gov/","code":""},{"path":"/reference/nass.corn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"U.S. historical crop yields by state — nass.corn","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(nass.corn) dat <- nass.corn # Use only states that grew at least 100K acres of corn in 2011 keep <- droplevels(subset(dat, year == 2011 & acres > 100000))$state dat <- droplevels(subset(dat, is.element(state, keep))) # Acres of corn grown each year libs(lattice) xyplot(acres ~ year|state, dat, type='l', as.table=TRUE, main=\"nass.corn: state trends in corn acreage\") ## Plain levelplot, using only states ## libs(reshape2) ## datm <- acast(dat, year~state, value.var='yield') ## redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) ## levelplot(datm, aspect=.7, col.regions=redblue, ## main=\"nass.corn\", ## scales=list(x=list(rot=90, cex=.7))) # Model the rate of genetic gain in Illinois as a piecewise regression # Breakpoints define periods of open-pollinated varieties, double-cross, # single-cross, and transgenic hybrids. dil <- subset(nass.corn, state==\"Illinois\" & year >= 1900) m1 <- lm(yield ~ pmin(year,1932) + pmax(1932, pmin(year, 1959)) + pmax(1959, pmin(year, 1995)) + pmax(1995, year), dil) signif(coef(m1)[-1],3) # Rate of gain for each segment plot(yield ~ year, dil, main=\"nass.corn: piecewise linear model of Illinois corn yields\") lines(dil$year, fitted(m1)) abline(v=c(1932,1959,1995), col=\"wheat\") } # }"},{"path":"/reference/nebraska.farmincome.html","id":null,"dir":"Reference","previous_headings":"","what":"Nebraska farm income in 2007 by county — nebraska.farmincome","title":"Nebraska farm income in 2007 by county — nebraska.farmincome","text":"Nebraska farm income 2007 county","code":""},{"path":"/reference/nebraska.farmincome.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Nebraska farm income in 2007 by county — nebraska.farmincome","text":"data frame 93 observations following 4 variables. county county crop crop income, thousand dollars animal livestock poultry income, thousand dollars area area county, square miles","code":""},{"path":"/reference/nebraska.farmincome.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Nebraska farm income in 2007 by county — nebraska.farmincome","text":"variables county : Value farm products sold - crops (NAICS) 2007 (adjusted) Value farm products sold - livestock, 2007 (adjusted). Area square miles. Note: Cuming county important beef-producing county. counties reported protect privacy. Western Nebraska dryer lower income. South-central Nebraska irrigated higher crop income per square mile.","code":""},{"path":"/reference/nebraska.farmincome.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Nebraska farm income in 2007 by county — nebraska.farmincome","text":"U.S. Department Agriculture-National Agriculture Statistics Service. https://censtats.census.gov/usa/usa.shtml","code":""},{"path":"/reference/nebraska.farmincome.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nebraska farm income in 2007 by county — nebraska.farmincome","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(nebraska.farmincome) dat <- nebraska.farmincome libs(maps, mapproj, latticeExtra) # latticeExtra for mapplot dat$stco <- paste0('nebraska,', dat$county) # Scale to million dollars per county dat <- transform(dat, crop=crop/1000, animal=animal/1000) # Raw, county-wide incomes. Note the outlier Cuming county redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) mapplot(stco ~ crop + animal, data = dat, colramp=redblue, main=\"nebraska.farmincome\", xlab=\"Farm income from animals and crops (million $ per county)\", scales = list(draw = FALSE), map = map('county', 'nebraska', plot = FALSE, fill = TRUE, projection = \"mercator\") ) # Now scale to income/mile^2 dat <- within(dat, { crop.rate <- crop/area animal.rate <- animal/area }) # And use manual breakpoints. mapplot(stco ~ crop.rate + animal.rate, data = dat, colramp=redblue, main=\"nebraska.farmincome: income per square mile (percentile breaks)\", xlab=\"Farm income (million $ / mi^2) from animals and crops\", scales = list(draw = FALSE), map = map('county', 'nebraska', plot = FALSE, fill = TRUE, projection = \"mercator\"), # Percentile break points # breaks=quantile(c(dat$crop.rate, dat$animal.rate), # c(0,.1,.2,.4,.6,.8,.9,1), na.rm=TRUE) # Fisher-Jenks breakpoints via classInt package # breaks=classIntervals(na.omit(c(dat$crop.rate, dat$animal.rate)), # n=7, style='fisher')$brks breaks=c(0,.049, .108, .178, .230, .519, .958, 1.31)) } # }"},{"path":"/reference/nonnecke.peas.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of canning peas — nonnecke.peas.uniformity","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"Uniformity trial canning peas southern Alberta, 1957.","code":""},{"path":"/reference/nonnecke.peas.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"","code":"data(\"nonnecke.peas.uniformity\")"},{"path":"/reference/nonnecke.peas.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"data frame 540 observations following 5 variables. block block factor row row col column vines vines weight, pounds peas shelled peas weight, pounds","code":""},{"path":"/reference/nonnecke.peas.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"Width basic plot 10 feet, length 5 feet, limited viner. two blocks/locations, planting consisted 18 rows (15 rows harvested) 10 feet wide 90 feet long. Rows separated 7 foot bare ground facilitate harvesting. Nonnecke 1960 shows map one block. Plots harvested five foot mower. Vines plot weighed, shelled. two blocks/locations side side combined Nonnecke. optimum plot size found 5 feet long 10 feet wide. Field width: 15 rows * 10 ft/row + 14 gaps * 7 ft/gap = 248 feet Field length: 18 plots * 5 ft/plot = 90 feet","code":""},{"path":"/reference/nonnecke.peas.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"Ib Libner Nonnecke. 1958. Yield variability sweet corn canning peas affected plot size shape. Thesis Oregon State College. https://hdl.handle.net/1957/23367","code":""},{"path":"/reference/nonnecke.peas.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":". L. Nonnecke, 1960. precision field experiments vegetable crops influenced plot block size shape: II. Canning peas. Canadian Journal Plant Science, 40(2): 396-404. https://doi.org/10.4141/cjps60-053","code":""},{"path":"/reference/nonnecke.peas.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(nonnecke.peas.uniformity) dat <- nonnecke.peas.uniformity libs(desplot) desplot(dat, vines~col*row|block, tick=TRUE, flip=TRUE, aspect=248/90, # true aspect main=\"nonnecke.peas.uniformity - vines\") desplot(dat, peas~col*row|block, tick=TRUE, flip=TRUE, aspect=248/90, # true aspect main=\"nonnecke.peas.uniformity - peas\") libs(lattice) xyplot(peas~vines|block,dat, xlab=\"vine weight\", ylab=\"shelled pea weight\", main=\"nonnecke.peas.uniformity\") } # }"},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"Uniformity trials sweet corn Alberta, 1956.","code":""},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"","code":"data(\"nonnecke.sweetcorn.uniformity\")"},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"data frame: loc location row row col column yield yield marketable ears, pounds","code":""},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"Experiments conducted three locations Southern Alberta Lethbridge, Vauxhall, Cranford 1956. Plot layout 32 rows, 179 feet long, allowing 18 ten-foot plots per row. Rows 3 feet apart, thinned one foot plants. double guard row surrounded entire plot. two persons assigned harvest corn locations. 576 plots harvested one day. Optimal plot sizes found 10ft x 6ft 20ft 3ft. R data uses row/column plot/row. Field width: 18 plots * 10 ft = 180 feet Field length: 32 rows * 3 ft = 96 feet","code":""},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"Ib Libner Nonnecke. 1958. Yield variability sweet corn canning peas affected plot size shape. Thesis Oregon State College. https://hdl.handle.net/1957/23367","code":""},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":". L. Nonnecke, 1959. precision field experiments vegetable crops influenced plot block size shape: . Sweet corn. Canadian Journal Plant Science, 39(4): 443-457. Tables 1-7. https://doi.org/10.4141/cjps59-061","code":""},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) # Corn 1 data(nonnecke.sweetcorn.uniformity) dat <- nonnecke.sweetcorn.uniformity libs(desplot) desplot(dat, yield~col*row|loc, flip=TRUE, tick=TRUE, aspect=96/180, # true aspect main=\"nonnecke.sweetcorn.uniformity\") } # }"},{"path":"/reference/obsi.potato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"Uniformity trial potato Africa 2001","code":""},{"path":"/reference/obsi.potato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"","code":"data(\"obsi.potato.uniformity\")"},{"path":"/reference/obsi.potato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"data frame 2569 observations following 4 variables. loc location, 2 levels row row col column yield yield, kg/m^2","code":""},{"path":"/reference/obsi.potato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"Data collected potato uniformity trials Hollota (L1) Kulumsa (L2). field 0.15 hectares. field, 75cm rows 60cm plants. basic units harvested 1.2m x 1.5m. clear way plots oriented field respect rows columns. location L1, plot (10,7) 22.5 source document, changed 2.25 electronic data. Hollota: Field width: 26 * 1.2 m Field length: 63 rows * 1.5 m Note horizontal banding 8 9 rows location L1. Kulumsa Field width: 19 * 1.2 m Field length: 49 * 1.5 m","code":""},{"path":"/reference/obsi.potato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"Dechassa Obsi. 2008. Application Spatial Modeling Study Soil Fertility Pattern. MS Thesis, Addis Ababa University. Page 122-125. https://etd.aau.edu.et/handle/123456789/3221","code":""},{"path":"/reference/obsi.potato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"None.","code":""},{"path":"/reference/obsi.potato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(obsi.potato.uniformity) dat <- obsi.potato.uniformity # Mean plot yield according to Obsi p. 54 # libs(dplyr) # dat <- group_by(dat, loc) # summarize(dat, yield=mean(yield)) ## loc yield ## ## 1 L1 2.54 # Obsi says 2.55 ## 2 L2 5.31 # Obsi says 5.36 libs(desplot) desplot(dat, yield ~ col*row, subset=loc==\"L1\", main=\"obsi.potato.uniformity - loc L1\", flip=TRUE, tick=TRUE) desplot(dat, yield ~ col*row, subset=loc==\"L2\", main=\"obsi.potato.uniformity - loc L2\", flip=TRUE, tick=TRUE) } # }"},{"path":"/reference/odland.soybean.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","title":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","text":"Uniformity trials soy hay soybeans Virginia Experiment Station, 1925-1926.","code":""},{"path":"/reference/odland.soybean.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","text":"Data frames 3 variables. row row col column yield yield: hay tons/acre, beans bushels/acre","code":""},{"path":"/reference/odland.soybean.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","text":"Grown West Virginia Experiment Station 1925 & 1926. Soy forage hay: 1925 crop harvested forage, 42 rows, 200 feet long. Yields 8-foot plats recorded nearest 0.1 tons. Field width: 42 plots * 30 / 12in/ft = 105 ft Field length: 24 plots * 8 feet = 192 feet + border = total 200 feet. Note, hay data Odland & Garber measured 0.1 tons, converted tons . Soy beans: Soybeans planted rows 30 inches apart. 1926 crop harvested seed, 55 rows, 232 feet long. Yields 8-foot plats recorded. 1926, data last row page 96 seems missing. Field width: 55 plots * 30 / 12in/ft = 137.5 feet Field length: 28 plots * 8 feet = 224 feet + border = total 232 feet. Odland Garber provide agronomic context yield variation.","code":""},{"path":"/reference/odland.soybean.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","text":"Odland, T.E. Garber, R.J. (1928). Size Plat Number Replications Field Experiments Soybeans. Agronomy Journal, 20, 93–108. https://doi.org/10.2134/agronj1928.00021962002000020002x","code":""},{"path":"/reference/odland.soybean.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(desplot) data(odland.soyhay.uniformity) dat1 <- odland.soyhay.uniformity desplot(dat1, yield ~ col*row, flip=TRUE, aspect=200/105, # true aspect main=\"odland.soyhay.uniformity\") data(odland.soybean.uniformity) dat2 <- odland.soybean.uniformity desplot(dat2, yield ~ col*row, flip=TRUE, aspect = 232/137, main=\"odland.soybean.uniformity\") } # }"},{"path":"/reference/omer.sorghum.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"Multi-environment trial sorghum, 6 environments","code":""},{"path":"/reference/omer.sorghum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"","code":"data(\"omer.sorghum\")"},{"path":"/reference/omer.sorghum.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"data frame 432 observations following 4 variables. env environment rep replication gen genotype factor yield yield, kg/ha","code":""},{"path":"/reference/omer.sorghum.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"Trials conducted Sudan, 3 years 2 locations, 4 reps RCBD location. year location combined form 6 environments. environments given data, individual year location.","code":""},{"path":"/reference/omer.sorghum.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"Siraj Osman Omer, Abdel Wahab Hassan Abdalla, Mohammed Hamza Mohammed, Murari Singh (2015). Bayesian estimation genotype--environment interaction sorghum variety trials Communications Biometry Crop Science, 10 (2), 82-95. Electronic data provided Siraj Osman Omer.","code":""},{"path":"/reference/omer.sorghum.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"None.","code":""},{"path":"/reference/omer.sorghum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(omer.sorghum) dat <- omer.sorghum # REML approach libs(lme4) libs(lucid) # 1 loc, 2 years. Match Omer table 1. m1 <- lmer(yield ~ 1 + env + (1|env:rep) + (1|gen) + (1|gen:env), data=subset(dat, is.element(env, c('E2','E4')))) vc(m1) ## grp var1 var2 vcov sdcor ## gen:env (Intercept) 17050 130.6 ## gen (Intercept) 2760 52.54 ## env:rep (Intercept) 959.1 30.97 ## Residual 43090 207.6 # 1 loc, 3 years. Match Omer table 1. m2 <- lmer(yield ~ 1 + env + (1|env:rep) + (1|gen) + (1|gen:env), data=subset(dat, is.element(env, c('E2','E4','E6')))) vc(m2) ## grp var1 var2 vcov sdcor ## gen:env (Intercept) 22210 149 ## gen (Intercept) 9288 96.37 ## env:rep (Intercept) 1332 36.5 ## Residual 40270 200.7 # all 6 locs. Match Omer table 3, frequentist approach m3 <- lmer(yield ~ 1 + env + (1|env:rep) + (1|gen) + (1|gen:env), data=dat) vc(m3) ## grp var1 var2 vcov sdcor ## gen:env (Intercept) 21340 146.1 ## env:rep (Intercept) 1152 33.95 ## gen (Intercept) 1169 34.2 ## Residual 24660 157 } # }"},{"path":"/reference/onofri.winterwheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"Multi-environment trial winter wheat, 7 years, 8 gen","code":""},{"path":"/reference/onofri.winterwheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"","code":"data(\"onofri.winterwheat\")"},{"path":"/reference/onofri.winterwheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"data frame 168 observations following 5 variables. year year, numeric block block, 3 levels plot plot, numeric gen genotype, 7 levels yield yield plot","code":""},{"path":"/reference/onofri.winterwheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"Yield 8 durum winter wheat varieties across 7 years 3 reps. Downloaded electronic version Nov 2015: https://www.casaonofri./Biometry/index.html Used permission Andrea Onofri.","code":""},{"path":"/reference/onofri.winterwheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"Andrea Onofri, Egidio Ciriciofolo (2007). Using R Perform AMMI Analysis Agriculture Variety Trials. R News, Vol. 7, . 1, pp. 14-19.","code":""},{"path":"/reference/onofri.winterwheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"F. Mendiburu. AMMI. https://tarwi.lamolina.edu.pe/~fmendiburu/AMMI.htm . Onofri. https://accounts.unipg./~onofri/RTutorial/CaseStudies/WinterWheat.htm","code":""},{"path":"/reference/onofri.winterwheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"","code":"library(agridat) data(onofri.winterwheat) dat <- onofri.winterwheat dat <- transform(dat, year=factor(dat$year)) m1 <- aov(yield ~ year + block:year + gen + gen:year, dat) anova(m1) # Matches Onofri figure 1 #> Analysis of Variance Table #> #> Response: yield #> Df Sum Sq Mean Sq F value Pr(>F) #> year 6 159.279 26.5466 178.3996 < 2.2e-16 *** #> gen 7 11.544 1.6491 11.0824 2.978e-10 *** #> year:block 14 3.922 0.2801 1.8826 0.03738 * #> year:gen 42 27.713 0.6598 4.4342 6.779e-10 *** #> Residuals 98 14.583 0.1488 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 libs(agricolae) m2 <- AMMI(dat$year, dat$gen, dat$block, dat$yield) plot(m2) title(\"onofri.winterwheat - AMMI biplot\")"},{"path":"/reference/ortiz.tomato.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"Multi-environment trial tomato Latin America, weight/yield environmental covariates","code":""},{"path":"/reference/ortiz.tomato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"","code":"data(\"ortiz.tomato.covs\") data(\"ortiz.tomato.yield\")"},{"path":"/reference/ortiz.tomato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"ortiz.tomato.covs data frame 18 observations following 18 variables. env environment Day degree days (base 10) Dha days harvest Driv drivings (0/1) ExK extra potassium (kg / ha) ExN extra nitrogen (kg / ha) ExP extra phosphorous (kg / ha) Irr irrigation (0/1) K potassium (/100 g) Lat latitude Long longitude MeT mean temperature (C) MnT min temperature (C) MxT max temperature (C) OM organic matter (percent) P phosphorous (ppm) pH soil pH Prec precipitation (mm) Tri trimming (0/1) ortiz.tomato.yield data frame 270 observations following 4 variables. env environment gen genotype yield marketable fruit yield t/ha weight fruit weight, g","code":""},{"path":"/reference/ortiz.tomato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"environment locations : Used permission Rodomiro Ortiz.","code":""},{"path":"/reference/ortiz.tomato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"Rodomiro Ortiz Jose Crossa Mateo Vargas Juan Izquierdo, 2007. Studying Effect Environmental Variables Genotype x Environment Interaction Tomato. Euphytica, 153, 119–134. https://doi.org/10.1007/s10681-006-9248-7","code":""},{"path":"/reference/ortiz.tomato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ortiz.tomato.covs) data(ortiz.tomato.yield) libs(pls, reshape2) # Double-centered yield matrix Y <- acast(ortiz.tomato.yield, env ~ gen, value.var='yield') Y <- sweep(Y, 1, rowMeans(Y, na.rm=TRUE)) Y <- sweep(Y, 2, colMeans(Y, na.rm=TRUE)) # Standardized covariates X <- ortiz.tomato.covs rownames(X) <- X$env X <- X[,c(\"MxT\", \"MnT\", \"MeT\", \"Prec\", \"Day\", \"pH\", \"OM\", \"P\", \"K\", \"ExN\", \"ExP\", \"ExK\", \"Trim\", \"Driv\", \"Irr\", \"Dha\")] X <- scale(X) # Now, PLS relating the two matrices. # Note: plsr deletes observations with missing values m1 <- plsr(Y~X) # Inner-product relationships similar to Ortiz figure 1. biplot(m1, which=\"x\", var.axes=TRUE, main=\"ortiz.tomato - env*cov biplot\") #biplot(m1, which=\"y\", var.axes=TRUE) } # }"},{"path":"/reference/pacheco.soybean.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","title":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","text":"Yields 18 soybean genotypes 11 environments Brazil.","code":""},{"path":"/reference/pacheco.soybean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","text":"gen genotype, 18 levels env environment, 11 levels yield yield, kg/ha","code":""},{"path":"/reference/pacheco.soybean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","text":"environment used RCB design 3 reps. means reps shown . Used permission Robert Pacheco.","code":""},{"path":"/reference/pacheco.soybean.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","text":"R M Pacheco, J B Duarte, R Vencovsky, J B Pinheiro, B Oliveira, (2005). Use supplementary genotypes AMMI analysis. Theor Appl Genet, 110, 812-818. https://doi.org/10.1007/s00122-004-1822-6","code":""},{"path":"/reference/pacheco.soybean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(pacheco.soybean) dat <- pacheco.soybean # AMMI biplot similar to Fig 2 of Pacheco et al. libs(agricolae) m1 <- with(dat, AMMI(env, gen, REP=1, yield)) bip <- m1$biplot[,1:3] # Fig 1 of Pacheco et al. with(bip, plot(yield, PC1, cex=0.0, text(yield,PC1,labels=row.names(bip), col=\"blue\"), xlim=c(1000,3000),main=\"pacheco.soybean - AMMI biplot\",frame=TRUE)) with(bip[19:29,], points(yield, PC1, cex=0.0, text(yield,PC1,labels=row.names(bip[19:29,]), col=\"darkgreen\"))) } # }"},{"path":"/reference/paez.coffee.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of coffee — paez.coffee.uniformity","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"Uniformity trial coffee Caldas Columbia","code":""},{"path":"/reference/paez.coffee.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"","code":"data(\"paez.coffee.uniformity\")"},{"path":"/reference/paez.coffee.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"data frame 4190 observations following 5 variables. plot plot number row row col column year year yield yield per tree, kilograms","code":""},{"path":"/reference/paez.coffee.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"field map Paez page 56, plots 1 838. data tables page 79-97 data plots 1 900. Note: 'row' ordinate data imply rows columns perpendicular. field map page 56 Paez shows rows 90-degree angle compared columns, 60-degree angle compared columns. words, columns vertical, rows sloping right 30 degrees. Paez looks blocks 1,2,...36 trees size. Page 30 shows annual CV.","code":""},{"path":"/reference/paez.coffee.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"Gilberto Paez Bogarin (1962). Estudios sobre tamano y forma de parcela para ensayos en cafe. Instituto Interamericano de Ciencias Agricolas de la O.E.. Centro Tropical de Investigacion y Ensenanza para Graduados. Costa Rica. https://hdl.handle.net/11554/1892","code":""},{"path":"/reference/paez.coffee.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"None","code":""},{"path":"/reference/paez.coffee.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(paez.coffee.uniformity) dat <- paez.coffee.uniformity libs(reshape2, corrgram) datt <- acast(dat, plot ~ year) corrgram(datt, lower.panel=panel.pts, main=\"paez.coffee.uniformity\") # Not quite right. The rows are not actually horizontal. See notes above. libs(desplot) desplot(dat, yield ~ col*row,subset=year==\"Y1\", tick=TRUE, aspect=1, main=\"paez.coffee.uniformity - Y1\") desplot(dat, yield ~ col*row,subset=year==\"Y2\", tick=TRUE, aspect=1, main=\"paez.coffee.uniformity - Y2\") desplot(dat, yield ~ col*row,subset=year==\"Y3\", tick=TRUE, aspect=1, main=\"paez.coffee.uniformity - Y3\") desplot(dat, yield ~ col*row,subset=year==\"Y4\", tick=TRUE, aspect=1, main=\"paez.coffee.uniformity - Y4\") desplot(dat, yield ~ col*row,subset=year==\"Y5\", tick=TRUE, aspect=1, main=\"paez.coffee.uniformity - Y5\") } # }"},{"path":"/reference/panse.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton — panse.cotton.uniformity","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"Uniformity trial cotton India 1934.","code":""},{"path":"/reference/panse.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"","code":"data(\"panse.cotton.uniformity\")"},{"path":"/reference/panse.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"data frame 1280 observations following 3 variables. row row col column yield total yield per plot, grams","code":""},{"path":"/reference/panse.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"uniformity trial cotton Institute Plant Industry, Indore, India. trial consisted 128 rows cotton spacing 14 inches rows length 186 feet 8 inches. harvested plot 4 rows wide 4 ft 8 long, measuring 1/2000 acre. Four pickings made Nov 1933 Jan 1934. data total yields. fertility map shows appreciable variation, following systematic pattern. Field length: 40 plots * 4 feet 8 inches = 206 feet 8 inches Field width: 32 plots * 4 rows/plot * 14 inches/row = 150 feet Conclusions: Lower error obtained plots long rows instead across rows. data typed K.Wright Panse (1941) p. 864-865.","code":""},{"path":"/reference/panse.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"V. G. Panse (1941). Studies technique field experiments. V. Size shape blocks arrangements plots cotton trials. Indian Journal Agricultural Science, 11, 850-867 https://archive.org/details/.ernet.dli.2015.271747/page/n955","code":""},{"path":"/reference/panse.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"Hutchinson, J. B. V. G. Panse (1936). Studies technique field experiments. . Size, shape arrangement plots cotton trials. Indian J. Agric. Sci., 5, 523-538. https://archive.org/details/.ernet.dli.2015.271739/page/n599 V.G. Panse P.V. Sukhatme. (1954). Statistical Methods Agricultural Workers. First edition page 137. Fourth edition, page 131.","code":""},{"path":"/reference/panse.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(panse.cotton.uniformity) dat <- panse.cotton.uniformity # match the CV of Panse 1954 # sd(dat$yield)/mean(dat$yield) * 100 # 32.1 # match the fertility map of Hutchinson, fig 1 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=207/150, # true aspect main=\"panse.cotton.uniformity\") } # }"},{"path":"/reference/parker.orange.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of oranges — parker.orange.uniformity","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"Uniformity trial oranges Riverside, CA, 1921-1927.","code":""},{"path":"/reference/parker.orange.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"","code":"data(\"parker.orange.uniformity\")"},{"path":"/reference/parker.orange.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"data frame 1364 observations following 4 variables. year year row row col column yield yield, pounds/tree plot","code":""},{"path":"/reference/parker.orange.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"orchard naval oranges planted 1917 University California Citrus Experiment Station Riverside. orchard maintained uniform conditions 10 years. Eight Washington Navel orange trees single row constituted plot. planting distance 20 feet trees within row 24 feet rows. Every row guard row, row 2 row 4 observational units, row 3 guard row. example, row 2 row 4 2*24 = 48 feet. Another way think plot 48 feet wide, middle 24 feet harvested. end plot one guard tree. Including guard trees row ends, row plot 10 trees * 20 feet = 200 feet long. Field width (west-east) 10 plots * 200 feet = 2000 feet. Field length (north-south) 27 plots * 48 feet = 1296 feet. investigation variability plots included systematic soil surveys, soil moisture, soil nitrates, inspection differences infestation citrus nematode. None factors considered primary cause variations yield. 7 years uniformity trials, different treatments applied plots. Parker et al. state soil heterogeneity considerable first-year yields predictive future yields. Table 25 mean top volume per tree plot 1926. Table 26 mean area trunk cross section.","code":""},{"path":"/reference/parker.orange.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"E. R. Parker & L. D. Batchelor. (1932). Variation Yields Fruit Trees Relation Planning Future Experiments. Hilgardia, 7(2), 81-161. Tables 3-9. https://doi.org/10.3733/hilg.v07n02p081","code":""},{"path":"/reference/parker.orange.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"Batchelor, L. D. (Leon Dexter), b. 1884; Parker, E. R. (Edwin Robert), 1896-1952; McBride, Robert, d. 1927. (1928) Studies preliminary establishment series fertilizer trials bearing citrus grove. Vol B451. Berkeley, Cal. : Agricultural Experiment Station https://archive.org/details/studiesprelimina451batc","code":""},{"path":"/reference/parker.orange.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(parker.orange.uniformity) dat <- parker.orange.uniformity # Parker fig 2, field plan libs(desplot) dat$year <- factor(dat$year) # 27 rows * 48 ft x 10 cols * 200 feet desplot(dat, yield ~ col*row|year, flip = TRUE, aspect = 27*48/(10*200), # true aspect main = \"parker.orange.uniformity\") # CV across plots in each year. Similar to Parker table 11 cv <- function(x) { x <- na.omit(x) sd(x)/mean(x) } round(100*tapply(dat$yield, dat$year, cv),2) # Correlation of plot yields across years. Similar to Parker table 15. # Paker et al may have calculated correlation differently. libs(reshape2) libs(corrgram) dat2 <- acast(dat, row+col ~ year, value.var = 'yield') round(cor(dat2, use = \"pair\"),3) corrgram(dat2, lower = panel.pts, upper = panel.conf, main=\"parker.orange.uniformity\") # Fertility index. Mean across years (ignoring 1921). Parker table 16 dat3 <- aggregate(yield ~ row+col, data = subset(dat, year !=1921 ), FUN = mean, na.rm = TRUE) round(acast(dat3, row ~ col, value.var = 'yield'),0) libs(desplot) desplot(dat3, yield ~ col*row, flip = TRUE, aspect = 27*48/(10*200), # true aspect main = \"parker.orange.uniformity - mean across years\") } # }"},{"path":"/reference/patterson.switchback.html","id":null,"dir":"Reference","previous_headings":"","what":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"Switchback experiment dairy cattle, milk yield 4 treatments","code":""},{"path":"/reference/patterson.switchback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"","code":"data(\"patterson.switchback\")"},{"path":"/reference/patterson.switchback.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"data frame 36 observations following 4 variables. y response, milk FCM trt treatment factor, 4 levels period period factor, 3 levls cow cow factor, 12 levels","code":""},{"path":"/reference/patterson.switchback.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"three periods. cow assigned one treatment cycle like T1-T2-T1, T1 treatment period P1 P3, T2 treatment period P2. four treatments. 4*3 = 12 treatment cycles represented. Data extracted Lowry, page 70.","code":""},{"path":"/reference/patterson.switchback.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"Patterson, H.D. Lucas, H.L. 1962. Change-designs. Technical Bulletin 147, North Carolina Agricultural Experimental Station.","code":""},{"path":"/reference/patterson.switchback.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"Lowry, S.R. 1989. Statistical design analysis dairy nutrition experiments improve detection milk response differences. Proceedings Conference Applied Statistics Agriculture, 1989. https://newprairiepress.org/agstatconference/1989/proceedings/7/","code":""},{"path":"/reference/patterson.switchback.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(patterson.switchback) dat <- patterson.switchback # Create groupings for first treatment, second treatment datp1 <- subset(dat, period==\"P1\") datp2 <- subset(dat, period==\"P2\") dat$p1trt <- datp1$trt[match(dat$cow, datp1$cow)] dat$p2trt <- datp2$trt[match(dat$cow, datp2$cow)] libs(latticeExtra) useOuterStrips(xyplot(y ~ period|p1trt*p2trt, data=dat, group=cow, type=c('l','r'), auto.key=list(columns=5), main=\"patterson.switchback\", xlab=\"First/Third period treatment\", ylab=\"Second period treatment\")) # Create a numeric period variable dat$per <- as.numeric(substring(dat$period,2)) # Need to use 'terms' to preserve the order of the model terms m1 <- aov(terms(y ~ cow + per:cow + period + trt, keep.order=TRUE), data=dat) anova(m1) # Match table 2 of Lowry ## Analysis of Variance Table ## Df Sum Sq Mean Sq F value Pr(>F) ## cow 11 3466.0 315.091 57.1773 2.258e-06 *** ## cow:per 12 953.5 79.455 14.4182 0.0004017 *** ## period 1 19.7 19.740 3.5821 0.0950382 . ## trt 3 58.3 19.418 3.5237 0.0685092 . ## Residuals 8 44.1 5.511 } # }"},{"path":"/reference/payne.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Long term rotation experiment at Rothamsted — payne.wheat","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"Long term rotation experiment Rothamsted","code":""},{"path":"/reference/payne.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"","code":"data(\"payne.wheat\")"},{"path":"/reference/payne.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"data frame 480 observations following 4 variables. rotation rotation treatment nitro nitrogen rate kg/ha year year yield metric tons per hectare","code":""},{"path":"/reference/payne.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"rotation treatments : AB = arable rotation spring barley. AF = arable rotation bare fallow. Ln3 = 3-year grass lay crops. Ln8 = 8-year grass lay crops. Lc3 = 3-year grass-clover lay crops. Lc8 = 8-year grass-clover lay crops. full data available via CC-4.0 license : Margaret Glendining, Paul Poulton, Andrew Macdonald, Chloe MacLaren, Suzanne Clark (2022). Dataset: Woburn Ley-arable experiment: yields wheat first test crop, 1976-2018 Electronic Rothamsted Archive, Rothamsted Research. https://doi.org/10.23637/wrn3-wheat7618-01 data used subset appearing paper Payne.","code":""},{"path":"/reference/payne.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"Payne, R. (2013) Design analysis long-term rotation experiments. Agronomy Journal, 107, 772-785. https://doi.org/10.2134/agronj2012.0411","code":""},{"path":"/reference/payne.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"None","code":""},{"path":"/reference/payne.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(payne.wheat) dat <- payne.wheat # make factors dat <- transform(dat, rotf = factor(rotation), yrf = factor(year), nitrof = factor(nitro)) # visualize the response to nitrogen libs(lattice) # Why does Payne use nitrogen factor, when it is an obvious polynomial term? # Probably doesn't matter too much. xyplot(yield ~ nitro|yrf, dat, groups=rotf, type='b', auto.key=list(columns=6), main=\"payne.wheat\") # What are the long-term trends? Yields are decreasing xyplot(yield ~ year | rotf, data=dat, groups=nitrof, type='l', auto.key=list(columns=4)) if(require(\"asreml\", quietly=TRUE)){ libs(asreml) # Model 5: drop 3-way interaction and return to pol function (easier prediction) m5 <- asreml(yield ~ rotf * nitrof * pol(year,2) - (rotf:nitrof:pol(year,2)), data=dat, random = ~yrf, residual = ~ dsum( ~ units|yrf)) summary(m5)$varcomp # Table 7 of Payne # lucid::vc(m5) # Table 8 of Payne wald(m5, denDF=\"default\") # Predictions of three-way interactions from final model p5 <- predict(m5, classify=\"rotf:nitrof:year\") p5 <- p5$pvals # Matches Payne table 8 head(p5) # Plot the predictions. Matches Payne figure 1 xyplot(predicted.value ~ year | rotf, data=p5, groups=nitrof, ylab=\"yield t/ha\", type='l', auto.key=list(columns=5)) } } # }"},{"path":"/reference/pearce.apple.html","id":null,"dir":"Reference","previous_headings":"","what":"Apple tree yields for 6 treatments with covariate — pearce.apple","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"Apple tree yields 6 treatments covariate previous yield.","code":""},{"path":"/reference/pearce.apple.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"data frame 24 observations following 4 variables. block block factor, 4 levels trt treatment factor, 6 levels prev previous yield boxes yield yield per plot","code":""},{"path":"/reference/pearce.apple.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"Treatment 'S' standard practice English apple orchards keeping land clean summer. previous yield number boxes fruit, four seasons previous application treatments.","code":""},{"path":"/reference/pearce.apple.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"S. C. Pearce (1953). Field Experiments Fruit Trees Perennial Plants. Commonwealth Bureau Horticulture Plantation Crops, Farnham Royal, Slough, England, App. IV.","code":""},{"path":"/reference/pearce.apple.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"James G. Booth, Walter T. Federer, Martin T. Wells Russell D. Wolfinger (2009). Multivariate Variance Components Model Analysis Covariance Designed Experiments. Statistical Science, 24, 223-237.","code":""},{"path":"/reference/pearce.apple.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(pearce.apple) dat <- pearce.apple libs(lattice) xyplot(yield~prev|block, dat, main=\"pearce.apple\", xlab=\"previous yield\") # Univariate fixed-effects model of Booth et al, using previous # yield as a covariate. m1 <- lm(yield ~ trt + block + prev, data=dat) # Predict values, holding the covariate at its overall mean of 8.3 newdat <- expand.grid(trt=c('A','B','C','D','E','S'), block=c('B1','B2','B3','B4'), prev=8.308333) newdat$pred <- predict(m1, newdata=newdat) # Average across blocks to get the adjusted mean, Booth et al. Table 1 tapply(newdat$pred, newdat$trt, mean) # A B C D E S # 280.4765 266.5666 274.0666 281.1370 300.9175 251.3357 # Same thing, but with blocks random libs(lme4) m2 <- lmer(yield ~ trt + (1|block) + prev, data=dat) newdat$pred2 <- predict(m2, newdata=newdat) tapply(newdat$pred2, newdat$trt, mean) # A B C D E S # 280.4041 266.5453 274.0453 281.3329 301.3432 250.8291 } # }"},{"path":"/reference/pearl.kernels.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","title":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","text":"Counts yellow/white sweet/starchy kernels 4 maize ears 15 observers.","code":""},{"path":"/reference/pearl.kernels.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","text":"data frame 59 observations following 6 variables. ear ear, 8-11 obs observer, 1-15 ys number yellow starchy kernels yt yellow sweet ws white starchy wt white sweet","code":""},{"path":"/reference/pearl.kernels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","text":"ear white sweet corn crossed ear yellow starchy corn. F1 kernels cross grown sample four ears harvested. F2 kernels ears classified 15 observers white/yellow sweet/starchy. Mendelian genetics, kernels occur ratio 9 yellow starch, 3 white starch, 3 yellow sweet, 1 white sweet. observers following positions:","code":""},{"path":"/reference/pearl.kernels.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","text":"Raymond Pearl, 1911. Personal Equation Breeding Experiments Involving Certain Characters Maize, Biol. Bull., 21, 339-366. https://www.biolbull.org/cgi/reprint/21/6/339.pdf","code":""},{"path":"/reference/pearl.kernels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(pearl.kernels) dat <- pearl.kernels libs(lattice) xyplot(ys+yt+ws+wt~obs|ear, dat, type='l', as.table=TRUE, auto.key=list(columns=4), main=\"pearl.kernels\", xlab=\"observer\",ylab=\"kernels\", layout=c(4,1), scales=list(x=list(rot=90))) # Test hypothesis that distribution is 'Mendelian' 9:3:3:1 dat$pval <- apply(dat[, 3:6], 1, function(x) chisq.test(x, p=c(9,3,3,1)/16)$p.val) dotplot(pval~obs|ear, dat, layout=c(1,4), main=\"pearl.kernels\", ylab=\"P-value for test of 9:3:3:1 distribution\") } # }"},{"path":"/reference/pederson.lettuce.repeated.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"Repeated measurements lettuce growth 3 treatments.","code":""},{"path":"/reference/pederson.lettuce.repeated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"","code":"data(\"pederson.lettuce.repeated\")"},{"path":"/reference/pederson.lettuce.repeated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"data frame 594 observations following 4 variables. plant plant number day day observation trt treatment weight weight","code":""},{"path":"/reference/pederson.lettuce.repeated.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"Experiment conducted greenhouse Silver Bay, Minnesota. Plants grown hydroponically. Treatment 1 9 plants per raft. Treatment 2 18 plants, treatment 3 36 plants. response variable weight plant, roots, soil, cup, water. plants measured repeatedly beginning Dec 1, ending Jan 9, plants harvested.","code":""},{"path":"/reference/pederson.lettuce.repeated.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"Levi Dawson Pederson (2015). Mixed Model Analysis Repeated Measures Lettuce Growth Thesis University Minnesota. Appendix C. https://scse.d.umn.edu/sites/scse.d.umn.edu/files/pedersonprojectthesis.pdf","code":""},{"path":"/reference/pederson.lettuce.repeated.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"None","code":""},{"path":"/reference/pederson.lettuce.repeated.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(pederson.lettuce.repeated) dat <- pederson.lettuce.repeated libs(lattice) dat <- dat[order(dat$day),] xyplot(weight ~ day|trt, dat, type='l', group=plant, layout=c(3,1), main=\"pederson.lettuce.repeated\") # Pederson used this SAS MIXED model for unstructured covariance # proc mixed data=Project.Spacingdata; # class trt plant day; # model weight=trt day trt*day; # repeated day / subject=plant type=un r rcorr; # This should give the same results as SAS, but does not. libs(nlme) dat <- transform(dat, plant=factor(plant), day=factor(day)) datg <- groupedData(weight ~ day|plant, data=dat) un1 <- gls(weight ~ trt * day, data=datg, correlation=corSymm(value=rep(.6,55), form = ~ 1 | plant), control=lmeControl(opt=\"optim\", msVerbose=TRUE, maxIter=500, msMaxIter=500)) logLik(un1)*2 # nlme has 1955, SAS had 1898.6 # Comparing the SAS results in Pederson (page 16) and the nlme results, we notice # the SAS correlations in table 5.2 are unusually low for the first # column. The nlme results have a higher correlation in the first column # and just \"look\" better un1 } # }"},{"path":"/reference/perry.springwheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"Yields wheat cultivars introduced 1860-1982. Grown 20 environments.","code":""},{"path":"/reference/perry.springwheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"","code":"data(\"perry.springwheat\")"},{"path":"/reference/perry.springwheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"data frame 560 observations following 6 variables. yield yield, kg/ha gen genotype/cultivar factor, 28 levels env environment factor, 20 levels site site factor year year, 1979-1982 yor year release, 1860-1982","code":""},{"path":"/reference/perry.springwheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"Twenty-eight significant wheat cultivars past century Western Australia, grown 20 field trials 4 years Central Eastern wheat-belt Australia. Wongan Hills site separate early late sown trials 1979 1980. Later sowing dates generally lower yields. Note: Although indicated original paper, may Merredin site 1979 also early/late sowing dates. Used permission Mario D'Antuono CSIRO Publishing.","code":""},{"path":"/reference/perry.springwheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"MW Perry MF D'Antuono. (1989). Yield improvement associated characteristics Australian spring wheat cultivars introduced 1860 1982. Australian Journal Agricultural Research, 40(3), 457–472. https://www.publish.csiro.au/nid/43/issue/1237.htm","code":""},{"path":"/reference/perry.springwheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(perry.springwheat) dat <- perry.springwheat libs(lattice) xyplot(yield~yor|env, dat, type=c('p','r'), xlab=\"year of release\", main=\"perry.springwheat\") # Show the genetic trend for each testing location * year. # libs(latticeExtra) # useOuterStrips(xyplot(yield~yor|site*factor(year), dat, # type=c('p','r'))) # Perry reports a rate of gain of 5.8 kg/ha/year. No model is given. # We fit a model with separate intercept/slope for each env m1 <- lm(yield ~ env + yor + env:yor, data=dat) # Average slope across environments mean(c(coef(m1)[21], coef(m1)[21]+coef(m1)[22:40])) ## [1] 5.496781 # ---------- # Now a mixed-effects model. Fixed overall int/slope. Random env int/slope. # First, re-scale response so we don't have huge variances dat$y <- dat$yield / 100 libs(lme4) # Use || for uncorrelated int/slope. Bad model. See below. # m2 <- lmer(y ~ 1 + yor + (1+yor||env), data=dat) ## Warning messages: ## 1: In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : ## Model failed to converge with max|grad| = 0.55842 (tol = 0.002, component 1) ## 2: In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : ## Model is nearly unidentifiable: very large eigenvalue ## - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio ## - Rescale variables? # Looks like lme4 is having trouble with variance of intercepts # There is nothing special about 1800 years, so change the # intercept -- 'correct' yor by subtracting 1800 and try again. dat$yorc <- dat$yor - 1800 m3 <- lmer(y ~ 1 + yorc + (1+yorc||env), data=dat) # Now lme4 succeeds. Rate of gain is 100*0.0549 = 5.49 fixef(m3) ## (Intercept) yorc ## 5.87492444 0.05494464 if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) m3a <- asreml(y ~ 1 + yorc, data=dat, random = ~ env + env:yorc) lucid::vc(m3) ## grp var1 var2 vcov sdcor ## env (Intercept) 11.61 3.407 ## env.1 yorc 0.00063 0.02511 ## Residual 3.551 1.884 lucid::vc(m3a) ## effect component std.error z.ratio con ## env!env.var 11.61 4.385 2.6 Positive ## env:yorc!env.var 0.00063 0.000236 2.7 Positive ## R!variance 3.551 0.2231 16 Positive } } # }"},{"path":"/reference/petersen.sorghum.cowpea.html","id":null,"dir":"Reference","previous_headings":"","what":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"Intercropping experiment sorghum/cowpea.","code":""},{"path":"/reference/petersen.sorghum.cowpea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"","code":"data(\"petersen.sorghum.cowpea\")"},{"path":"/reference/petersen.sorghum.cowpea.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"data frame 18 observations following 5 variables. block block srows sorghum rows crows cowpea rows syield sorghum yield, kg/ha cyield cowpea yield, kg/ha","code":""},{"path":"/reference/petersen.sorghum.cowpea.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"intercropping experiment Tanzania. treatments consisted four ratios sorghum rows cowpea rows 1:4, 2:3, 3:2, 4:1. sole-crop yields 5 rows per crop also given (part blocks).","code":""},{"path":"/reference/petersen.sorghum.cowpea.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"Roger G Petersen (1994). Agricultural Field Experiments. Marcel Dekker Inc, New York. Page 372.","code":""},{"path":"/reference/petersen.sorghum.cowpea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"None","code":""},{"path":"/reference/petersen.sorghum.cowpea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"","code":"if (FALSE) { # \\dontrun{ libs(agridat) data(petersen.sorghum.cowpea) dat <- petersen.sorghum.cowpea # Petersen figure 10.4a tmp <- dat with(tmp, plot(srows, syield + cyield, col=\"blue\", type='l', xlim=c(0,5), ylim=c(0,4000)) ) with(tmp, lines(srows, syield) ) with(tmp, lines(srows, cyield, col=\"red\") ) title(\"Cow Pea (red), Sorghum (black), Total (blue)\") title(\"petersen.sorghum.cowpea\", line=0.5) } # }"},{"path":"/reference/piepho.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of barley — piepho.barley.uniformity","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"Uniformity trial barley Germany","code":""},{"path":"/reference/piepho.barley.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"","code":"data(\"piepho.barley.uniformity\")"},{"path":"/reference/piepho.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"data frame 1080 observations following 5 variables. row row ordinate col column ordinate yield yield per plot","code":""},{"path":"/reference/piepho.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"Uniformity trial barley Ihinger Hof farm, conducted University Hohenheim, Germany, 2007. Note: paper Piepho says \"trial 30 rows 36 columns. Plot widths 1.90 m along rows 3.73 m along columns.\" confirmed variograms Figure 1. clear \"along rows\" \"along columns\" means English. However, SAS code supplement paper, called \"PBR_1654_sm_example1.sas\", row=1-36, col=1-30.","code":""},{"path":"/reference/piepho.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"H. P. Piepho & E. R. Williams (2010). Linear variance models plant breeding trials. Plant Breeding, 129, 1-8. https://doi.org/10.1111/j.1439-0523.2009.01654.x","code":""},{"path":"/reference/piepho.barley.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"None","code":""},{"path":"/reference/piepho.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ data(piepho.barley.uniformity) dat <- piepho.barley.uniformity libs(desplot) desplot(dat, yield ~ col*row, tick=TRUE, aspect=(36*3.73)/(30*1.90), main=\"piepho.barley.uniformity.csv\") if(require(\"asreml\", quietly=TRUE)){ libs(asreml,dplyr,lucid) dat <- mutate(dat, x=factor(col), y=factor(row)) dat <- arrange(dat, x, y) # Piepho AR1xAR1 model (in random term, NOT residual) m1 <- asreml(data=dat, yield ~ 1, random = ~ x + y + ar1(x):ar1(y), residual = ~ units, na.action=na.method(x=\"keep\") ) m1 <- update(m1) # Match Piepho table 3, footnote 4: .9671, .9705 for col,row correlation # Note these parameters are basically at the boundary of the parameter # space. Questionable fit. lucid::vc(m1) } } # }"},{"path":"/reference/piepho.cocksfoot.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"Multi-environment trial cock's foot, heading dates 25 varieties 7 yearsyears","code":""},{"path":"/reference/piepho.cocksfoot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"","code":"data(\"piepho.cocksfoot\")"},{"path":"/reference/piepho.cocksfoot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"data frame 111 observations following 3 variables. gen genotype factor, 25 levels year year, numeric date heading date (days April 1)","code":""},{"path":"/reference/piepho.cocksfoot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"data heading dates (days April 1 heading) 25 cock's foot Dactylis glomerata varieties trials Hannover, Germany, repeated seven years. Values means replications. Piepho fits model similar Finlay-Wilkinson regression, genotype environment swapped.","code":""},{"path":"/reference/piepho.cocksfoot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"Hans-Pieter Piepho. (1999). Fitting Regression Model Genotype--Environment Data Heading Dates Grasses Methods Nonlinear Mixed Models. Biometrics, 55, 1120-1128. https://doi.org/10.1111/j.0006-341X.1999.01120.x","code":""},{"path":"/reference/piepho.cocksfoot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(piepho.cocksfoot) dat <- piepho.cocksfoot dat$year <- factor(dat$year) libs(lattice) # Gaussian, not gamma distn densityplot(~date|year, data=dat, main=\"piepho.cocksfoot - heading date\") if(require(\"mumm\", quietly=TRUE)){ libs(mumm) # The mumm package can reproduce Piepho's results levelplot(date ~ year*gen, dat) # note mp(random,fixed) mod3 <- mumm(date ~ -1 + gen + (1|year) + mp(year, gen), dat) # Compare to Piepho table 3, \"full maximum likelihood\" mod3$sigmas^2 # variances for year:gen, residual match # year mp year:gen Residual # 17.70287377 0.02944158 0.49024737 # mod3$par_fix # fixed genotypes match # mod3$sdreport # estim/stderr # Estimate Std. Error # nu 49.0393183 1.55038652 # nu 42.0889493 1.67597832 # nu 45.3411252 1.59818620 # etc # mod3$par_rand # random year:gen match # $`mp year:gen` # 1990 1991 1992 1993 1994 1995 # 0.10595661 -0.05298523 0.08228274 -0.09629696 -0.11045540 0.29637268 } } # }"},{"path":"/reference/polson.safflower.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of safflower — polson.safflower.uniformity","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"Uniformity trial safflower Farmington, Utah, 1962.","code":""},{"path":"/reference/polson.safflower.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"","code":"data(\"polson.safflower.uniformity\")"},{"path":"/reference/polson.safflower.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"data frame 1716 observations following 3 variables. row row col column yield yield (grams)","code":""},{"path":"/reference/polson.safflower.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"uniformity trial safflower Utah State University field station Farmington, Utah, 1962. field approximately 0.5 acres size, 110 x 189 feet. four-row planter used, 22 inches rows. Four rows either side 12 feet ends removed harvesting. Yield threshed grain recorded grams. Field width: (52 rows + 8 border rows) * 22 = 110 ft Field length: 33 sections * 5ft + 2 borders * 12 ft = 189 ft","code":""},{"path":"/reference/polson.safflower.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"David Polson. 1964. Estimation Optimum Size, Shape, Replicate Number Safflower Plots Yield Trials. Utah State University, Graduate Theses Dissertations, 2979. Table 6, p. 52. https://digitalcommons.usu.edu/etd/2979","code":""},{"path":"/reference/polson.safflower.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"None.","code":""},{"path":"/reference/polson.safflower.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(polson.safflower.uniformity) dat <- polson.safflower.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=189/110, # true aspect main=\"polson.safflower.uniformity\") libs(agricolae) libs(reshape2) dmat <- acast(dat, row~col, value.var=\"yield\") # Similar to Polson fig 4. tab <- index.smith(dmat, col=\"red\", main=\"polson.safflower.uniformity - Smith Index\", xlab=\"Plot size in number of basic plots\") # Polson p. 25 said CV decreased from 14.3 to 4.5 # for increase from 1 unit to 90 units. Close match. tab <- data.frame(tab$uniformity) # Polson only uses log(Size) < 2 in his Fig 5, obtained slope -0.63 coef(lm(log(Vx) ~ log(Size), subset(tab, Size <= 6))) # -0.70 # Polson table 2 reported labor for # K1, number of plots, 133 hours 75 # K2, size of plot, 43.5 hours 24 # Optimum plot size # X = b K1 / ((1-b) K2) # Polson suggests optimum plot size 2.75 to 11 basic plots } # }"},{"path":"/reference/ratkowsky.onions.html","id":null,"dir":"Reference","previous_headings":"","what":"Onion yields for different densities at two locations — ratkowsky.onions","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"Onion yields different densities two locations","code":""},{"path":"/reference/ratkowsky.onions.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"data frame contains following columns: density planting density (plants per square meter) yield yield (g / plant) loc location, Purnong Landing Virginia","code":""},{"path":"/reference/ratkowsky.onions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"Spanish white onions.","code":""},{"path":"/reference/ratkowsky.onions.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"Ratkowsky, D. . (1983). Nonlinear Regression Modeling: Unified Practical Approach. New York: Marcel Dekker.","code":""},{"path":"/reference/ratkowsky.onions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"Ruppert, D., Wand, M.P. Carroll, R.J. (2003). Semiparametric Regression. Cambridge University Press. https://stat.tamu.edu/~carroll/semiregbook/","code":""},{"path":"/reference/ratkowsky.onions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ratkowsky.onions) dat <- ratkowsky.onions # Model inverse yield as a quadratic. Could be better... libs(lattice) dat <- transform(dat, iyield = 1/yield) m1 <- lm(iyield ~ I(density^2)*loc, dat) dat$pred <- predict(m1) libs(latticeExtra) foo <- xyplot(iyield ~ density, data=dat, group=loc, auto.key=TRUE, main=\"ratkowski.onions\",ylab=\"Inverse yield\") foo + xyplot(pred ~ density, data=dat, group=loc, type='l') } # }"},{"path":"/reference/reid.grasses.html","id":null,"dir":"Reference","previous_headings":"","what":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"Yields four grasses wide range nitrogen fertilizer, conducted 3 years.","code":""},{"path":"/reference/reid.grasses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"","code":"data(\"reid.grasses\")"},{"path":"/reference/reid.grasses.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"data frame 210 observations following 5 variables. nitro nitrogen, 21 numeric levels year Y1, Y2, Y3 gen genotype drymatter dry matter content protein protein content","code":""},{"path":"/reference/reid.grasses.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"Experiment Hannah Research Institute, Ayr. Single plots planted 4 different kinds grasses. Within plot, 21 nitrogen treatments randomized. Reid modeled dry matter yield four-parameter logistic curves form y = - b exp(-cx^d).","code":""},{"path":"/reference/reid.grasses.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"D. Reid (1985). comparison yield responses four grasses wide range nitrogen application rates. J. Agric. Sci., 105, 381-387. Table 1 & 3. https://doi.org/10.1017/S0021859600056434","code":""},{"path":"/reference/reid.grasses.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"None","code":""},{"path":"/reference/reid.grasses.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(reid.grasses) dat <- reid.grasses libs(latticeExtra) foo <- xyplot(drymatter + protein ~ nitro|year, dat, group=gen, auto.key=list(columns=4), as.table=TRUE, type=c('p','l'), main=\"reid.grasses\",ylab=\"drymatter/protein trait value\", scales=list(y=list(relation=\"free\"))) combineLimits(foo) # devtools::run_examples does NOT like groupedData if (0){ libs(nlme) dat2 <- dat dat2$indiv <- paste(dat$year, dat$gen) # individual year+genotype curves # use all data to get initial values inits <- getInitial(drymatter ~ SSfpl(nitro, A, B, xmid, scal), data = dat2) inits ## A B xmid scal ## -4.167902 12.139796 68.764796 128.313106 xvals <- 0:800 y1 <- with(as.list(inits), SSfpl(xvals, A, B, xmid, scal)) plot(drymatter ~ nitro, dat2) lines(xvals,y1) # must have groupedData object to use augPred dat2 <- groupedData(drymatter ~ nitro|indiv, data=dat2) plot(dat2) # without 'random', all effects are included in 'random' m1 <- nlme(drymatter ~ SSfpl(nitro, A, B, xmid,scale), data= dat2, fixed= A + B + xmid + scale ~ 1, random = A + B + xmid + scale ~ 1|indiv, start=inits) fixef(m1) summary(m1) plot(augPred(m1, level=0:1), main=\"reid.grasses - observed/predicted data\") # only works with groupedData object } # if(0) } # }"},{"path":"/reference/riddle.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Modified Latin Square experiments of wheat — riddle.wheat","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"Modified Latin Square experiments wheat two varieties 2 years","code":""},{"path":"/reference/riddle.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"","code":"data(\"riddle.wheat\")"},{"path":"/reference/riddle.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"data frame 650 observations following 7 variables. expt experiment strain strain rep replicate row row (nested column) year year yield yield, grams col column (group rows)","code":""},{"path":"/reference/riddle.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"experiment \"Baart\" varieties 1939 another experiment \"White Federation\" varieties 1939. experiments repeated 1940. experimental design Modified Latin Square. 5 reps, horizontal. 5 \"columns\". rep*column contains multiple plots strain planted 16-foot row. Field length: 5 reps * 16 feet Field width: 25 30 rows, perhaps 0.5 feet rows Riddle & Baker note: Two strains, 5129 (Baart) 1617 (White Federation) reversed position significantly LOWER 1939 significantly HIGHER general mean 1940.","code":""},{"path":"/reference/riddle.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"Riddle, O. C. G. . Baker. (1944). Biases encountered large-scale yield tests. Hilgardia, 16, 1-14. https://doi.org/10.3733/hilg.v16n01p001","code":""},{"path":"/reference/riddle.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"None","code":""},{"path":"/reference/riddle.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(riddle.wheat) dat <- riddle.wheat datb39 <- subset(dat, expt==\"Baart\" & year==1939) datb40 <- subset(dat, expt==\"Baart\" & year==1940) datw39 <- subset(dat, expt==\"WhiteFed\" & year==1939) datw40 <- subset(dat, expt==\"WhiteFed\" & year==1940) # Match table 4, sections a, b, d, e anova(aov(yield ~ factor(rep) + factor(col) + strain, datb39)) anova(aov(yield ~ factor(rep) + factor(col) + strain, datb40)) anova(aov(yield ~ factor(rep) + factor(col) + strain, datw39)) anova(aov(yield ~ factor(rep) + factor(col) + strain, datw40)) libs(desplot) # Show the huge variaion between reps dat$yrexpt <- paste0(dat$year, dat$expt) desplot(dat, yield ~ row*rep|yrexpt, tick=TRUE, out1=col, main=\"riddle.wheat\", aspect=(5*16)/(30*.5)) # Show the randomization was the same in each year (but not each expt). desplot(dat, strain ~ row*rep|yrexpt, tick=TRUE, out1=col, main=\"riddle.wheat\") } # }"},{"path":"/reference/ridout.appleshoots.html","id":null,"dir":"Reference","previous_headings":"","what":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"Root counts propagated columnar apple shoots.","code":""},{"path":"/reference/ridout.appleshoots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"","code":"data(\"ridout.appleshoots\")"},{"path":"/reference/ridout.appleshoots.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"data frame 270 observations following 4 variables. roots number roots per shoot trtn number shoots per treatment combination photo photoperiod, 8 16 bap BAP concentration, numeric","code":""},{"path":"/reference/ridout.appleshoots.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"270 micropropagated shoots columnar apple cultivar Trajan. rooting period, shoot tips length 1.0-1.5 cm cultured media different concentrations cytokinin BAP two growth chambers 8 16 hour photoperiod. response variable number roots 4 weeks 22 degrees C. Almost shoots 8 hour photoperiod rooted. 16 hour photoperiod half rooted. High BAP concentrations often inhibit root formation apples, perhaps columnar varieties. Used permission Martin Ridout.","code":""},{"path":"/reference/ridout.appleshoots.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"Ridout, M. S., Hinde, J. P., Demetrio, C. G. B. (1998). Models Count Data Many Zeros. Proceedings 19th International Biometric Conference, 179-192.","code":""},{"path":"/reference/ridout.appleshoots.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"SAS. Fitting Zero-Inflated Count Data Models Using PROC GENMOD. support.sas.com/rnd/app/examples/stat/GENMODZIP/roots.pdf","code":""},{"path":"/reference/ridout.appleshoots.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ridout.appleshoots) dat <- ridout.appleshoots # Change photo and bap to factors dat <- transform(dat, photo=factor(photo), bap=factor(bap)) libs(lattice) # histogram(~roots, dat, breaks=0:18-0.5) # For photo=8, Poisson distribution looks reasonable. # For photo=16, half of the shoots had no roots # Also, photo=8 has very roughly 1/45 as many zeros as photo=8, # so we anticipate prob(zero) is about 1/45=0.22 for photo=8. histogram(~roots|photo, dat, breaks=0:18-0.5, main=\"ridout.appleshoots\") libs(latticeExtra) foo.obs <- histogram(~roots|photo*bap, dat, breaks=0:18-0.5, type=\"density\", xlab=\"Number of roots for photoperiod 8, 16\", ylab=\"Density for BAP levels\", main=\"ridout.appleshoots\") useOuterStrips(foo.obs) # Ordinary (non-ZIP) Poisson GLM m1 <- glm(roots ~ bap + photo + bap:photo, data=dat, family=\"poisson\") summary(m1) # Appears to have overdispersion # ----- Fit a Zero-Inflated Poisson model ----- libs(pscl) # Use SAS contrasts to match SAS output oo <- options(contrasts=c('contr.SAS','contr.poly')) # There are unequal counts for each trt combination, which obviously affects # the distribution of counts, so use log(trtn) as an offset. dat$ltrtn <- log(dat$trtn) # Ordinary Poisson GLM: 1 + bap*photo. # Zero inflated probability depends only on photoperiod: 1 + photo m2 <- zeroinfl(roots ~ 1 + bap*photo | 1 + photo, data=dat, dist=\"poisson\", offset=ltrtn) logLik(m2) # -622.2283 matches SAS Output 1 -2 * logLik(m2) # 1244.457 Matches Ridout Table 2, ZIP, H*P, P summary(m2) # Coefficients match SAS Output 3. exp(coef(m2, \"zero\")) # Photo=8 has .015 times as many zeros as photo=16 # Get predicted _probabilities_ # Prediction data newdat <- expand.grid(photo=c(8,16), bap=c(2.2, 4.4, 8.8, 17.6)) newdat <- aggregate(trtn~bap+photo, dat, FUN=mean) newdat$ltrtn <- log(newdat$trtn) # The predicted (Poisson + Zero) probabilities d2 <- cbind(newdat[,c('bap','photo')], predict(m2, newdata=newdat, type=\"prob\")) libs(reshape2) d2 <- melt(d2, id.var = c('bap','photo')) # wide to tall d2$xpos <- as.numeric(as.character(d2$variable)) foo.poi <- xyplot(value~xpos|photo*bap, d2, col=\"black\", pch=20, cex=1.5) # Plot data and model foo.obs <- update(foo.obs, main=\"ridout.appleshoots: observed (bars) & predicted (dots)\") useOuterStrips(foo.obs + foo.poi) # Restore contrasts options(oo) } # }"},{"path":"/reference/robinson.peanut.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of peanuts — robinson.peanut.uniformity","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"Uniformity trial peanuts North Carolina 1939, 1940.","code":""},{"path":"/reference/robinson.peanut.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"","code":"data(\"robinson.peanut.uniformity\")"},{"path":"/reference/robinson.peanut.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"data frame 1152 observations following 4 variables. row row col column yield yield grams/plot year year","code":""},{"path":"/reference/robinson.peanut.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"Two crops peanuts grown North Carolina 1939 1940. different field used year. block 36 rows 3 feet wide 200 feet long harvested 12.5 foot lengths. Field length: 36 plots * 12.5 feet = 200 feet Field width: 16 plots * 3 feet = 48 feet Widening plot effective increasing plot length order reduce error. agrees results uniformity studies. Assuming 30 percent total cost experiment proportional size plots used, optimum plot size approximately 3.2 units.","code":""},{"path":"/reference/robinson.peanut.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"H.F. Robinson J..Rigney P.H.Harvey (1948). Investigations Peanut Plot Technique Peanuts. Univ California Tech. Bul. 86.","code":""},{"path":"/reference/robinson.peanut.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"None","code":""},{"path":"/reference/robinson.peanut.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(robinson.peanut.uniformity) dat <- robinson.peanut.uniformity # Mean yield per year. Robinson has 703.9, 787.3 # tapply(dat$yield, dat$year, mean) # 1939 1940 # 703.7847 787.8125 libs(desplot) desplot(dat, yield ~ col*row|year, flip=TRUE, tick=TRUE, aspect=200/48, main=\"robinson.peanut.uniformity\") } # }"},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"Uniformity trial sugar beets","code":""},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"","code":"data(\"roemer.sugarbeet.uniformity\")"},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"data frame 192 observations following 4 variables. row row ordinate col column ordinate yield yield per plot, kg year year experiment","code":""},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"Roemer p 27: Eigene Versuche mit Zuckerrüben, ausgeführt auf dem Neßthaler Zuchtfeld des Kaiser-Wilhelm-Institutes, Bromberg, den Jahren 1916, 1917 und 1918. 1916 und 1918 war die Versuchsfläche ein und dieselbe, 6,80 groß und den beiden Jahren mit Original Klein-Wanzlebener Zuckerrüben auf 30 X 40 cm bebaut. Vorfrucht für 1916 war Hafer, für 1918 Roggen; 1917 war eine andere Fläche, ebenfalls 6,80 groß, für den Versuch benußt; gesät wurden zwei verschiedene Zuchten von Strube, Schlanstedt. Beide Flächen sind von sehr gleichmäßiger Bodenbeschaffenheit. Bei der Fläche 1916 und 1918 machte sich im ersten Jahre bei den Reihen 31-33 eine geringe Stelle bemerkbar, die 1918 weit weniger Erscheinung trat. Die Bodenunterschiede sind allen drei Jahren geringer als die durch die Versuchstechnik bedingten Fehler. Translated: (Roemer) experiments sugar beets, carried Neßthal breeding field Kaiser Wilhelm Institute, Bromberg, years 1916, 1917 1918. 1916 1918 test area one , 6.80 large original years Klein-Wanzleben sugar beets cultivated 30 x 40 cm. previous crop 1916 oats, 1918 rye; 1917 another area, also 6.80 large, used experiment; Two different varieties Strube, Schlanstedt sown. areas uniform soil conditions. 1916 1918 area, small spot noticeable rows 31-33 first year, much less noticeable 1918. three years soil differences smaller errors caused experimental technology. Field width: 2 plots * 17 m = 34 m Field length: 48 plots * 4.17 m = 200 m Total area = 34 m * 200 m = 6800 sq m = 6.8 . Cochran says: 96 plots, 1 row x 55.8 ft (17m). Two sets (years) 1916 1918. Data typed K.Wright Roemer (1920).","code":""},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"Roemer, T. (1920). Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. Table 1, page 62. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ","code":""},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"Neyman, J., & Iwaszkiewicz, K. (1935). Statistical problems agricultural experimentation. Supplement Journal Royal Statistical Society, 2(2), 107-180.","code":""},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(roemer.sugarbeet.uniformity) dat <- roemer.sugarbeet.uniformity libs(desplot) desplot(dat, yield~col*row|year, aspect=(48*4.16)/(2*17), flip=TRUE, tick=TRUE, main=\"roemer.sugarbeet.uniformity\") } # }"},{"path":"/reference/rothamsted.brussels.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"RCB experiment brussels sprouts, 9 fertilizer treatments","code":""},{"path":"/reference/rothamsted.brussels.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"data frame 48 observations following 5 variables. row row col column yield yield saleable sprouts, pounds trt treatment, 9 levels block block, 4 levels","code":""},{"path":"/reference/rothamsted.brussels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"block numbers arbitrary, may match orignal source. Plots 10 yards x 14 yards. Plot orientation clear.","code":""},{"path":"/reference/rothamsted.brussels.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"Rothamsted Experimental Station Report 1934-36. Brussels sprouts: effect sulphate ammonia, poultry manure, soot rape dust, pp. 191-192. Harpenden: Lawes Agricultural Trust.","code":""},{"path":"/reference/rothamsted.brussels.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667","code":""},{"path":"/reference/rothamsted.brussels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(rothamsted.brussels) dat <- rothamsted.brussels libs(lattice) bwplot(yield~trt, dat, main=\"rothamsted.brussels\") libs(desplot) desplot(dat, yield~col*row, num=trt, out1=block, cex=1, # aspect unknown main=\"rothamsted.brussels\") } # }"},{"path":"/reference/rothamsted.oats.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"RCB experiment oats, straw grain, 9 fertilizer treatments","code":""},{"path":"/reference/rothamsted.oats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"","code":"data(\"rothamsted.oats\")"},{"path":"/reference/rothamsted.oats.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"data frame 96 observations following 6 variables. block block trt fertilizer treatment 9 levels grain grain, pounds per plot straw straw, pounds per plot row row col column","code":""},{"path":"/reference/rothamsted.oats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"Oats (Grey Winter) grown Rothamsted, Long Hoos field 1926. Values grain straw actual weights pounds. plot 1/40 acre. plot dimensions given, Rothamsted report shows field square. treatment codes : OA,OB,OC,OD = top dressing. E/L = Early/late application. S/M = Sulphate muriate ammonia. 1/2 = Single double dressing.","code":""},{"path":"/reference/rothamsted.oats.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"Rothamsted Report 1925-26, p. 146. https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1925-26-138-155 Electronic version data supplied David Clifford.","code":""},{"path":"/reference/rothamsted.oats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667","code":""},{"path":"/reference/rothamsted.oats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(rothamsted.oats) dat <- rothamsted.oats libs(desplot) desplot(dat, grain~col*row, out1=block, text=trt, cex=1, shorten=FALSE, aspect=1, main=\"rothamsted.oats\") desplot(dat, straw~col*row, out1=block, text=trt, cex=1, shorten=FALSE, aspect=1, main=\"rothamsted.oats\") libs(lattice) xyplot(grain~straw, dat, main=\"rothamsted.oats\") # traits are correlated if(0){ # compare to summary at bottom of page 146, first 3 columns libs(dplyr) dat = mutate(dat, nfert=trt, # number of fertilizer applications nfert=dplyr::recode(nfert, \"oa\"=\"None\", \"ob\"=\"None\", \"oc\"=\"None\", \"od\"=\"None\", \"1se\"=\"Single\", \"1sl\"=\"Single\", \"1me\"=\"Single\", \"1ml\"=\"Single\", \"2se\"=\"Double\", \"2sl\"=\"Double\", \"2me\"=\"Double\", \"2ml\"=\"Double\")) # English ton = 2240 pounds, cwt = 112 pounds # multiply by 40 to get pounds/acre # divide by: 112 to get hundredweight/acre, 42 to get bushels/acre # Avoid pipe operator in Rd examples! dat <- group_by(dat, nfert) dat <- summarize(dat, straw=mean(straw), grain=mean(grain)) dat <- mutate(dat, straw= straw * 40/112, grain = grain * 40/42) ## # A tibble: 3 x 3 ## nfert straw grain ## ## 1 Single 50.3 78.9 ## 2 Double 53.7 77.7 ## 3 None 44.1 75.4 } } # }"},{"path":"/reference/ryder.groundnut.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","title":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","text":"RCB experiment groundut, wet dry yields","code":""},{"path":"/reference/ryder.groundnut.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","text":"data frame 24 observations following 6 variables. block block row row col column gen genotype factor wet wet yield, kg/plot dry dry yield, kg/plot","code":""},{"path":"/reference/ryder.groundnut.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","text":"Ryder (1981) uses data discuss importance looking field plan experiment. Based analysis residuals, suggests varieties B block 3 may data swapped.","code":""},{"path":"/reference/ryder.groundnut.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","text":"K. Ryder (1981). Field plans: biometrician finds useful, Experimental Agriculture, 17, 243–256. https://doi.org/10.1017/S0014479700011601","code":""},{"path":"/reference/ryder.groundnut.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ryder.groundnut) dat <- ryder.groundnut # RCB model m1 <- lm(dry~block+gen,dat) dat$res1 <- resid(m1) # Table 3 of Ryder. Scale up from kg/plot to kg/ha round(dat$res1 * 596.6,0) # Visually. Note largest positive/negative residuals are adjacent libs(desplot) desplot(dat, res1 ~ col + row, text=gen, # aspect unknown main=\"ryder.groundnut - residuals\") libs(desplot) # Swap the dry yields for two plots and re-analyze dat[dat$block==\"B3\" & dat$gen==\"A\", \"dry\"] <- 2.8 dat[dat$block==\"B3\" & dat$gen==\"B\", \"dry\"] <- 1.4 m2 <- lm(dry~block+gen, dat) dat$res2 <- resid(m2) desplot(dat, res2 ~ col+row, # aspect unknown text=gen, main=\"ryder.groundnut\") } # }"},{"path":"/reference/salmon.bunt.html","id":null,"dir":"Reference","previous_headings":"","what":"Fungus infection in varieties of wheat — salmon.bunt","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"Fungus infection varieties wheat","code":""},{"path":"/reference/salmon.bunt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"data frame 400 observations following 4 variables. bunt bunt factor, 20 levels pct percent infected rep rep factor, 2 levels gen genotype factor, 10 levels","code":""},{"path":"/reference/salmon.bunt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"Note: Salmon (1938) gives results 69 types bunt, just 20 shown paper. H. . Rodenhiser C. S. Holton (1937) say races two different species bunt used, Tilletia tritici T. levis. data gives results 20 types bunt (fungus) winter wheat varieties Kearneysville, W. Va., 1935. Altogether 69 types bunt included experiment, 20 data representative. type wheat grown short row (5 8 feet), seed innoculated spores bunt. entire seeding repeated order. Infection recorded percentage total number heads counted near harvest. number counted seldom less 200 sometimes 400 per row.","code":""},{"path":"/reference/salmon.bunt.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"S.C. Salmon, 1938. Generalized standard errors evaluating bunt experiments wheat. Agronomy Journal, 30, 647–663. Table 1. https://doi.org/10.2134/agronj1938.00021962003000080003x","code":""},{"path":"/reference/salmon.bunt.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"Salmon says data came : H. . Rodenhiser C. S. Holton (1937). Physiologic races Tilletia tritici T. levis. Journal Agricultural Research, 55, 483-496. naldc.nal.usda.gov/download/IND43969050/PDF","code":""},{"path":"/reference/salmon.bunt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(salmon.bunt) dat <- salmon.bunt d2 <- aggregate(pct~bunt+gen, dat, FUN=mean) # average reps d2$gen <- reorder(d2$gen, d2$pct) d2$bunt <- reorder(d2$bunt, d2$pct) # Some wheat varieties (Hohenheimer) are resistant to all bunts, and some (Hybrid128) # are susceptible to all bunts. Note the groups of bunt races that are similar, # such as the first 4 rows of this plot. Also note the strong wheat*bunt interaction. libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(pct~gen+bunt,d2, col.regions=redblue, main=\"salmon.bunt percent of heads infected\", xlab=\"Wheat variety\", ylab=\"bunt line\") # We don't have individual counts, so use beta regression libs(betareg) dat$y <- dat$pct/100 + .001 # Beta regression does not allow 0 dat$gen <- reorder(dat$gen, dat$pct) # For a prettier dot plot m1 <- betareg(y ~ gen + bunt + gen:bunt, data=dat) # Construct 95 percent confidence intervals p1 <- cbind(dat, lo = predict(m1, type='quantile', at=.025), est = predict(m1, type='quantile', at=.5), up = predict(m1, type='quantile', at=.975)) p1 <- subset(p1, rep==\"R1\") # Plot the model intervals over the original data libs(latticeExtra) dotplot(bunt~y|gen, data=dat, pch='x', col='red', main=\"Observed data and 95 pct intervals for bunt infection\") + segplot(bunt~lo+up|gen, data=p1, centers=est, draw.bands=FALSE) # To evaluate wheat, we probably want to include bunt as a random effect... } # }"},{"path":"/reference/saunders.maize.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"Uniformity trial maize South Africa","code":""},{"path":"/reference/saunders.maize.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"","code":"data(\"saunders.maize.uniformity\")"},{"path":"/reference/saunders.maize.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"data frame 2500 observations following 4 variables. row row ordinate col column ordinate yield yield per plot, pounds year year","code":""},{"path":"/reference/saunders.maize.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"two maize uniformity trials conducted Potchefstroom Experiment Station, South Africa. harvested unit plot 10 plants, planted 3 feet 3 feet individual hills. Dataset 1928-1929 experiment Rows 41-43 missing. Field width: 4 plots * 10 yards = 40 yards Field length : 250 plots * 1 yard = 250 yards Dataset 1929-30 experiment Row 255 missing obvious edge effect first column. Field width: 5 plots * 20 yards = 100 yards Field length: 300 plots * 1 yard = 300 yards Two possible outliers 1929-30 data verified correctly transcribed source document. data made available special help staff Rothamsted Research Library. Rothamsted library scanned paper documents pdf. Screen captures pdf saved jpg files, uploaded OCR conversion site. resulting text 95 percent accurate carefully hand-checked formatted csv files.","code":""},{"path":"/reference/saunders.maize.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 5.","code":""},{"path":"/reference/saunders.maize.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"Rayner & . R. Saunders. Statistical Methods, Special Reference Field Experiments.","code":""},{"path":"/reference/saunders.maize.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(saunders.maize.uniformity) dat <- saunders.maize.uniformity libs(desplot) desplot(dat, yield ~ col*row, subset=year==1929, flip=TRUE, aspect=250/40, main=\"saunders.maize.uniformity 1928-29\") desplot(dat, yield ~ col*row, subset=year==1930, flip=TRUE, aspect=300/100, main=\"saunders.maize.uniformity 1929-30\") } # }"},{"path":"/reference/sawyer.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"Uniformity trials wheat, swedes, oats Rothamsted, England, 1925-1927.","code":""},{"path":"/reference/sawyer.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"","code":"data(\"sawyer.multi.uniformity\")"},{"path":"/reference/sawyer.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"data frame 48 observations following 7 variables. year year crop crop row row col column grain wheat/oats grain weight, pounds straw wheat/oats straw weight, pounds leafwt swedes leaf weight, pounds rootwt swedes root weight, pounds rootct swedes root count","code":""},{"path":"/reference/sawyer.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"experiment conducted Rothamsted, England, 1925-1927, Sawyers Field. Row 6, column 1 planted year. 1925: Wheat harvested Row 1, column 1 partially missing data wheat values 1925 used Rothamsted summary statistics page 155. 1926: Swedes harvested 1927: Oats harvested Note summaries statistics bottom page report calibrated ACRES. Field width: 8 plots * 22 feet = 528 feet Field length: 6 plots * 22 feet = 396 feet field 8 plots wide, 6 plots long. plots drawn source documents squares .098 acres (1 chain = 66 feet side). Eden & Maskell (page 165) say field clover, ploughed autumn 1924. field laid uniformly lands one chain width plot width made coincide land width ridge ridge. length plot also one chain point view yield data trial comprised 47 plots 8x6 except run hedge allowed rank five plots one ends.","code":""},{"path":"/reference/sawyer.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"Rothamsted Experimental Station, Report 1925-26. Lawes Agricultural Trust, p. 154-155. https://www.era.rothamsted.ac.uk/eradoc/book/84 Rothamsted Experimental Station, Report 1927-1928. Lawes Agricultural Trust, p. 153. https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1927-28-131-175","code":""},{"path":"/reference/sawyer.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"Eden, T. E. J. Maskell. (1928). influence soil heterogeneity growth yield successive crops. Jour Agricultural Science, 18, 163-185. https://archive.org/stream/.ernet.dli.2015.25895/2015.25895.Journal--Agricultural-Science-Vol-xviii-1928#page/n175 McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667 Winifred . Mackenzie. (1926) Note remarkable correlation grain straw, obtained Rothamsted. Journal Agricultural Science, 16, 275-279. https://doi.org/10.1017/S0021859600018256","code":""},{"path":"/reference/sawyer.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(\"sawyer.multi.uniformity\") dat <- sawyer.multi.uniformity libs(desplot) # The field plan shows square plots desplot(dat, grain~col*row, subset= year==1925, main=\"sawyer.multi.uniformity - 1925 wheat grain yield\", aspect=(6)/(8)) # true aspect desplot(dat, rootwt~col*row, subset= year==1926, main=\"sawyer.multi.uniformity - 1926 root weight of swedes\", aspect=(6)/(8)) desplot(dat, grain~col*row, subset= year==1927, main=\"sawyer.multi.uniformity - 1927 oats grain yield\", aspect=(6)/(8)) # This plot shows the \"outlier\" in the wheat data reported by Mackenzie. libs(lattice) xyplot(grain ~ straw, data=subset(dat, year==1925)) round(cor(dat[,7:9], use=\"pair\"),2) # Matches McCullagh p 2121 ## leafwt rootwt rootct ## leafwt 1.00 0.66 0.47 ## rootwt 0.66 1.00 0.43 ## rootct 0.47 0.43 1.00 ## pairs(dat[,7:9], ## main=\"sawyer.multi.uniformity\") } # }"},{"path":"/reference/sayer.sugarcane.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"Uniformity trial sugarcane India, 1932, 1933 & 1934.","code":""},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"","code":"data(\"sayer.sugarcane.uniformity\")"},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"data frame following 4 variables. row row col column yield yield, pounds/plot year year","code":""},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"1932 Experiment, 20 col x 48 row = 960 plots Sayer (1936a, page 685): tonnage Experiment sugarcane, Co. 205, un-irrigated, conducted Harpur Jhilli 1932; 42 rows cane space 3 ft rows selected cut sections, section 30 feet 3 inches long. Thus yield figures plot sizes 30 feet 3 inches 3 feet (.e. 1/480 acre ), numbering 840 plots , available statistical analysis ; convenience data yields first forty rows also considered separately. Field width: 20 sections x 30 ft 3 = 605 feet Field length: 48 rows x 3 feet = 144 feet Note data Rothamsted library contains 48 rows, missing values rows 43-48. may Sayer (1963b) used 42 rows. ———- 1933 Experiment, 8 col x 136 row = 1088 plots Sayer (1936a, page 688). experiment conducted 1933 Meghaul (Monghyr). road cut field, creating blocks 480 ft x 315 ft 480 ft x 93 ft. (See Plate XLI). 136 rows, 3 feet apart, 480 feet long . required 16 days harvest 1088 plots. plot 1/242 acre. authors conclude long narrow plots 12/242 16/242 acre best. Field width: 8 plots * 60 feet = 480 feet Field length: 136 rows * 3 feet = 408 feet ———- 1934 Experiment, 8 col x 121 row = 968 plots experiment conducted New Area, Pusa. experiment laid 6 blocks, separated 3-foot bund. cutting canes began Jan 1934, taking 24 days. (earthquake 15 January delayed harvesting). Conclusion: Variation reduced increasing plot size 9/242 acre. Field width: 8 plots * 60 feet = 480 feet Field length: 121 rows * 3 feet = 363 feet 1932 data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"1932 Data Rothamsted Research Library, Box STATS17 WG Cochran, Folder 5. 1933 Data Wynne Sayer, M. Vaidyanathan S. Subrammonia Iyer (1936a). Ideal size shape sugar-cane experimental plots based upon tonnage experiments Co 205 Co 213 conducted Pusa. Indian J. Agric. Sci., 1936, 6, 684-714. Appendix, page 712. https://archive.org/details/.ernet.dli.2015.271737 1934 data Wynne Sayer Krishna Iyer. (1936b). factors influence error field experiments special reference sugar cane. Indian J. Agric. Sci., 1936, 6, 917-929. Appendix, page 927. https://archive.org/details/.ernet.dli.2015.271737","code":""},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"None","code":""},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(sayer.sugarcane.uniformity) dat32 <- subset(sayer.sugarcane.uniformity, year==1932) dat33 <- subset(sayer.sugarcane.uniformity, year==1933) dat34 <- subset(sayer.sugarcane.uniformity, year==1934) # The 1933 data have a 15-foot road between row 105 & row 106. # Add 5 to row number of row 106 and above. dat33$row <- ifelse(dat33$row >= 106, dat33$row + 5, dat33$row) b1 <- subset(dat33, row<31) b2 <- subset(dat33, row > 30 & row < 61) b3 <- subset(dat33, row > 60 & row < 91) b4 <- subset(dat33, row > 105 & row < 136) mean(b1$yield) # 340.7 vs Sayer 340.8 mean(b2$yield) # 338.2 vs Sayer 338.6 mean(b3$yield) # 331.3 vs Sayer 330.2 mean(b4$yield) # 295.4 vs Sayer 295.0 mean(dat34$yield) # 270.83 vs Sayer 270.83 libs(desplot) desplot(dat33, yield ~ col*row, flip=TRUE, aspect=408/480, # true aspect main=\"sayer.sugarcane.uniformity 1933\") desplot(dat34, yield ~ col*row, flip=TRUE, aspect=363/480, # true aspect main=\"sayer.sugarcane.uniformity 1934\") } # }"},{"path":"/reference/senshu.rice.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"Response rice solar radiation temperature","code":""},{"path":"/reference/senshu.rice.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"data frame 40 observations following 7 variables. country country loc location year year planting, last two digits month month planting rad solar radiation mint minimum temperature yield yield t/ha","code":""},{"path":"/reference/senshu.rice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"Minimum temperature average across 30 days post flowering. Opinion: Fitting quadratic model data makes sense.","code":""},{"path":"/reference/senshu.rice.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"Seshu, D. V. Cady, F. B. 1984. Response rice solar radiation temperature estimated international yield trials. Crop Science, 24, 649-654. https://doi.org/10.2135/cropsci1984.0011183X002400040006x","code":""},{"path":"/reference/senshu.rice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"Walter W. Piegorsch, . John Bailer. (2005) Analyzing Environmental Data, Wiley.","code":""},{"path":"/reference/senshu.rice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(senshu.rice) dat <- senshu.rice # Model 1 of Senshu & Cady m1 <- lm(yield ~ 1 + rad + mint + I(mint^2), dat) coef(m1) # Use Fieller to calculate conf int around optimum minimum temp # See: Piegorsch & Bailer, p. 31. # Calculation derived from vegan:::fieller.MOStest m2 <- lm(yield ~ 1 + mint + I(mint^2), dat) b1 <- coef(m2)[2] b2 <- coef(m2)[3] vc <- vcov(m2) sig11 <- vc[2,2] sig12 <- vc[2,3] sig22 <- vc[3,3] u <- -b1/2/b2 tval <- qt(1-.05/2, nrow(dat)-3) gam <- tval^2 * sig22 / b2^2 x <- u + gam * sig12 / (2 * sig22) f <- tval / (-2*b2) sq <- sqrt(sig11 + 4*u*sig12 + 4*u^2*sig22 - gam * (sig11 - sig12^2 / sig22) ) ci <- (x + c(1,-1)*f*sq) / (1-gam) plot(yield ~ mint, dat, xlim=c(17, 32), main=\"senshu.rice: Quadratic fit and Fieller confidence interval\", xlab=\"Minimum temperature\", ylab=\"Yield\") lines(17:32, predict(m2, new=data.frame(mint=17:32))) abline(v=ci, col=\"blue\") } # }"},{"path":"/reference/shafi.tomato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of tomato — shafi.tomato.uniformity","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"Uniformity trial tomato India","code":""},{"path":"/reference/shafi.tomato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"","code":"data(\"shafi.tomato.uniformity\")"},{"path":"/reference/shafi.tomato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"data frame 200 observations following 3 variables. row row ordinate col column ordinate yield yield, kg/plot","code":""},{"path":"/reference/shafi.tomato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"experiment conducted Regional Research Station Faculty Agriculture, SKUAST-K Wadura Campus 2006. original data collected 1m x 1m plots. data aggregated 2m x 2m plots. Field length: 20 row * 2 m = 40 m Field width: 10 col * 2 m = 20 m","code":""},{"path":"/reference/shafi.tomato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"Shafi, Sameera (2007). Aspects Plot Techniques Field Experiments Tomato (Lycopersicon esculentum mill.) Soils Kashmir. Thesis. Univ. Ag. Sciences & Technology Kashmir. Table 2.2.1. https://krishikosh.egranth.ac./assets/pdfjs/web/viewer.html?file=https","code":""},{"path":"/reference/shafi.tomato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"Shafi, Sameera; S..Mir, Nageena Nazir, Anjum Rashid. (2010). Optimum plot size tomato using S-PLUS R-software's soils Kashmir. Asian J. Soil Sci., 4, 311-314. http://researchjournal.co./upload/assignments/4_311-314.pdf","code":""},{"path":"/reference/shafi.tomato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(shafi.tomato.uniformity) dat <- shafi.tomato.uniformity libs(desplot) desplot(dat, yield ~ col*row, aspect=40/20, # true aspect main=\"shafi.tomato.uniformity\") } # }"},{"path":"/reference/shafii.rapeseed.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"Rapeseed yield multi-environment trial, 6 genotypes, 3 years, 14 loc, 3 rep","code":""},{"path":"/reference/shafii.rapeseed.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"data frame 648 observations following 5 variables. year year, numeric: 87, 88, 89 loc location, 14 levels rep rep, 3 levels gen genotype, 6 levels yield yield, kg/ha","code":""},{"path":"/reference/shafii.rapeseed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"data U.S. National Winter Rapeseed trials conducted 1986, 1987, 1988. Trial locations included Georgia (GGA, TGA), Idaho (ID), Kansas (KS), Mississippi (MS), Montana (MT), New York (NY), North Carolina (NC), Oregon (), South Carolina (SC), Tennessee (TN), Texas (TX), Virginia (VA), Washington (WA). SAS codes analysis can found https://webpages.uidaho.edu/cals-statprog/ammi/index.html Electronic version : https://www.uiweb.uidaho.edu/ag/statprog/ammi/yld.data Used permission Bill Price.","code":""},{"path":"/reference/shafii.rapeseed.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"Bahman Shafii William J Price, 1998. Analysis Genotype--Environment Interaction Using Additive Main Effects Multiplicative Interaction Model Stability Estimates. JABES, 3, 335–345. https://doi.org/10.2307/1400587","code":""},{"path":"/reference/shafii.rapeseed.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"Matthew Kramer (2018). Using Posterior Predictive Distribution Diagnostic Tool Mixed Models. Joint Statistical Meetings 2018, Biometrics Section. https://www.ars.usda.gov/ARSUserFiles/3122/KramerProceedingsJSM2018.pdf Reyhaneh Bijari Sigurdur Olafsson (2022). Accounting G×E interactions plant breeding: probabilistic approach https://doi.org/10.21203/rs.3.rs-2052233/v1","code":""},{"path":"/reference/shafii.rapeseed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"","code":"library(agridat) data(shafii.rapeseed) dat <- shafii.rapeseed dat$gen <- with(dat, reorder(gen, yield, mean)) dat$loc <- with(dat, reorder(loc, yield, mean)) dat$yield <- dat$yield/1000 dat <- transform(dat, rep=factor(rep), year=as.factor(as.character(year))) dat$locyr = paste(dat$loc, dat$year, sep=\"\") # The 'means' of reps datm <- aggregate(yield~gen+year+loc+locyr, data=dat, FUN=mean) datm <- datm[order(datm$gen),] datm$gen <- as.character(datm$gen) datm$gen <- factor(datm$gen, levels=c(\"Bienvenu\",\"Bridger\",\"Cascade\", \"Dwarf\",\"Glacier\",\"Jet\")) dat$locyr <- reorder(dat$locyr, dat$yield, mean) libs(lattice) # This picture tells most of the story dotplot(loc~yield|gen,group=year,data=dat, auto.key=list(columns=3), par.settings=list(superpose.symbol=list(pch = c('7','8','9'))), main=\"shafii.rapeseed\",ylab=\"Location\") # AMMI biplot. Remove gen and locyr effects. m1.lm <- lm(yield ~ gen + locyr, data=datm) datm$res <- resid(m1.lm) # Convert to a matrix libs(reshape2) dm <- melt(datm, measure.var='res', id.var=c('gen', 'locyr')) dmat <- acast(dm, gen~locyr) # AMMI biplot. Figure 1 of Shafii (1998) biplot(prcomp(dmat), main=\"shafii.rapeseed - AMMI biplot\")"},{"path":"/reference/sharma.met.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial — sharma.met","title":"Multi-environment trial — sharma.met","text":"Multi-environment trial","code":""},{"path":"/reference/sharma.met.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial — sharma.met","text":"","code":"data(\"sharma.met\")"},{"path":"/reference/sharma.met.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial — sharma.met","text":"data frame 126 observations following 5 variables. gen genotype loc location year year rep replicate yield yield","code":""},{"path":"/reference/sharma.met.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial — sharma.met","text":"Yield 7 genotypes, 3 years, 2 locations per year, 3 replicates. Might simulated data.","code":""},{"path":"/reference/sharma.met.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial — sharma.met","text":"Jawahar R. Sharma. 1988. Statistical Biometrical Techniques Plant Breeding. New Age International Publishers.","code":""},{"path":"/reference/sharma.met.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial — sharma.met","text":"Andrea Onofri, 2020. Fitting complex mixed models nlme: Example #5. https://www.statforbiology.com/2020/stat_met_jointreg/","code":""},{"path":"/reference/sharma.met.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial — sharma.met","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(sharma.met) dat <- sharma.met dat$env = paste0(dat$year, dat$loc) # Define environment # Calculate environment index as loc mean - overall mean --- libs(dplyr) dat <- group_by(dat, env) dat <- mutate(dat, eix = mean(yield)-mean(dat$yield)) libs(nlme) ## Finlay-Wilkinson model plot-level model --- m1fw <- lme(yield ~ gen/eix - 1, random = list(env = pdIdent(~ gen - 1), env = pdIdent(~ rep - 1)), data=dat) summary(m1fw)$tTable # Match Sharma table 9.6 VarCorr(m1fw) ## Eberhart-Russell plot-level model --- # Use pdDiag to get variance for each genotype m1er <- lme(yield ~ gen/eix - 1, random = list(env = pdDiag(~ gen - 1), env = pdIdent(~ rep - 1)), data=dat) summary(m1er)$tTable # same as FW VarCorr(m1er) # genotype variances differ # Calculate GxE cell means and environment index --- dat2 <- group_by(dat, gen, env) dat2 <- summarize(dat2, yield=mean(yield)) dat2 <- group_by(dat2, env) dat2 <- mutate(dat2, eix=mean(yield)-mean(dat2$yield)) ## Finlay-Wilkinson cell-means model --- m2fw <- lm(yield ~ gen/eix - 1, data=dat2) summary(m2fw) ## Eberhart-Russell cell-means model --- # Note, using varIdent(form=~1) is same as FW model m2er <- gls(yield ~ gen/eix - 1, weights=varIdent(form=~1|gen), data=dat) summary(m2er)$tTable sigma <- summary(m2er)$sigma sigma2i <- (c(1, coef(m2er$modelStruct$varStruct, uncons = FALSE)) * sigma)^2 names(sigma2i)[1] <- \"A\" sigma2i # shifted from m1er because variation from reps was swept out } # }"},{"path":"/reference/shaw.oats.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of oats in India — shaw.oats","title":"Multi-environment trial of oats in India — shaw.oats","text":"Multi-environment trial oats India, 13 genotypes, 3 year, 2 loc, 5 reps","code":""},{"path":"/reference/shaw.oats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of oats in India — shaw.oats","text":"","code":"data(\"shaw.oats\")"},{"path":"/reference/shaw.oats.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of oats in India — shaw.oats","text":"data frame 390 observations following 5 variables. env environment, 2 levels year year, 3 levels block block, 5 levels gen genotype variety, 13 levels yield yield oats, pounds per plot","code":""},{"path":"/reference/shaw.oats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of oats in India — shaw.oats","text":"oat trial India 11 hybrid oats compared 2 established high-yielding varieties, labeled L M. trail conducted 2 locations. size exact locations plots varied year year. Pusa, crop grown without irrigation. Karnal crop given 2-3 irrigations. Five blocks used, plot 1000 square feet. 1932, variety L high-yielding Pusa, low-yielding Karnal. Shaw used data illustrate ANOVA multi-environment trial.","code":""},{"path":"/reference/shaw.oats.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of oats in India — shaw.oats","text":"F.J.F. Shaw (1936). Handbook Statistics Use Plant Breeding Agricultural Problems. Imperial Council Agricultural Research, India. https://archive.org/details/HandbookStatistics1936/page/n12 P. 126","code":""},{"path":"/reference/shaw.oats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of oats in India — shaw.oats","text":"None","code":""},{"path":"/reference/shaw.oats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of oats in India — shaw.oats","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(shaw.oats) dat <- shaw.oats # sum(dat$yield) # 16309 matches Shaw p. 125 # sum( (dat$yield-mean(dat$yield)) ^2) # total SS matches Shaw p. 141 dat$year <- factor(dat$year) libs(lattice) dotplot(yield ~ gen|env, data=dat, groups=year, main=\"shaw.oats\", par.settings=list(superpose.symbol=list(pch=c('2','3','4'))), panel=function(x,y,...){ panel.dotplot(x,y,...) panel.superpose(x,y,..., panel.groups=function(x,y,col.line,...) { dd<-aggregate(y~x,data.frame(x,y),mean) panel.xyplot(x=dd$x, y=dd$y, col=col.line, type=\"l\") })}, auto.key=TRUE) # Shaw & Bose meticulously calculate the ANOVA table, p. 141 m1 <- aov(yield ~ year*env*block*gen - year:env:block:gen, dat) anova(m1) } # }"},{"path":"/reference/siao.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of cotton in China — siao.cotton.uniformity","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"Uniformity trials cotton China","code":""},{"path":"/reference/siao.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"","code":"data(\"siao.cotton.uniformity\")"},{"path":"/reference/siao.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"data frame 858 observations following 4 variables. row row ordinate col column ordinate yield yield, catties per mou crop crop trial number","code":""},{"path":"/reference/siao.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"1930 test blank test carried Provincial Cotton Station Yuyao, Chekiang, China. 200 rows, 24 feet long, 1 foot apart, planted single series. Seed sown drills, thinned 8 inches plant--plant, 30 plants one row. Appendix Table , Actual yield 200 rows 1930 test. 1931 test piece land, culture, fertilization previous year. Yields much lower due weather. Appendix Table II, Actual yield 200 rows 1931 test. 1931 test B 24 long ridges cotton. ridge 3 rows 1.2 feet apart (rows 3.6 feet wide). ridge cut 12 sections 16.66 feet long plants one foot apart. Siao notes yield border plots lower inner plots. correlation yield number plants plot .09. Appendix Table III, Actual yield 264 rows 1931 test (12 col, 22 row). 1932 test Another 200 rows 24 feet long planted cultural practice 1930 test. Weather unfavorable. Appendix Table IV, Actual yield 194 rows 1932 test. \"catty\" 1.33 pounds (Love & Reisner). \"mou\" 1/6 acre (Siao page 12). See also \"Cornell-Nanking Story\" Love & Reisner tangential information.","code":""},{"path":"/reference/siao.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"Siao, Fu. field plot technic study cotton. Found : Harry H. Love papers, 1907-1964. Box 3, folder 34, Cotton - Plot Technic Study 1930-1932. https://rmc.library.cornell.edu/EAD/htmldocs/RMA00890.html","code":""},{"path":"/reference/siao.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"Siao, Fu (1935). Uniformity trials cotton, J. Amer. Soc. Agron., 27, 974-979 https://doi.org/10.2134/agronj1935.00021962002700120004x","code":""},{"path":[]},{"path":"/reference/silva.cotton.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of cotton bolls for different levels of defoliation. — silva.cotton","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"Number cotton bolls, nodes, plant height, plant weight different levels defoliation.","code":""},{"path":"/reference/silva.cotton.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"","code":"data(\"silva.cotton\")"},{"path":"/reference/silva.cotton.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"data frame 125 observations following 4 variables. stage growth stage defoliation level defoliation, 0, 25, 50, 75, 100 plant plant number rep replicate reproductive number reproductive structures bolls number bolls height plant height nodes number nodes weight weight bolls","code":""},{"path":"/reference/silva.cotton.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"Data come greenhouse experiment cotton plants. Completely randomized design 5 replicates, 2 plants per pot. Artificial defoliation used levels 0, 25, 50, 75, 100 percent. Data collected per plant five growth stages: vegetative, flower-bud, blossom, fig cotton boll. primary response variable number bolls. data counts, underdispersed, correlated. Zeviana et al. used data compared Poisson, Gamma-count, quasi-Poisson GLMs. Bonat & Zeviani used data fit multivariate correlated generalized linear model. Used permission Walmes Zeviani. Electronic version : https://www.leg.ufpr.br/~walmes/data/desfolha_algodao.txt","code":""},{"path":"/reference/silva.cotton.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"Silva, Anderson Miguel da; Degrande, Paulo Eduardo; Suekane, Renato; Fernandes, Marcos Gino; & Zeviani, Walmes Marques. (2012). Impacto de diferentes niveis de desfolha artificial nos estadios fenologicos algodoeiro. Revista de Ciencias Agrarias, 35(1), 163-172. https://www.scielo.mec.pt/scielo.php?script=sci_arttext&pid=S0871-018X2012000100016&lng=pt&tlng=pt.","code":""},{"path":"/reference/silva.cotton.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"Zeviani, W. M., Ribeiro, P. J., Bonat, W. H., Shimakura, S. E., Muniz, J. . (2014). Gamma-count distribution analysis experimental underdispersed data. Journal Applied Statistics, 41(12), 1-11. https://doi.org/10.1080/02664763.2014.922168 Online supplement: https://leg.ufpr.br/doku.php/publications:papercompanions:zeviani-jas2014 Regression Models Count Data. https://cursos.leg.ufpr.br/rmcd/applications.html#cotton-bolls Wagner Hugo Bonat & Walmes Marques Zeviani (2017). Multivariate Covariance Generalized Linear Models Analysis Experimental Data. Short-cource : 62nd RBras 17th SEAGRO meeting/ https://github.com/leg-ufpr/mcglm4aed","code":""},{"path":"/reference/silva.cotton.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(silva.cotton) dat <- silva.cotton dat$stage <- ordered(dat$stage, levels=c(\"vegetative\",\"flowerbud\",\"blossom\",\"boll\",\"bollopen\")) # make stage a numeric factors dat <- transform(dat, stage = factor(stage, levels = unique(stage), labels = 1:nlevels(stage))) # sum data across plants, 1 pot = 2 plants dat <- aggregate(cbind(weight,height,bolls,nodes) ~ stage+defoliation+rep, data=dat, FUN=sum) # all traits, plant-level data libs(latticeExtra) foo <- xyplot(weight + height + bolls + nodes ~ defoliation | stage, data = dat, outer=TRUE, xlab=\"Defoliation percent\", ylab=\"\", main=\"silva.cotton\", as.table = TRUE, jitter.x = TRUE, type = c(\"p\", \"smooth\"), scales = list(y = \"free\")) combineLimits(useOuterStrips(foo)) if(0){ # poisson glm with quadratic effect for defoliation m0 <- glm(bolls ~ 1, data=dat, family=poisson) m1 <- glm(bolls ~ defoliation+I(defoliation^2), data=dat, family=poisson) m2 <- glm(bolls ~ stage:defoliation+I(defoliation^2), data=dat, family=poisson) m3 <- glm(bolls ~ stage:(defoliation+I(defoliation^2)), data=dat, family=poisson) par(mfrow=c(2,2)); plot(m3); layout(1) anova(m0, m1, m2, m3, test=\"Chisq\") # predicted values preddat <- expand.grid(stage=levels(dat$stage), defoliation=seq(0,100,length=20)) preddat$pred <- predict(m3, newdata=preddat, type=\"response\") # Zeviani figure 3 libs(latticeExtra) xyplot(bolls ~ jitter(defoliation)|stage, dat, as.table=TRUE, main=\"silva.cotton - observed and model predictions\", xlab=\"Defoliation percent\", ylab=\"Number of bolls\") + xyplot(pred ~ defoliation|stage, data=preddat, as.table=TRUE, type='smooth', col=\"black\", lwd=2) } if(0){ # ----- mcglm ----- dat <- transform(dat, deffac=factor(defoliation)) libs(car) vars <- c(\"weight\",\"height\",\"bolls\",\"nodes\") splom(~dat[vars], data=dat, groups = stage, auto.key = list(title = \"Growth stage\", cex.title = 1, columns = 3), par.settings = list(superpose.symbol = list(pch = 4)), as.matrix = TRUE) splom(~dat[vars], data=dat, groups = defoliation, auto.key = list(title = \"Artificial defoliation\", cex.title = 1, columns = 3), as.matrix = TRUE) # multivariate linear model. m1 <- lm(cbind(weight, height, bolls, nodes) ~ stage * deffac, data = dat) anova(m1) summary.aov(m1) r0 <- residuals(m1) # Checking the models assumptions on the residuals. car::scatterplotMatrix(r0, gap = 0, smooth = FALSE, reg.line = FALSE, ellipse = TRUE, diagonal = \"qqplot\") } } # }"},{"path":"/reference/sinclair.clover.html","id":null,"dir":"Reference","previous_headings":"","what":"Clover yields in a factorial fertilizer experiment — sinclair.clover","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"Clover yields factorial fertilizer experiment","code":""},{"path":"/reference/sinclair.clover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"","code":"data(\"sinclair.clover\")"},{"path":"/reference/sinclair.clover.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"data frame 25 observations following 3 variables. yield yield t/ha P phosphorous fertilizer kg/ha S sulfur fertilizer kg/ha","code":""},{"path":"/reference/sinclair.clover.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"phosphorous sulfur factorial experiment Dipton Southland, New Zealand. 3 reps. Plots harvested repeatedly Dec 1992 Mar 1994. Yields reported total dry matter across cuttings.","code":""},{"path":"/reference/sinclair.clover.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"Sinclair AG, Risk WH, Smith LC, Morrison JD & Dodds KG (1994) Sulphur phosphorus balanced pasture nutrition. Proc N Z Grass Assoc, 56, 13-16.","code":""},{"path":"/reference/sinclair.clover.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"Dodds, KG Sinclair, AG Morrison, JD. (1995). bivariate response surface growth data. Fertilizer research, 45, 117-122. https://doi.org/10.1007/BF00790661","code":""},{"path":"/reference/sinclair.clover.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(sinclair.clover) dat <- sinclair.clover libs(lattice) xyplot(yield~P|factor(S), dat, layout=c(5,1), main=\"sinclair.clover - Yield by sulfur levels\", xlab=\"Phosphorous\") # Dodds fits a two-dimensional Mitscherlich-like model: # z = a*(1+b*{(s+t*x)/(x+1)}^y) * (1+d*{(th+r*y)/(y+1)}^x) # First, re-scale the problem to a more stable part of the parameter space dat <- transform(dat, x=P/10, y=S/10) # Response value for (x=0, y=maximal), (x=maximal, y=0), (x=max, y=max) z0m <- 5 zm0 <- 5 zmm <- 10.5 # The parameters are somewhat sensitive to starting values. # I had to try a couple different initial values to match the paper by Dodds m1 <- nls(yield ~ alpha*(1 + beta*{(sig+tau*x)/(x+1)}^y) * (1 + del*{(th+rho*y)/(y+1)}^x), data=dat, # trace=TRUE, start=list(alpha=zmm, beta=(zm0/zmm)-1, del=(z0m/zmm)-1, sig=.51, tau=.6, th=.5, rho=.7)) summary(m1) # Match Dodds Table 2 ## Parameters: ## Estimate Std. Error t value Pr(>|t|) ## alpha 11.15148 0.66484 16.773 1.96e-12 *** ## beta -0.61223 0.03759 -16.286 3.23e-12 *** ## del -0.48781 0.04046 -12.057 4.68e-10 *** ## sig 0.26783 0.16985 1.577 0.13224 ## tau 0.68030 0.06333 10.741 2.94e-09 *** ## th 0.59656 0.16716 3.569 0.00219 ** ## rho 0.83273 0.06204 13.421 8.16e-11 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Residual standard error: 0.5298 on 18 degrees of freedom pred <- expand.grid(x=0:17, y=0:9) pred$z <- predict(m1, pred) # 3D plot of data with fitted surface. Matches Dodds figure 2. libs(rgl) bg3d(color = \"white\") clear3d() spheres3d(dat$x, dat$y, dat$yield, radius=.2, col = rep(\"navy\", nrow(dat))) surface3d(seq(0, 17, by = 1), seq(0, 9, by = 1), pred$z, alpha=0.9, col=\"wheat\", front=\"fill\", back=\"fill\") axes3d() title3d(\"sinclair.clover - yield\",\"\", xlab=\"Phosphorous/10\", ylab=\"Sulfur/10\", zlab=\"\", line=3, cex=1.5) view3d(userMatrix=matrix(c(.7,.2,-.7,0, -.7,.2,-.6,0, 0,.9,.3,0, 0,0,0,1),ncol=4)) # snapshot3d(file, \"png\") close3d() } # }"},{"path":"/reference/smith.beans.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"Uniformity trials beans California, 1954-1955, 2 species 2 years","code":""},{"path":"/reference/smith.beans.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"","code":"data(\"smith.beans.uniformity\")"},{"path":"/reference/smith.beans.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"data frame 912 observations following 4 variables. expt experiment row row col column yield yield, kg","code":""},{"path":"/reference/smith.beans.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"Trials conducted California. 1955 plots twice wide twice long 1954. Red Kidney bush variety bean, Standard Pink viny variety. Smith randomly assigned ,B,C,D plots used 'varieties' calculating ANOVA tables. Plots combined side--side end--end make larger plots. Decreasing LSDs observed increases plot sizes. LSDs seldom 200, considered noticeable difference farmers. four datasets: —– 1954 Experiment 1: Red Kidney. 1954 Experiment 2: Standard Pink Field width: 18 plots * 30 inches = 45 ft Field length: 12 plots * 15 ft = 180 ft —– 1955 Experiment 3: Red Kidney. 1955 Experiment 4: Standard Pink Field width: 16 plots * 2 rows * 30 = 80 ft Field length: 15 plots * 30 ft = 450 ft","code":""},{"path":"/reference/smith.beans.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"Francis L. Smith, 1958. Effects plot size, plot shape, number replications efficacy bean yield trials. Hilgardia, 28, 43-63. https://doi.org/10.3733/hilg.v28n02p043","code":""},{"path":"/reference/smith.beans.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"None.","code":""},{"path":"/reference/smith.beans.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(smith.beans.uniformity) dat1 <- subset(smith.beans.uniformity, expt==\"E1\") dat2 <- subset(smith.beans.uniformity, expt==\"E2\") dat3 <- subset(smith.beans.uniformity, expt==\"E3\") dat4 <- subset(smith.beans.uniformity, expt==\"E4\") cv <- function(x) { sd(x)/mean(x) } cv(dat1$yield) cv(dat2$yield) # Does not match Smith. Checked all values by hand. cv(dat3$yield) cv(dat4$yield) libs(\"desplot\") desplot(dat1, yield ~ col*row, aspect=180/45, flip=TRUE, # true aspect main=\"smith.beans.uniformity, expt 1 (true aspect)\") desplot(dat2, yield ~ col*row, aspect=180/45, flip=TRUE, # true aspect main=\"smith.beans.uniformity, expt 2 (true aspect)\") desplot(dat3, yield ~ col*row, aspect=450/80, flip=TRUE, # true aspect main=\"smith.beans.uniformity, expt 3 (true aspect)\") desplot(dat4, yield ~ col*row, aspect=450/80, flip=TRUE, # true aspect main=\"smith.beans.uniformity expt 4, (true aspect)\") } # }"},{"path":"/reference/smith.corn.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","title":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","text":"Uniformity trial corn, 3 years ground, 1895-1897, Illinois.","code":""},{"path":"/reference/smith.corn.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","text":"data frame 360 observations following 5 variables. row row col column plot plot number, consistent across years year year. Last two digits 1895, 1896, 1897 yield yield, bushels / acre","code":""},{"path":"/reference/smith.corn.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","text":"Data come Illinois Experiment Station. data values Smith (1910) field map Harris (1920). plot 1/10 acre, dimensions given. Note 1/10 acre also area square 1 chain (66 feet) side. following text abridged Smith (1910). much variability may reasonably expect land apparently uniform? data among records soil plots Illinois Experiment station furnish interesting material study connection. field lain sixteen years pasture broken 1895 laid plots subsequently used soil experiments. land slightly rolling otherwise quite uniform appearance. series considered connection 120 one-tenth acre plots. plots planted corn three consecutive years without soil treatment, records offer rather exceptional opportunity study kind. study data reveals striking variations. noticed first place tremendous difference production different years. first year, 1895, extremely unfavorable one corn yields exceptionally low. weather records show season unusually dry, also cool early part. following year exceptionally favorable corn season, yields run unusually high. third year also good one, yields perhaps somewhat normal locality. observed certain plots appear abnormal. Thus plots 117, 118, 119, 120 give abnormally high yield first season abnormally low one two following years. accounted topography land. plots lie low spot favorable dry year 1895, unfavorable 1896 1897. reason four plots rejected consideration study, also plots 616, 617, 618, 619, 620. leaves 111 plots whose variations apparently unaccounted furnish data following results taken. noticeable variability measured standard deviation becomes less succeeding year. suggests question whether continued cropping might tend induce uniformity. records plots continued corn three years longer, however, support conclusion. seems reasonable expect greater variability seasons unfavorable production, 1895, much may depend upon certain critical factors production coming play suggestion may explanation high standard deviation first year. Results extending longer series years extremely interesting connection. consider total range variation single year, find differences follows: Plots lying adjoining shown following maximum variations: 18 bushels 1895; 11 bushels 1896; 8 bushels 1897. results give us conception unaccountable plot variations deal field tests. possibility remains still closer study might detect abnormal factors play account variations certain cases, study certainly suggests importance conservatism arriving conclusions based upon plot tests. particular value writer derived study strengthening conviction dependence placed upon variety tests field experiments records involving average liberal numbers extending long periods time.","code":""},{"path":"/reference/smith.corn.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","text":"Smith, L.H. 1910. Plot arrangement variety experiments corn. Agronomy Journal, 1, 84–89. Table 1. https://books.google.com/books?id=mQT0AAAAMAAJ&pg=PA84 Harris, J.. 1920. Practical universality field heterogeneity factor influencing plot yields. Journal Agricultural Research, 19, 279–314. Page 296-297. https://books.google.com/books?id=jyEXAAAAYAAJ&pg=PA279","code":""},{"path":"/reference/smith.corn.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(smith.corn.uniformity) dat <- smith.corn.uniformity dat = transform(dat, year=factor(year)) libs(desplot) desplot(dat, yield~col*row|year, layout=c(2,2), aspect=1, main=\"smith.corn.uniformity: yield across years 1895-1987\") ## # Outliers are obvious ## libs(lattice) ## xyplot(yield~row|factor(col), dat, groups=year, ## auto.key=list(columns=3), main=\"smith.corn.uniformity\") libs(rgl) # A few odd pairs of outliers in column 6 # black/gray dots very close to each other plot3d(dat$col, dat$row, dat$yield, col=dat$year, xlab=\"col\",ylab=\"row\",zlab=\"yield\") close3d() } # }"},{"path":"/reference/smith.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — smith.wheat.uniformity","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"Uniformity trial wheat Australia.","code":""},{"path":"/reference/smith.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"","code":"data(\"smith.wheat.uniformity\")"},{"path":"/reference/smith.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"data frame 1080 observations following 4 variables. row row ordinate col column ordinate yield grain yield per plot, grams ears number ears per plot","code":""},{"path":"/reference/smith.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"Experiment grown Canberra, Australia, 1934. data yield grain per plot number \"ears\". plot 1 foot long 0.5 foot. Field width: 36 columns x 1 foot = 36 feet. Field length: 30 rows x 0.5 foot = 15 feet. Notes: 2 copies yield data Rothamsted library. Let Copy one dark, hand-drawn grid lines, Copy B one without hand-drawn grid lines. copies hand-written, likely copied original data. row 4 (top) column 34: Copy yield 164 Copy B yield 154. value 154 appears correct, since leads row column totals shown Copy Copy B. row 20, column 28, Copy Copy B show yield 283. appears copy error. replaced value 283 203, row column totals match values Copy Copy B, also variance data matches value Smith (1938), 2201 page 7. documents Rothamsted claim grain yield shown \"Yields grain decigrams per foot length\". However, believe actual unit weight grams. Note yield values high-yielding parts field close 200 g per plot, plot 0.5 sq feet. Multiply 8 get 1600 g per 4 sq feet. Smith's paper, fertility contour map figure 1 shows high-yielding part field yield close \"16 d.kg per 4 sq ft\", 16 d.kg = 16 kg = 1600 g. data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/smith.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 7.","code":""},{"path":"/reference/smith.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"H. Fairfield Smith (1938). empirical law describing heterogeneity yields agricultural crops. Journal Agricultural Science, volume 28, Issue 1, January 1938, pp. 1 - 23. https://doi.org/10.1017/S0021859600050516 Peter McCullagh & David Clifford. (2006). Evidence conformal invariance crop yields. Proc. R. Soc. (2006) 462, 2119–2143 http://www.stat.uchicago.edu/~pmcc/reml/ https://doi.org/:10.1098/rspa.2006.1667","code":""},{"path":"/reference/smith.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(smith.wheat.uniformity) dat <- smith.wheat.uniformity libs(desplot) desplot(dat, yield ~ col*row, main=\"smith.wheat.uniformity\", flip=TRUE, aspect=15/30) xyplot(yield ~ ears, data=dat) libs(agricolae,reshape2) # Compare to Smith Fig. 2 m1 <- index.smith(acast(dat, row~col, value.var='yield'), main=\"smith.wheat.uniformity\", col=\"red\")$uni m1 # Compare to Smith table I } # }"},{"path":"/reference/snedecor.asparagus.html","id":null,"dir":"Reference","previous_headings":"","what":"Asparagus yields for different cutting treatments — snedecor.asparagus","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"Asparagus yields different cutting treatments, 4 years.","code":""},{"path":"/reference/snedecor.asparagus.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"data frame 64 observations following 4 variables. block block factor, 4 levels year year, numeric trt treatment factor final cutting date yield yield, ounces","code":""},{"path":"/reference/snedecor.asparagus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"Planted 1927. Cutting began 1929. Yield weight asparagus cuttings Jun 1 plot. plots received continued cuttings Jun 15, Jul 1, Jul 15. past, repeated-measurement experiments like sometimes analyzed split-plot experiment. violates indpendence assumptions.","code":""},{"path":"/reference/snedecor.asparagus.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"Snedecor Cochran, 1989. Statistical Methods.","code":""},{"path":"/reference/snedecor.asparagus.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"Mick O'Neill, 2010. Guide Linear Mixed Models Experimental Design Context. Statistical Advisory & Training Service Pty Ltd.","code":""},{"path":"/reference/snedecor.asparagus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(snedecor.asparagus) dat <- snedecor.asparagus dat <- transform(dat, year=factor(year)) dat$trt <- factor(dat$trt, levels=c(\"Jun-01\", \"Jun-15\", \"Jul-01\", \"Jul-15\")) # Continued cutting reduces plant vigor and yield libs(lattice) dotplot(yield ~ trt|year, data=dat, xlab=\"Cutting treatment\", main=\"snedecor.asparagus\") # Split-plot if(0){ libs(lme4) m1 <- lmer(yield ~ trt + year + trt:year + (1|block) + (1|block:trt), data=dat) } # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Split-plot with asreml m2 <- asreml(yield ~ trt + year + trt:year, data=dat, random = ~ block + block:trt) lucid::vc(m2) ## effect component std.error z.ratio bound ## block 354.3 405 0.87 P 0.1 ## block:trt 462.8 256.9 1.8 P 0 ## units!R 404.7 82.6 4.9 P 0 ## # Antedependence with asreml. See O'Neill (2010). dat <- dat[order(dat$block, dat$trt), ] m3 <- asreml(yield ~ year * trt, data=dat, random = ~ block, residual = ~ block:trt:ante(year,1), max=50) m3 <- update(m3) m3 <- update(m3) ## # Extract the covariance matrix for years and convert to correlation ## covmat <- diag(4) ## covmat[upper.tri(covmat,diag=TRUE)] <- m3$R.param$`block:trt:year`$year$initial ## covmat[lower.tri(covmat)] <- t(covmat)[lower.tri(covmat)] ## round(cov2cor(covmat),2) # correlation among the 4 years ## # [,1] [,2] [,3] [,4] ## # [1,] 1.00 0.45 0.39 0.31 ## # [2,] 0.45 1.00 0.86 0.69 ## # [3,] 0.39 0.86 1.00 0.80 ## # [4,] 0.31 0.69 0.80 1.00 ## # We can also build the covariance Sigma by hand from the estimated ## # variance components via: Sigma^-1 = U D^-1 U' ## vv <- vc(m3) ## print(vv) ## ## effect component std.error z.ratio constr ## ## block!block.var 86.56 156.9 0.55 pos ## ## R!variance 1 NA NA fix ## ## R!year.1930:1930 0.00233 0.00106 2.2 uncon ## ## R!year.1931:1930 -0.7169 0.4528 -1.6 uncon ## ## R!year.1931:1931 0.00116 0.00048 2.4 uncon ## ## R!year.1932:1931 -1.139 0.1962 -5.8 uncon ## ## R!year.1932:1932 0.00208 0.00085 2.4 uncon ## ## R!year.1933:1932 -0.6782 0.1555 -4.4 uncon ## ## R!year.1933:1933 0.00201 0.00083 2.4 uncon ## U <- diag(4) ## U[1,2] <- vv[4,2] ; U[2,3] <- vv[6,2] ; U[3,4] <- vv[8,2] ## Dinv <- diag(c(vv[3,2], vv[5,2], vv[7,2], vv[9,2])) ## # solve(U ## solve(crossprod(t(U), tcrossprod(Dinv, U)) ) ## ## [,1] [,2] [,3] [,4] ## ## [1,] 428.4310 307.1478 349.8152 237.2453 ## ## [2,] 307.1478 1083.9717 1234.5516 837.2751 ## ## [3,] 349.8152 1234.5516 1886.5150 1279.4378 ## ## [4,] 237.2453 837.2751 1279.4378 1364.8446 } } # }"},{"path":"/reference/snijders.fusarium.html","id":null,"dir":"Reference","previous_headings":"","what":"Fusarium infection in wheat varieties — snijders.fusarium","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"Infection wheat different strains Fusarium.","code":""},{"path":"/reference/snijders.fusarium.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"data frame 204 observations following 4 variables. gen wheat genotype strain fusarium strain year year y percent infected","code":""},{"path":"/reference/snijders.fusarium.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"data percent leaf area affected Fusarium head blight, averaged 4-5 reps, 17 winter wheat genotypes. Van Eeuwijk fit generalized ammi-2 model data. generalized model sense link function used, non-linear AMMI model main effects variety year-strain, additional multiplicative effects interactions. Note, value strain F348 1988, gen SVP75059-32 28.3 (shown VanEeuwijk 1995) 38.3 (shown Snijders 1991). Used permission Fred van Eeuwijk.","code":""},{"path":"/reference/snijders.fusarium.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"Snijders, CHA Van Eeuwijk, FA. 1991. Genotype x strain interactions resistance Fusarium head blight caused Fusarium culmorum winter wheat. Theoretical Applied Genetics, 81, 239–244. Table 1. https://doi.org/10.1007/BF00215729","code":""},{"path":"/reference/snijders.fusarium.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"Fred van Eeuwijk. 1995. Multiplicative interaction generalized linear models. Biometrics, 51, 1017-1032. https://doi.org/10.2307/2533001","code":""},{"path":"/reference/snijders.fusarium.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(snijders.fusarium) dat <- snijders.fusarium aggregate(y ~ strain + year, dat, FUN=mean) # Match means in Snijders table 1 dat <- transform(dat, y=y/100, year=factor(year), yrstr=factor(paste0(year,\"-\",strain))) # Strain F329 shows little variation across years. F39 shows a lot. libs(lattice) dotplot(gen~y|strain, data=dat, group=year, main=\"snijders.fusarium : infection by strain\", xlab=\"Fraction infected\", ylab=\"variety\", auto.key=list(columns=3)) # Logit transform dat <- transform(dat, logit=log(y/(1-y))) m1 <- aov(logit ~ yrstr + gen, data=dat) # Match SS in VanEeuwijk table 4 anova(m1) # Match SS in VanEeuwijk table 4 m2 <- aov(logit ~ year*strain + gen + gen:year + gen:strain, data=dat) anova(m2) # Match to VanEeuwijk table 5 # GLM on untransformed data using logit link, variance mu^2(1-mu)^2 libs(gnm) # for 'wedderburn' family m2 <- glm(y ~ yrstr + gen, data=dat, family=\"wedderburn\") anova(m2) # Main effects match VanEeuwijk table 6 # Generalized AMMI-2 model. Matches VanEeuwijk table 6 bilin2 <- gnm(y ~ yrstr + gen + instances(Mult(yrstr, gen), 2), data=dat, family = wedderburn) # plot(bilin2,1) # Resid vs fitted plot matches VanEeuwijk figure 3c ## anova(bilin2) ## Df Deviance Resid. Df Resid. Dev ## NULL 203 369.44 ## yrstr 11 150.847 192 218.60 ## gen 16 145.266 176 73.33 ## Mult(yrstr, gen, inst = 1) 26 26.128 150 47.20 ## Mult(yrstr, gen, inst = 2) 24 19.485 126 27.72 # Manually extract coordinates for biplot cof <- coef(bilin2) y1 <- cof[29:40] g1 <- cof[41:57] y2 <- cof[58:69] g2 <- cof[70:86] g12 <- cbind(g1,g2) rownames(g12) <- substring(rownames(g12), 29) y12 <- cbind(y1,y2) rownames(y12) <- substring(rownames(y12), 31) g12[,1] <- -1 * g12[,1] y12[,1] <- -1 * y12[,1] # GAMMI biplot. Inner-products of points projected onto # arrows match VanEeuwijk figure 4. Slight rotation of graph is ignorable. biplot(y12, g12, cex=.75, main=\"snijders.fusarium\") # Arrows to genotypes. } # }"},{"path":"/reference/stephens.sorghum.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","title":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","text":"Uniformity trial sorghum silage Chillicothe, Texas, 1915.","code":""},{"path":"/reference/stephens.sorghum.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","text":"data frame 2000 observations following 3 variables. row row col column / rod yield yield, ounces","code":""},{"path":"/reference/stephens.sorghum.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","text":"Grown near Chillicothe, TX 1915. Rows 40 inches apart. row harvested 1-rod (16.5 ft) lengths. East side higher yielding west side. Yields weight (ounces) green forage rod-row. Total area harvested: 100*40/12 = 333.33 feet 20*16.5=330 feet. Field width: 20 plots * 16.5 ft (1 rod) = 330 feet. Field length: 100 plots * 40 = 333 feet","code":""},{"path":"/reference/stephens.sorghum.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","text":"Stephens, Joseph C. 1928. Experimental methods probable error field experiments sorghum. Journal Agricultural Research, 37, 629–646. https://naldc.nal.usda.gov/catalog/IND43967516","code":""},{"path":"/reference/stephens.sorghum.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(stephens.sorghum.uniformity) dat <- stephens.sorghum.uniformity dat <- subset(dat, row>2 & row<99) # omit outer two rows # mean(dat$yield) # 180.27 # range(dat$yield) # 75,302 matches Stephens # densityplot(~dat$yield) # Stephens figure 3 # Aggregate 4 side-by-side rows. d4 <- dat d4$row2 <- ceiling((d4$row-2)/4) d4 <- aggregate(yield ~ row2+col, data=d4, FUN=sum) d4$row2 <- 25-d4$row2 # flip horizontally libs(desplot) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) desplot(d4, yield ~ row2*col, aspect=333/330, flip=TRUE, # true aspect main=\"stephens.sorghum.uniformity\", col.regions=grays(3), at=c(500,680,780,1000)) # Similar to Stephens Figure 7. North at top. East at right. } # }"},{"path":"/reference/steptoe.morex.pheno.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"Phenotypic genotypic data barley population Steptoe x Morex. 150 doubled haploid crosses, evaluated 223 markers. Phenotypic data wascollected 8 traits 16 environments.","code":""},{"path":"/reference/steptoe.morex.pheno.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"","code":"data(\"steptoe.morex.pheno\")"},{"path":"/reference/steptoe.morex.pheno.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"steptoe.morex.pheno data.frame phenotypic data 2432 observations 10 variables: gen genotype factor parents Steptoe Morex, 150 crosses SM1, SM2, ..., SM200. 200 numbers used. env environment, 16 levels amylase alpha amylase (20 Deg Units) diapow diastatic power (degree units) hddate heading date (julian days) lodging lodging (percent) malt malt extract (percent) height plant height (centimeters) protein grain protein (percent) yield grain yield (Mt/Ha) steptoe.morex.geno cross object qtl package genotypic data 223 markers 150 crosses Steptoe x Morex.","code":""},{"path":"/reference/steptoe.morex.pheno.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"described Hayes et al (1993), population 150 barley doubled haploid (DH) lines developed Oregon State University Barley Breeding Program North American Barley Genome Mapping Project. parentage population Steptoe / Morex. Steptoe dominant feed barley northwestern U.S. Morex spring U.S. malting quality standard. Seed single head parent used create F1, set 150 lines developed. Phenotypic values parents Steptoe Morex : https://wheat.pw.usda.gov/ggpages/SxM/parental_values.html 16 locations, average across locations column 17. traits collected every location. location, 150 lines included block 1, random subset 50 lines used block 2. traits : Alpha Amylase (20 Deg Units), Diastatic Power (Deg Units), Heading Date (Julian Days), Lodging (percent), Malt Extract (percent), Grain Protein (percent), Grain Yield (Mt/Ha). Phenotypic values 150 lines F1 population : https://wheat.pw.usda.gov/ggpages/SxM/phenotypes.html trait different file, block numbers represents one location. 223-markers Steptoe/Morex base map : https://wheat.pw.usda.gov/ggpages/SxM/smbasev2.map data markers 150 lines https://wheat.pw.usda.gov/ggpages/SxM/smbasev2.mrk hand-assembled (e.g. marker distances cumulated marker positions) .csv file imported R using qtl::read.cross. class manually changed c('bc','cross') c('dh','cross'). marker data coded = Steptoe, B = Morex, - = missing. pedigrees 150 lines found : https://wheat.pw.usda.gov/ggpages/SxM/pedigrees.html Data provided United States Department Agriculture.","code":""},{"path":"/reference/steptoe.morex.pheno.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"Steptoe x Morex Barley Mapping Population. Map: Version 2, August 1, 1995 https://wheat.pw.usda.gov/ggpages/SxM. Accessed Jan 2015.","code":""},{"path":"/reference/steptoe.morex.pheno.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"P.M. Hayes, B.H. Liu, S.J. Knapp, F. Chen, B. Jones, T. Blake, J. Franckowiak, D. Rasmusson, M. Sorrells, S.E. Ullrich, others. 1993. Quantitative trait locus effects environmental interaction sample North American barley germplasm. Theoretical Applied Genetics, 87, 392–401. https://doi.org/10.1007/BF01184929 Ignacio Romagosa, Steven E. Ullrich, Feng Han, Patrick M. Hayes. 1996. Use additive main effects multiplicative interaction model QTL mapping adaptation barley. Theor Appl Genet, 93, 30-37. https://doi.org/10.1007/BF00225723 Piepho, Hans-Peter. 2000. mixed-model approach mapping quantitative trait loci barley basis multiple environment data. Genetics, 156, 2043-2050. M. Malosetti, J. Voltas, . Romagosa, S.E. Ullrich, F.. van Eeuwijk. (2004). Mixed models including environmental covariables studying QTL environment interaction. Euphytica, 137, 139-145. https://doi.org/10.1023/B:EUPH.0000040511.4638","code":""},{"path":"/reference/steptoe.morex.pheno.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(steptoe.morex.pheno) dat <- steptoe.morex.pheno # Visualize GxE of traits libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(amylase~env*gen, data=dat, col.regions=redblue, scales=list(x=list(rot=90)), main=\"amylase\") ## levelplot(diapow~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"diapow\") ## levelplot(hddate~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"hddate\") ## levelplot(lodging~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"lodging\") ## levelplot(malt~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"malt\") ## levelplot(height~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"height\") ## levelplot(protein~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"protein\") ## levelplot(yield~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"yield\") # Calculate avg yield for each loc as in Romagosa 1996, table 3 # t(t(round(tapply(dat$yield, dat$env, FUN=mean),2))) # SKo92,SKg92 means in table 3 are switched. Who is right, him or me? # Draw marker map libs(qtl) data(steptoe.morex.geno) datg <- steptoe.morex.geno qtl::plot.map(datg, main=\"steptoe.morex.geno\") qtl::plotMissing(datg) # This is a very rudimentary example. # The 'wgaim' function works interactively, but fails during # devtools::check(). if(0 & require(\"asreml\", quietly=TRUE)){ libs(asreml) # Fit a simple multi-environment mixed model m1 <- asreml(yield ~ env, data=dat, random=~gen) libs(wgaim) wgaim::linkMap(datg) # Create an interval object for wgaim dati <- wgaim::cross2int(datg, id=\"gen\") # Whole genome qtl q1 <- wgaim::wgaim(m1, intervalObj=dati, merge.by=\"gen\", na.action=na.method(x=\"include\")) #wgaim::linkMap(q1, dati) # Visualize wgaim::outStat(q1, dati) # outlier statistic summary(q1, dati) # Table of important intervals # Chrom Left Marker dist(cM) Right Marker dist(cM) Size Pvalue # 3 ABG399 52.6 BCD828 56.1 0.254 0.000 45.0 # 5 MWG912 148 ABG387A 151.2 0.092 0.001 5.9 # 6 ABC169B 64.8 CDO497 67.5 -0.089 0.001 5.6 } } # }"},{"path":"/reference/stickler.sorghum.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sorghum — stickler.sorghum.uniformity","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"Uniformity trial sorghum Manhattan, Kansas, 1958-1959.","code":""},{"path":"/reference/stickler.sorghum.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"","code":"data(\"stickler.sorghum.uniformity\")"},{"path":"/reference/stickler.sorghum.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"data frame 1600 observations following 4 variables. expt experiment row row col col yield yield, pounds","code":""},{"path":"/reference/stickler.sorghum.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"Four sorghum experiments Agronomy Farm Manhattan, Kansas. Experiments E1,E2 grown 1958. Expts E3,E5 grown 1959. Experiment E1. Field width = 20 units * 14 inches = 23.3 ft. Field length = 20 units * 10 feet = 200 feet. Experiment E2-E3. Field width = 20 units * 40 inches = 73 feet Field length = 20 units * 5 ft = 100 feet.","code":""},{"path":"/reference/stickler.sorghum.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"F. C. Stickler (1960). Estimates Optimum Plot Size Grain Sorghum Uniformity Trial Data. Technical bulletin, Kansas Agricultural Experiment Station, page 17-20. https://babel.hathitrust.org/cgi/pt?id=uiug.30112019584322&view=1up&seq=21","code":""},{"path":"/reference/stickler.sorghum.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"None.","code":""},{"path":"/reference/stickler.sorghum.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(stickler.sorghum.uniformity) dat <- stickler.sorghum.uniformity dat1 <- subset(dat, expt==\"E1\") dat2 <- subset(dat, expt!=\"E1\") libs(desplot) desplot(dat, yield ~ col*row|expt, subset=expt==\"E1\", #cex=1,text=yield, shorten=\"none\", xlab=\"row\",ylab=\"range\", flip=TRUE, tick=TRUE, aspect=(20*10)/(20*14/12), # true aspect main=\"stickler.sorghum.uniformity: expt E1\") desplot(dat, yield ~ col*row|expt, subset=expt!=\"E1\", xlab=\"row\",ylab=\"range\", flip=TRUE, tick=TRUE, aspect=(20*5)/(20*44/12), # true aspect main=\"stickler.sorghum.uniformity: expt E2,E3,E4\") # Stickler, p. 10-11 has # E1 E2 E3 E4 # 34.81 11.53 11.97 14.10 cv <- function(x) 100*sd(x)/mean(x) tapply(dat$yield, dat$expt, cv) # 35.74653 11.55062 11.97011 14.11389 } # }"},{"path":"/reference/stirret.borers.html","id":null,"dir":"Reference","previous_headings":"","what":"Corn borer control by application of fungal spores. — stirret.borers","title":"Corn borer control by application of fungal spores. — stirret.borers","text":"Corn borer control application fungal spores.","code":""},{"path":"/reference/stirret.borers.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Corn borer control by application of fungal spores. — stirret.borers","text":"data frame 60 observations following 4 variables. block block, 15 levels trt treatment, 4 levels count1 count borers August 18 count2 count borers October 19","code":""},{"path":"/reference/stirret.borers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Corn borer control by application of fungal spores. — stirret.borers","text":"Experiment conducted 1935, Ottawa. European corn borer infestation established application egg masses plants. Treatments applied July 8 July 19 two levels, 0 40 grams per acre. number borers per plot counted Aug 18 Oct 19.","code":""},{"path":"/reference/stirret.borers.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Corn borer control by application of fungal spores. — stirret.borers","text":"Stirrett, George M Beall, Geoffrey Timonin, M. (1937). field experiment control European corn borer, Pyrausta nubilalis Hubn, Beauveria bassiana Vuill. Sci. Agric., 17, 587–591. Table 2.","code":""},{"path":"/reference/stirret.borers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Corn borer control by application of fungal spores. — stirret.borers","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(stirret.borers) dat <- stirret.borers libs(lattice) xyplot(count2~count1|trt,dat, main=\"stirret.borers - by treatment\", xlab=\"Early count of borers\", ylab=\"Late count\") # Even though the data are counts, Normal distribution seems okay # qqmath(~count1|trt, dat, main=\"stirret.borers\") m1 <- lm(count1 ~ -1 + trt + block, dat) anova(m1) # predicted means = main effect + average of 15 block effects # note block 1 effect is 0 # coef(m1)[1:4] + sum(coef(m1)[-c(1:4)])/15 ## trtBoth trtEarly trtLate trtNone ## 47.86667 62.93333 40.93333 61.13333 } # }"},{"path":"/reference/streibig.competition.html","id":null,"dir":"Reference","previous_headings":"","what":"Competition experiment between barley and sinapis. — streibig.competition","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"Competition experiment barley sinapis, different planting rates.","code":""},{"path":"/reference/streibig.competition.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"data frame 135 observations following 8 variables. pot pot number bseeds barley seeds sown sseeds sinapis seeds sown block block bfwt barley fresh weight sfwt sinapis fresh weight bdwt barley dry weight sdwt sinapis dry weight","code":""},{"path":"/reference/streibig.competition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"source data (McCullagh) also contains count plants harvested (included ) sometimes greater number seeds planted. Used permission Jens Streibig.","code":""},{"path":"/reference/streibig.competition.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"Peter McCullagh, John . Nelder. Generalized Linear Models, page 318-320.","code":""},{"path":"/reference/streibig.competition.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"Oliver Schabenberger Francis J Pierce. 2002. Contemporary Statistical Models Plant Soil Sciences. CRC Press. Page 370-375.","code":""},{"path":"/reference/streibig.competition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(streibig.competition) dat <- streibig.competition # See Schaberger and Pierce, pages 370+ # Consider only the mono-species barley data (no competition from sinapis) d1 <- subset(dat, sseeds<1) d1 <- transform(d1, x=bseeds, y=bdwt, block=factor(block)) # Inverse yield looks like it will be a good fit for Gamma's inverse link libs(lattice) xyplot(1/y~x, data=d1, group=block, auto.key=list(columns=3), xlab=\"Seeding rate\", ylab=\"Inverse yield of barley dry weight\", main=\"streibig.competition\") # linear predictor is quadratic, with separate intercept and slope per block m1 <- glm(y ~ block + block:x + x+I(x^2), data=d1, family=Gamma(link=\"inverse\")) # Predict and plot newdf <- expand.grid(x=seq(0,120,length=50), block=factor(c('B1','B2','B3')) ) newdf$pred <- predict(m1, new=newdf, type='response') plot(y~x, data=d1, col=block, main=\"streibig.competition - by block\", xlab=\"Barley seeds\", ylab=\"Barley dry weight\") for(bb in 1:3){ newbb <- subset(newdf, block==c('B1','B2','B3')[bb]) lines(pred~x, data=newbb, col=bb) } } # }"},{"path":"/reference/strickland.apple.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial in apple — strickland.apple.uniformity","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"Uniformity trial apple Australia","code":""},{"path":"/reference/strickland.apple.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"","code":"data(\"strickland.apple.uniformity\")"},{"path":"/reference/strickland.apple.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"data frame 198 observations following 3 variables. row row col column yield yield per tree, pounds","code":""},{"path":"/reference/strickland.apple.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"recently re-worked trees removed data. distance trees uncertain, likely range 20-30 feet.","code":""},{"path":"/reference/strickland.apple.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial in apple — strickland.apple.uniformity","text":". G. Strickland (1935). Error horticultural experiments. Journal Agriculture, Victoria, 33, 408-416. https://handle.slv.vic.gov.au/10381/386642","code":""},{"path":"/reference/strickland.apple.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"None","code":""},{"path":"/reference/strickland.apple.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(strickland.apple.uniformity) dat <- strickland.apple.uniformity libs(desplot) desplot(dat, yield ~ col*row, main=\"strickland.apple.uniformity\", flip=TRUE, aspect=(18/11)) } # }"},{"path":"/reference/strickland.grape.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of grape — strickland.grape.uniformity","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"Uniformity trial grape Australia","code":""},{"path":"/reference/strickland.grape.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"","code":"data(\"strickland.grape.uniformity\")"},{"path":"/reference/strickland.grape.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"data frame 155 observations following 3 variables. row row col column yield yield per vine, pounds","code":""},{"path":"/reference/strickland.grape.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"Yields individual grape vines, planted 8 feet apart rows 10 feet apart. Grown Rutherglen, North-East Victoria, Australia, 1930. Certain sections omitted missing vines.","code":""},{"path":"/reference/strickland.grape.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of grape — strickland.grape.uniformity","text":". G. Strickland (1932). vine uniformity trial. Journal Agriculture, Victoria, 30, 584-593. https://handle.slv.vic.gov.au/10381/386462","code":""},{"path":"/reference/strickland.grape.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"None","code":""},{"path":"/reference/strickland.grape.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(strickland.grape.uniformity) dat <- strickland.grape.uniformity libs(desplot) desplot(dat, yield ~ col*row, main=\"strickland.grape.uniformity\", flip=TRUE, aspect=(31*8)/(5*10) ) # CV 43.4 sd(dat$yield, na.rm=TRUE)/mean(dat$yield, na.rm=TRUE) # anova like Strickland, appendix 1 anova(aov(yield ~ factor(row) + factor(col), data=dat)) # numbers ending in .5 much more common than .0 # table(substring(format(na.omit(dat$yield)),4,4)) # 0 5 # 25 100 } # }"},{"path":"/reference/strickland.peach.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of peach — strickland.peach.uniformity","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"Uniformity trial peach trees Australia.","code":""},{"path":"/reference/strickland.peach.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"","code":"data(\"strickland.peach.uniformity\")"},{"path":"/reference/strickland.peach.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"data frame 144 observations following 3 variables. row row col column yield yield, pounds per tree","code":""},{"path":"/reference/strickland.peach.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"Yields weight peaches per individual tree pounds.","code":""},{"path":"/reference/strickland.peach.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of peach — strickland.peach.uniformity","text":". G. Strickland (1935). Error horticultural experiments. Journal Agriculture, Victoria, 33, 408-416. https://handle.slv.vic.gov.au/10381/386642","code":""},{"path":"/reference/strickland.peach.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"None","code":""},{"path":"/reference/strickland.peach.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(strickland.peach.uniformity) dat <- strickland.peach.uniformity mean(dat$yield) # 131.3, Strickland has 131.3 sd(dat$yield)/mean(dat$yield) # 31.1, Strickland has 34.4 libs(desplot) desplot(dat, yield ~ col*row, main=\"strickland.peach.uniformity\", flip=TRUE, aspect=1) } # }"},{"path":"/reference/strickland.tomato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of tomato — strickland.tomato.uniformity","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"Uniformity trial tomato Australia","code":""},{"path":"/reference/strickland.tomato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"","code":"data(\"strickland.tomato.uniformity\")"},{"path":"/reference/strickland.tomato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"data frame 180 observations following 3 variables. row row col column yield yield per plot, pounds","code":""},{"path":"/reference/strickland.tomato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"Tomato plants placed 2 feet apart rows 4 feet apart. plot contained 6 plants. Field dimensions given, likely design : Field length: 6 plots * 6 plants * 2 feet = 72 feet Field width: 30 plots * 4 feet = 120 feet","code":""},{"path":"/reference/strickland.tomato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":". G. Strickland (1935). Error horticultural experiments. Journal Agriculture, Victoria, 33, 408-416. https://handle.slv.vic.gov.au/10381/386642","code":""},{"path":"/reference/strickland.tomato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"None","code":""},{"path":"/reference/strickland.tomato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(strickland.tomato.uniformity) dat <- strickland.tomato.uniformity mean(dat$yield) sd(dat$yield) libs(desplot) desplot(dat, yield ~ col*row, main=\"strickland.tomato.uniformity\", flip=TRUE, aspect=(6*12)/(30*4)) } # }"},{"path":"/reference/stroup.nin.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"yield data advanced Nebraska Intrastate Nursery (NIN) breeding trial conducted Alliance, Nebraska, 1988/89.","code":""},{"path":"/reference/stroup.nin.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"gen genotype, 56 levels rep replicate, 4 levels yield yield, bu/ac col column row row","code":""},{"path":"/reference/stroup.nin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"Four replicates 19 released cultivars, 35 experimental wheat lines 2 additional triticale lines laid 22 row 11 column rectangular array plots. varieties allocated plots using randomised complete block (RCB) design. plot sown four rows 4.3 m long 0.3 m apart. Plots trimmed 2.4 m length harvest. orientation plots clear paper, data Littel et al given meters make orientation clear. Field length: 11 plots * 4.3 m = 47.3 m Field width: 22 plots * 1.2 m = 26.4 m plots missing data coded gen = \"Lancer\". (ASREML, missing plots need included spatial analysis level 'gen' needs one already data.) data first analyzed Stroup et al (1994) subsequently Littell et al (1996, page 321), Pinheiro Bates (2000, page 260), Butler et al (2004). version data give yield bushels per acre. yield values published Stroup et al (1994) expressed kg/ha. wheat, 1 bu/ac = 67.25 kg/ha. gen names different Stroup et al (1994). (Sometimes experimental genotype given new name released commercial use.) minimum, following differences gen names noted: published versions data use long/lat instead col/row. obtain correct value 'long', multiply 'col' 1.2. obtain correct value 'lat', multiply 'row' 4.3. Relatively low yields clustered northwest corner, explained low rise part field, causing increased exposure winter kill wind damage thus depressed yield. genotype 'Buckskin' known superior variety, disadvantaged assignment unfavorable locations within blocks. Note figures Stroup 2002 claim based data, number rows columns 1 positions Buckskin shown Stroup 2002 appear quite right.","code":""},{"path":"/reference/stroup.nin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"Stroup, Walter W., P Stephen Baenziger, Dieter K Mulitze (1994) Removing Spatial Variation Wheat Yield Trials: Comparison Methods. Crop Science, 86:62–66. https://doi.org/10.2135/cropsci1994.0011183X003400010011x","code":""},{"path":"/reference/stroup.nin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"Littell, R.C. Milliken, G.. Stroup, W.W. Wolfinger, R.D. 1996. SAS system mixed models, SAS Institute, Cary, NC. Jose Pinheiro Douglas Bates, 2000, Mixed Effects Models S S-Plus, Springer. Butler, D., B R Cullis, R Gilmour, B J Goegel. (2004) Spatial Analysis Mixed Models S language environments W. W. Stroup (2002). Power Analysis Based Spatial Effects Mixed Models: Tool Comparing Design Analysis Strategies Presence Spatial Variability. Journal Agricultural, Biological, Environmental Statistics, 7(4), 491-511. https://doi.org/10.1198/108571102780","code":""},{"path":[]},{"path":"/reference/stroup.nin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(stroup.nin) dat <- stroup.nin # Experiment layout. All \"Buckskin\" plots are near left side and suffer # from poor fertility in two of the reps. libs(desplot) desplot(dat, yield~col*row, aspect=47.3/26.4, out1=\"rep\", num=gen, cex=0.6, # true aspect main=\"stroup.nin - yield heatmap (true shape)\") # Dataframe to hold model predictions preds <- data.frame(gen=levels(dat$gen)) # ----- # nlme libs(nlme) # Random RCB model lme1 <- lme(yield ~ 0 + gen, random=~1|rep, data=dat, na.action=na.omit) preds$lme1 <- fixef(lme1) # Linear (Manhattan distance) correlation model lme2 <- gls(yield ~ 0 + gen, data=dat, correlation = corLin(form = ~ col + row, nugget=TRUE), na.action=na.omit) preds$lme2 <- coef(lme2) # Random block and spatial correlation. # Note: corExp and corSpher give nearly identical results lme3 <- lme(yield ~ 0 + gen, data=dat, random = ~ 1 | rep, correlation = corExp(form = ~ col + row), na.action=na.omit) preds$lme3 <- fixef(lme3) # AIC(lme1,lme2,lme3) # lme2 is lowest ## df AIC ## lme1 58 1333.702 ## lme2 59 1189.135 ## lme3 59 1216.704 # ----- # SpATS libs(SpATS) dat <- transform(dat, yf = as.factor(row), xf = as.factor(col)) # what are colcode and rowcode??? sp1 <- SpATS(response = \"yield\", spatial = ~ SAP(col, row, nseg = c(10,20), degree = 3, pord = 2), genotype = \"gen\", #fixed = ~ colcode + rowcode, random = ~ yf + xf, data = dat, control = list(tolerance = 1e-03)) #plot(sp1) preds$spats <- predict(sp1, which=\"gen\")$predicted.value # ----- # Template Model Builder # See the ar1xar1 example: # https://github.com/kaskr/adcomp/tree/master/TMB/inst/examples # This example uses dpois() in the cpp file to model a Poisson response # with separable AR1xAR1. I think this example could be used for the # stroup.nin data, changing dpois() to something Normal. # ----- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # RCB analysis as1 <- asreml(yield ~ gen, random = ~ rep, data=dat, na.action=na.method(x=\"omit\")) preds$asreml1 <- predict(as1, data=dat, classify=\"gen\")$pvals$predicted.value # Two-dimensional AR1xAR1 spatial model dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf, dat$yf),] as2 <- asreml(yield~gen, data=dat, residual = ~ar1(xf):ar1(yf), na.action=na.method(x=\"omit\")) preds$asreml2 <- predict(as2, data=dat, classify=\"gen\")$pvals$predicted.value lucid::vc(as2) ## effect component std.error z.ratio constr ## R!variance 48.7 7.155 6.8 pos ## R!xf.cor 0.6555 0.05638 12 unc ## R!yf.cor 0.4375 0.0806 5.4 unc # Compare the estimates from the two asreml models. # We see that Buckskin has correctly been shifted upward by the spatial model plot(preds$as1, preds$as2, xlim=c(13,37), ylim=c(13,37), xlab=\"RCB\", ylab=\"AR1xAR1\", type='n') title(\"stroup.nin: Comparison of predicted values\") text(preds$asreml1, preds$asreml2, preds$gen, cex=0.5) abline(0,1) } # ----- # sommer # Fixed gen, random row, col, 2D spline libs(sommer) dat <- stroup.nin dat <- transform(dat, yf = as.factor(row), xf = as.factor(col)) so1 <- mmer(yield ~ 0+gen, random = ~ vs(xf) + vs(yf) + spl2Db(row,col), data=dat) preds$so1 <- coef(so1)[,\"Estimate\"] # spatPlot # ----- # compare variety effects from different packages lattice::splom(preds[,-1], main=\"stroup.nin\") } # }"},{"path":"/reference/stroup.splitplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-plot experiment of simulated data — stroup.splitplot","title":"Split-plot experiment of simulated data — stroup.splitplot","text":"simulated dataset simple split-plot experiment, used illustrate details calculating predictable functions (broad space, narrow space, etc.). example, density narrow, intermediate broad-space predictable function factor level A1 shown (html help )","code":""},{"path":"/reference/stroup.splitplot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-plot experiment of simulated data — stroup.splitplot","text":"y simulated response rep replicate, 4 levels b sub-plot, 2 levels whole-plot, 3 levels Used permission Walt Stroup.","code":""},{"path":"/reference/stroup.splitplot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-plot experiment of simulated data — stroup.splitplot","text":"Walter W. Stroup, 1989. Predictable functions prediction space mixed model procedure. Applications Mixed Models Agriculture Related Disciplines.","code":""},{"path":"/reference/stroup.splitplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-plot experiment of simulated data — stroup.splitplot","text":"Wolfinger, R.D. Kass, R.E., 2000. Nonconjugate Bayesian analysis variance component models, Biometrics, 56, 768–774. https://doi.org/10.1111/j.0006-341X.2000.00768.x","code":""},{"path":"/reference/stroup.splitplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-plot experiment of simulated data — stroup.splitplot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(stroup.splitplot) dat <- stroup.splitplot # ---- lme4 --- # libs(lme4) # m0 <- lmer(y~ -1 + a + b + a:b + (1|rep) + (1|a:rep), data=dat) # No predict function # ----- nlme --- # libs(nlme) # m0 <- lme(y ~ -1 + a + b + a:b, data=dat, random = ~ 1|rep/a) # ----- ASREML model --- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) m1 <- asreml(y~ -1 + a + b + a:b, random=~ rep + a:rep, data=dat) # vc(m1) # Variance components match Stroup p. 41 ## effect component std.error z.ratio bound ## rep 62.42 56.41 1.1 P ## a:rep 15.39 11.8 1.3 P ## units(R) 9.364 4.415 2.1 P # Narrow space predictions predict(m1, data=dat, classify=\"a\", average=list(rep=NULL)) # a Predicted Std Err Status # a1 32.88 1.082 Estimable # a2 34.12 1.082 Estimable # a3 25.75 1.082 Estimable # Intermediate space predictions predict(m1, data=dat, classify=\"a\", ignore=\"a:rep\", average=list(rep=NULL)) # a Predicted Std Err Status # a1 32.88 2.24 Estimable # a2 34.12 2.24 Estimable # a3 25.75 2.24 Estimable # Broad space predictions predict(m1, data=dat, classify=\"a\") # a Predicted Std Err Status # a1 32.88 4.54 Estimable # a2 34.12 4.54 Estimable # a3 25.75 4.54 Estimable } # ----- MCMCglmm model ----- # Use the point estimates from REML with a prior distribution libs(lattice,MCMCglmm) prior2 = list( G = list(G1=list(V=62.40, nu=1), G2=list(V=15.38, nu=1)), R = list(V = 9.4, nu=1) ) m2 <- MCMCglmm(y~ -1 + a + b + a:b, random=~ rep + a:rep, data=dat, pr=TRUE, # save random effects as columns of 'Sol' nitt=23000, # double the default 13000 prior=prior2, verbose=FALSE) # posterior.mode(m2$VCV) # rep a:rep units # 39.766020 9.617522 7.409334 # plot(m2$VCV) # Now create a matrix of coefficients for the prediction. # Each column is for a different prediction. For example, # the values in the column called 'a1a2n' are multiplied times # the model coefficients (identified at the right side) to create # the linear contrast for the the narrow-space predictions # (also called adjusted mean) for the a1:a2 interaction. # a1n a1i a1b a1a2n a1a2ib cm <- matrix(c(1, 1, 1, 1, 1, # a1 0, 0, 0, -1, -1, # a2 0, 0, 0, 0, 0, # a3 1/2, 1/2, 1/2, 0, 0, # b2 0, 0, 0, -1/2, -1/2, # a2:b2 0, 0, 0, 0, 0, # a3:b2 1/4, 1/4, 0, 0, 0, # r1 1/4, 1/4, 0, 0, 0, # r2 1/4, 1/4, 0, 0, 0, # r3 1/4, 1/4, 0, 0, 0, # r4 1/4, 0, 0, 1/4, 0, # a1r1 0, 0, 0, -1/4, 0, # a2r1 0, 0, 0, 0, 0, # a3r1 1/4, 0, 0, 1/4, 0, # a1r2 0, 0, 0, -1/4, 0, # a2r2 0, 0, 0, 0, 0, # a3r2 1/4, 0, 0, 1/4, 0, # a1r3 0, 0, 0, -1/4, 0, # a2r3 0, 0, 0, 0, 0, # a3r3 1/4, 0, 0, 1/4, 0, # a1r4 0, 0, 0, -1/4, 0, # a2r4 0, 0, 0, 0, 0), # a3r4 ncol=5, byrow=TRUE) rownames(cm) <- c(\"a1\", \"a2\", \"a3\", \"b2\", \"a2:b2\", \"a3:b2\", \"r1\", \"r2\", \"r3\", \"r4\", \"a1r1\", \"a1r2\", \"a1r3\", \"a1r4\", \"a2r1\", \"a2r2\", \"a2r3\", \"a2r4\", \"a3r1\", \"a3r2\", \"a3r3\", \"a3r4\") colnames(cm) <- c(\"A1n\",\"A1i\",\"A1b\", \"A1-A2n\", \"A1-A2ib\") print(cm) # post2 <- as.mcmc(m2$Sol post2 <- as.mcmc(crossprod(t(m2$Sol), cm)) # Following table has columns for A1 estimate (narrow, intermediate, broad) # A1-A2 estimate (narrow and intermediat/broad). # The REML estimates are from Stroup 1989. est <- rbind(\"REML est\"=c(32.88, 32.88, 32.88, -1.25, -1.25), \"REML stderr\"=c(1.08, 2.24, 4.54, 1.53, 3.17), \"MCMC mode\"=posterior.mode(post2), \"MCMC stderr\"=apply(post2, 2, sd)) round(est,2) # A1n A1i A1b A1-A2n A1-A2ib # REML est 32.88 32.88 32.88 -1.25 -1.25 # REML stderr 1.08 2.24 4.54 1.53 3.17 # MCMC mode 32.95 32.38 31.96 -1.07 -1.17 # MCMC stderr 1.23 2.64 5.93 1.72 3.73 # plot(post2) post22 <- lattice::make.groups( Narrow=post2[,1], Intermediate=post2[,2], Broad=post2[,3]) print(densityplot(~data|which, data=post22, groups=which, cex=.25, lty=1, layout=c(1,3), main=\"stroup.splitplot\", xlab=\"MCMC model value of predictable function for A1\")) } # }"},{"path":"/reference/student.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley — student.barley","title":"Multi-environment trial of barley — student.barley","text":"Yield two varieties barley grown 51 locations years 1901 1906.","code":""},{"path":"/reference/student.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley — student.barley","text":"data frame 102 observations following 7 variables. year year, 1901-1906 farmer farmer name place place (nearest town) district district, geographical area gen genotype, Archer Goldthorpe yield yield, 'stones' per acre (1 stone = 14 pounds) income income per acre shillings, based yield quality","code":""},{"path":"/reference/student.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley — student.barley","text":"Experiments conducted six years Department Agriculture Ireland. total seven varieties tested, Archer Goldthorpe tested six years (others dropped found inferior, added later). Plots two acres size. value grain depended yield quality. Quality varied much farm farm, much within farm. phrase \"analysis variance\" first appears abstract () 1918 paper Fisher. 1923 paper Student contained first analysis variance table (data). One stone 14 pounds. convert lb/ac tonnes/ha, multiply 0.00112085116 Note: analysis Student reproduced exactly. example, Student states maximum income Goldthorpe 230 shillings. quick glance Table Student shows maximum income Goldthorpe 220 shillings (11 pounds, 0 shillings) 1901 Thurles. Also, results Kempton reproduced exactly, perhaps due rounding conversion factor used.","code":""},{"path":"/reference/student.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley — student.barley","text":"Student. 1923. Testing Varieties Cereals. Biometrika, 15, 271–293. https://doi.org/10.1093/biomet/15.3-4.271","code":""},{"path":"/reference/student.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of barley — student.barley","text":"R Kempton P N Fox, 1997. Statistical Methods Plant Variety Evaluation.","code":""},{"path":"/reference/student.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley — student.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(student.barley) dat <- student.barley libs(lattice) bwplot(yield ~ gen|district, dat, main=\"student.barley - yield\") dat$year <- factor(dat$year) dat$income <- NULL # convert to tons/ha dat <- transform(dat, yield=yield*14 * 0.00112085116) # Define 'loc' the way that Kempton does dat$loc <- rep(\"\",nrow(dat)) dat[is.element(dat$farmer, c(\"Allardyce\",\"Roche\",\"Quinn\")),\"loc\"] <- \"1\" dat[is.element(dat$farmer, c(\"Luttrell\",\"Dooley\")), \"loc\"] <- \"2\" dat[is.element(dat$year, c(\"1904\",\"1905\",\"1906\")) & dat$farmer==\"Kearney\",\"loc\"] <- \"2\" dat[dat$farmer==\"Mulhall\",\"loc\"] <- \"3\" dat <- transform(dat, loc=factor(paste(place,loc,sep=\"\"))) libs(reshape2) datm <- melt(dat, measure.var='yield') # Kempton Table 9.5 round(acast(datm, loc+gen~year),2) # Kempton Table 9.6 d2 <- dcast(datm, year+loc~gen) mean(d2$Archer) mean(d2$Goldthorpe) mean(d2$Archer-d2$Goldthorpe) sqrt(var(d2$Archer-d2$Goldthorpe)/51) cor(d2$Archer,d2$Goldthorpe) if(0){ # Kempton Table 9.6b libs(lme4) m2 <- lmer(yield~1 + (1|loc) + (1|year) + (1|loc:year) + (1|gen:loc) + (1|gen:year), data=dat, control=lmerControl(check.nobs.vs.rankZ=\"ignore\")) } } # }"},{"path":"/reference/summerby.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"Uniformity trial maize, oat, alfalfa, mangolds","code":""},{"path":"/reference/summerby.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"","code":"data(\"summerby.multi.uniformity\")"},{"path":"/reference/summerby.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"data frame 2600 observations following 6 variables. col column ordinate row row ordinate yield yield range range (block field) year year crop crop","code":""},{"path":"/reference/summerby.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"Note plots range across years. example plots range R2 1922, 1923, 1924, 1925. Grown Macdonald College, Quebec. Four ranges land 760 x 100 links used. years 1922-1926, crops harvested 20 link 20 links plots. oats, yields cleaned grain. mangolds alfalfa, yields dry matter calculated. maize, green weights fodder obtained. 1925, range R3 oats damaged birds. 1927, range R4 oats lodges harvested. 1924 range R5 flooding considered 'inadvisable' use. 1914 range R3 oat yield variable, perhaps poor germination. Data included completeness, perhaps included. row numbers data based figure page 13 Summerby. Row 1 bottom. appears approximately blank row ranges. paper Summerby year/range combinations, plots 20 links 100 links single plot wide. data converted PDF png images, OCR converted text, hand-checked K.Wright.","code":""},{"path":"/reference/summerby.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"Summerby, R. (1934). value preliminary uniformity trials increasing precision field experiments. Macdonald College. https://books.google.com/books?id=6zlMAAAAYAAJ&pg=RA14-PA47","code":""},{"path":"/reference/summerby.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"None","code":""},{"path":"/reference/summerby.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(summerby.multi.uniformity) dat <- summerby.multi.uniformity libs(desplot) dat <- mutate(dat, env=paste(range, year, crop)) desplot(dat, yield ~ col*row|env, aspect=(5*20)/(35*20), main=\"summerby.multi.uniformity\") # Show all ranges for a single year. # dat # Compare the variance for each dataset in Summerby, page 18, column (a) # with what we calculate. Very slight differences. # libs(dplyr) # dat ## range year var summerby ## 1 R2 1922 82404 82404 ## 2 R2 1923 254780. 254780 ## 3 R2 1924 111978. 111978 ## 4 R2 1925 84515. 84515 ## 5 R2 1926 101008. 100960 ## 6 R3 1922 185031. 185031 ## 7 R3 1923 154777. 154784 ## 8 R3 1924 252451. 252451 ## 9 R3 1926 472087. 472088 ## 10 R4 1924 19.3 19.341 ## 11 R4 1925 14.2 14.234 ## 12 R4 1926 14.2 14.236 ## 13 R5 1924 134472. 134472 ## 14 R5 1925 289001. 289026 ## 15 R5 1926 131714. 131714 ## 16 R5 1927 8.62 8.622 } # }"},{"path":"/reference/tai.potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of potato — tai.potato","title":"Multi-environment trial of potato — tai.potato","text":"Multi-environment trial potato tuber yields","code":""},{"path":"/reference/tai.potato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of potato — tai.potato","text":"","code":"data(\"tai.potato\")"},{"path":"/reference/tai.potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of potato — tai.potato","text":"data frame 48 observations following 6 variables. yield yield, kg/plot gen genotype code variety variety name env environment code loc location year year","code":""},{"path":"/reference/tai.potato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of potato — tai.potato","text":"Mean tuber yield 8 genotypes 3 locations two years. Katahdin Sebago check varieties. location planted 4-rep RCB design. Tai's plot stability parameters, F5751 Sebago average stability area. highest yielding genotype F6032 unstable performance.","code":""},{"path":"/reference/tai.potato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of potato — tai.potato","text":"G.C.C. Tai, 1971. Genotypic stability analysis application potato regional trials. Crop Sci 11, 184-190. Table 2, p. 187. https://doi.org/10.2135/cropsci1971.0011183X001100020006x","code":""},{"path":"/reference/tai.potato.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of potato — tai.potato","text":"George Fernandez (1991). Analysis Genotype x Environment Interaction Stability Estimates. Hort Science, 26, 947-950.","code":""},{"path":"/reference/tai.potato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of potato — tai.potato","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(tai.potato) dat <- tai.potato libs(lattice) dotplot(variety ~ yield|env, dat, main=\"tai.potato\") # fixme - need to add tai() example # note, st4gi::tai assumes there are replications in the data # https://github.com/reyzaguirre/st4gi/blob/master/R/tai.R } # }"},{"path":"/reference/talbot.potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"Yield 14 trait scores 9 potato varieties 12 locations UK.","code":""},{"path":"/reference/talbot.potato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"","code":"data(\"talbot.potato.traits\") data(\"talbot.potato.yield\")"},{"path":"/reference/talbot.potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"talbot.potato.yield dataframe 126 observations following 3 variables. gen genotype/variety trait trait score trait score, 1-9 talbot.potato.yield dataframe 108 observations following 3 variables. gen genotype/variety loc location/center yield yield, t/ha","code":""},{"path":"/reference/talbot.potato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"talbot.potato.yield dataframe contains mean tuber yields (t/ha) 9 varieties potato 12 centers United Kingdom five years 1983-1987. following abbreviations used centers. Used permission Mike Talbot.","code":""},{"path":"/reference/talbot.potato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"Mike Talbot V Wheelwright, 1989, analysis genotype x analysis interactions partial least squares regression. Biuletyn Oceny Odmian, 21/22, 19–25.","code":""},{"path":"/reference/talbot.potato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(pls, reshape2) data(talbot.potato.traits) datt <- talbot.potato.traits data(talbot.potato.yield) daty <- talbot.potato.yield datt <- acast(datt, gen ~ trait, value.var='score') daty <- acast(daty, gen ~ loc, value.var='yield') # Transform columns to zero mean and unit variance datt <- scale(datt) daty <- scale(daty) m1 <- plsr(daty ~ datt, ncomp=3) summary(m1) # Loadings factor 1 lo <- loadings(m1)[,1,drop=FALSE] round(-1*lo[order(-1*lo),1,drop=FALSE],2) biplot(m1, main=\"talbot.potato - biplot\") } # }"},{"path":"/reference/tesfaye.millet.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of millet — tesfaye.millet","title":"Multi-environment trial of millet — tesfaye.millet","text":"Multi-environment trial millet","code":""},{"path":"/reference/tesfaye.millet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of millet — tesfaye.millet","text":"","code":"data(\"tesfaye.millet\")"},{"path":"/reference/tesfaye.millet.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of millet — tesfaye.millet","text":"data frame 415 observations following 9 variables. year year site site (location) rep replicate col column ordinate row row ordinate plot plot number gen genotype entry_number entry yield yield, kg/ha","code":""},{"path":"/reference/tesfaye.millet.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of millet — tesfaye.millet","text":"Experiments conducted Bako Assosa research centers Ethiopia. data : 4 years, 2 sites = 7 environments, 2-3 reps per trial, 47 genotypes. Tesfaye et al used asreml fit GxE model Factor Analytic covariance structure GxE part AR1xAR1 spatial residuals site. Data PloS ONE published Creative Commons Attribution License.","code":""},{"path":"/reference/tesfaye.millet.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of millet — tesfaye.millet","text":"Tesfaye K, Alemu T, Argaw T, de Villiers S, Assefa E (2023) Evaluation finger millet (Eleusine coracana (L.) Gaertn.) multi-environment trials using enhanced statistical models. PLoS ONE 18(2): e0277499. https://doi.org/10.1371/journal.pone.0277499","code":""},{"path":"/reference/tesfaye.millet.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of millet — tesfaye.millet","text":"None","code":""},{"path":"/reference/tesfaye.millet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of millet — tesfaye.millet","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(tesfaye.millet) dat <- tesfaye.millet dat <- transform(dat, year=factor(year), site=factor(site)) libs(dplyr,asreml,lucid) dat <- mutate(dat, env=factor(paste0(site,year)), gen=factor(gen), rep=factor(rep), xfac=factor(col), yfac=factor(row)) libs(desplot) desplot(dat, yield~col*row|env, main=\"tesfaye.millet\") dat <- arrange(dat, env, xfac, yfac) # Fixed environment # Random row/col within environment, Factor Analytic GxE # AR1xAR1 spatial residuals within each environment if(require(\"asreml\", quietly=TRUE)){ libs(asreml) m1 <- asreml(yield ~ 1 + env, data=dat, random = ~ at(env):xfac + at(env):yfac + gen:fa(env), residual = ~ dsum( ~ ar1(xfac):ar1(yfac)|env) ) m1 <- update(m1) lucid::vc(m1) } } # }"},{"path":"/reference/theobald.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"Barley yields multiple locs, years, fertilizer levels","code":""},{"path":"/reference/theobald.barley.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"","code":"data(\"theobald.barley\")"},{"path":"/reference/theobald.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"data frame 105 observations following 5 variables. yield yield, tonnes/ha gen genotype loc location, 5 levels nitro nitrogen kg/ha year year, 2 levels","code":""},{"path":"/reference/theobald.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"Theobald Talbot used BUGS fit fully Bayesian model yield response curves. Locations experiment north-east Scotland. Assumed nitrogen cost 400 pounds per tonne. Grain prices used 100, 110, 107.50 pounds per tonne Georgie, Midas Sundance.","code":""},{"path":"/reference/theobald.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"Chris M. Theobald Mike Talbot, (2002). Bayesian choice crop variety fertilizer dose. Appl Statistics, 51, 23-36. https://doi.org/10.1111/1467-9876.04863 Data provided Chris Theobald Mike Talbot.","code":""},{"path":"/reference/theobald.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(theobald.barley) dat <- theobald.barley dat <- transform(dat, env=paste(loc,year,sep=\"-\")) dat <- transform(dat, income=100*yield - 400*nitro/1000) libs(lattice) xyplot(income~nitro|env, dat, groups=gen, type='b', auto.key=list(columns=3), main=\"theobald.barley\") } # }"},{"path":"/reference/theobald.covariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","title":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","text":"Corn silage yields maize 5 years 7 districts 10 hybrids.","code":""},{"path":"/reference/theobald.covariate.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","text":"data frame 256 observations following 5 variables. year year, 1990-1994 env environment/district, 1-7 gen genotype, 1-10 yield dry-matter silage yield corn chu corn heat units, thousand degrees Celsius Used permission Chris Theobald.","code":""},{"path":"/reference/theobald.covariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","text":"trials carried seven districts maritime provinces Eastern Canada. Different fields used successive years. covariate CHU (Corn Heat Units) accumulated average daily temperatures (thousands degrees Celsius) growing season location.","code":""},{"path":"/reference/theobald.covariate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","text":"Chris M. Theobald Mike Talbot Fabian Nabugoomu, 2002. Bayesian Approach Regional Local-Area Prediction Crop Variety Trials. Journ Agric Biol Env Sciences, 7, 403–419. https://doi.org/10.1198/108571102230","code":""},{"path":"/reference/theobald.covariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(theobald.covariate) dat <- theobald.covariate libs(lattice) xyplot(yield ~ chu|gen, dat, type=c('p','smooth'), xlab = \"chu = corn heat units\", main=\"theobald.covariate - yield vs heat\") # REML estimates (Means) in table 3 of Theobald 2002 libs(lme4) dat <- transform(dat, year=factor(year)) m0 <- lmer(yield ~ -1 + gen + (1|year/env) + (1|gen:year), data=dat) round(fixef(m0),2) # Use JAGS to fit Theobald (2002) model 3.2 with 'Expert' prior # Requires JAGS to be installed if(0) { libs(reshape2) ymat <- acast(dat, year+env~gen, value.var='yield') chu <- acast(dat, year+env~., mean, value.var='chu', na.rm=TRUE) chu <- as.vector(chu - mean(chu)) # Center the covariate dat$yr <- as.numeric(dat$year) yridx <- as.vector(acast(dat, year+env~., mean, value.var='yr', na.rm=TRUE)) dat$loc <- as.numeric(dat$env) locidx <- acast(dat, year+env~., mean, value.var='loc', na.rm=TRUE) locidx <- as.vector(locidx) jdat <- list(nVar = 10, nYear = 5, nLoc = 7, nYL = 29, yield = ymat, chu = chu, year = yridx, loc = locidx) libs(rjags) m1 <- jags.model(file=system.file(package=\"agridat\", \"files/theobald.covariate.jag\"), data=jdat, n.chains=2) # Table 3, Variety deviations from means (Expert prior) c1 <- coda.samples(m1, variable.names=(c('alpha')), n.iter=10000, thin=10) s1 <- summary(c1) effs <- s1$statistics[,'Mean'] # Perfect match (different order?) rev(sort(round(effs - mean(effs), 2))) } } # }"},{"path":"/reference/thompson.cornsoy.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"Average yield corn soybeans five U.S. states (IA, IL, , MO, OH) years 1930-1962. Pre-season precipitation average temperature precipitation month growing season included.","code":""},{"path":"/reference/thompson.cornsoy.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"state state year year, 1930-1962 rain0 pre-season precipitation inches temp5 may temperature, Fahrenheit rain6 june rain, inches temp6 june temp rain7 july rain temp7 july temp rain8 august rain temp8 august temp corn corn yield, bu/acre soy soybean yield, bu/acre","code":""},{"path":"/reference/thompson.cornsoy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"Note: Iowa corn data sometimes identified ( sources) \"Iowa wheat\" data, incorrect. 'year' variable affects yield (1) improvements plant genetics (2) changes management techniques fertilizer, chemicals, tillage, planting date, (3) climate, pest infestations, etc. Double-cross corn hybrids introduced 1920s. Single-cross hybrids became common around 1960. World War II, nitrogen used production TNT bombs. war, factories switched producing ammonia fertilizer. Nitrogen fertilizer use greatly increased WWII major reason yield gains corn. Soybeans gain little benefit nitrogen fertilizer. major reason increasing yields crops due improved plant genetics. Crops often planted May, harvest begins September. Yields 1936 low due July one hottest driest record. relevant maps yield, heat, precipitation can found Atlas crop yield summer weather patterns, 1931-1975, https://www.isws.illinois.edu/pubdoc/C/ISWSC-150.pdf following notes pertain Iowa data. 1947 June precipitation 10.33 inches wettest June record (new Iowa June record 10.34 inches set 2010). quoted Monthly Weather Review (Dec 1957, p. 396) \"dependence Iowa agriculture upon vagaries weather closely demonstrated 1947 season. cool wet spring delayed crop planting activity plant growth; , addition, hard freeze May 29th ... set back corn. heavy rains subsequent floods June caused appreciable crop acreage abandoned ... followed hot dry weather regime persisted mid-July first week September.\" 1949 soybean yields average corn yields low. source , \"year 1949 saw greatest infestation corn borer history corn Iowa\". 1955 yields reduced due dry weather late July August.","code":""},{"path":"/reference/thompson.cornsoy.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"Thompson, L.M., 1963. Weather technology production corn soybeans. CAED Report 17. Center Agriculture Economic Development, Iowa State University, Ames, Iowa.","code":""},{"path":"/reference/thompson.cornsoy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"Draper, N. R. Smith, H. (1981). Applied Regression Analysis, second ed., Wiley, New York.","code":""},{"path":"/reference/thompson.cornsoy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(thompson.cornsoy) dat <- thompson.cornsoy # The droughts of 1934/36 were severe in IA/MO. Less so in OH. libs(lattice) xyplot(corn+soy~year|state, dat, type=c('p','l','r'), auto.key=list(columns=2), main=\"thompson.cornsoy\", layout=c(5,1),ylab='yield') # In 1954, only Missouri suffered very hot, dry weather ## xyplot(corn~year, dat, ## groups=state, type=c('p','l'), ## main=\"thompson.cornsoy\", ## auto.key=list(columns=5), ylab='corn yield') # Rain and temperature have negative correlation in each month. # July is a critical month: temp and yield are negatively correlated, # while rain and yield are positively correlated. # splom(~dat[-1,-1], col=dat$state, cex=.5, main=\"thompson.cornsoy\") # Plots similar to those in Venables' Exegeses paper. dat.ia <- subset(dat, state==\"Iowa\") libs(splines) m2 <- aov(corn ~ ns(rain0, 3) + ns(rain7, 3) + ns(temp8, 3) + ns(year,3), dat.ia) op <- par(mfrow=c(2,2)) termplot(m2, se=TRUE, rug=TRUE, partial=TRUE, main=\"thompson.cornsoy\") par(op) # do NOT use gam package libs(mgcv) m1 <- gam(corn ~ s(year, k=5) + s(rain0, k=5) + s(rain7, k=5) + s(temp8, k=5), data=dat.ia) op <- par(mfrow=c(2,2)) plot.gam(m1, residuals=TRUE, se=TRUE, cex=2, main=\"thompson.cornsoy\") par(op) } # }"},{"path":"/reference/tulaikow.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"Uniformity trial winter/spring wheat Russia","code":""},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"","code":"data(\"tulaikow.wheat.uniformity\")"},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"data frame 480 observations following 4 variables. row row ordinate col column ordinate yield yield grams per plot season winter summer","code":""},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"Land fallow 1911, harvested 1912 Bezenchuk Experimental Station Russia. winter wheat field 240 square sazhen (24 x 10 sazhen) divided separate plots 1 square sazhen, cut, threshed weighed separately. way, plot Poltavka spring wheat harvested plot 240 square sazhen dimensions 15 16 sazhen divided plots 1 square sazhen. Winter wheat: Field length: 10 rows * 1 sazhen. Field width: 24 columns * 1 sazhen. Summer wheat: Field length: 16 rows * 1 sazhen. Field width: 15 columns * 1 sazhen. Note: Russian word (looks like \"cax\" vertical line \"x\") refers unit measurement. Specifically, represents sazhen, used traditional Russian systems measurement. sazhen approximately 3 meters (7 feet) long. Google Translate sometimes converts \"sazhen\" \"soot\", \"meter\" \"fathom\". data typed K.Wright Roemer (1920), table 4, p. 63.","code":""},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"N. Tulaikow (1913) Resultate einer mathematischen Bearbeitung von Ernteergebnissen. Russian Journal fur Exp Landw., 14, 88-113. https://www.google.com/books/edition/Journal_de_l_agriculture_experimentale/i2EjAQAAIAAJ?hl=en&gbpv=1&dq=tulaikow","code":""},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"Neyman, J., & Iwaszkiewicz, K. (1935). Statistical problems agricultural experimentation. Supplement Journal Royal Statistical Society, 2(2), 107-180. Roemer, T. (1920). Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ","code":""},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(tulaikow.wheat.uniformity) dat <- tulaikow.wheat.uniformity libs(desplot) desplot(dat, yield~col*row, subset=season==\"winter\", aspect=10/24, flip=TRUE, tick=TRUE, main=\"tulaikow.wheat.uniformity (winter)\") desplot(dat, yield~col*row, subset=season==\"summer\", aspect=16/15, flip=TRUE, tick=TRUE, main=\"tulaikow.wheat.uniformity (summer)\") } # }"},{"path":"/reference/turner.herbicide.html","id":null,"dir":"Reference","previous_headings":"","what":"Herbicide control of larkspur — turner.herbicide","title":"Herbicide control of larkspur — turner.herbicide","text":"Herbicide control larkspur","code":""},{"path":"/reference/turner.herbicide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Herbicide control of larkspur — turner.herbicide","text":"","code":"data(\"turner.herbicide\")"},{"path":"/reference/turner.herbicide.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Herbicide control of larkspur — turner.herbicide","text":"data frame 12 observations following 4 variables. rep rep factor rate rate herbicide live number live plants application dead number plants killed herbicide","code":""},{"path":"/reference/turner.herbicide.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Herbicide control of larkspur — turner.herbicide","text":"Effectiveness herbicide Picloram larkspur plants 4 doses (0, 1.1, 2.2, 4.5) 3 reps. Experiment done 1986 Manti, Utah.","code":""},{"path":"/reference/turner.herbicide.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Herbicide control of larkspur — turner.herbicide","text":"David L. Turner Michael H. Ralphs John O. Evans (1992). Logistic Analysis Monitoring Assessing Herbicide Efficacy. Weed Technology, 6, 424-430. https://www.jstor.org/stable/3987312","code":""},{"path":"/reference/turner.herbicide.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Herbicide control of larkspur — turner.herbicide","text":"Christopher Bilder, Thomas Loughin. Analysis Categorical Data R.","code":""},{"path":"/reference/turner.herbicide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Herbicide control of larkspur — turner.herbicide","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(turner.herbicide) dat <- turner.herbicide dat <- transform(dat, prop=dead/live) # xyplot(prop~rate,dat, pch=20, main=\"turner.herbicide\", ylab=\"Proportion killed\") m1 <- glm(prop~rate, data=dat, weights=live, family=binomial) coef(m1) # -3.46, 2.6567 Same as Turner eqn 3 # Make conf int on link scale and back-transform p1 <- expand.grid(rate=seq(0,to=5,length=50)) p1 <- cbind(p1, predict(m1, newdata=p1, type='link', se.fit=TRUE)) p1 <- transform(p1, lo = plogis(fit - 2*se.fit), fit = plogis(fit), up = plogis(fit + 2*se.fit)) # Figure 2 of Turner libs(latticeExtra) foo1 <- xyplot(prop~rate,dat, cex=1.5, main=\"turner.herbicide (model with 2*S.E.)\", xlab=\"Herbicide rate\", ylab=\"Proportion killed\") foo2 <- xyplot(fit~rate, p1, type='l') foo3 <- xyplot(lo+up~rate, p1, type='l', lty=1, col='gray') print(foo1 + foo2 + foo3) # What dose gives a LD90 percent kill rate? # libs(MASS) # dose.p(m1, p=.9) ## Dose SE ## p = 0.9: 2.12939 0.128418 # Alternative method # libs(car) # logit(.9) = 2.197225 # deltaMethod(m1, g=\"(log(.9/(1-.9))-b0)/(b1)\", parameterNames=c('b0','b1')) ## Estimate SE ## (2.197225 - b0)/(b1) 2.12939 0.128418 # What is a 95 percent conf interval for LD90? Bilder & Loughin page 138 root <- function(x, prob=.9, alpha=0.05){ co <- coef(m1) # b0,b1 covs <- vcov(m1) # b00,b11,b01 # .95 = b0 + b1*x # (b0+b1*x) + Z(alpha/2) * sqrt(b00 + x^2*b11 + 2*x*b01) > .95 # (b0+b1*x) - Z(alpha/2) * sqrt(b00 + x^2*b11 + 2*x*b01) < .95 f <- abs(co[1] + co[2]*x - log(prob/(1-prob))) / sqrt(covs[1,1] + x^2 * covs[2,2] + 2*x*covs[1,2]) return( f - qnorm(1-alpha/2)) } lower <- uniroot(f=root, c(0,2.13)) upper <- uniroot(f=root, c(2.12, 5)) c(lower$root, upper$root) # 1.92 2.45 } # }"},{"path":"/reference/urquhart.feedlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Weight gain calves in a feedlot — urquhart.feedlot","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"Weight gain calves feedlot, given three different diets.","code":""},{"path":"/reference/urquhart.feedlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"","code":"data(\"urquhart.feedlot\")"},{"path":"/reference/urquhart.feedlot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"data frame 67 observations following 5 variables. animal animal ID herd herd ID diet diet: Low, Medium, High weight1 initial weight weight2 slaughter weight","code":""},{"path":"/reference/urquhart.feedlot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"Calves born 1975 11 different herds entered feedlot yearlings. animal fed one three diets low, medium, high energy. original sources explored use contrasts comparing breeds.","code":""},{"path":"/reference/urquhart.feedlot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"N. Scott Urquhart (1982). Adjustment Covariance One Factor Affects Covariate Biometrics, 38, 651-660. Table 4, p. 659. https://doi.org/10.2307/2530046","code":""},{"path":"/reference/urquhart.feedlot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"N. Scott Urquhart David L. Weeks (1978). Linear Models Messy Data: Problems Alternatives Biometrics, 34, 696-705. https://doi.org/10.2307/2530391 Also available 'emmeans' package 'feedlot' data.","code":""},{"path":"/reference/urquhart.feedlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(urquhart.feedlot) dat <- urquhart.feedlot libs(reshape2) d2 <- melt(dat, id.vars=c('animal','herd','diet')) libs(latticeExtra) useOuterStrips(xyplot(value ~ variable|diet*herd, data=d2, group=animal, type='l', xlab=\"Initial & slaughter timepoint for each diet\", ylab=\"Weight for each herd\", main=\"urquhart.feedlot - weight gain by animal\")) # simple fixed-effects model dat <- transform(dat, animal = factor(animal), herd=factor(herd)) m1 <- lm(weight2 ~ weight1 + herd*diet, data = dat) coef(m1) # weight1 = 1.1373 match Urquhart table 5 common slope # random-effects model might be better, for example # libs(lme4) # m1 <- lmer(weight2 ~ -1 + diet + weight1 + (1|herd), data=dat) # summary(m1) # weight1 = 1.2269 } # }"},{"path":"/reference/usgs.herbicides.html","id":null,"dir":"Reference","previous_headings":"","what":"Concentrations of herbicides in streams in the United States — usgs.herbicides","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"Concentrations selected herbicides degradation products determined laboratory method analysis code GCS water samples collected 51 streams nine Midwestern States,2002","code":""},{"path":"/reference/usgs.herbicides.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"","code":"data(\"usgs.herbicides\")"},{"path":"/reference/usgs.herbicides.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"data frame 184 observations following 19 variables. mapnum map number usgsid USGS ID long longitude lat latitude site site name city city sampletype sample type code date date sample collected hour hour sample collected acetochlor concentration character alachlor concentration character ametryn concentration character atrazine concentration character CIAT concentration character CEAT concentration character cyanazine concentration character CAM concentration character dimethenamid concentration character flufenacet concentration character","code":""},{"path":"/reference/usgs.herbicides.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"Concentrations selected herbicides degradation products determined laboratory method analysis code GCS water samples collected 51 streams nine Midwestern States, 2002. concentrations micrograms/liter, \"<\" means \"less \". data character format allow \"<\". original report contains data herbicides. data illustrative purposes. Sample types: CR = concurrent replicate sample, FB = field blank, LD = laboratory duplicate, S1 = sample pre-emergence runoff, S2 = sample post-emergence runoff, S3 = sample harvest-season runoff.","code":""},{"path":"/reference/usgs.herbicides.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"Scribner, E.., Battaglin, W.., Dietze, J.E., Thurman, E.M., \"Reconnaissance Data Glyphosate, Selected Herbicides, Degradation Products, Antibiotics 51 streams Nine Midwestern States, 2002\". U.S. Geological Survey Open File Report 03-217. Herbicide data table 5, page 30-37. Site coordinates page 7-8. https://ks.water.usgs.gov/pubs/reports/ofr.03-217.html","code":""},{"path":"/reference/usgs.herbicides.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"None.","code":""},{"path":"/reference/usgs.herbicides.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(usgs.herbicides) dat <- usgs.herbicides libs(NADA) # create censored data for one trait dat$y <- as.numeric(dat$atrazine) dat$ycen <- is.na(dat$y) dat$y[is.na(dat$y)] <- .05 # percent censored with(dat, censummary(y, censored=ycen)) # median/mean with(dat, cenmle(y, ycen, dist=\"lognormal\")) # boxplot with(dat, cenboxplot(obs=y, cen=ycen, log=FALSE, main=\"usgs.herbicides\")) # with(dat, boxplot(y)) pp <- with(dat, ros(obs=y, censored=ycen, forwardT=\"log\")) # default lognormal plot(pp) plotfun <- function(vv){ dat$y <- as.numeric(dat[[vv]]) dat$ycen <- is.na(dat$y) dat$y[is.na(dat$y)] <- .01 # qqnorm(log(dat$y), main=vv) # ordinary qq plot shows censored values pp <- with(dat, ros(obs=y, censored=ycen, forwardT=\"log\")) plot(pp, main=vv) # omits censored values } op <- par(mfrow=c(3,3)) vnames <- c(\"acetochlor\", \"alachlor\", \"ametryn\", \"atrazine\",\"CIAT\", \"CEAT\", \"cyanazine\", #\"CAM\", \"dimethenamid\", \"flufenacet\") for(vv in vnames) plotfun(vv) par(op) } # }"},{"path":"/reference/vaneeuwijk.drymatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"Multi-environment trial maize, dry matter content","code":""},{"path":"/reference/vaneeuwijk.drymatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"","code":"data(\"vaneeuwijk.drymatter\")"},{"path":"/reference/vaneeuwijk.drymatter.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"data frame 168 observations following 5 variables. year year site site, 4 levels variety variety, 6 levels y dry matter percent","code":""},{"path":"/reference/vaneeuwijk.drymatter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"Percent dry matter given. Site codes soil type classifications: SS=Southern Sand, CS=Central Sand, NS=Northern Sand, RC=River Clay. data balanced subset data analyzed van Eeuwijk, Keizer, Bakker (1995b) Kroonenberg, Basford, Ebskamp (1995). Used permission Fred van Eeuwijk.","code":""},{"path":"/reference/vaneeuwijk.drymatter.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"van Eeuwijk, Fred . Pieter M. Kroonenberg (1998). Multiplicative Models Interaction Three-Way ANOVA, Applications Plant Breeding Biometrics, 54, 1315-1333. https://doi.org/10.2307/2533660","code":""},{"path":"/reference/vaneeuwijk.drymatter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"Kroonenberg, P.M., Basford, K.E. & Ebskamp, .G.M. (1995). Three-way cluster component analysis maize variety trials. Euphytica, 84(1):31-42. https://doi.org/10.1007/BF01677554 van Eeuwijk, F.., Keizer, L.C.P. & Bakker, J.J. Van Eeuwijk. (1995b). Linear bilinear models analysis multi-environment trials: II. application data Dutch Maize Variety Trials Euphytica, 84(1):9-22. https://doi.org/10.1007/BF01677552 Hardeo Sahai, Mario M. Ojeda. Analysis Variance Random Models, Volume 1. Page 261.","code":""},{"path":"/reference/vaneeuwijk.drymatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(vaneeuwijk.drymatter) dat <- vaneeuwijk.drymatter dat <- transform(dat, year=factor(year)) dat <- transform(dat, env=factor(paste(year,site))) libs(HH) HH::interaction2wt(y ~ year+site+variety,dat,rot=c(90,0), x.between=0, y.between=0, main=\"vaneeuwijk.drymatter\") # anova model m1 <- aov(y ~ variety+env+variety:env, data=dat) anova(m1) # Similar to VanEeuwijk table 2 m2 <- aov(y ~ year*site*variety, data=dat) anova(m2) # matches Sahai table 5.5 # variance components model libs(lme4) libs(lucid) m3 <- lmer(y ~ (1|year) + (1|site) + (1|variety) + (1|year:site) + (1|year:variety) + (1|site:variety), data=dat) vc(m3) # matches Sahai page 266 ## grp var1 var2 vcov sdcor ## year:variety (Intercept) 0.3187 0.5645 ## year:site (Intercept) 7.735 2.781 ## site:variety (Intercept) 0.03502 0.1871 ## year (Intercept) 6.272 2.504 ## variety (Intercept) 0.4867 0.6976 ## site (Intercept) 6.504 2.55 ## Residual 0.8885 0.9426 } # }"},{"path":"/reference/vaneeuwijk.fusarium.html","id":null,"dir":"Reference","previous_headings":"","what":"Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium","title":"Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium","text":"Infection wheat varieties Fusarium strains 1990 1993","code":""},{"path":"/reference/vaneeuwijk.fusarium.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium","text":"","code":"data(\"vaneeuwijk.fusarium\")"},{"path":"/reference/vaneeuwijk.fusarium.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium","text":"data frame 560 observations following 4 variables. year year, 1990-1993 strain strain fusarium gen genotype/variety y","code":""},{"path":"/reference/vaneeuwijk.fusarium.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium","text":"Data come Hungary. 20 wheat varieties infected 7 strains Fusarium years 1990-1993. measured value rating severity disease due Fusarium head blight, expressed number 1-100. Three-way interactions varieties 21 23 ones 1992 suffering strain infections. due incorrect storage innoculum (strain) rendered incapable infecting varieties. data subset data analyzed VanEeuwijk et al. 1995. Used permission Fred van Eeuwijk.","code":""},{"path":"/reference/vaneeuwijk.fusarium.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium","text":"van Eeuwijk, Fred . Pieter M. Kroonenberg (1998). Multiplicative Models Interaction Three-Way ANOVA, Applications Plant Breeding Biometrics, 54, 1315-1333. https://doi.org/10.2307/2533660","code":""},{"path":"/reference/vaneeuwijk.fusarium.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium","text":"F. . van Eeuwijk, . Mesterhazy, Ch. . Kling, P. Ruckenbauer, L. Saur, H. Burstmayr, M. Lemmens, L. C. P. Keizer, N. Maurin, C. H. . Snijders. (1995). Assessing non-specificity resistance wheat head blight caused inoculation European strains Fusarium culmorum, F. graminearum F. nivale using multiplicative model interaction. Theor Appl Genet. 90(2), 221-8. https://doi.org/10.1007/BF00222205","code":""},{"path":"/reference/vaneeuwijk.fusarium.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Infection of wheat varieties by Fusarium strains from 1990 to 1993 — vaneeuwijk.fusarium","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(vaneeuwijk.fusarium) dat <- vaneeuwijk.fusarium dat <- transform(dat, year=factor(year)) dat <- transform(dat, logity=log((y/100)/(1-y/100))) libs(HH) position(dat$year) <- c(3,9,14,19) position(dat$strain) <- c(2,5,8,11,14,17,20) HH::interaction2wt(logity ~ gen+year+strain,dat,rot=c(90,0), x.between=0, y.between=0, main=\"vaneeuwijk.fusarium\") # anova on logit scale. Near match to VanEeuwijk table 6 m1 <- aov(logity ~ gen*strain*year, data=dat) anova(m1) ## Response: logity ## Df Sum Sq Mean Sq F value Pr(>F) ## gen 19 157.55 8.292 ## strain 6 91.54 15.256 ## year 3 321.99 107.331 ## gen:strain 114 34.03 0.299 ## gen:year 57 140.94 2.473 ## strain:year 18 236.95 13.164 ## gen:strain:year 342 93.15 0.272 } # }"},{"path":"/reference/vaneeuwijk.nematodes.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes","title":"Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes","text":"number cysts 11 potato genotypes 5 potato cyst nematode populations.","code":""},{"path":"/reference/vaneeuwijk.nematodes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes","text":"","code":"data(\"vaneeuwijk.nematodes\")"},{"path":"/reference/vaneeuwijk.nematodes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes","text":"data frame 55 observations following 3 variables. gen potato genotype pop nematode population y number cysts","code":""},{"path":"/reference/vaneeuwijk.nematodes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes","text":"number cysts 11 potato genotypes 5 potato cyst nematode populations belonging species Globodera pallida. part larger table . numbers means four five replicates. Van Eeuwijk used data illustrate fitting generalized linear model.","code":""},{"path":"/reference/vaneeuwijk.nematodes.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes","text":"Fred . van Eeuwijk, (1995). Multiplicative Interaction Generalized Linear Models. Biometrics, 51, 1017-1032. https://doi.org/10.2307/2533001","code":""},{"path":"/reference/vaneeuwijk.nematodes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes","text":"Arntzen, F.K. & van Eeuwijk (1992). Variation resistance level potato genotypes virulence level potato cyst nematode populations. Euphytica, 62, 135-143. https://doi.org/10.1007/BF00037939","code":""},{"path":"/reference/vaneeuwijk.nematodes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. — vaneeuwijk.nematodes","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(vaneeuwijk.nematodes) dat <- vaneeuwijk.nematodes # show non-normality op <- par(mfrow=c(2,1), mar=c(5,4,3,2)) boxplot(y ~ pop, data=dat, las=2, ylab=\"number of cysts\") title(\"vaneeuwijk.nematodes - cysts per nematode pop\") boxplot(y ~ gen, data=dat, las=2) title(\"vaneeuwijk.nematodes - cysts per potato\") par(op) # normal distribution lm1 <- lm(y ~ gen + pop, data=dat) # poisson distribution glm1 <- glm(y ~ gen+pop,data=dat,family=quasipoisson(link=log)) anova(glm1) libs(gnm) # main-effects non-interaction model gnm0 <- gnm(y ~ pop + gen, data=dat, family=quasipoisson(link=log)) # one interaction gnm1 <- gnm(y ~ pop + gen + Mult(pop,gen,inst=1), data=dat, family=quasipoisson(link=log)) # two interactions gnm2 <- gnm(y ~ pop + gen + Mult(pop,gen,inst=1) + Mult(pop,gen,inst=2), data=dat, family=quasipoisson(link=log)) # anova(gnm0, gnm1, gnm2, test=\"F\") # only 2, not 3 axes needed # match vaneeuwijk table 2 # anova(gnm2) ## Df Deviance Resid. Df Resid. Dev ## NULL 54 8947.4 ## pop 4 690.6 50 8256.8 ## gen 10 7111.4 40 1145.4 ## Mult(pop, gen, inst = 1) 13 716.0 27 429.4 ## Mult(pop, gen, inst = 2) 11 351.1 16 78.3 # compare residual qq plots from models op <- par(mfrow=c(2,2)) plot(lm1, which=2, main=\"LM\") plot(glm1, which=2, main=\"GLM\") plot(gnm0, which=2, main=\"GNM, no interaction\") plot(gnm2, which=2, main=\"GNM, 2 interactions\") par(op) # extract interaction-term coefficients, make a biplot pops <- pickCoef(gnm2, \"[.]pop\") gens <- pickCoef(gnm2, \"[.]gen\") coefs <- coef(gnm2) A <- matrix(coefs[pops], nc = 2) B <- matrix(coefs[gens], nc = 2) A2=scale(A) B2=scale(B) rownames(A2) <- levels(dat$pop) rownames(B2) <- levels(dat$gen) # near-match with vaneeuwijk figure 1 biplot(A2,B2, expand=2.5,xlim=c(-2,2),ylim=c(-2,2), main=\"vaneeuwijk.nematodes - GAMMI biplot\") } # }"},{"path":"/reference/vargas.txe.html","id":null,"dir":"Reference","previous_headings":"","what":"Treatment x environment interaction in agronomy trials — vargas.txe","title":"Treatment x environment interaction in agronomy trials — vargas.txe","text":"Treatment x environment interaction agronomy trials","code":""},{"path":"/reference/vargas.txe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Treatment x environment interaction in agronomy trials — vargas.txe","text":"","code":"data(\"vargas.txe.covs\") data(\"vargas.txe.yield\")"},{"path":"/reference/vargas.txe.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Treatment x environment interaction in agronomy trials — vargas.txe","text":"'vargas.txe.covs' data 10 years measurements 28 environmental covariates: year year MTD mean maximum temperature December MTJ mean maximum temperature January MTF mean maximum temperature February MTM mean maximum temperature March MTA mean maximum temperature April mTD mean minimum temperature December mTJ mean minimum temperature January mTF mean minimum temperature February mTM mean minimum temperature March mTA mean minimum temperature April mTUD mean minimum temperature December mTUJ mean minimum temperature January mTUF mean minimum temperature February mTUM mean minimum temperature March mTUA mean minimum temperature April PRD total monthly precipitation December PRJ total monthly precipitation Jan PRF total monthly precipitation Feb PRM total monthly precipitation Mar SHD sun hours per day Dec SHJ sun hours per day Jan SHF sun hours per day Feb EVD total monthly evaporation Dec EVJ total monthly evaporation Jan EVF total monthly evaporation Feb EVM total monthly evaporation Mar EVA total monthly evaporation Apr 'vargas.txe.yield' dataframe contains 240 observations three variables year Year trt Treatment. See details section yield Grain yield, kg/ha","code":""},{"path":"/reference/vargas.txe.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Treatment x environment interaction in agronomy trials — vargas.txe","text":"treatment names indicate: Used permission Jose Crossa.","code":""},{"path":"/reference/vargas.txe.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Treatment x environment interaction in agronomy trials — vargas.txe","text":"Vargas, Mateo Crossa, Jose van Eeuwijk, Fred Sayre, Kenneth D. Reynolds, Matthew P. (2001). Interpreting Treatment x Environment Interaction Agronomy Trials. Agron. J., 93, 949-960. Table A1, A3. https://doi.org/10.2134/agronj2001.934949x","code":""},{"path":"/reference/vargas.txe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Treatment x environment interaction in agronomy trials — vargas.txe","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(vargas.txe.covs) data(vargas.txe.yield) libs(reshape2) libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) Z <- vargas.txe.yield Z <- acast(Z, year ~ trt, value.var='yield') levelplot(Z, col.regions=redblue, main=\"vargas.txe.yield\", xlab=\"year\", ylab=\"treatment\", scales=list(x=list(rot=90))) # Double-centered like AMMI Z <- sweep(Z, 1, rowMeans(Z)) Z <- sweep(Z, 2, colMeans(Z)) # Vargas figure 1 biplot(prcomp(Z, scale.=FALSE), main=\"vargas.txe.yield\") # Now, PLS relating the two matrices U <- vargas.txe.covs U <- scale(U) # Standardized covariates libs(pls) m1 <- plsr(Z~U) # Vargas Fig 2, flipped vertical/horizontal biplot(m1, which=\"x\", var.axes=TRUE) } # }"},{"path":"/reference/vargas.wheat1.html","id":null,"dir":"Reference","previous_headings":"","what":"Wheat yields in 7 years with genetic and environment covariates — vargas.wheat1","title":"Wheat yields in 7 years with genetic and environment covariates — vargas.wheat1","text":"Yield Durum wheat, 7 genotypes, 6 years, 16 genotypic variates 16 environment variates.","code":""},{"path":"/reference/vargas.wheat1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wheat yields in 7 years with genetic and environment covariates — vargas.wheat1","text":"","code":"data(\"vargas.wheat1.covs\") data(\"vargas.wheat1.traits\")"},{"path":"/reference/vargas.wheat1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Wheat yields in 7 years with genetic and environment covariates — vargas.wheat1","text":"vargas.wheat1.covs dataframe 6 observations following 17 variables. year year, 1990-1995 MTD Mean daily max temperature December, deg C MTJ Mean max January MTF Mean max February MTM Mean max March mTD Mean daily minimum temperature December, deg C mTJ Mean min January mTF Mean min February mTM Mean min March PRD Monthly precipitation December, mm PRJ Precipitation January PRF Precipitation February PRM Precipitation March SHD Sun hours December SHJ Sun hours January SHF Sun hours February SHM Sun hours March vargas.wheat1.traits dataframe 126 observations following 19 variables. year year, 1990-1995 rep replicate, 3 levels gen genotype, 7 levels yield yield, kg/ha ANT anthesis, days emergence MAT maturity, days emergence GFI grainfill, MAT-ANT PLH plant height, cm BIO biomass ground, kg/ha HID harvest index STW straw yield, kg/ha NSM spikes / m^2 NGM grains / m^2 NGS grains per spike TKW thousand kernel weight, g WTI weight per tiller, g SGW spike grain weight, g VGR vegetative growth rate, kg/ha/day, STW/ANT KGR kernel growth rate, mg/kernel/day","code":""},{"path":"/reference/vargas.wheat1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wheat yields in 7 years with genetic and environment covariates — vargas.wheat1","text":"Conducted Ciudad Obregon, Mexico.","code":""},{"path":"/reference/vargas.wheat1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Wheat yields in 7 years with genetic and environment covariates — vargas.wheat1","text":"Mateo Vargas Jose Crossa Ken Sayre Matthew Renolds Martha E Ramirez Mike Talbot, 1998. Interpreting Genotype x Environment Interaction Wheat Partial Least Squares Regression. Crop Science, 38, 679-689. https://doi.org/10.2135/cropsci1998.0011183X003800030010x Data provided Jose Crossa.","code":""},{"path":"/reference/vargas.wheat1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wheat yields in 7 years with genetic and environment covariates — vargas.wheat1","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(vargas.wheat1.covs) data(vargas.wheat1.traits) libs(pls) libs(reshape2) # Yield as a function of non-yield traits Y0 <- vargas.wheat1.traits[,c('gen','rep','year','yield')] Y0 <- acast(Y0, gen ~ year, value.var='yield', fun=mean) Y0 <- sweep(Y0, 1, rowMeans(Y0)) Y0 <- sweep(Y0, 2, colMeans(Y0)) # GxE residuals Y1 <- scale(Y0) # scaled columns X1 <- vargas.wheat1.traits[, -4] # omit yield X1 <- aggregate(cbind(ANT,MAT,GFI,PLH,BIO,HID,STW,NSM,NGM, NGS,TKW,WTI,SGW,VGR,KGR) ~ gen, data=X1, FUN=mean) rownames(X1) <- X1$gen X1$gen <- NULL X1 <- scale(X1) # scaled columns m1 <- plsr(Y1~X1) loadings(m1)[,1,drop=FALSE] # X loadings in Table 1 of Vargas biplot(m1, cex=.5, which=\"x\", var.axes=TRUE, main=\"vargas.wheat1 - gen ~ trait\") # Vargas figure 2a # Yield as a function of environment covariates Y2 <- t(Y0) X2 <- vargas.wheat1.covs rownames(X2) <- X2$year X2$year <- NULL Y2 <- scale(Y2) X2 <- scale(X2) m2 <- plsr(Y2~X2) loadings(m2)[,1,drop=FALSE] # X loadings in Table 2 of Vargas } # }"},{"path":"/reference/vargas.wheat2.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat with environmental covariates — vargas.wheat2","title":"Multi-environment trial of wheat with environmental covariates — vargas.wheat2","text":"yield 8 wheat genotypes measured 21 low-humidity environments. environment 13 covariates recorded.","code":""},{"path":"/reference/vargas.wheat2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat with environmental covariates — vargas.wheat2","text":"","code":"data(\"vargas.wheat2.covs\") data(\"vargas.wheat2.yield\")"},{"path":"/reference/vargas.wheat2.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat with environmental covariates — vargas.wheat2","text":"'vargas.wheat2.covs' data frame 21 observations following 14 variables. env environment CYC length growth cycle days mTC mean daily minimum temperature degrees Celsius MTC mean daily maximum temperature SHC sun hours per day mTV mean daily minimum temp vegetative stage MTV mean daily maximum temp vegetative stage SHV sun hours per day vegetative stage mTS mean daily minimum temp spike growth stage MTS mean daily maximum temp spike growth stage SHS sun hours per day spike growth stage mTG mean daily minimum temp grainfill stage MTG mean daily maximum temp grainfill stage SHG sun hours per day grainfill stage 'vargas.wheat2.yield' data frame 168 observations following 3 variables. env environment gen genotype yield yield (kg/ha)","code":""},{"path":"/reference/vargas.wheat2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat with environmental covariates — vargas.wheat2","text":"Grain yields (kg/ha) 8 wheat genotypes 21 low-humidity environments grown 1990-1994. data environment-centered genotype-centered. rows columns GxE matrix mean zero. locations experiments :","code":""},{"path":"/reference/vargas.wheat2.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat with environmental covariates — vargas.wheat2","text":"Mateo Vargas Jose Crossa Ken Sayre Matthew Renolds Martha E Ramirez Mike Talbot, 1998. Interpreting Genotype x Environment Interaction Wheat Partial Least Squares Regression, Crop Science, 38, 679–689. https://doi.org/10.2135/cropsci1998.0011183X003800030010x Data provided Jose Crossa.","code":""},{"path":"/reference/vargas.wheat2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat with environmental covariates — vargas.wheat2","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(pls,reshape2) data(vargas.wheat2.covs) datc <- vargas.wheat2.covs data(vargas.wheat2.yield) daty <- vargas.wheat2.yield # Cast to matrix daty <- acast(daty, env ~ gen, value.var='yield') rownames(datc) <- datc$env datc$env <- NULL # The pls package centers, but does not (by default) use scaled covariates # Vargas says you should # daty <- scale(daty) datc <- scale(datc) m2 <- plsr(daty ~ datc) # Plot predicted vs observed for each genotype using all components plot(m2) # Loadings # plot(m2, \"loadings\", xaxt='n') # axis(1, at=1:ncol(datc), labels=colnames(datc), las=2) # Biplots biplot(m2, cex=.5, which=\"y\", var.axes=TRUE, main=\"vargas.wheat2 - daty ~ datc\") # Vargas figure 2a biplot(m2, cex=.5, which=\"x\", var.axes=TRUE) # Vectors form figure 2 b # biplot(m2, cex=.5, which=\"scores\", var.axes=TRUE) # biplot(m2, cex=.5, which=\"loadings\", var.axes=TRUE) } # }"},{"path":"/reference/verbyla.lupin.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of lupin, multiple varieties and densities — verbyla.lupin","title":"Multi-environment trial of lupin, multiple varieties and densities — verbyla.lupin","text":"Yield 9 varieties lupin different planting densities across 2 years multiple locations.","code":""},{"path":"/reference/verbyla.lupin.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of lupin, multiple varieties and densities — verbyla.lupin","text":"gen genotype, 9 varieties site site, 11 levels rep rep, 2-3 levels rate seeding rate plants/m^2 row row col column serp factor 4 levels serpentine seeding effect linrow centered row position numeric variate (row-8.5)/10 lincol centered column position numeric variate (col-3.5) linrate linear effect seedrate, scaled (seedrate-41.92958)/10 yield yield tons/hectare year year, 1991-1992 loc location","code":""},{"path":"/reference/verbyla.lupin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of lupin, multiple varieties and densities — verbyla.lupin","text":"Nine varieties lupin tested yield response plant density 11 sites. target density 1991 10, 20, ..., 60 plants per m^2, 1992 20, 30, ..., 70 plants per m^2. Plot dimensions given. variety Myallie grown 1992. site 2 reps 1991 3 reps 1992. rep laid factorial RCB design; one randomization used sites 1991 one (different) randomization used sites 1992. (confirmed principal investigator.) 1991 Mt. Barker location, data columns 5 6 discarded due problems weeds. Variety 'Myallie' called '84L:439' Verbyla 1997. year release varieties Data retrieved Oct 2010 https://www.blackwellpublishers.co.uk/rss. (longer available). Used permission Blackwell Publishing.","code":""},{"path":"/reference/verbyla.lupin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of lupin, multiple varieties and densities — verbyla.lupin","text":"Arunas P. Verbyla Brian R. Cullis Michael G. Kenward Sue J. Welham, (1999). analysis designed experiments longitudinal data using smoothing splines. Appl. Statist., 48, 269–311. https://doi.org/10.1111/1467-9876.00154 Arunas P. Verbyla Brian R. Cullis Michael G. Kenward Sue J. Welham, (1997). analysis designed experiments longitudinal data using smoothing splines. University Adelaide, Department Statistics, Research Report 97/4. https://https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.808","code":""},{"path":"/reference/verbyla.lupin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of lupin, multiple varieties and densities — verbyla.lupin","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(verbyla.lupin) dat <- verbyla.lupin # The same RCB randomization was used at all sites in each year libs(desplot) desplot(dat, gen~col+row|site, out1=rep, num=rate, # aspect unknown main=\"verbyla.lupin - experiment design\") # Figure 3 of Verbyla libs(lattice) foo <- xyplot(yield ~ rate|loc*gen, data=dat, subset=year==92, type=c('p','smooth'), cex=.5, main=\"verbyla.lupin: 1992 yield response curves\", xlab=\"Seed rate (plants/m^2)\", ylab=\"Yield (tons/ha)\", strip=strip.custom(par.strip.text=list(cex=.7))) libs(latticeExtra) # for useOuterStrips useOuterStrips(foo, strip=strip.custom(par.strip.text=list(cex=.7)), strip.left=strip.custom(par.strip.text=list(cex=.7))) # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # We try to reproduce the analysis of Verbyla 1999. # May not be exactly the same, but is pretty close. # Check nlevels for size of random-coefficient structures # length(with(dat, table(gen))) # 9 varieties for RC1 # length(with(dat, table(gen,site))) # 99 site:gen combinations for RC2 # Make row and col into factors dat <- transform(dat, colf=factor(col), rowf=factor(row)) # sort for asreml dat <- dat[order(dat$site, dat$rowf, dat$colf),] # Make site names more useful for plots # dat <- transform(dat, site=factor(paste0(year,\".\",substring(loc,1,4)))) # Initial model from top of Verbyla table 9. m0 <- asreml(yield ~ 1 + site + linrate + site:linrate, data = dat, random = ~ spl(rate) + dev(rate) + site:spl(rate) + site:dev(rate) + str(~gen+gen:linrate, ~us(2):id(9)) # RC1 + gen:spl(rate) + gen:dev(rate) + str(~site:gen+site:gen:linrate, ~us(2):id(99)) # RC2 + site:gen:spl(rate) + site:gen:dev(rate), residual = ~ dsum( ~ ar1(rowf):ar1(colf)|site) # Spatial AR1 x AR1 ) m0 <- update(m0) m0 <- update(m0) m0 <- update(m0) m0 <- update(m0) m0 <- update(m0) # Variograms match Verbyla 1999 figure 7 (scale slightly different) plot(varioGram(m0), xlim=c(1:19), zlim=c(0,2), main=\"verbyla.lupin - variogram by site\") # Sequence of models in Verbyla 1999 table 10 m1 <- update(m0, fixed= ~ . + at(site, c(2,5,6,8,9,10)):lincol + at(site, c(3,5,7,8)):linrow + at(site, c(2,3,5,7,8,9,11)):serp , random = ~ . + at(site, c(3,6,7,9)):rowf + at(site, c(1,2,3,9,10)):colf + at(site, c(5,7,8,10)):units) m1 <- update(m1) m2 <- update(m1, random = ~ . - site:gen:spl(rate) - site:gen:dev(rate)) m3 <- update(m2, random = ~ . - site:dev(rate) - gen:dev(rate)) m4 <- update(m3, random = ~ . - dev(rate)) m5 <- update(m4, random = ~ . - at(site, c(5,7,8,10)):units + at(site, c(5,7,8)):units) # Variance components are a pretty good match to Verbyla 1997, table 15 libs(lucid) vc(m5) .001004/sqrt(.005446*.0003662) # .711 correlation for RC1 .00175/sqrt(.01881*.000167) # .987 correlation for RC2 # Matches Verbyla 1999 figure 5 plot(varioGram(m5), main=\"verbyla.lupin - final model variograms\", xlim=c(1:19), zlim=c(0,1.5)) } } # }"},{"path":"/reference/vishnaadevi.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice — vishnaadevi.rice.uniformity","title":"Uniformity trial of rice — vishnaadevi.rice.uniformity","text":"Uniformity trial rice Madurai, India.","code":""},{"path":"/reference/vishnaadevi.rice.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of rice — vishnaadevi.rice.uniformity","text":"","code":"data(\"vishnaadevi.rice.uniformity\")"},{"path":"/reference/vishnaadevi.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice — vishnaadevi.rice.uniformity","text":"data frame 288 observations following 3 variables. row row ordinate col column ordinate yield yield per plot, grams","code":""},{"path":"/reference/vishnaadevi.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice — vishnaadevi.rice.uniformity","text":"uniformity trial rice raised 2017 late samba season near Madurai, India. Note: clear outlier value '685'. outlier included, calculated value CV matches value Vishnaadevi et al. remove outlier, CV smaller value paper. means outlier value simple typo publication, actual value original data. Field width: 12 columns x 1m = 12 m Field length: 24 rows x 1m = 24m","code":""},{"path":"/reference/vishnaadevi.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice — vishnaadevi.rice.uniformity","text":"Vishnaadevi, S.; K. Prabakaran, E. Subramanian, P. Arunachalam. (2019). Determination fertility gradient direction optimum plot shape paddy crop Madurai District. Green Farming, 10, 155-159. https://www.researchgate.net/publication/333892867","code":""},{"path":"/reference/vishnaadevi.rice.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of rice — vishnaadevi.rice.uniformity","text":"None","code":""},{"path":"/reference/vishnaadevi.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice — vishnaadevi.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(vishnaadevi.rice.uniformity) dat <-vishnaadevi.rice.uniformity # CV in Table 2 for 1x1 is reported as 2.8 # sd(dat$yield)/mean(dat$yield) = .0277 # If we remove the outlier yield 685, then we calculate .0256 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=24/12, main=\"vishnaadevi.rice.uniformity\") } # }"},{"path":"/reference/vold.longterm.html","id":null,"dir":"Reference","previous_headings":"","what":"Long-term barley yields at different fertilizer levels — vold.longterm","title":"Long-term barley yields at different fertilizer levels — vold.longterm","text":"Long-term barley yields different fertilizer levels","code":""},{"path":"/reference/vold.longterm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Long-term barley yields at different fertilizer levels — vold.longterm","text":"","code":"data(\"vold.longterm\")"},{"path":"/reference/vold.longterm.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Long-term barley yields at different fertilizer levels — vold.longterm","text":"data frame 76 observations following 3 variables. year year nitro nitrogen fertilizer, grams/m^2 yield yield, grams/m^2","code":""},{"path":"/reference/vold.longterm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Long-term barley yields at different fertilizer levels — vold.longterm","text":"Trials conducted Osaker, Norway. Nitrogen fertilizer amounts increased twenty percent 1978. Vold (1998) fit Michaelis-Menten type equation different maximum year decreasing covariate non-fertilizer nitrogen. Miguez used non-linear mixed effects model asymptotic curve.","code":""},{"path":"/reference/vold.longterm.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Long-term barley yields at different fertilizer levels — vold.longterm","text":"Arild Vold (1998). generalization ordinary yield response functions. Ecological modelling, 108, 227-236. https://doi.org/10.1016/S0304-3800(98)00031-3","code":""},{"path":"/reference/vold.longterm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Long-term barley yields at different fertilizer levels — vold.longterm","text":"Fernando E. Miguez (2008). Using Non-Linear Mixed Models Agricultural Data.","code":""},{"path":"/reference/vold.longterm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Long-term barley yields at different fertilizer levels — vold.longterm","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(vold.longterm) dat <- vold.longterm libs(lattice) foo1 <- xyplot(yield ~ nitro | factor(year), data = dat, as.table=TRUE, type = \"o\", main=list(\"vold.longterm\", cex=1.5), xlab = list(\"N fertilizer\",cex=1.5,font=4), ylab = list(\"Yield\", cex=1.5)) # Long term trend shows decreasing yields xyplot(yield ~ year , data = dat, group=nitro, type='o', main=\"vold.longterm - yield level by nitrogen\", auto.key=list(columns=4)) if(0){ # Global model m1.nls <- nls(yield ~ SSasymp(nitro, max, int, lograte), data=dat) summary(m1.nls) libs(MASS) # for 'confint' confint(m1.nls) # Raw data plus global model. Year variation not modeled. pdat <- data.frame(nitro=seq(0,14,0.5)) pdat$pred <- predict(m1.nls, newdata=pdat) libs(latticeExtra) # for layers foo1 + xyplot(pred ~ nitro , data = pdat, as.table=TRUE, type='l', col='red', lwd=2) } # Separate fit for each year. Overfitting with 3x19=57 params. libs(nlme) m2.lis <- nlsList(yield ~ SSasymp(nitro,max,int,lograte) | year, data=dat) plot(intervals(m2.lis),layout = c(3,1), main=\"vold.longterm\") # lograte might be same for each year # Fixed overall asymptotic model, plus random deviations for each year # Simpler code, but less clear about what model is fit: m3.lme <- nlme(m2.lis) libs(nlme) m3.lme <- nlme(yield ~ SSasymp(nitro, max, int, lograte), data=dat, groups = ~ year, fixed = list(max~1, int~1, lograte~1), random= max + int + lograte ~ 1, start= c(max=300, int=100, rate=-2)) ## # Fixed effects are similar for the nls/lme models ## coef(m1.nls) ## fixef(m3.lme) ## # Random effects are normally distributed ## qqnorm(m3.lme, ~ ranef(.),col=\"black\") ## # Note the trend in intercept effects over time ## plot(ranef(m3.lme),layout=c(3,1)) ## # Correlation between int,lograte int,max may not be needed ## intervals(m3.lme,which=\"var-cov\") ## pairs(m3.lme,pch=19,col=\"black\") ## # Model with int uncorrelated with max,lograte. AIC is worse. ## # fit4.lm3 <- update(m3.lme, random=pdBlocked(list(max+lograte~1,int ~ 1))) ## # intervals(fit4.lm3, which=\"var-cov\") ## # anova(m3.lme, fit4.lm3) # Plot the random-effect model. Excellent fit with few parameters. pdat2 <- expand.grid(year=1970:1988, nitro=seq(0,15,length=50)) pdat2$pred <- predict(m3.lme, new=pdat2) pdat2$predf <- predict(m3.lme, new=pdat2, level=0) foo1 <- update(foo1, type='p', key=simpleKey(c(\"Observed\",\"Fixed\",\"Random\"), col=c(\"blue\",\"red\",\"darkgreen\"), points=FALSE, columns=3)) libs(latticeExtra) foo2 <- xyplot(pred~nitro|year, data=pdat2, type='l', col=\"darkgreen\", lwd=2) foo3 <- xyplot(predf~nitro|year, data=pdat2, type='l', col=\"red\",lwd=1) foo1 + foo2 + foo3 ## # Income is maximized at about 15 ## pdat2 <- transform(pdat2, income = predf*2 - 7*nitro) ## with(pdat2, xyplot(income~nitro)) } # }"},{"path":"/reference/vsn.lupin3.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of lupin, early generation trial — vsn.lupin3","title":"Multi-environment trial of lupin, early generation trial — vsn.lupin3","text":"Early generation lupin trial 3 sites, 330 test lines, 6 check lines.","code":""},{"path":"/reference/vsn.lupin3.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of lupin, early generation trial — vsn.lupin3","text":"data frame 1236 observations following 5 variables. site site, levels S1 S2 S3 col column row row gen genotype yield yield","code":""},{"path":"/reference/vsn.lupin3.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of lupin, early generation trial — vsn.lupin3","text":"early-stage multi-environment trial, 6 check lines 300 test lines. 6 check lines replicated environment. Used permission Arthur Gilmour, Brian Cullis, Robin Thompson.","code":""},{"path":"/reference/vsn.lupin3.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of lupin, early generation trial — vsn.lupin3","text":"Multi-Environment Trials - Lupins. https://www.vsni.co.uk/software/asreml/htmlhelp/asreml/xlupin.htm","code":""},{"path":"/reference/vsn.lupin3.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of lupin, early generation trial — vsn.lupin3","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(vsn.lupin3) dat <- vsn.lupin3 # Split gen into check/test, make factors dat <- within(dat, { check <- ifelse(gen>336, 0, gen) check <- ifelse(check<7, check, 7) check <- factor(check) test <- factor(ifelse(gen>6 & gen<337, gen, 0)) gen=factor(gen) }) libs(desplot) desplot(dat, yield~ col*row|site, # midpoint=\"midrange\", # aspect unknown main=\"vsn.lupin3 - yield\") # Site 1 & 2 used same randomization desplot(dat, check~ col*row|site, main=\"vsn.lupin3: check plot placement\") if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Single-site analyses suggested random row term for site 3, # random column terms for all sites, # AR1 was unnecessary for the col dimension of site 3 dat <- transform(dat, colf=factor(col), rowf=factor(row)) dat <- dat[order(dat$site, dat$colf, dat$rowf),] # Sort for asreml m1 <- asreml(yield ~ site + check:site, data=dat, random = ~ at(site):colf + at(site,3):rowf + test, residual = ~ dsum( ~ ar1(colf):ar1(rowf) + id(colf):ar1(rowf) | site, levels=list(1:2, 3) ) ) m1$loglik ## [1] -314.2616 lucid::vc(m1) ## effect component std.error z.ratio constr ## at(site, S1):colf!colf.var 0.6228 0.4284 1.5 pos ## at(site, S2):colf!colf.var 0.159 0.1139 1.4 pos ## at(site, S3):colf!colf.var 0.04832 0.02618 1.8 pos ## at(site, S3):rowf!rowf.var 0.0235 0.008483 2.8 pos ## test!test.var 0.1031 0.01468 7 pos ## site_S1!variance 2.771 0.314 8.8 pos ## site_S1!colf.cor 0.1959 0.05375 3.6 uncon ## site_S1!rowf.cor 0.6503 0.03873 17 uncon ## site_S2!variance 0.9926 0.1079 9.2 pos ## site_S2!colf.cor 0.2868 0.05246 5.5 uncon ## site_S2!rowf.cor 0.5744 0.0421 14 uncon ## site_S3!variance 0.1205 0.01875 6.4 pos ## site_S3!rowf.cor 0.6394 0.06323 10 uncon # Add site:test m2 <- update(m1, random=~. + site:test) m2$loglik ## [1] -310.8794 # CORUH structure on the site component of site:test m3 <- asreml(yield ~ site + check:site, data=dat, random = ~ at(site):colf + at(site,3):rowf + corh(site):test, residual = ~ dsum( ~ ar1(colf):ar1(rowf) + id(colf):ar1(rowf) | site, levels=list(1:2, 3) )) m3$loglik ## [1] -288.4837 # Unstructured genetic variance matrix m4 <- asreml(yield ~ site + check:site, data=dat, random = ~ at(site):colf + at(site,3):rowf + us(site):test, residual = ~ dsum( ~ ar1(colf):ar1(rowf) + id(colf):ar1(rowf) | site, levels=list(1:2, 3) )) m4$loglik ## [1] -286.8239 # Note that a 3x3 unstructured matrix can be written LL'+Psi with 1 factor L # Explicitly fit the factor analytic model m5 <- asreml(yield ~ site + check:site, data=dat, random = ~ at(site):colf + at(site,3):rowf + fa(site,1, init=c(.7,.1,.1,.5,.3,.2)):test, residual = ~ dsum( ~ ar1(colf):ar1(rowf) + id(colf):ar1(rowf) | site, levels=list(1:2, 3) )) m5$loglik # Same as m4 ## [1] -286.8484 # Model 4, Unstructured (symmetric) genetic variance matrix un <- diag(3) un[upper.tri(un,TRUE)] <- m4$vparameters[5:10] round(un+t(un)-diag(diag(un)),3) ## [,1] [,2] [,3] ## [1,] 0.992 0.158 0.132 ## [2,] 0.158 0.073 0.078 ## [3,] 0.132 0.078 0.122 # Model 5, FA matrix = LL'+Psi. Not quite the same as unstructured, # since the FA model fixes site 2 variance at 0. psi <- diag(m5$vparameters[5:7]) lam <- matrix(m5$vparameters[8:10], ncol=1) round(tcrossprod(lam,lam)+psi,3) ## [,1] [,2] [,3] ## [1,] 0.991 0.156 0.133 ## [2,] 0.156 0.092 0.078 ## [3,] 0.133 0.078 0.122 } } # }"},{"path":"/reference/wallace.iowaland.html","id":null,"dir":"Reference","previous_headings":"","what":"Iowa farmland values by county in 1925 — wallace.iowaland","title":"Iowa farmland values by county in 1925 — wallace.iowaland","text":"Iowa farmland values county 1925","code":""},{"path":"/reference/wallace.iowaland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Iowa farmland values by county in 1925 — wallace.iowaland","text":"","code":"data(\"wallace.iowaland\")"},{"path":"/reference/wallace.iowaland.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Iowa farmland values by county in 1925 — wallace.iowaland","text":"data frame 99 observations following 10 variables. county county factor, 99 levels fips FIPS code (state+county) lat latitude long longitude yield average corn yield per acre (bu) corn percent land corn grain percent land small grains untillable percent land untillable fedval land value (excluding buildings) per acre, 1925 federal census stval land value (excluding buildings) per acre, 1925 state census","code":""},{"path":"/reference/wallace.iowaland.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Iowa farmland values by county in 1925 — wallace.iowaland","text":"None.","code":""},{"path":"/reference/wallace.iowaland.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Iowa farmland values by county in 1925 — wallace.iowaland","text":"H.. Wallace (1926). Comparative Farm-Land Values Iowa. Journal Land & Public Utility Economics, 2, 385-392. Page 387-388. https://doi.org/10.2307/3138610","code":""},{"path":"/reference/wallace.iowaland.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Iowa farmland values by county in 1925 — wallace.iowaland","text":"Larry Winner. Spatial Data Analysis. https://www.stat.ufl.edu/~winner/data/iowaland.txt","code":""},{"path":"/reference/wallace.iowaland.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Iowa farmland values by county in 1925 — wallace.iowaland","text":"","code":"library(agridat) data(wallace.iowaland) dat <- wallace.iowaland # Interesting trends involving latitude libs(lattice) splom(~dat[,-c(1:2)], type=c('p','smooth'), lwd=2, main=\"wallace.iowaland\") # Means. Similar to Wallace table 1 apply(dat[, c('yield','corn','grain','untillable','fedval')], 2, mean) #> yield corn grain untillable fedval #> 39.11111 32.47475 21.55556 18.84848 118.67677 # Correlations. Similar to Wallace table 2 round(cor(dat[, c('yield','corn','grain','untillable','fedval')]),2) #> yield corn grain untillable fedval #> yield 1.00 0.30 0.27 -0.16 0.61 #> corn 0.30 1.00 0.61 -0.82 0.81 #> grain 0.27 0.61 1.00 -0.59 0.62 #> untillable -0.16 -0.82 -0.59 1.00 -0.69 #> fedval 0.61 0.81 0.62 -0.69 1.00 m1 <- lm(fedval ~ yield + corn + grain + untillable, dat) summary(m1) # estimates similar to Wallace, top of p. 389 #> #> Call: #> lm(formula = fedval ~ yield + corn + grain + untillable, data = dat) #> #> Residuals: #> Min 1Q Median 3Q Max #> -36.392 -7.797 -0.110 6.068 31.778 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) -64.7071 17.3420 -3.731 0.000326 *** #> yield 3.1488 0.3717 8.472 3.24e-13 *** #> corn 1.8175 0.3090 5.881 6.20e-08 *** #> grain 0.5394 0.2566 2.102 0.038229 * #> untillable -0.5527 0.2697 -2.049 0.043270 * #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Residual standard error: 11.61 on 94 degrees of freedom #> Multiple R-squared: 0.819,\tAdjusted R-squared: 0.8113 #> F-statistic: 106.3 on 4 and 94 DF, p-value: < 2.2e-16 #> # Choropleth map libs(maps) data(county.fips) dat <- transform(dat, polnm = paste0('iowa,',county)) # polnm example: iowa,adair libs(\"latticeExtra\") # for mapplot redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) mapplot(polnm~fedval , data=dat, colramp=redblue, main=\"wallace.iowaland - Federal land values\", xlab=\"Land value, dollars per acre\", scales=list(draw=FALSE), map=map('county', 'iowa', plot=FALSE, fill=TRUE, projection=\"mercator\")) #> Warning: 6 unmatched regions: iowa,blackhawk, iowa,buenavista, iowa,cerrogordo, iowa,d...."},{"path":"/reference/walsh.cottonprice.html","id":null,"dir":"Reference","previous_headings":"","what":"Acres and price of cotton 1910-1943 — walsh.cottonprice","title":"Acres and price of cotton 1910-1943 — walsh.cottonprice","text":"Acres price cotton 1910-1943","code":""},{"path":"/reference/walsh.cottonprice.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Acres and price of cotton 1910-1943 — walsh.cottonprice","text":"data frame 34 observations following 9 variables. year year, numeric 1910-1943 acres acres cototn (1000s) cotton price per pound (cents) previous year cottonseed price per ton (dollars) previous year combined cotton price/pound + 1.857 x cottonseed price/pound (cents) index price index, 1911-1914=100 adjcotton adjusted cotton price per pound (cents) previous year adjcottonseed adjusted cottonseed price per ton (dollars) previous year adjcombined adjusted combined price/pound (cents)","code":""},{"path":"/reference/walsh.cottonprice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Acres and price of cotton 1910-1943 — walsh.cottonprice","text":"'index' price index farm commodities.","code":""},{"path":"/reference/walsh.cottonprice.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Acres and price of cotton 1910-1943 — walsh.cottonprice","text":"R.M. Walsh (1944). Response Price Production Cotton Cottonseed, Journal Farm Economics, 26, 359-372. https://doi.org/10.2307/1232237","code":""},{"path":"/reference/walsh.cottonprice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acres and price of cotton 1910-1943 — walsh.cottonprice","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(walsh.cottonprice) dat <- walsh.cottonprice dat <- transform(dat, acres=acres/1000) # convert to million acres percentchg <- function(x){ # percent change from previous to current ix <- 2:(nrow(dat)) c(NA, (x[ix]-x[ix-1])/x[ix-1]) } # Compare percent change in acres with percent change in previous price # using constant dollars dat <- transform(dat, chga = percentchg(acres), chgp = percentchg(adjcombined)) with(dat, cor(chga, chgp, use='pair')) # .501 correlation libs(lattice) xyplot(chga~chgp, dat, type=c('p','r'), main=\"walsh.cottonprice\", xlab=\"Percent change in previous price\", ylab=\"Percent change in acres\") } # }"},{"path":"/reference/wassom.brome.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of bromegrass — wassom.brome.uniformity","title":"Uniformity trials of bromegrass — wassom.brome.uniformity","text":"Uniformity trials bromegrass Ames, Iowa, 1950-1951.","code":""},{"path":"/reference/wassom.brome.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of bromegrass — wassom.brome.uniformity","text":"","code":"data(\"wassom.brome.uniformity\")"},{"path":"/reference/wassom.brome.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of bromegrass — wassom.brome.uniformity","text":"data frame 1296 observations following 3 variables. expt experiment row row col column yield forage yield, pounds","code":""},{"path":"/reference/wassom.brome.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of bromegrass — wassom.brome.uniformity","text":"Experiments conducted Ames, Iowa. response variable forage yield pounds green weight. Optimum plot size estimated 3.5 x 7.5 feet. Wassom Kalton used two different methods estimate optimum plot size. 1. Relative efficiency different plot sizes. 2. Regression log variance yield vs log plot size. three Experiments: Experiment E1 broadcast seeded, harvested 1950. Experiment E2 row planted, harvested 1950. Experiment E3 broadcast seeded, harvested 1951. field contained mixture alfalfa brome equal proportions. plot 3.5 ft x 4 ft, orientation plot clear. Field width: 36 plots Field length: 36 plots","code":""},{"path":"/reference/wassom.brome.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of bromegrass — wassom.brome.uniformity","text":"Wassom R.R. Kalton. (1953). Estimations Optimum Plot Size Using Data Bromegrass Uniformity Trials. Agricultural Experiment Station, Iowa State College, Bulletin 396, page 314-319. https://dr.lib.iastate.edu/handle/20.500.12876/62735 https://babel.hathitrust.org/cgi/pt?id=uiug.30112019570701&view=1up&seq=26&skin=2021","code":""},{"path":"/reference/wassom.brome.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of bromegrass — wassom.brome.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(wassom.brome.uniformity) dat <- wassom.brome.uniformity libs(desplot) desplot(dat, yield~col*row|expt, flip=TRUE, aspect=1, # approximate aspect main=\"wassom.brome.uniformity\") } # }"},{"path":"/reference/waynick.soil.html","id":null,"dir":"Reference","previous_headings":"","what":"Soil nitrogen and carbon in two fields — waynick.soil","title":"Soil nitrogen and carbon in two fields — waynick.soil","text":"Soil nitrogen carbon two fields","code":""},{"path":"/reference/waynick.soil.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Soil nitrogen and carbon in two fields — waynick.soil","text":"data frame 200 observations following 6 variables. field field name, 2 levels sample sample number x x ordinate y y ordinate nitro nitrogen content, percent carbon carbon content, percent","code":""},{"path":"/reference/waynick.soil.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Soil nitrogen and carbon in two fields — waynick.soil","text":"Two fields studied, one University Farm Davis, near Oakley. Davis field silty clay loam, Oakley field blow sand.","code":""},{"path":"/reference/waynick.soil.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Soil nitrogen and carbon in two fields — waynick.soil","text":"Waynick, Dean, Sharp, Leslie. (1918). Variability soils significance past future soil investigations, -II. University California press. https://archive.org/details/variabilityinsoi45wayn","code":""},{"path":"/reference/waynick.soil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Soil nitrogen and carbon in two fields — waynick.soil","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(waynick.soil) dat <- waynick.soil # Strong relationship between N,C libs(lattice) xyplot(nitro~carbon|field, data=dat, main=\"waynick.soil\") # Spatial plot libs(sp, gstat) d1 <- subset(dat, field==\"Davis\") d2 <- subset(dat, field==\"Oakley\") coordinates(d1) <- data.frame(x=d1$x, y=d1$y) coordinates(d2) <- data.frame(x=d2$x, y=d2$y) spplot(d1, zcol = \"nitro\", cuts=8, cex = 1.6, main = \"waynick.soil - Davis field - nitrogen\", col.regions = bpy.colors(8), key.space = \"right\") # Variogram v1 <- gstat::variogram(nitro~1, data=d1) plot(v1, main=\"waynick.soil - Davis field - nitrogen\") # Maybe hasn't reached sill } # }"},{"path":"/reference/wedderburn.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley, percent of leaves affected by leaf blotch — wedderburn.barley","title":"Multi-environment trial of barley, percent of leaves affected by leaf blotch — wedderburn.barley","text":"Percent leaf area affected leaf blotch 10 varieties barley 9 sites.","code":""},{"path":"/reference/wedderburn.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley, percent of leaves affected by leaf blotch — wedderburn.barley","text":"data frame 90 observations following 3 variables. y Percent leaf area affected, 0-100. site Site factor, 9 levels gen Variety factor, 10 levels","code":""},{"path":"/reference/wedderburn.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley, percent of leaves affected by leaf blotch — wedderburn.barley","text":"Incidence Rhynchosporium secalis (leaf blotch) leaves 10 varieties barley grown 9 sites 1965.","code":""},{"path":"/reference/wedderburn.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley, percent of leaves affected by leaf blotch — wedderburn.barley","text":"Wedderburn, R W M (1974). Quasilikelihood functions, generalized linear models Gauss-Newton method. Biometrika, 61, 439–47. https://doi.org/10.2307/2334725 Wedderburn credits original data unpublished thesis J. F. Jenkyn.","code":""},{"path":"/reference/wedderburn.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of barley, percent of leaves affected by leaf blotch — wedderburn.barley","text":"McCullagh, P Nelder, J (1989). Generalized Linear Models (2nd ed). R. B. Millar. Maximum Likelihood Estimation Inference: Examples R, SAS ADMB. Chapter 8.","code":""},{"path":"/reference/wedderburn.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley, percent of leaves affected by leaf blotch — wedderburn.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(wedderburn.barley) dat <- wedderburn.barley dat$y <- dat$y/100 libs(lattice) dotplot(gen~y|site, dat, main=\"wedderburn.barley\") # Use the variance function mu(1-mu). McCullagh page 330 # Note, 'binomial' gives same results as 'quasibinomial', but also a warning m1 <- glm(y ~ gen + site, data=dat, family=\"quasibinomial\") summary(m1) # Same shape (different scale) as McCullagh fig 9.1a plot(m1, which=1, main=\"wedderburn.barley\") # Compare data and model dat$pbin <- predict(m1, type=\"response\") dotplot(gen~pbin+y|site, dat, main=\"wedderburn.barley: observed/predicted\") # Wedderburn suggested variance function: mu^2 * (1-mu)^2 # Millar shows how to do this explicitly. wedder <- list(varfun=function(mu) (mu*(1-mu))^2, validmu=function(mu) all(mu>0) && all(mu<1), dev.resids=function(y,mu,wt) wt * ((y-mu)^2)/(mu*(1-mu))^2, initialize=expression({ n <- rep.int(1, nobs) mustart <- pmax(0.001, pmin(0.99,y)) }), name=\"(mu(1-mu))^2\") m2 <- glm(y ~ gen + site, data=dat, family=quasi(link=\"logit\", variance=wedder)) #plot(m2) # Alternatively, the 'gnm' package has the 'wedderburn' family. libs(gnm) m3 <- glm(y ~ gen + site, data=dat, family=\"wedderburn\") summary(m3) # Similar to McCullagh fig 9.2 plot(m3, which=1) title(\"wedderburn.barley\") # Compare data and model dat$pwed <- predict(m3, type=\"response\") dotplot(gen~pwed+y|site, dat, main=\"wedderburn.barley\") } # }"},{"path":"/reference/weiss.incblock.html","id":null,"dir":"Reference","previous_headings":"","what":"Soybean balanced incomplete block experiment — weiss.incblock","title":"Soybean balanced incomplete block experiment — weiss.incblock","text":"Soybean balanced incomplete block experiment","code":""},{"path":"/reference/weiss.incblock.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Soybean balanced incomplete block experiment — weiss.incblock","text":"","code":"data(\"weiss.incblock\")"},{"path":"/reference/weiss.incblock.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Soybean balanced incomplete block experiment — weiss.incblock","text":"data frame 186 observations following 5 variables. block block factor gen genotype (variety) factor yield yield (bu/ac) row row col column","code":""},{"path":"/reference/weiss.incblock.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Soybean balanced incomplete block experiment — weiss.incblock","text":"Grown Ames, Iowa 1937. plot 6 feet 16 feet (2 rows, 3 feet apart). Including space plots, entire experiment 252 ft x 96 feet (7 block * 6 plots * 6 feet = 252, 16*5 plots plus 4 gaps 4 feet). Weiss shows figure field ( later doubled dize via using two rows per plot). Note 30 varieties tested. Varieties 7 14 variety (Mukden). Although total yields varieties equal, correction blocks adjusted means identical values. accuracy , however, claimed constant characteristic design. Field width: 96 feet Field length: 252 feet","code":""},{"path":"/reference/weiss.incblock.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Soybean balanced incomplete block experiment — weiss.incblock","text":"Weiss, Martin G. Cox, Gertrude M. (1939). Balanced Incomplete Block Lattice Square Designs Testing Yield Differences Among Large Numbers Soybean Varieties. Agricultural Research Bulletins, Nos. 251-259. https://lib.dr.iastate.edu/ag_researchbulletins/24/","code":""},{"path":"/reference/weiss.incblock.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Soybean balanced incomplete block experiment — weiss.incblock","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(weiss.incblock) dat <- weiss.incblock # True aspect as shown in Weiss and Cox libs(desplot) desplot(dat, yield~col*row, text=gen, shorten='none', cex=.6, out1=block, aspect=252/96, # true aspect main=\"weiss.incblock\") if(require(\"asreml\", quietly=TRUE)){ # Standard inc block analysis used by Weiss and Cox libs(asreml) m1 <- asreml(yield ~ gen + block , data=dat) predict(m1, data=dat, classify=\"gen\")$pvals ## gen pred.value std.error est.stat ## G01 24.59 0.8312 Estimable ## G02 26.92 0.8312 Estimable ## G03 32.62 0.8312 Estimable ## G04 26.97 0.8312 Estimable ## G05 26.02 0.8312 Estimable } } # }"},{"path":"/reference/weiss.lattice.html","id":null,"dir":"Reference","previous_headings":"","what":"Lattice experiment in soybeans. — weiss.lattice","title":"Lattice experiment in soybeans. — weiss.lattice","text":"Lattice experiment soybeans.","code":""},{"path":"/reference/weiss.lattice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lattice experiment in soybeans. — weiss.lattice","text":"","code":"data(\"weiss.lattice\")"},{"path":"/reference/weiss.lattice.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Lattice experiment in soybeans. — weiss.lattice","text":"data frame 196 observations following 5 variables. yield yield (bu/ac) gen genotype factor, 49 levels rep rep factor, 4 levels col column row row","code":""},{"path":"/reference/weiss.lattice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lattice experiment in soybeans. — weiss.lattice","text":"Yield test 49 soybean varieties, grown Ames, IA, 1938. Plot dimensions 3x16 feeet. varieties compared variety 26 (Mukden). clear reps positioned field. one hand, middle three columns rep/square higher yielding, giving appearance reps stacked top . hand, analysis Weiss uses 24 degrees freedom 4*(7-1) fit separate effect column rep (instead across reps).","code":""},{"path":"/reference/weiss.lattice.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Lattice experiment in soybeans. — weiss.lattice","text":"Weiss, Martin G. Cox, Gertrude M. (1939). Balanced Incomplete Block Lattice Square Designs Testing Yield Differences Among Large Numbers Soybean Varieties. Table 5. Agricultural Research Bulletins, Nos. 251-259. https://lib.dr.iastate.edu/ag_researchbulletins/24/","code":""},{"path":"/reference/weiss.lattice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lattice experiment in soybeans. — weiss.lattice","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(weiss.lattice) dat <- weiss.lattice libs(desplot) desplot(dat, yield~col*row|rep, text=gen, shorten=\"none\", cex=.8, aspect=3/16, # true aspect main=\"weiss.lattice (layout uncertain)\", xlab=\"Soybean yields\") dat <- transform(dat, xf=factor(col), yf=factor(row)) m1 <- lm(terms(yield ~ rep + rep:xf + rep:yf + gen, keep.order=TRUE), data=dat) anova(m1) # Matches Weiss table 7 ## Response: yield ## Df Sum Sq Mean Sq F value Pr(>F) ## rep 3 91.57 30.525 4.7414 0.0039709 ** ## rep:xf 24 2913.43 121.393 18.8557 < 2.2e-16 *** ## rep:yf 24 390.21 16.259 2.5254 0.0007734 *** ## gen 48 1029.87 21.456 3.3327 2.652e-07 *** ## Residuals 96 618.05 6.438 # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml) m2 <- asreml(yield ~ rep + rep:xf + rep:yf + gen, data=dat) # Weiss table 6 means wald(m2) predict(m2, data=dat, classify=\"gen\")$pvals ## gen pred.value std.error est.stat ## G01 27.74 1.461 Estimable ## G02 24.95 1.461 Estimable ## G03 24.38 1.461 Estimable ## G04 28.05 1.461 Estimable ## G05 19.6 1.461 Estimable ## G06 23.79 1.461 Estimable } } # }"},{"path":"/reference/welch.bermudagrass.html","id":null,"dir":"Reference","previous_headings":"","what":"Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers — welch.bermudagrass","title":"Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers — welch.bermudagrass","text":"Factorial experiment bermuda grass, 4x4x4, N, P, K fertilizers.","code":""},{"path":"/reference/welch.bermudagrass.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers — welch.bermudagrass","text":"data frame 64 observations following 4 variables. n nitrogen fertilizer, 4 levels p phosphorus, 4 levels k potassium, 4 levels yield yield grass, tons/ac","code":""},{"path":"/reference/welch.bermudagrass.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers — welch.bermudagrass","text":"experiment conducted 1955, 1956, 1957. 3 treatment factors: 4 n nitrogen levels: 0, 100, 200, 400 pounds/acre 4 p phosphorous levels: 0, 22, 44, 88 pounds/acre 4 k potassium levels: 0, 42, 84, 168 pounds/acre 3 blocks. harvests oven-dried. value mean 3 years 3 replications. cases, yield increased additions fertilizer nutrients.","code":""},{"path":"/reference/welch.bermudagrass.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers — welch.bermudagrass","text":"Welch, Louis Frederick Adams, William Eugenius Carmon, JL. (1963). Yield response surfaces, isoquants, economic fertilizer optima Coastal Bermudagrass. Agronomy Journal, 55, 63-67. Table 1. https://doi.org/10.2134/agronj1963.00021962005500010023x","code":""},{"path":"/reference/welch.bermudagrass.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers — welch.bermudagrass","text":"Jim Albert. Bayesian Computation R. Page 256. Peter Congdon. Bayesian Statistical Modeling. Page 124-125. P. McCullagh, John . Nelder. Generalized Linear Models, 2nd ed. Page 382.","code":""},{"path":"/reference/welch.bermudagrass.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers — welch.bermudagrass","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(welch.bermudagrass) dat <- welch.bermudagrass # Welch uses 100-pound units of n,p,k. dat <- transform(dat, n=n/100, p=p/100, k=k/100) libs(latticeExtra) useOuterStrips(xyplot(yield~n|factor(p)*factor(k), data=dat, type='b', main=\"welch.bermudagrass: yield for each P*K\", xlab=\"Nitro for each Phosphorous level\", ylab=\"Yield for each Potassim level\")) # Fit a quadratic model m1 <- lm(yield ~ n + p + k + I(n^2) + I(p^2) + I(k^2) + n:p + n:k + p:k + n:p:k, data=dat) signif(coef(m1),4) # These match the 3-yr coefficients of Welch, Table 2 ## (Intercept) n p k I(n^2) I(p^2) ## 1.94300 2.00700 1.47100 0.61880 -0.33150 -1.29500 ## I(k^2) n:p n:k p:k n:p:k ## -0.37430 0.20780 0.18740 0.23480 0.02789 # Welch Fig 4. Modeled response curves d1 <- expand.grid(n=seq(0, 4, length=50), p=0, k=0) d1$pred <- predict(m1, d1) d2 <- expand.grid(n=0, p=0, k=seq(0, 1.68, length=50)) d2$pred <- predict(m1, d2) d3 <- expand.grid(n=0, p=seq(0, .88, length=50), k=0) d3$pred <- predict(m1, d3) op <- par(mfrow=c(1,3), mar=c(5,3,4,1)) plot(pred~n, data=d1, type='l', ylim=c(0,6), xlab=\"N 100 lb/ac\", ylab=\"\") plot(pred~k, data=d2, type='l', ylim=c(0,6), xlab=\"K 100 lb/ac\", ylab=\"\") title(\"welch.bermudagrass - Predicted yield vs fertilizer\", outer=TRUE, line= -3) plot(pred~p, data=d3, type='l', ylim=c(0,6), xlab=\"P 100 lb/ac\", ylab=\"\") par(op) # Brute-force grid-search optimization of fertilizer quantities, using # $25/ton for grass, $.12/lb for N, $.18/lb for P, $.07/lb for K # Similar to Example 5 in Table 4 of Welch d4 <- expand.grid(n=seq(3,4,length=20), p=seq(.5, 1.5, length=20), k=seq(.8, 1.8, length=20)) d4$pred <- predict(m1, newdata=d4) d4 <- transform(d4, income = 25*pred - .12*n*100 + -.18*p*100 -.07*k*100) d4[which.max(d4$income),] # Optimum at 300 lb N, 71 lb P, 148 lb K # ----- JAGS ----- if(0){ # Congdon (2007) p. 124, provides a Bayesian model based on a GLM # by McCullagh & Nelder. We use JAGS and simplify the code. # y ~ gamma with shape = nu, scale = nu * eps_i # 1/eps = b0 + b1/(N+a1) + b2/(P+a2) + b3/(K+a3) # N,P,K are added fertilizer amounts, a1,a2,a3 are background # nutrient levels and b1,b2,b3 are growth parameters. libs(rjags) mod.bug = \"model { for(i in 1:nobs) { yield[i] ~ dgamma(nu, mu[i]) mu[i] <- nu * eta[i] eta[i] <- b0 + b1 / (N[i]+a1) + b2 / (P[i]+a2) + b3 / (K[i]+a3) yhat[i] <- 1 / eta[i] } # Hyperparameters nu ~ dgamma(0.01, 0.01) a1 ~ dnorm(40, 0.01) # Informative priors a2 ~ dnorm(22, 0.01) a3 ~ dnorm(32, 0.01) b0 ~ dnorm(0, 0.0001) b1 ~ dnorm(0, 0.0001) I(0,) # Keep b1 non-negative b2 ~ dnorm(0, 0.0001) I(0,) b3 ~ dnorm(0, 0.0001) I(0,) }\" jdat <- with(welch.bermudagrass, list(yield=yield, N=n, P=p, K=k, nobs=64)) jinit = list(a1=40, a2=22, a3=32, b0=.1, b1=10, b2=1, b3=1) oo <- textConnection(mod.bug) j1 <- jags.model(oo, data=jdat, inits=jinit, n.chains=3) close(oo) c1 <- coda.samples(j1, c(\"b0\",\"b1\",\"b2\",\"b3\", \"a1\",\"a2\",\"a3\"), n.iter=10000) # Results nearly identical go Congdon print(summary(c1)$statistics[,1:2],dig=1) # libs(lucid) # print(vc(c1),3) ## Mean SD ## a1 44.85 4.123 ## a2 23.63 7.37 ## a3 35.42 8.57 ## b0 0.092 0.0076 ## b1 13.23 1.34 ## b2 1.186 0.47 ## b3 1.50 0.48 d2 <- coda.samples(j1, \"yhat\", n.iter=10000) dat$yhat <- summary(d2)$statistics[,1] with(dat, plot(yield, yield-yhat)) } } # }"},{"path":"/reference/wheatley.carrot.html","id":null,"dir":"Reference","previous_headings":"","what":"Insecticide treatments for carrot fly larvae — wheatley.carrot","title":"Insecticide treatments for carrot fly larvae — wheatley.carrot","text":"Insecticide treatments carrot fly larvae. Two insecticides five depths.","code":""},{"path":"/reference/wheatley.carrot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Insecticide treatments for carrot fly larvae — wheatley.carrot","text":"","code":"data(\"wheatley.carrot\")"},{"path":"/reference/wheatley.carrot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Insecticide treatments for carrot fly larvae — wheatley.carrot","text":"data frame 36 observations following 6 variables. treatment treatment factor, 11 levels insecticide insecticide factor depth depth rep block damaged number damaged plants total total number plants","code":""},{"path":"/reference/wheatley.carrot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Insecticide treatments for carrot fly larvae — wheatley.carrot","text":"1964 experiment conducted microplots evaluate effectiveness treatments carrot fly larvae. treatment factor combination insecticide depth. Hardin & Hilbe used data fit generalized binomial model. Famoye (1995) used data fit generalized binomial regression model. Results Famoye shown.","code":""},{"path":"/reference/wheatley.carrot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Insecticide treatments for carrot fly larvae — wheatley.carrot","text":"G Wheatley & H Freeman. (1982). method using proportions undamaged carrots parsnips estimate relative population densities carrot fly (Psila rosae) larvae, practical applications. Annals Applied Biology, 100, 229-244. Table 2. https://doi.org/10.1111/j.1744-7348.1982.tb01935.x","code":""},{"path":"/reference/wheatley.carrot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Insecticide treatments for carrot fly larvae — wheatley.carrot","text":"James William Hardin, Joseph M. Hilbe. Generalized Linear Models Extensions, 2nd ed. F Famoye (1995). Generalized Binomial Regression. Biom J, 37, 581-594.","code":""},{"path":"/reference/wheatley.carrot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Insecticide treatments for carrot fly larvae — wheatley.carrot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(wheatley.carrot) dat <- wheatley.carrot # Observed proportions of damage dat <- transform(dat, prop=damaged/total) libs(lattice) xyplot(prop~depth|insecticide, data=dat, subset=treatment!=\"T11\", cex=1.5, main=\"wheatley.carrot\", ylab=\"proportion damaged\") # Model for Wheatley. Deviance for treatment matches Wheatley, but other # deviances do not. Why? # treatment:rep is the residual m1 <- glm(cbind(damaged,total-damaged) ~ rep + treatment + treatment:rep, data=dat, family=binomial(\"cloglog\")) anova(m1) # GLM of Hardin & Hilbe p. 161. By default, R uses T01 as the base, # but Hardin uses T11. Results match. m2 <- glm(cbind(damaged,total-damaged) ~ rep + C(treatment, base=11), data=dat, family=binomial(\"cloglog\")) summary(m2) } # }"},{"path":"/reference/wiebe.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — wiebe.wheat.uniformity","title":"Uniformity trial of wheat — wiebe.wheat.uniformity","text":"Uniformity trial wheat Aberdeen, Idaho, 1927.","code":""},{"path":"/reference/wiebe.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — wiebe.wheat.uniformity","text":"data frame 1500 observations following 3 variables. row row col column (series) yield yield grams per plot","code":""},{"path":"/reference/wiebe.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — wiebe.wheat.uniformity","text":"Yield trial conducted 1927 near Aberdeen, Idaho. crop Federation wheat (C.. 4734). Plots seeded April 18 drill sowed eight rows time. Individual rows harvested August threshed small nursery thresher. authors recommend analyzing square root yields. Rows 15 feet long, 1 foot apart. Field width: 12 columns * 15 feet = 180 feet wide. Field length: 125 rows * 12 = 125 feet","code":""},{"path":"/reference/wiebe.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — wiebe.wheat.uniformity","text":"Wiebe, G.. 1935. Variation Correlation Grain Yield among 1,500 Wheat Nursery Plots. Journal Agricultural Research, 50, 331-357. https://naldc.nal.usda.gov/download/IND43968632/PDF","code":""},{"path":"/reference/wiebe.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — wiebe.wheat.uniformity","text":"D.. Preece, 1981, Distributions final digits data, Statistician, 30, 31–60. https://doi.org/10.2307/2987702 Wilkinson et al. (1983). Nearest Neighbour (NN) Analysis Field Experiments. J. R. Statist. Soc. B, 45, 151-211. https://doi.org/10.1111/j.2517-6161.1983.tb01240.x https://www.jstor.org/stable/2345523 Wiebe, G.. 1937. Error grain yield attending misspaced wheat nursery rows extent misspacing effect. Journal American Society Agronomy, 29, 713-716. F. Yates (1939). comparative advantages systematic randomized arrangements design agricultural biological experiments. Biometrika, 30, 440-466, p. 465 https://archive.org/details/.ernet.dli.2015.231848/page/n473","code":""},{"path":"/reference/wiebe.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — wiebe.wheat.uniformity","text":"","code":"library(agridat) data(wiebe.wheat.uniformity) dat <- wiebe.wheat.uniformity libs(desplot) desplot(dat, yield~col+row, aspect=125/180, flip=TRUE, # true aspect main=\"wiebe.wheat.uniformity: yield\") # row 1 is at south # Preece (1981) found the last digits have an interesting distribution # with 0 and 5 much more common than other digits. dig <- substring(dat$yield, nchar(dat$yield)) dig <- as.numeric(dig) hist(dig, breaks=0:10-.5, xlab=\"Last digit\", main=\"wiebe.wheat.uniformity - histogram of last digit\") table(dat$col, dig) # Table 3 of Preece #> dig #> 0 1 2 3 4 5 6 7 8 9 #> 1 61 0 0 0 0 64 0 0 0 0 #> 2 67 0 0 0 0 58 0 0 0 0 #> 3 65 0 0 0 0 60 0 0 0 0 #> 4 66 0 0 0 0 59 0 0 0 0 #> 5 55 0 0 0 0 70 0 0 0 0 #> 6 54 0 0 0 0 71 0 0 0 0 #> 7 69 0 0 0 0 56 0 0 0 0 #> 8 65 0 0 0 0 60 0 0 0 0 #> 9 9 10 7 9 19 19 13 16 13 10 #> 10 13 18 12 10 11 17 14 9 8 13 #> 11 13 9 16 13 16 18 8 6 16 10 #> 12 17 0 11 16 12 17 1 16 20 15 # Wilkinson (1983, p. 152) noted that an 8-row planter was used which # produced a recurring pattern of row effects on yield. This can be seen # in the high autocorrelations of row means at lag 8 and lag 16 rowm <- tapply(dat$yield, dat$row, mean) acf(rowm, main=\"wiebe.wheat.uniformity row means\") # Plot the row mean against the planter row unit 1-8 libs(\"lattice\") xyplot(rowm~rep(1:8, length=125), main=\"wiebe.wheat.uniformity\", xlab=\"Planter row unit\", ylab=\"Row mean yield\") # Wiebe (1937) and Yates (1939) show the effect of \"guess rows\" # caused by the 8-row drill passing back and forth through # the field. # Yates gives the distance between strips (8 rows per strip) as: # 10.2,12.4,11.7,13.4,10.6,14.2,11.8,13.8,12.2,13.1,11.2,14,11.3,12.9,12.4 # First give each row 12 inches of growing width between rows tmp <- data.frame(row=1:125,area=12) # Distance between rows 8,9 is 10.2 inches, so we give these two # rows 6 inches (on the 'inside' of the strip) and 10.2/2=5.1 inches # on the outside of the strip, total 11.1 inches tmp$area[8:9] <- 6 + 10.2/2 tmp$area[16:17] <- 6 + 12.4/2 tmp$area[24:25] <- 6 + 11.7/2 tmp$area[32:33] <- 6 + 13.4/2 tmp$area[40:41] <- 6 + 10.6/2 tmp$area[48:49] <- 6 + 14.2/2 tmp$area[56:57] <- 6 + 11.8/2 tmp$area[64:65] <- 6 + 13.8/2 tmp$area[72:73] <- 6 + 12.2/2 tmp$area[80:81] <- 6 + 13.1/2 tmp$area[88:89] <- 6 + 11.2/2 tmp$area[96:97] <- 6 + 14.0/2 tmp$area[104:105] <- 6 + 11.3/2 tmp$area[112:113] <- 6 + 12.9/2 tmp$area[120:121] <- 6 + 12.4/2 dat <- merge(dat, tmp) # It's not clear if Wiebe used border rows...we delete them dat <- subset(dat, row > 1 & row < 125) # Wiebe (1937) calculated a moving average to adjust for fertility # effects, then used only the OUTER rows of each 8-row drill strip # and found 21.5 g / inch of space between rows. We used all the # data without correcting for fertility and obtained 33.1 g / inch. xyplot(yield ~ area, dat, type=c('p','r'), main=\"wiebe.wheat.uniformity\", xlab=\"Average area per row\", ylab=\"Yield\") coef(lm(yield ~ area, dat))[2] #> area #> 33.1033 # 33.1"},{"path":"/reference/wiedemann.safflower.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of safflower — wiedemann.safflower.uniformity","title":"Uniformity trial of safflower — wiedemann.safflower.uniformity","text":"Uniformity trial safflower Farmington, Utah, 1960.","code":""},{"path":"/reference/wiedemann.safflower.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of safflower — wiedemann.safflower.uniformity","text":"","code":"data(\"wiedemann.safflower.uniformity\")"},{"path":"/reference/wiedemann.safflower.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of safflower — wiedemann.safflower.uniformity","text":"data frame 1782 observations following 3 variables. row row col column yield yield, grams","code":""},{"path":"/reference/wiedemann.safflower.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of safflower — wiedemann.safflower.uniformity","text":"trial planted University Field Station, Farmington, Utah, 1960, plot land one half acre size. soil uniform...northern third field clay rest gravelly. Rows planted 22 inches apart, 62 rows total, row running length field. harvest, 4 rows removed side, 12 feet removed end. row harvested five-foot lengths, threshed, seed weighed nearest gram. northern third field yields twice high remaining part field soil better moisture retention. remaining part field yields uniform. Wiedemann determined optimum plot size 8 basic plots. shape plot important. , two-row plots recommended simplicity harvest, 3.33 feet 20 feet. Based operational costs, K1=74 percent K2=26 percent. Field width: 33 plots/ranges * 5ft = 165 feet Field length: 54 rows * 22 /row = 99 feet original source document columns labeled 33, 32, ... 1. columns labeled 1:33 plotting tools work normally. See Wiedemann figure 8. Wiedemann notes statistical analysis data required 100 hours labor. Today analysis takes second. R package, tables Wiedemann converted OCR digital format, values checked hand.","code":""},{"path":"/reference/wiedemann.safflower.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of safflower — wiedemann.safflower.uniformity","text":"Wiedemann, Alfred Max. 1962. Estimation Optimum Plot Size Shape Use Safflower Yield Trails. Table 5. Graduate Theses Dissertations. Paper 3600. Table 5. https://digitalcommons.usu.edu/etd/3600 https://doi.org/10.26076/7184-afa1","code":""},{"path":"/reference/wiedemann.safflower.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of safflower — wiedemann.safflower.uniformity","text":"None.","code":""},{"path":"/reference/wiedemann.safflower.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of safflower — wiedemann.safflower.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(wiedemann.safflower.uniformity) dat <- wiedemann.safflower.uniformity # CV of entire field = 39 sd(dat$yield)/mean(dat$yield) libs(desplot) desplot(dat, yield~col*row, flip=TRUE, tick=TRUE, aspect =99/165, # true aspect main=\"wiedemann.safflower.uniformity (true shape)\") libs(agricolae) libs(reshape2) dmat <- acast(dat, row~col, value.var='yield') agricolae::index.smith(dmat, main=\"wiedemann.safflower.uniformity\", col=\"red\") } # }"},{"path":"/reference/williams.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of barley — williams.barley.uniformity","title":"Uniformity trial of barley — williams.barley.uniformity","text":"Uniformity trial barley Narrabri, New South Wales, 1984.","code":""},{"path":"/reference/williams.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of barley — williams.barley.uniformity","text":"data frame 720 observations following 3 variables. row row col column yield grain yield kg/ha divided 10","code":""},{"path":"/reference/williams.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of barley — williams.barley.uniformity","text":"Grown Roseworthy Agricultural College. Plots 5 m long (4 m sown, 3.3 m harvested) 0.75 m wide. three-plot seeder used, planting serpentine fashion. Williams noted appears middle plot pass lower yield, possibly due soil compaction tractor. Field width: 48 plots * .75 m = 36 m Field length: 15 plots * 5 m = 75 m","code":""},{"path":"/reference/williams.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of barley — williams.barley.uniformity","text":"Williams, ER Luckett, DJ. 1988. use uniformity data design analysis cotton barley variety trials. Australian Journal Agricultural Research, 39, 339-350. https://doi.org/10.1071/AR9880339","code":""},{"path":"/reference/williams.barley.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of barley — williams.barley.uniformity","text":"Maria Xose Rodriguez-Alvarez, Martin P. Boer, Fred . van Eeuwijk, Paul H. C. Eilersd (2018). Correcting spatial heterogeneity plant breeding experiments P-splines. Spatial Statistics, 23, 52-71. https://doi.org/10.1016/j.spasta.2017.10.003","code":""},{"path":"/reference/williams.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of barley — williams.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(williams.barley.uniformity) dat <- williams.barley.uniformity libs(desplot) desplot(dat, yield ~ col*row, aspect= 75/36, # true aspect main=\"williams.barley.uniformity\") # Smoothed contour/persp plot like Williams Fig 1b, 2b libs(lattice) dat$fit <- fitted(loess(yield~col*row, dat, span=.1)) contourplot(fit~col*row, data=dat, aspect=75/36, region=TRUE, col.regions=RedGrayBlue, main=\"williams.barley.uniformity\") wireframe(fit~col*row, data=dat, zlim=c(100, 350), main=\"williams.barley.uniformity\") # Williams table 1 anova(aov(yield ~ factor(row) + factor(col), dat)) } # }"},{"path":"/reference/williams.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton — williams.cotton.uniformity","title":"Uniformity trial of cotton — williams.cotton.uniformity","text":"Uniformity trial cotton Narrabri, New South Wales, 1984.","code":""},{"path":"/reference/williams.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton — williams.cotton.uniformity","text":"data frame 288 observations following 3 variables. row row col column yield lint yield, kg/ha divided 10","code":""},{"path":"/reference/williams.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton — williams.cotton.uniformity","text":"Cotton uniformity trial grown Narrabri, New South Wales, 1984-1985. Plots 12m long, 1m apart, 12 rows 24 columns, irrigation furrow columns. Field width: 24 plots * 1 m = 24 m Field length: 12 plots * 12 m = 144 m","code":""},{"path":"/reference/williams.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton — williams.cotton.uniformity","text":"Williams, ER Luckett, DJ. 1988. use uniformity data design analysis cotton barley variety trials. Australian Journal Agricultural Research, 39, 339-350. https://doi.org/10.1071/AR9880339","code":""},{"path":"/reference/williams.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton — williams.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(williams.cotton.uniformity) dat <- williams.cotton.uniformity libs(desplot) desplot(dat, yield ~ col*row, aspect=144/24, # true aspect main=\"williams.cotton.uniformity\") # Smoothed contour/persp plot like Williams 1988 Fig 1a, 2a dat$fit <- fitted(loess(yield~col*row, dat, span=.5)) libs(\"lattice\") contourplot(fit~col*row, data=dat, aspect=144/24, region=TRUE, cuts=6, col.regions=RedGrayBlue, main=\"williams.cotton.uniformity\") # wireframe(fit~col*row, data=dat, zlim=c(100, 250), # main=\"williams.cotton.uniformity\") # Williams table 1 anova(aov(yield ~ factor(row) + factor(col), dat)) } # }"},{"path":"/reference/williams.trees.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand — williams.trees","title":"Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand — williams.trees","text":"Multi-environment trial trees, height / survival 37 species 6 sites Thailand","code":""},{"path":"/reference/williams.trees.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand — williams.trees","text":"data frame 222 observations following 4 variables. env Environment factor, 6 levels gen Genetic factor, 37 levels height Height (cm) survival Survival percentage","code":""},{"path":"/reference/williams.trees.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand — williams.trees","text":"Planted 1985 six sites Thailand. RCB 3 reps. data mean three reps. Plots 5 meters square spacing 2m x 2m. Measurements collected 24 months. gen column data actually seedlot, tree species multiple seed lots. trees mostly acacia eucalyptus. Used permission Emlyn Williams.","code":""},{"path":"/reference/williams.trees.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand — williams.trees","text":"Williams, ER Luangviriyasaeng, V. 1989. Statistical analysis tree species trial seedlot:site interaction Thailand. Chapter 14 Trees Tropics: Growing Australian Multipurpose Trees Shrubs Developing Countries. Pages 145–152. https://aciar.gov.au/publication/MN010","code":""},{"path":"/reference/williams.trees.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand — williams.trees","text":"E. R. Williams . C. Matheson C. E Harwood, Experimental Design Analysis Tree Improvement. CSIRO Publishing, 2002.","code":""},{"path":"/reference/williams.trees.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand — williams.trees","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(williams.trees) dat <- williams.trees libs(lattice) xyplot(survival~height|env,dat, main=\"williams.trees\", xlab=\"Height\", ylab=\"Percent surviving\") } # }"},{"path":"/reference/woodman.pig.html","id":null,"dir":"Reference","previous_headings":"","what":"Weight gain in pigs for different treatments — woodman.pig","title":"Weight gain in pigs for different treatments — woodman.pig","text":"Weight gain pigs different treatments, initial weight feed eaten covariates.","code":""},{"path":"/reference/woodman.pig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weight gain in pigs for different treatments — woodman.pig","text":"","code":"data(\"woodman.pig\")"},{"path":"/reference/woodman.pig.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weight gain in pigs for different treatments — woodman.pig","text":"data frame 30 observations following 7 variables. pen pen treatment diet pig pig number sex sex weight1 initial weight pounds, week 0 weight2 final weight pounds, week 16 feed feed eaten pounds w0 initial weight g average weekly gain h half rate change growth","code":""},{"path":"/reference/woodman.pig.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Weight gain in pigs for different treatments — woodman.pig","text":"Six pigs 5 pens fed individually. litter 3 males 3 females chosen pen. Three different diet treatments used. Note: Woodman gives initial weights nearest 0.5 pounds. w0, g, h columns Wishart 1938. Wishart used weekly weight measurements (available) fit quadratic growth curves pig reported constants. data widely used many authors.","code":""},{"path":"/reference/woodman.pig.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Weight gain in pigs for different treatments — woodman.pig","text":"Woodman, Evans, Callow & Wishart (1936). nutrition bacon pig. . influence high levels protein intake growth, conformation quality bacon pig. Journal Agricultural Science, 26, 546 - 619. Table V, Page 557. https://doi.org/10.1017/S002185960002308X Wishart, J. (1938). Growth-rate determinations nutrition studies bacon pig analysis. Biometrika, 30: 16-28. Page 20, table 2. https://doi.org/10.2307/2332221","code":""},{"path":"/reference/woodman.pig.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Weight gain in pigs for different treatments — woodman.pig","text":"Wishart (1950) Table 2, p 17. Bernard Ostle (1963). Statistics Research, 2nd ed. Page 455. https://archive.org/details/secondeditionsta001000mbp Henry Scheffe (1999). Analysis Variance. Page 217. Peter H Westfall, Randall Tobias, Russell D Wolfinger (2011). Multiple Comparisons Multiple Tests using SAS. Sec 8.3.","code":""},{"path":"/reference/woodman.pig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weight gain in pigs for different treatments — woodman.pig","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(woodman.pig) dat <- woodman.pig # add day of year for each weighing dat <- transform(dat, date1=36, date2=148) plot(NA, xlim=c(31,153), ylim=c(28,214), xlab=\"day of year\", ylab=\"weight\") segments(dat$date1, dat$weight1, dat$date2, dat$weight2, col=as.numeric(as.factor(dat$treatment))) title(\"woodman.pig\") # Average gain per week dat <- transform(dat, pen=factor(pen), treatment=factor(treatment), sex=factor(sex)) m1 <- lm(g ~ -1 + pen + treatment +sex + treatment:sex + w0, data=dat) anova(m1) # Compare diets. Results similar to Westfall 8.13 libs(emmeans) pairs(emmeans(m1, \"treatment\")) # NOTE: Results may be misleading due to involvement in interactions # contrast estimate SE df t.ratio p.value # A - B 0.4283 0.288 19 1.490 0.3179 # A - C 0.5200 0.284 19 1.834 0.1857 # B - C 0.0918 0.288 19 0.319 0.9456 } # }"},{"path":"/reference/wyatt.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity","title":"Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity","text":"Uniformity trial oats wheat ground.","code":""},{"path":"/reference/wyatt.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity","text":"","code":"data(\"wyatt.multi.uniformity\")"},{"path":"/reference/wyatt.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity","text":"data frame 258 observations following 5 variables. col column row row yield yield, bu/ac year year crop crop","code":""},{"path":"/reference/wyatt.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity","text":"Experiments conducted Soils Experimental field University Alberta, Canada. Oats grown 1925, average yield 88 bu/ac. Wheat grown 1926, average yield 32.2 bu/ac. data reported relative yields within year. plot size rows 1 2 (Series B original paper) 1/10th acre. plot size row 3 1/11 acre. Field length: 3 plots (140 ft, 140 ft, 128 ft) + 2 roads * 16 feet = 440 feet. Field width: 43 plots * 37 ft = 1591 feet.","code":""},{"path":"/reference/wyatt.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity","text":"F. . Wyatt (1927). Variation plot yields due soil heterogeneity. Scientific Agriculture, 7, 248-256. Table 1. https://doi.org/10.4141/sa-1927-0020","code":""},{"path":"/reference/wyatt.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity","text":"None","code":""},{"path":"/reference/wyatt.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of oats and wheat on the same ground. — wyatt.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(wyatt.multi.uniformity) dat <- wyatt.multi.uniformity # range of yields. Wyatt has 48.6 bu/ac for oats, 10.4 for wheat # diff(range(na.omit(subset(dat, crop==\"oats\")$yield)/100*88)) # 48.4 # diff(range(na.omit(subset(dat, crop==\"wheat\")$yield)/100*32.8)) # 10.5 # std dev. Wyatt has 9.18 bu/ac for oats, 2.06 for wheat, 2.06 for wheat # sd(na.omit(subset(dat, crop==\"oats\")$yield)/100*88) # 9.11 # sd(na.omit(subset(dat, crop==\"wheat\")$yield)/100*32.8) # 2.14 # correlation across years. Wyatt has .08 # cor(reshape2::acast(dat, row+col ~ crop, value.var=\"yield\"), use=\"pair\") # Fig 3 libs(lattice) xyplot(yield ~ col|factor(row), dat, group=crop, main=\"wyatt.multi.uniformity\", type='l', layout=c(1,3), auto.key=TRUE ) libs(desplot) desplot(dat, yield ~ col*row, subset=crop==\"oats\", tick=TRUE, aspect=(440)/(1591), # true aspect main=\"wyatt.multi.uniformity - 1925 oats\") desplot(dat, yield ~ col*row, subset=crop==\"wheat\", aspect=(440)/(1591), # true aspect main=\"wyatt.multi.uniformity - 1926 wheat\") } # }"},{"path":"/reference/yan.winterwheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of winter wheat in Ontario — yan.winterwheat","title":"Multi-environment trial of winter wheat in Ontario — yan.winterwheat","text":"Yield 18 varieties winter wheat grown 9 environments Ontario 1993.","code":""},{"path":"/reference/yan.winterwheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of winter wheat in Ontario — yan.winterwheat","text":"yield mean several reps, measured metric tons per hectare. data often used illustrate GGE biplots.","code":""},{"path":"/reference/yan.winterwheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of winter wheat in Ontario — yan.winterwheat","text":"data frame 162 observations following 3 variables. gen genotype env environment yield yield metric tons per hectare Used permission Weikai Yan.","code":""},{"path":"/reference/yan.winterwheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of winter wheat in Ontario — yan.winterwheat","text":"Weikai Yan M.S. Kang (2002). GGE biplot analysis: graphical tool breeders, geneticists, agronomists. CRC. Page 59. Weikai Yan Nicholas . Tinker. 2006. Biplot analysis multi-environment trial data: Principles applications. Table 1.","code":""},{"path":"/reference/yan.winterwheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of winter wheat in Ontario — yan.winterwheat","text":"Weikai Yan Manjit S. Kang Baoluo Ma Sheila Woods, 2007, GGE Biplot vs. AMMI Analysis Genotype--Environment Data, Crop Science, 2007, 47, 641–653. https://doi.org/10.2135/cropsci2006.06.0374","code":""},{"path":"/reference/yan.winterwheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of winter wheat in Ontario — yan.winterwheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(yan.winterwheat) dat <- yan.winterwheat libs(gge) m1 <- gge(dat, yield ~ gen*env) biplot(m1, flip=c(1,1), hull=TRUE, main=\"yan.winterwheat - GGE biplot\") } # }"},{"path":"/reference/yang.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley","title":"Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley","text":"Yield 6 barley varieties 18 locations Alberta.","code":""},{"path":"/reference/yang.barley.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley","text":"","code":"data(\"yang.barley\")"},{"path":"/reference/yang.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley","text":"data frame 108 observations following 3 variables. site site factor, 18 levels gen genotype factor, 6 levels yield yield, Mg/ha","code":""},{"path":"/reference/yang.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley","text":"experiment 2003. Yang (2013) uses data illustrate procedure bootstrapping biplots. Used permission Rong-Cai Yang.","code":""},{"path":"/reference/yang.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley","text":"Rong-Cai Yang (2007). Mixed-Model Analysis Crossover Genotype-Environment Interactions. Crop Science, 47, 1051-1062. https://doi.org/10.2135/cropsci2006.09.0611","code":""},{"path":"/reference/yang.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley","text":"Zhiqiu Hu Rong-Cai Yang, (2013). Improved Statistical Inference Graphical Description Interpretation Genotype x Environment Interaction. Crop Science, 53, 2400-2410. https://doi.org/10.2135/cropsci2013.04.0218","code":""},{"path":"/reference/yang.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. — yang.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(yang.barley) dat <- yang.barley libs(reshape2) dat <- acast(dat, gen~site, value.var='yield') ## For bootstrapping of a biplot, see the non-cran packages: ## 'bbplot' and 'distfree.cr' ## https://statgen.ualberta.ca/index.html?open=software.html ## install.packages(\"https://statgen.ualberta.ca/download/software/bbplot_1.0.zip\") ## install.packages(\"https://statgen.ualberta.ca/download/software/distfree.cr_1.5.zip\") ## libs(SDMTools) ## libs(distfree.cr) ## libs(bbplot) ## d1 <- bbplot.boot(dat, nsample=2000) # bootstrap the data ## plot(d1) # plot distributions of principal components ## b1 <- bbplot(d1) # create data structures for the biplot ## plot(b1) # create the confidence regions on the biplot } # }"},{"path":"/reference/yates.missing.html","id":null,"dir":"Reference","previous_headings":"","what":"Factorial experiment of potato, 3x3 with missing values — yates.missing","title":"Factorial experiment of potato, 3x3 with missing values — yates.missing","text":"Factorial experiment potato, 3x3 missing values.","code":""},{"path":"/reference/yates.missing.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Factorial experiment of potato, 3x3 with missing values — yates.missing","text":"data frame 80 observations following 3 variables. trt treatment factor, 8 levels block block, 10 levels y infection intensity n nitrogen treatment, 2 levels p phosphorous treatment, 2 levels k potassium treatment, 2 levels","code":""},{"path":"/reference/yates.missing.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Factorial experiment of potato, 3x3 with missing values — yates.missing","text":"response variable y intensity infection potato tubers innoculated Phytophthora Erythroseptica. 3 treatment factors: 2 nitrogen levels 2 phosphorous levels 2 potassium levels Yates (1933) presents iterative algorithm estimate missing values matrix, using data example.","code":""},{"path":"/reference/yates.missing.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Factorial experiment of potato, 3x3 with missing values — yates.missing","text":"F. Yates (1933). analysis replicated experiments field results incomplete. Emp. J. Exp. Agric., 1, 129–142.","code":""},{"path":"/reference/yates.missing.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Factorial experiment of potato, 3x3 with missing values — yates.missing","text":"Steel & Torrie (1980). Principles Procedures Statistics, 2nd Edition, page 212.","code":""},{"path":"/reference/yates.missing.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factorial experiment of potato, 3x3 with missing values — yates.missing","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(yates.missing) dat <- yates.missing libs(lattice) bwplot(y ~ trt, data=dat, xlab=\"Treatment\", ylab=\"Infection intensity\", main=\"yates.missing\") libs(reshape2) mat0 <- acast(dat[, c('trt','block','y')], trt~block, id.var=c('trt','block'), value.var='y') # Use lm to estimate missing values. The estimated missing values # are the same as in Yates (1933) m1 <- lm(y~trt+block, dat) dat$pred <- predict(m1, new=dat[, c('trt','block')]) dat$filled <- ifelse(is.na(dat$y), dat$pred, dat$y) mat1 <- acast(dat[, c('trt','block','pred')], trt~block, id.var=c('trt','block'), value.var='pred') # Another method to estimate missing values via PCA libs(\"nipals\") m2 <- nipals(mat0, center=FALSE, ncomp=3, fitted=TRUE) # mat2 <- m2$scores mat2 <- m2$fitted # See also pcaMethods::svdImpute # Compare ord <- c(\"0\",\"n\",\"k\",\"p\",\"nk\",\"np\",\"kp\",\"nkp\") print(mat0[ord,], na.print=\".\") round(mat1[ord,] ,2) round(mat2[ord,] ,2) # mat2 SVD with 3 components recovers original data better than # mat1 from lm() sum((mat0-mat1)^2, na.rm=TRUE) sum((mat0-mat2)^2, na.rm=TRUE) # Smaller SS => better fit } # }"},{"path":"/reference/yates.oats.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-plot experiment of oats — yates.oats","title":"Split-plot experiment of oats — yates.oats","text":"yield oats split-plot field trial conducted Rothamsted 1931. Varieties applied main plots. Manurial (nitrogen) treatments applied sub-plots. plot 1/80 acre = 28.4 links * 44 links. Field width: 4 plots * 44 links = 176 links. Field length: 18 rows * 28.4 links = 511 links 'block' numbers data given Rothamsted Report. 'grain' 'straw' values actual pounds per sub-plot shown Rothamsted Report. sub-plot 1/80 acre, 'hundredweight (cwt)' 112 pounds, converting sub-plot weight hundredweight/acre needs conversion factor 80/112. 'yield' values values appeared paper Yates, used 1/4-pounds units (.e. multiplied original weight 4) simpler calculations.","code":""},{"path":"/reference/yates.oats.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-plot experiment of oats — yates.oats","text":"row row col column yield yield 1/4 pounds per sub-plot, 1/80 acre nitro nitrogen treatment hundredweight per acre gen genotype, 3 levels block block, 6 levels grain grain weight pounds per sub-plot straw straw weight pounds per sub-plot","code":""},{"path":"/reference/yates.oats.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-plot experiment of oats — yates.oats","text":"Report 1931. Rothamsted Experiment Station. Page 143. https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1931-141-159","code":""},{"path":"/reference/yates.oats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-plot experiment of oats — yates.oats","text":"Yates, Frank (1935) Complex experiments, Journal Royal Statistical Society Supplement 2, 181-247. Figure 2. https://doi.org/10.2307/2983638","code":""},{"path":"/reference/yates.oats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-plot experiment of oats — yates.oats","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(yates.oats) dat <- yates.oats ## # Means match Rothamsted report p. 144 ## libs(dplyr) ## dat ## summarize(grain=mean(grain)*80/112, ## straw=mean(straw)*80/112) libs(desplot) # Experiment design & yield heatmap desplot(dat, block ~ col*row, col.regions=c(\"black\",\"yellow\"), out1=block, num=nitro, col=gen, cex=1, aspect=511/176, # true aspect main=\"yates.oats\") # Roughly linear gradient across the field. The right-half of each # block has lower yield. The blocking is inadequate! libs(\"lattice\") xyplot(yield ~ col|factor(nitro), dat, type = c('p', 'r'), xlab='col', as.table = TRUE, main=\"yates.oats\") libs(lme4) # Typical split-plot analysis. Non-significant gen differences m3 <- lmer(yield ~ factor(nitro) * gen + (1|block/gen), data=dat) # Residuals still show structure xyplot(resid(m3) ~ dat$col, xlab='col', type=c('p','smooth'), main=\"yates.oats\") # Add a linear trend for column m4 <- lmer(yield ~ col + factor(nitro) * gen + (1|block/gen), data=dat) # xyplot(resid(m4) ~ dat$col, type=c('p','smooth'), xlab='col') ## Compare fits AIC(m3,m4) ## df AIC ## m3 9 581.2372 ## m4 10 557.9424 # Substantially better # ---------- # Marginal predictions # --- nlme --- libs(nlme) libs(emmeans) # create unbalance dat2 <- yates.oats[-c(1,2,3,5,8,13,21,34,55),] m5l <- lme(yield ~ factor(nitro) + gen, random = ~1 | block/gen, data = dat2) # asreml r 4 has a bug with asreml( factor(nitro)) dat2$nitrof <- factor(dat2$nitro) # --- asreml --- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) m5a <- asreml(yield ~ nitrof + gen, random = ~ block + block:gen, data=dat2) lucid::vc(m5l) lucid::vc(m5a) emmeans::emmeans(m5l, \"gen\") predict(m5a, data=dat2, classify=\"gen\")$pvals } } # }"},{"path":"/reference/zuidhof.broiler.html","id":null,"dir":"Reference","previous_headings":"","what":"Daily weight, feed, egg measurements for a broiler chicken — zuidhof.broiler","title":"Daily weight, feed, egg measurements for a broiler chicken — zuidhof.broiler","text":"Daily weight, feed, egg measurements broiler chicken","code":""},{"path":"/reference/zuidhof.broiler.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Daily weight, feed, egg measurements for a broiler chicken — zuidhof.broiler","text":"data frame 59 observations following 6 variables. bw Body weight, grams targetbw Target body weight, grams adfi Average daily feed intake, grams adg Average daily gain, grams eggwt Egg weight, grams age Age, days","code":""},{"path":"/reference/zuidhof.broiler.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Daily weight, feed, egg measurements for a broiler chicken — zuidhof.broiler","text":"Using graphs like one examples section, authors discovered drop body weight commonly occurs around time first egg production. Used permission Martin Zuidhof.","code":""},{"path":"/reference/zuidhof.broiler.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Daily weight, feed, egg measurements for a broiler chicken — zuidhof.broiler","text":"Martin J. Zuidhof Robert . Renema Frank E. Robinson, (2008). Understanding Multiple, Repeated Animal Measurements Help PROC GPLOT. SAS Global Forum 2008, Paper 250-2008. https://support.sas.com/resources/papers/proceedings/pdfs/sgf2008/250-2008.pdf","code":""},{"path":"/reference/zuidhof.broiler.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Daily weight, feed, egg measurements for a broiler chicken — zuidhof.broiler","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(zuidhof.broiler) dat <- zuidhof.broiler dat <- transform(dat, age=age/7) # Change days into weeks # Reproducing figure 1 of Zuidhof et al. # Plot using left axis op <- par(mar=c(5,4,4,4)) plot(bw~age, dat, xlab=\"Age (weeks)\", ylab=\"Bodyweight (g)\", main=\"zuidhof.broiler\", xlim=c(20,32), ylim=c(0,4000), pch=20) lines(targetbw~age, subset(dat, !is.na(targetbw)), col=\"black\") # Now plot using the right axis par(new=TRUE) plot(adfi~age, subset(dat, !is.na(adfi)), xlab=\"\", ylab=\"\", xlim=c(20,32), xaxt=\"n\",yaxt=\"n\", ylim=c(-50,175), type=\"s\", lty=2) axis(4, at=c(-50,-25,0,25,50,75,100,125,150,175), col=\"red\", col.axis=\"red\") mtext(\"Weight (g)\", side=4, line=2, col=\"red\") lines(adg~age, subset(dat, !is.na(adg)), col=\"red\", type=\"s\", lty=1, lwd=2) abline(h=c(0,52), col=\"red\") with(dat, segments(age, 0, age, eggwt, col=\"red\")) legend(20, -40, c(\"Body weight\", \"Target BW\", \"Feed/day\", \"Gain/day\", \"Egg wt\"), bty=\"n\", cex=.5, ncol=5, col=c(\"black\",\"black\",\"red\",\"red\",\"red\"), lty=c(-1,1,2,1,1), lwd=c(1,1,1,2,1), pch=c(20,-1,-1,-1,-1)) par(op) } # }"},{"path":"/news/index.html","id":"agridat-124-2024-10-25","dir":"Changelog","previous_headings":"","what":"agridat 1.24 (2024-10-25)","title":"agridat 1.24 (2024-10-25)","text":"CRAN release: 2024-10-27","code":""},{"path":"/news/index.html","id":"new-datasets-1-24","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.24 (2024-10-25)","text":"haritonenko.sugarbeet.uniformity jegorow.oats.uniformity jurowski.wheat.uniformity roemer.sugarbeet.uniformity siao.cotton.uniformity tulaikow.wheat.uniformity","code":""},{"path":"/news/index.html","id":"other-notes-1-24","dir":"Changelog","previous_headings":"","what":"Other notes","title":"agridat 1.24 (2024-10-25)","text":"baker.strawberry.uniformity: inserted 3 columns layout field closely align experiment layout. garber.multi.uniformity: data reshaped wide tall additional years added. minnesota.barley.yield: Added 10+ years data. odland.soyhay.uniformity: Yields divided 10 yield values now units “tons” instead “0.1 tons”. shafi.tomato.uniformity: incorrect yield scale, now divided 1000. Documentation pages now created via Github Actions. (Issue #12). Thanks E.Tanaka.","code":""},{"path":"/news/index.html","id":"agridat-123-2024-01-30","dir":"Changelog","previous_headings":"","what":"agridat 1.23 (2024-01-30)","title":"agridat 1.23 (2024-01-30)","text":"CRAN release: 2024-02-06","code":""},{"path":[]},{"path":"/news/index.html","id":"other-notes-1-23","dir":"Changelog","previous_headings":"","what":"Other notes","title":"agridat 1.23 (2024-01-30)","text":"Removed suggested use sf/terra packages examples. Add (require(\"asreml\", quietly=TRUE)) example sections. way can allow people (GitHub Actions) force run dontrun sections, even asreml installed. cochran.eelworms - Fix typo reported U.Genschel, added columns grain yield, straw yield, weeds. Updates documentation. gartner.corn - Remove ‘rgdal’ package example (Issue #11).","code":""},{"path":"/news/index.html","id":"agridat-122-2023-08-24","dir":"Changelog","previous_headings":"","what":"agridat 1.22 (2023-08-24)","title":"agridat 1.22 (2023-08-24)","text":"CRAN release: 2023-08-25","code":""},{"path":"/news/index.html","id":"new-datasets-1-22","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.22 (2023-08-24)","text":"bailey.cotton.uniformity belamkar.augmented (augmented design multiple locations) chakravertti.factorial (3 block, 3x5x3x3 factorial large factorial experiment) coombs.rice.uniformity evans.sugarcane.uniformity (Data Rothamsted archive) forster.wheat.uniformity goulden.barley.uniformity (expanded previously published 20x20 48x48) hansen.multi.uniformity (multi-year uniformity trials) heath.cabbage.uniformity hutchinson.cotton.uniformity (Data Rothamsted archive) immer.sugarbeet.uniformity (add year 1931 results Rothamsted archive) khan.brassica.uniformity kirk.potato (20 varieties 15 reps) mckinstry.cotton.uniformity (Data Rothamsted archive) payne.wheat (long term rotation experiment) riddle.wheat (Modified Latin Square, “significant” variety switches high/low) saunders.maize.uniformity (Data Rothamsted archive) smith.wheat.uniformity (classic data measuring field heterogeneity) summerby.multi.uniformity tesfaye.millet (GxE dataset multiple reps, xy coordinates) vishnaadevi.rice.uniformity","code":""},{"path":"/news/index.html","id":"other-notes-1-22","dir":"Changelog","previous_headings":"","what":"Other notes","title":"agridat 1.22 (2023-08-24)","text":"Changed package MIT license. Modified factorial-experiment datasets separate treatment factors individual treatment factors. Add docType{package} fix CRAN. Removed ‘donttest’ examples.","code":""},{"path":"/news/index.html","id":"agridat-121-2022-06-15","dir":"Changelog","previous_headings":"","what":"agridat 1.21 (2022-06-15)","title":"agridat 1.21 (2022-06-15)","text":"CRAN release: 2022-06-15","code":""},{"path":"/news/index.html","id":"new-datasets-1-21","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.21 (2022-06-15)","text":"bachmaier.quadratic (confidence intervals optimum quadratic) ducker.groundnut.uniformity kling.augmented (augmented design) sharma.met (Finlay-Wilkinson regression)","code":""},{"path":"/news/index.html","id":"agridat-120-2021-12-20","dir":"Changelog","previous_headings":"","what":"agridat 1.20 (2021-12-20)","title":"agridat 1.20 (2021-12-20)","text":"CRAN release: 2021-12-20","code":""},{"path":"/news/index.html","id":"new-datasets-1-20","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.20 (2021-12-20)","text":"arankacami.groundnut.uniformity barrero.maize buntaran.wheat gomez.heterogeneity grover.diallel grover.rcb.subsample hadasch.lettuce hadasch.lettuce.markers jones.corn.uniformity petersen.sorghum.cowpea woodman.pig","code":""},{"path":"/news/index.html","id":"other-notes-1-20","dir":"Changelog","previous_headings":"","what":"Other notes","title":"agridat 1.20 (2021-12-20)","text":"Re-named mead.cowpeamaize mead.cowpea.maize. Re-named correa.soybean.uniformity dasilva.soybean.uniformity. 200 URLs vignettes slow check generated many false warnings. Stopped Rmd html conversion creating URL links via option Rmd YAML.","code":""},{"path":"/news/index.html","id":"agridat-118-2021-01-12","dir":"Changelog","previous_headings":"","what":"agridat 1.18 (2021-01-12)","title":"agridat 1.18 (2021-01-12)","text":"CRAN release: 2021-01-12","code":""},{"path":"/news/index.html","id":"new-datasets-1-18","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.18 (2021-01-12)","text":"damesa.maize jayaraman.bamboo nair.turmeric.uniformity shafi.tomato.uniformity","code":""},{"path":"/news/index.html","id":"agridat-117-2020-08-03","dir":"Changelog","previous_headings":"","what":"agridat 1.17 (2020-08-03)","title":"agridat 1.17 (2020-08-03)","text":"CRAN release: 2020-08-03","code":""},{"path":"/news/index.html","id":"new-datasets-1-17","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.17 (2020-08-03)","text":"alwan.lamb, baker.strawberry.uniformity, besag.checks, bryan.corn.uniformity, davidian.soybean, devries.pine, edwards.oats, george.wheat, hartman.tomato.uniformity, heath.radish.uniformity, johnson.douglasfir, kayad.alfalfa, kerr.sugarcane.uniformity, laycock.tea.uniformity, lehmann.millet.uniformity, linder.wheat, loesell.bean.uniformity, miguez.biomass, obsi.potato.uniformity, paez.coffee.uniformity, pederson.lettuce.repeated, piepho.barley.uniformity, rothamsted.oats, shaw.oats, wyatt.multi.uniformity","code":""},{"path":"/news/index.html","id":"other-notes-1-17","dir":"Changelog","previous_headings":"","what":"Other notes","title":"agridat 1.17 (2020-08-03)","text":"Examples now use asreml version 4. Re-named hutchinson.cotton.uniformity panse.cotton.uniformity. Added INLA example crowder.seeds. Wrapped example sections dontrun. New function libs(), basically pacman::p_load(), without dependency pacman. Example sections now use library(agridat) libs() load packages fly needed. Website built pkgdown.","code":""},{"path":"/news/index.html","id":"agridat-116-2018-07-06","dir":"Changelog","previous_headings":"","what":"agridat 1.16 (2018-07-06)","title":"agridat 1.16 (2018-07-06)","text":"CRAN release: 2018-07-06 Minor release make small change next release broom package.","code":""},{"path":"/news/index.html","id":"agridat-115-2018-06-28","dir":"Changelog","previous_headings":"","what":"agridat 1.15 (2018-06-28)","title":"agridat 1.15 (2018-06-28)","text":"CRAN release: 2018-06-27","code":""},{"path":"/news/index.html","id":"new-datasets-1-15","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.15 (2018-06-28)","text":"ansari.wheat.uniformity, baker.wheat.uniformity, bancroft.peanut.uniformity, bose.multi.uniformity, christidis.cotton.uniformity, dasilva.soybean.uniformity, davies.pasture.uniformity, eden.tea.uniformity, hutchinson.cotton.uniformity, igue.sugarcane.uniformity, kulkarni.sorghum.uniformity, lander.multi.uniformity, lord.rice.uniformity, magistad.pineapple.uniformity, nagai.strawberry.uniformity, narain.sorghum.uniformity, robinson.peanut.uniformity, sayer.sugarcane.uniformity, strickland.apple.uniformity, strickland.grape.uniformity, strickland.peach.uniformity, strickland.tomato.uniformity dasilva.maize, mead.turnip","code":""},{"path":"/news/index.html","id":"other-notes-1-15","dir":"Changelog","previous_headings":"","what":"Other notes","title":"agridat 1.15 (2018-06-28)","text":"nonnecke.corn.uniformity renamed nonnecke.sweetcorn.uniformity moore.carrots.uniformity renamed moore.carrot.uniformity Switch lsmeans emmeans examples.","code":""},{"path":"/news/index.html","id":"agridat-113-2017-11-30","dir":"Changelog","previous_headings":"","what":"agridat 1.13 (2017-11-30)","title":"agridat 1.13 (2017-11-30)","text":"CRAN release: 2017-11-29","code":""},{"path":"/news/index.html","id":"new-datasets-for-uniformity-trials-1-13","dir":"Changelog","previous_headings":"","what":"New datasets for uniformity trials","title":"agridat 1.13 (2017-11-30)","text":"bradley.multi.uniformity, christidis.wheat.uniformity, day.wheat.uniformity, draper.safflower.uniformity, holtsmark.timothy.uniformity, iyer.wheat.uniformity, kadam.millet.uniformity, kalamkar.wheat.uniformity, khin.rice.uniformity, kiesselbach.oats.uniformity, kristensen.barley.uniformity, lessman.sorghum.uniformity, love.cotton.uniformity, masood.rice.uniformity, mcclelland.corn.uniformity, montgomery.wheat.uniformity, moore.polebean.uniformity, moore.bushbean.uniformity, moore.sweetcorn.uniformity, moore.carrot.uniformity, moore.springcauliflower.uniformity, moore.fallcauliflower.uniformity, nonnecke.corn.uniformity, nonnecke.peas.uniformity, parker.orange.uniformity, polson.safflower.uniformity, sawyer.multi.uniformity, smith.beans.uniformity, stickler.sorghum.uniformity, wiedemann.safflower.uniformity","code":""},{"path":"/news/index.html","id":"new-datasets-for-stability-1-13","dir":"Changelog","previous_headings":"","what":"New datasets for stability","title":"agridat 1.13 (2017-11-30)","text":"fisher.barley, lu.stability, tai.potato","code":""},{"path":"/news/index.html","id":"new-datasets-1-13","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.13 (2017-11-30)","text":"acorsi.grayleafspot, becker.chicken, chinloy.fractionalfactorial, cramer.cucumber, crampton.pig, battese.survey, christidis.competition, depalluel.sheep. eden.nonnormal, gartner.corn, giles.wheat, gomez.heteroskedastic, gomez.nonnormal1, gomez.nonnormal2, gomez.nonnormal3, gomez.wetdry, goulden.eggs, goulden.splitsplit, gregory.cotton, hanover.whitepine, harvey.lsmeans, harville.lamb, huehn.wheat, kenward.cattle, kreusler.maize, lehner.soybeanmold, lillemo.wheat, lin.superiority, lin.unbalanced, little.splitblock, mead.lamb, omer.sorghum, onofri.winterwheat, reid.grasses, silva.cotton, urquhart.feedlot, usgs.herbicides, vaneeuwijk.fusarium, vaneeuwijk.nematodes, vaneeuwijk.drymatter, wheatley.carrot","code":""},{"path":"/news/index.html","id":"other-notes-1-13","dir":"Changelog","previous_headings":"","what":"Other notes","title":"agridat 1.13 (2017-11-30)","text":"following data objects changed lists 2 matrices tidy dataframes: aastveit.barley, box.cork, ortiz.tomato, talbot.potato, vargas.txe, vargas.wheat1, vargas.wheat2 devtools::run_examples() now works even without suggested packages installed. Changed extensions inst/files .bug .jag . Changed hyperlinks plain text (faster checking, avoids re-direct errors). Replaced use gam package examples, favor mgcv package. Added package logo github.","code":""},{"path":"/news/index.html","id":"agridat-112-2015-06-29","dir":"Changelog","previous_headings":"","what":"agridat 1.12 (2015-06-29)","title":"agridat 1.12 (2015-06-29)","text":"CRAN release: 2015-06-29","code":""},{"path":"/news/index.html","id":"new-datasets-1-12","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.12 (2015-06-29)","text":"cochran.beets cochran.lattice jansen.apple jansen.carrot","code":""},{"path":"/news/index.html","id":"agridat-111-2015-03-03","dir":"Changelog","previous_headings":"","what":"agridat 1.11 (2015-03-03)","title":"agridat 1.11 (2015-03-03)","text":"CRAN release: 2015-03-03","code":""},{"path":"/news/index.html","id":"new-datasets-1-11","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.11 (2015-03-03)","text":"besag.beans besag.triticale burgeuno.alpha burgueno.rowcol burgueno.unreplicated steptoe.morex.pheno","code":""},{"path":"/news/index.html","id":"other-1-11","dir":"Changelog","previous_headings":"","what":"Other","title":"agridat 1.11 (2015-03-03)","text":"Removed asreml package Suggests (due CRAN check problems).","code":""},{"path":"/news/index.html","id":"agridat-110-2014-11-26","dir":"Changelog","previous_headings":"","what":"agridat 1.10 (2014-11-26)","title":"agridat 1.10 (2014-11-26)","text":"CRAN release: 2014-11-26","code":""},{"path":"/news/index.html","id":"new-datasets-1-10","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.10 (2014-11-26)","text":"beaven.barley, perry.springwheat, ridout.appleshoots","code":""},{"path":"/news/index.html","id":"other-1-10","dir":"Changelog","previous_headings":"","what":"Other","title":"agridat 1.10 (2014-11-26)","text":"Move packages Depends Imports.","code":""},{"path":"/news/index.html","id":"agridat-19-2014-07-02","dir":"Changelog","previous_headings":"","what":"agridat 1.9 (2014-07-02)","title":"agridat 1.9 (2014-07-02)","text":"CRAN release: 2014-07-02","code":""},{"path":"/news/index.html","id":"new-datasets-1-9","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.9 (2014-07-02)","text":"beall.webworms, besag.endive, brandt.switchback, butron.maize, carlson.germination, cochran.factorial, connolly.potato, cornelius.maize, cullis.earlygen, fisher.latin, foulley.calving, fox.wheat, gomez.splitplot.subsample, goulden.latin, gumpertz.pepper, harrison.priors, hazell.vegetables, heady.fertilizer, holland.arthropods, hunter.corn, jansen.strawberry, kalamkar.potato.uniformity, kang.maize, kang.peanut, karcher.turfgrass, keen.potatodamage, lasrosas.corn, lee.potatoblight, lonnquist.maize, lucas.switchback, maindonald.barley, mead.cauliflower, mercer.mangold.uniformity, patterson.switchback, piepho.cocksfoot, sinclair.clover, snijders.fusarium, stirret.borers, theobald.barley, turner.herbicide, vargas.txe, vold.longterm, wallace.iowaland, walsh.cottonprice, wassom.brome.uniformity, welch.bermudagrass, weiss.incblock, weiss.lattice, yang.barley","code":""},{"path":"/news/index.html","id":"other-1-9","dir":"Changelog","previous_headings":"","what":"Other","title":"agridat 1.9 (2014-07-02)","text":"Use (require(lme4)) examples. B.Ripley request. data (almost) now example graphic.","code":""},{"path":"/news/index.html","id":"agridat-18-2013-09-23","dir":"Changelog","previous_headings":"","what":"agridat 1.8 (2013-09-23)","title":"agridat 1.8 (2013-09-23)","text":"CRAN release: 2013-09-23","code":""},{"path":"/news/index.html","id":"new-datasets-1-8","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.8 (2013-09-23)","text":"brandle.rape salmon.bunt","code":""},{"path":"/news/index.html","id":"agridat-17-2013-09-06","dir":"Changelog","previous_headings":"","what":"agridat 1.7 (2013-09-06)","title":"agridat 1.7 (2013-09-06)","text":"CRAN release: 2013-09-06","code":""},{"path":"/news/index.html","id":"new-datasets-1-7","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.7 (2013-09-06)","text":"baker.barley.uniformity bliss.borers bond.diallel harris.wateruse hayman.tobacco holshouser.splitstrip pearce.apple waynick.soil","code":""},{"path":"/news/index.html","id":"agridat-16-2013-06-04","dir":"Changelog","previous_headings":"","what":"agridat 1.6 (2013-06-04)","title":"agridat 1.6 (2013-06-04)","text":"CRAN release: 2013-06-04","code":""},{"path":"/news/index.html","id":"new-datasets-1-6","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.6 (2013-06-04)","text":"crossa.wheat, garber.multi.uniformity, gomez.nitrogen, harris.multi.uniformity, hughes.grapes, li.millet.uniformity, odland.soybean.uniformity, odland.soyhay.uniformity, ratkowsky.onions, stephens.sorghum.uniformity","code":""},{"path":"/news/index.html","id":"agridat-15-2013-04-26","dir":"Changelog","previous_headings":"","what":"agridat 1.5 (2013-04-26)","title":"agridat 1.5 (2013-04-26)","text":"CRAN release: 2013-04-26","code":""},{"path":"/news/index.html","id":"new-datasets-1-5","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.5 (2013-04-26)","text":"adugna.sorghum, ars.earlywhitecorn96, besag.bayesian, box.cork, broadbalk.wheat, byers.apple, caribbean.maize, carmer.density, cate.potassium, cleveland.soil, cochran.eelworms, cochran.wireworms, fan.stability, gomez.seedrate, gotway.hessianfly, goulden.barley.uniformity, henderson.milkfat, hernandez.nitrogen, hessling.argentina, immer.sugarbeet.uniformity, ivins.herbs, jenkyn.mildew, johnson.blight, lambert.soiltemp, lavoranti.eucalyptus, lyon.potato.uniformity, lyons.wheat, mead.cowpeamaize, mead.germination, minnesota.barley.weather, minnesota.barley.yield, nebraska.farmincome, nass.barley, nass.corn, nass.cotton, nass.hay, nass.rice, nass.sorghum, nass.soybean, nass.wheat, ortiz.tomato, pacheco.soybean, senshu.rice, snedecor.asparagus, streibig.competition, zuidhof.broiler","code":""},{"path":"/news/index.html","id":"agridat-14-2012-03-14","dir":"Changelog","previous_headings":"","what":"agridat 1.4 (2012-03-14)","title":"agridat 1.4 (2012-03-14)","text":"CRAN release: 2012-03-14","code":""},{"path":"/news/index.html","id":"new-datasets-1-4","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.4 (2012-03-14)","text":"archbold.apple, blackman.wheat, cochran.crd, cochran.latin, darwin.maize, denis.ryegrass, digby.jointregression, engelstad.nitro, federer.diagcheck, gilmour.slatehall, john.alpha, ilra.sheep, kempton.slatehall, ryder.groundnut, vsn.lupin3","code":""},{"path":"/news/index.html","id":"agridat-13-2011-10-20","dir":"Changelog","previous_headings":"","what":"agridat 1.3 (2011-10-20)","title":"agridat 1.3 (2011-10-20)","text":"CRAN release: 2011-10-20","code":""},{"path":"/news/index.html","id":"new-datasets-1-3","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.3 (2011-10-20)","text":"bridges.cucumber, cox.stripsplit, diggle.cow, eden.potato, gauch.soy, graybill.heteroskedastic, hildebrand.systems, hanks.sprinkler, mcconway.turnip, pearl.kernels, williams.barley.uniformity, williams.cotton.uniformity","code":""},{"path":"/news/index.html","id":"agridat-12-2011-06-30","dir":"Changelog","previous_headings":"","what":"agridat 1.2 (2011-06-30)","title":"agridat 1.2 (2011-06-30)","text":"CRAN release: 2011-06-30","code":""},{"path":"/news/index.html","id":"new-datasets-1-2","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.2 (2011-06-30)","text":"aastveit.barley, gathmann.bt, kempton.competition, wedderburn.barley","code":""},{"path":"/news/index.html","id":"agridat-10-2011-04-27","dir":"Changelog","previous_headings":"","what":"agridat 1.0 (2011-04-27)","title":"agridat 1.0 (2011-04-27)","text":"First release CRAN","code":""},{"path":"/news/index.html","id":"new-datasets-1-0","dir":"Changelog","previous_headings":"","what":"New datasets","title":"agridat 1.0 (2011-04-27)","text":"allcroft.lodging, australia.soybean, batchelor.apple.uniformity, batchelor.lemon.uniformity, batchelor.navel1.uniformity, batchelor.navel2.uniformity, batchelor.valencia.uniformity, batchelor.walnut.uniformity, besag.elbatan, besag.met, cochran.bib, corsten.interaction, crowder.germination, denis.missing, durban.competition, durban.rowcol, durban.splitplot, federer.tobacco, gilmour.serpentine, gomez.fractionalfactorial, gomez.groupsplit, gomez.multilocsplitplot, gomez.splitsplit, gomez.stripplot, gomez.stripsplitplot, gomez.rice.uniformity, hughes.grapes, kempton.rowcol, kempton.barley.uniformity, mead.strawberry, mercer.wheat.uniformity, rothamsted.brussels, shafii.rapeseed, smith.uniformity3, stroup.nin, stroup.splitplot, student.barley, talbot.potato, theobald.covariate, thompson.cornsoy, vargas.wheat1, vargas.wheat2, verbyla.lupin, williams.trees, wiebe.wheat.uniformity, yan.winterwheat, yates.missing, yates.oats","code":""},{"path":"/news/index.html","id":"agridat-00-2010-10-01","dir":"Changelog","previous_headings":"","what":"agridat 0.0 (2010-10-01)","title":"agridat 0.0 (2010-10-01)","text":"Development begins","code":""}]
+[{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 agridat authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"rothamsted-library","dir":"Articles","previous_headings":"Other","what":"Rothamsted Library","title":"Additional sources of agricultural data","text":"https://www.rothamsted.ac.uk/library--information-services now scanned PDFs put GitHub repository agridat package. Box uniformity trial data","code":"STATS17 WG Cochran 1. Uniformity trial data. 2. Genstat data. Data received since publication of the catalogue. 1935-1943. 3. Uniformity trial data. 1930-1936. 4. Uniformity trials. 1936-1938. 5. Uniformity trials. R data. 1936-1937. 6. O. V. S. Heath. Cotton uniformity trial data. 1934-1935. 7. Data. Yields of grain per foot length. 1934. 8. Catalogue of field uniformity trial data. N. d. 9. Demandt. 1931. One box"},{"path":[]},{"path":"/articles/agridat_data.html","id":"die-landwirtschaftlichen-versuchs-stations","dir":"Articles","previous_headings":"Books","what":"“Die Landwirtschaftlichen Versuchs-Stations”","title":"Additional sources of agricultural data","text":"https://catalog.hathitrust.org/Record/000549685 Full view research station reports 1859-1920. German.","code":""},{"path":"/articles/agridat_data.html","id":"d--f--andrews-and-a--m--herzberg-1985--data-","dir":"Articles","previous_headings":"Books","what":"D. F. Andrews and A. M. Herzberg (1985). “Data”.","title":"Additional sources of agricultural data","text":"https://www2.stat.duke.edu/courses/Spring01/sta114/data/andrews.html","code":"Table 2.1: agridat::darwin.maize Table 5.1: agridat::broadbalk.wheat Table 6.1: agridat::mercer.wheat.uniformity Table 6.2: agridat::wiebe.wheat.uniformity Table 58.1: agridat::caribbean.maize"},{"path":"/articles/agridat_data.html","id":"gemechu-application-of-spatial-mixed-model-in-agricultural-field-experiment","dir":"Articles","previous_headings":"Books","what":"Gemechu, “Application of Spatial Mixed Model in Agricultural Field Experiment”","title":"Additional sources of agricultural data","text":"Dibaba Bayisa Gemechu Aweke, Girma (maybe Girma Taye) Master thesis. Department Statistics, Addis Ababa University. One dataset wheat, RCB, field coordinates. Note: Forkman cites author “D. Bayisa”","code":""},{"path":"/articles/agridat_data.html","id":"m--n--das-narayan-c--giri-1987--design-and-analysis-of-experiments-","dir":"Articles","previous_headings":"Books","what":"M. N. Das & Narayan C. Giri (1987). “Design and Analysis of Experiments”.","title":"Additional sources of agricultural data","text":"","code":"31 wool from 24 ewes, 6 cuttings 116 grass NPK factorial, 3 years, 36 obs 116 2^5 factorial, 1 rep, 32 obs 117 2^3 factorial, 3 rep 117 sugar beet 3^3 factorial, 2 rep, 54 obs 139 alfalfa 3x2^2 factorial 149 cabbage NPK split-plot, xy, 2 rep, 108 obs 150 soybean nitro-variety split-plot 193 wheat variety inc block, 9 block 201 rice variety balanced lattice, 80 obs 279 maize covariate, yield & plant count, 4 rep, 32 obs"},{"path":"/articles/agridat_data.html","id":"peter-diggle-patrick-heagerty-kung-yee-liang-scott-zeger--analysis-of-longitudinal-data-","dir":"Articles","previous_headings":"Books","what":"Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger. “Analysis of Longitudinal Data”.","title":"Additional sources of agricultural data","text":"https://faculty.washington.edu/heagerty/Books/AnalysisLongitudinal/datasets.html Pig weight data found SemiPar::pig.weights Sitka spruce data found : geepack::spruce Milk protein data found : nlme::Milk. thorough description data can found Molenberghs & Kenward, “Missing Data Clinical Studies”, p. 377. Original source: . P. Verbyla B. R. Cullis, Modelling Repeated Measures Experiments.","code":""},{"path":"/articles/agridat_data.html","id":"federer-walt-1955--experimental-design-","dir":"Articles","previous_headings":"Books","what":"Federer, Walt (1955). “Experimental Design”.","title":"Additional sources of agricultural data","text":"","code":"192 3x3 factorial 204 3x2 factorial 236 2x2x2 factorial with confounding 257 2x3x2 factorial with confounding 276 split-plot with layout 285 nested multi-loc (Also problems page 22) 350 cubic lattice 420 balanced inc block 491 Latin square with covariate"},{"path":"/articles/agridat_data.html","id":"finney-1972--an-introduction-to-statistical-science-in-agriculture-","dir":"Articles","previous_headings":"Books","what":"Finney 1972. “An Introduction to Statistical Science in Agriculture”.","title":"Additional sources of agricultural data","text":"Small, mostly simulated data.","code":""},{"path":"/articles/agridat_data.html","id":"galwey-n-w--2014--introduction-to-mixed-modelling-2nd-ed-","dir":"Articles","previous_headings":"Books","what":"Galwey, N.W. (2014). “Introduction to Mixed Modelling”, 2nd ed.","title":"Additional sources of agricultural data","text":"https://www.wiley.com/en-us/Introduction++Mixed+Modelling%3A+Beyond+Regression++Analysis++Variance%2C+2nd+Edition-p-9781119945499","code":"2 83 variety x nitro split-plot - agridat::yates.oats 3 104 doubled-haploid barley 3 135 wheat/rye competition, heritability 5 190 chickpea flowering in families 7 250 canola oil gxe, sowing date, rainfall, oil. Si & Walton 2004. 7 284 pig growth, 4 diets 7 285 sheep milk fat and lactose 7 290 wheat anoxia root porosity, 9 gen 7 291 wool fibers, 3 trt, 21 animals 9 370 alphalpha design (row-column inc block for 2 reps) (not latinized row col) 10 434 hollamby wheat trial - agridat::gilmour.serpentine"},{"path":"/articles/agridat_data.html","id":"grover-deepak-lajpat-rai--experimental-designing-and-data-analysis-in-agriculture-and-biology-","dir":"Articles","previous_headings":"Books","what":"Grover, Deepak & Lajpat Rai. “Experimental Designing And Data Analysis In Agriculture And Biology”.","title":"Additional sources of agricultural data","text":"Agrotech Publishing Academy, 2010. https://archive.org/details/expldesnanddatanalinagblg00023","code":"43 Percent insect survival in 12 rice varieties, 3 reps 50 CRD 57 RCBD 67 Latin Square 85 Sampling, 4 rep, 9 trt, 4 sub-samples agridat::grover.rcb.subsample 88 Split-plot, 3 rep, 2 measurements/plot, plant height (unusual subsample example) 97 Missing plot 105 Latin square with missing plot 115 2^2 factorial, 6 block 118 2^3 factorial, 3 block 120 Two factor asymmetrical, 5 rep 140 2^3 fractional factorial, 3 rep 160 Split-plot (planting date, variety), 3 rep 168 Strip-plot, 3 rep 176 Milk yield with covariate 188 Multi-year nitrogen treatment 197 BIBD 13 varieties 205 Lattice 4 blocks, 3 reps, 16 trt 226 Augmented BIBD 236 Group-divisible 239 PBIB 241 Augmented group-divisible 245 Augmented PBIB 250 6x6 full diallel, 4 rep agridat::grover.diallel"},{"path":"/articles/agridat_data.html","id":"o-v-s--heath-1970--investigation-by-experiment-","dir":"Articles","previous_headings":"Books","what":"O.V.S. Heath (1970). “Investigation by experiment”.","title":"Additional sources of agricultural data","text":"https://archive.org/details/investigationbye0000heat","code":"23 uniformity trial of radish - agridat::heath.raddish.uniformity 50 uniformity trial of cabbage - agridat::heath.cabbage.uniformity"},{"path":"/articles/agridat_data.html","id":"kwanchai-a--gomez-gomez-1984--statistical-procedures-for-agricultural-research-","dir":"Articles","previous_headings":"Books","what":"Kwanchai A. Gomez & Gomez (1984). “Statistical Procedures for Agricultural Research”.","title":"Additional sources of agricultural data","text":"Extensive collection datasets rice experiments. Many added agridat.","code":""},{"path":"/articles/agridat_data.html","id":"cyril-h--goulden-1939-methods-of-statistical-analysis-","dir":"Articles","previous_headings":"Books","what":"Cyril H. Goulden (1939), “Methods of Statistical Analysis”.","title":"Additional sources of agricultural data","text":"First edition: https://archive.org/details/methodsofstatist031744mbp Second edition: http://krishikosh.egranth.ac./handle/1/2034118 (broken)","code":"18 Uniformity trial - agridat::goulden.barley.uniformity 153 Split-split plot with factorial sub-plot treatment - agridat::goulden.splitsplit 194 Incomplete block 197 Inc block 205 Latin square 208 Inc block 255 Covariates in feeding trial - agridat::crampton.pig 216 Latin square - agridat::goulden.latin 423 Control chart with egg weights - agridat::goulden.eggs"},{"path":"/articles/agridat_data.html","id":"harry-love-1936--applications-of-statistical-methods-to-agricultural-research-","dir":"Articles","previous_headings":"Books","what":"Harry Love (1936). “Applications of Statistical Methods to Agricultural Research”.","title":"Additional sources of agricultural data","text":"","code":"379 MET 4 year, 2 field, 5 block, 5 gen"},{"path":[]},{"path":"/articles/agridat_data.html","id":"kuehl-robert--design-of-experiments-2nd-ed-","dir":"Articles","previous_headings":"Books","what":"Kuehl, Robert. “Design of Experiments”, 2nd ed.","title":"Additional sources of agricultural data","text":"","code":"357 alfalfa quadruple lattice 358 alpha design 488 split-plot sorghum hybrid,density 516 alfalfa rcb, two-year 521 crossover design cattle feedstuff"},{"path":"/articles/agridat_data.html","id":"erwin-leclerg-warren-leonard-andrew-clark-1962--field-plot-technique","dir":"Articles","previous_headings":"Books","what":"Erwin LeClerg, Warren Leonard, Andrew Clark (1962). “Field Plot Technique”","title":"Additional sources of agricultural data","text":"https://archive.org/details/fieldplottechniq00leon Many small datasets.","code":"27 uniformity - agridat::goulden.barley.uniformity 213 split-plot 234 immer multi-environment 260 lattice pinto-bean 276 triple lattice cotton 280 lattice sugar beet 289 balanced lattice 336 repeated wheat"},{"path":"/articles/agridat_data.html","id":"thomas-m-little-f--jackson-hills-1978--agricultural-experimentation-","dir":"Articles","previous_headings":"Books","what":"Thomas M Little & F. Jackson Hills (1978). “Agricultural Experimentation”.","title":"Additional sources of agricultural data","text":"","code":"79 Latin square 89 Split-plot 103 Split-split 117 Split-block - agridat::little.splitblock 126 Repeated harvests. In data-unused. 144 Non-IID errors 155 Square root transform 158 Germination, 3 reps, 24 treatments 261 Response surface, nitrogen, harvest 277 Count data"},{"path":"/articles/agridat_data.html","id":"harald-martens-magni-martens--multivariate-analysis-of-quality","dir":"Articles","previous_headings":"Books","what":"Harald Martens & Magni Martens. “Multivariate Analysis of Quality”","title":"Additional sources of agricultural data","text":"https://www.wiley.com/legacy/wileychi/chemometrics/datasets.html ‘NIR’ data NIR spectra measurements wheat purpose understanding protein quality.","code":""},{"path":"/articles/agridat_data.html","id":"roger-mead-robert-n--curnow-anne-m--hasted-2002--statistical-methods-in-agriculture-and-experimental-biology-3rd-ed-","dir":"Articles","previous_headings":"Books","what":"Roger Mead, Robert N. Curnow, Anne M. Hasted (2002). “Statistical Methods in Agriculture and Experimental Biology”, 3rd ed.","title":"Additional sources of agricultural data","text":"","code":"10 weekly milk yields 24 carrot weight 96 cabbage fertilizer 143 intercropping cowpea maize 177 honeybee repellent non-normal 251 cauliflower poisson - agridat::mead.cauliflower 273 rhubarb RCB covariate 296 onion density 316 lambs 341 germination 350 germination factorial - agridat::mead.germination 352 poppy 359 lamb loglinear - agridat::mead.lambs 375 rats 386 intercrop 390 intercrop cowpea maize - agridat::mead.cowpeamaize 404 apple characteristics (incomplete)"},{"path":"/articles/agridat_data.html","id":"roger-mead-1988--the-design-of-experiments","dir":"Articles","previous_headings":"Books","what":"Roger Mead (1988). “The Design of Experiments”","title":"Additional sources of agricultural data","text":"https://books.google.com/books?id=CaFZPbCllrMC&pg=PA323","code":"323 Turnip spacing data - agridat::mead.turnip"},{"path":"/articles/agridat_data.html","id":"leonard-c--onyiah-2008-","dir":"Articles","previous_headings":"Books","what":"Leonard C. Onyiah (2008).","title":"Additional sources of agricultural data","text":"“Design Analysis Experiments: Classical Regression Approaches SAS”. https://books.google.com/books?id=_P3LBQAAQBAJ&pg=PA334","code":"334 Two examples of 5x5 Graeco-Latin squares in cassava and maize"},{"path":"/articles/agridat_data.html","id":"bernard-ostle-1963--statistics-in-research-2nd-ed-","dir":"Articles","previous_headings":"Books","what":"Bernard Ostle (1963). “Statistics in Research”, 2nd ed.","title":"Additional sources of agricultural data","text":"https://archive.org/details/secondeditionsta001000mbp","code":"455 2 factors, 1 covariate - agridat::woodman.pig 458 1 factor, 2 covariates - agridat::crampton.pig"},{"path":"/articles/agridat_data.html","id":"v--g--panse-and-p--v--sukhatme-1957--statistical-methods-for-agricultural-workers-","dir":"Articles","previous_headings":"Books","what":"V. G. Panse and P. V. Sukhatme (1957). “Statistical Methods for Agricultural Workers”.","title":"Additional sources of agricultural data","text":"Note: 1954 edition can found https://archive.org/details/dli.scoerat.949statisticalmethodsforagriculturalworkers/page/138/mode/2up","code":"3 Length and number of grains per ear of wheat 138 Uniformity trial - agridat::panse.cotton.uniformity 154 RCB 8 blocks 167 two factorial, 6 rep trial 178 2^4 factorial, 8 blocks, partial confounding 192 3^3 factorial, 3 reps/9 blocks, partial confounding 200 split-plot, 6 rep 212 strip-plot, 6 rep 219 cotton variety trial, yield & stand counts 256 8x8 simpple lattice, 4 reps 282 5 varieties at 6 locations 295 5 N levels at 5 locations 332 4 regions, 9-11 villages in each region, 3 fertilizer treatments"},{"path":"/articles/agridat_data.html","id":"d--d--paterson-1939--statistical-technique-in-agricultural-research-","dir":"Articles","previous_headings":"Books","what":"D. D. Paterson (1939). “Statistical Technique in Agricultural Research”.","title":"Additional sources of agricultural data","text":"https://archive.org/details/statisticaltechn031729mbp","code":"84 Distribution of purple/white starchy/sweet seeds from 11 ears 190 Sugar cane MET: 2 year, 5 block, 5 variety 199 Tea MET: 3 year, 2^2 factorial fertilizer 206 Grass: 4 rep, 2 gen, 4 cutting treatments 211 Cotton: 4 dates, 3 spacings, 3 irrigation, 2 nitro - agridat::gregory.cotton"},{"path":"/articles/agridat_data.html","id":"roger-petersen-agricultural-field-experiments","dir":"Articles","previous_headings":"Books","what":"Roger Petersen, “Agricultural Field Experiments”","title":"Additional sources of agricultural data","text":"","code":"8 Uniformity trial 18 * 6 plots 56 RCB 4 rep, 5 trt 71 Latin square 5x5 86 Factorial 4x2, 3 rep 97 Factorial 2x3x2, 3 rep 125 Fertilizer trial, 3 rep, 5 levels 136 Split plot variety x planting date, 3 rep 148 Strip plot 2 potash x 3 potassium, 3 rep 170 Augmented breeding trial with 3 checks, 6 inc blocks 174 Inc Block 182 Lattice 5x5, 2 rep 192 GxE 10 gen, 12 env. Stability analysis. 208 Factorial 2x3 at 8 locs, homogeneous variance, early lentils 217 GxE 8 gen, 5 loc, heterogeneous variance 232 Factorial 2x3 at 8 locs, late lentils (see also page 208) 249 On-farm trial, 24 entries, 3 rep RCB 257 Demonstration trials, 5 locs 272 Covariance example, RCB 6 rep, 4rt 278 Multi-year 2x2 factorial, 4 rep 309 Pasture trial 323 On-farm trial, 2 variety 8 loc 327 On-farm trial 6 trt, 5 loc 334 On-farm trial 4 trt, 6 loc 343 On-farm trial 2x3 factorial, 3 loc 351 Feeding trial, 2 trt, 2 periods 357 Intercrop, 2 crops 372 Intercrop, 2 crop, 4 mixtures, 4 rep. agridat::petersen.sorghum.cowpea"},{"path":"/articles/agridat_data.html","id":"richard-plant-spatial-data-analysis-in-ecology-and-agriculture-using-r","dir":"Articles","previous_headings":"Books","what":"Richard Plant, “Spatial Data Analysis in Ecology and Agriculture using R”","title":"Additional sources of agricultural data","text":"https://psfaculty.plantsciences.ucdavis.edu/plant/","code":""},{"path":"/articles/agridat_data.html","id":"arthur-asquith-rayner-1969--a-first-course-in-biometry-for-agriculture-students-","dir":"Articles","previous_headings":"Books","what":"Arthur Asquith Rayner (1969). “A First Course In Biometry For Agriculture Students”.","title":"Additional sources of agricultural data","text":"","code":"19 456 2x2x4 Factorial, 2 rep 19 466 2x4 factorial, layout, plot size, kale (from Rothamsted) 19 466 3x5 factorial, 3 rep, potato 20 494 3x4 Split-plot with layout 21 505 2x2x2 Factorial, 5 rep 21 515 2x2x2x2 Factorial, 3 rep, with layout. (Evaluated, rejected as too variable) 22 537 2x2x2 factorial, 6 rep, potato 22 537 2x2x2x2 factorial, 2 rep, wheat, layout"},{"path":"/articles/agridat_data.html","id":"f-s-f--shaw-1936--a-handbook-of-statistics-for-use-in-plant-breeding-and-agricultural-problems","dir":"Articles","previous_headings":"Books","what":"F.S.F. Shaw (1936). “A Handbook of Statistics For Use in Plant Breeding and Agricultural Problems”","title":"Additional sources of agricultural data","text":"https://archive.org/details/.ernet.dli.2015.176662","code":"5 Length of ear head and number of grains per ear, 400 ears. 95 variety RCB, 5 gen, 25 rep, diagonal layout 107 Latin square, 8 entries. 117 Factorial: 8 blocks, 3 varieties, 5 treatments, 2 infections 126 Multi-environment trial, 3 year, 13 varieties, 2 loc, 5 blocks agridat::shaw.oats"},{"path":"/articles/agridat_data.html","id":"g--w--snedecor-w--g--cochran--statistical-methods-","dir":"Articles","previous_headings":"Books","what":"G. W. Snedecor & W. G. Cochran. “Statistical Methods”.","title":"Additional sources of agricultural data","text":"","code":"168 regression 352 3x3 factorial, 4 blocks 359 2x2x2 factorial, 8 blocks, daily pig gain 362 2x3x4 factorial, 2 blocks, daily pig gain 371 3x4 split-plot, 3 var, 4 date, 6 blocks 374 2x3x3 split-split-plot, irrig, stand, fert, block 378 4x4 split-plot, 4 block, 4 year, 4 cuttings asparagus 384 regression with 2 predictors 428 covariates, 6 varieties, 4 blocks, yield vs stand 440 pig gain vs initial weight, 4 treatments, 40 pigs 454 protein vs yield for wheat, 91 plots, quadratic regression"},{"path":"/articles/agridat_data.html","id":"robert-g--d--steel-james-hiram-torrie--principles-and-procedures-of-statistics-2nd-ed-","dir":"Articles","previous_headings":"Books","what":"Robert G. D. Steel & James Hiram Torrie. “Principles and Procedures of Statistics”, 2nd ed.","title":"Additional sources of agricultural data","text":"","code":"154 Mint plant growth, 2-way + pot + plant 244 Trivariate data 319 Regression with three predictors 384 Split-plot yield 387 Split-plot row spacing 400 Soybean 3 loc 423 Pig weight gain 429 Guinea pig weight gain 434 Soybean lodging"},{"path":"/articles/agridat_data.html","id":"oliver-schabenberger-and-francis-j--pierce--contemporary-statistical-models-for-the-plant-and-soil-sciences-","dir":"Articles","previous_headings":"Books","what":"Oliver Schabenberger and Francis J. Pierce. “Contemporary Statistical Models for the Plant and Soil Sciences”.","title":"Additional sources of agricultural data","text":"Many datasets. added agridat.","code":""},{"path":"/articles/agridat_data.html","id":"s--j--welham-et-al--2015--statistical-methods-in-biology-","dir":"Articles","previous_headings":"Books","what":"S. J. Welham et al. (2015). “Statistical Methods In Biology”.","title":"Additional sources of agricultural data","text":"online-supplements contain many small datasets examples exercises.","code":""},{"path":"/articles/agridat_data.html","id":"pesticides-in-the-nations-streams-and-ground-water-1992-2001","dir":"Articles","previous_headings":"Books","what":"Pesticides in the Nation’s Streams and Ground Water, 1992-2001","title":"Additional sources of agricultural data","text":"Extensive data detection pesticides water samples. See Appendix 5 Appendix 6 supporting info. https://water.usgs.gov/nawqa/pnsp/pubs/circ1291/supporting_info.php","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"ag-data-commons","dir":"Articles","previous_headings":"Data Repositories","what":"Ag Data Commons","title":"Additional sources of agricultural data","text":"https://data.nal.usda.gov/-ag-data-commons https://data.nal.usda.gov/search/type/dataset","code":""},{"path":"/articles/agridat_data.html","id":"cyverse-data-commons","dir":"Articles","previous_headings":"Data Repositories","what":"CyVerse Data Commons","title":"Additional sources of agricultural data","text":"https://datacommons.cyverse.org/ https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"harvard-dataverse","dir":"Articles","previous_headings":"Data Repositories","what":"Harvard Dataverse","title":"Additional sources of agricultural data","text":"https://dataverse.harvard.edu/ IRRI Rice Research includes plot-level data long term rice experiments. https://dataverse.harvard.edu/dataverse/RiceResearch","code":""},{"path":"/articles/agridat_data.html","id":"kellogg-biological-station-long-term-research","dir":"Articles","previous_headings":"Data Repositories","what":"Kellogg Biological Station Long-Term Research","title":"Additional sources of agricultural data","text":"KBS037:Precision Agriculture Yield Monitoring Row Crop Agriculture https://lter.kbs.msu.edu/datasets/40 https://doi.org/10.6073/pasta/423c07d6ea3317c545beabb4b8e502c8 Yield monitor data across several years crops. Un-friendly license.","code":""},{"path":"/articles/agridat_data.html","id":"nature-scientific-data","dir":"Articles","previous_headings":"Data Repositories","what":"Nature Scientific Data","title":"Additional sources of agricultural data","text":"https://www.nature.com/sdata/","code":""},{"path":"/articles/agridat_data.html","id":"open-data-journal-for-agricultural-research","dir":"Articles","previous_headings":"Data Repositories","what":"Open Data Journal for Agricultural Research","title":"Additional sources of agricultural data","text":"https://library.wur.nl/ojs/index.php/odjar/","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"wolfram-data-repository","dir":"Articles","previous_headings":"Data Repositories","what":"Wolfram Data Repository","title":"Additional sources of agricultural data","text":"https://datarepository.wolframcloud.com/","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"iowa-state-agricultural-research-bulletins","dir":"Articles","previous_headings":"Journals - Bulletins","what":"Iowa State Agricultural Research Bulletins","title":"Additional sources of agricultural data","text":"https://lib.dr.iastate.edu/ag_researchbulletins/","code":"Vol 26/ 281. Cox: Analysis of Lattice and Triple Lattice. Page 11: Lattice, 81 hybs, 4 reps Page 24: Triple lattice, 81 hybs, 6 reps Vol 29/347. Homeyer. Punched Card and Calculating Machine Methods for Analyzing Lattice Experiments Including Lattice Squares and the Cubic Lattice. Page 37: Triple lattice (9 blocks * 9 hybrids) with 6 reps. Page 60: Simple lattice, 8 blocks * 8 hybrids, 4 reps. Page 76: Balanced lattice, 25 hybrids Page 87: Lattice square with (k+1)/2 reps, 121 hybrids, 6 rep Page 109: Lattice square with k+1 reps, 7 blocks * 7 hyb, 8 reps Page 126: Cubic lattice, 16 blocks * 4 plots = 64 varieties, 9 reps, cotton Vol 32/396. Wassom. Bromegrass Uniformity Trial: agridat::wassom.bromegrass.uniformity Vol 33/424. Heady. Crop Response Surfaces and Economic Optima in Fertilizer - agridat::heady.fertilizer Vol 34/358. Schwab. Research on Irrigation of Corn and Soybeans At Conesville. Page 257. 2 year, 2 loc, 4 rep, 2 nitro. Stand & yield. Nice graph of soil moisture deficit (fig 9) Vol. 34/463. Doll. Fertilizer Production Functions for Corn and Oats. Table 1, 1954 Clarion Loam. N,P,K. Table 14, 1955 McPaul Silt Loam. N,P. Table 25, 1955 corn. K,P,N. Table 31, 1956 oats, K,P,N. Trends difficult to establish. Vol 34/472. Pesek. Production Surfaces and Economic Optima For Corn Yields. Same data published in SSA journal? Vol 34/488. Walker. Application of Game Theory Models to Decisions. Vol 35/494. North Central Regional Potassium Studies with Alfalfa. Page 176. Two years, several locs per state, multiple states, multiple fertilizer levels, multiple cuttings. Soil test attributes. Page 183. Yield and %K. Vol 35/503. North Central Regional Potassium Studies with Corn."},{"path":[]},{"path":"/articles/agridat_data.html","id":"bakare-et-al","dir":"Articles","previous_headings":"Papers","what":"Bakare et al","title":"Additional sources of agricultural data","text":"Exploring genotype environment interaction cassava yield yield related traits using classical statistical methods https://doi.org/10.1371/journal.pone.0268189 36 gen, 20 env, 3 rep. Analysis data : https://github.com/mab658/classical_analysis_GxE","code":""},{"path":"/articles/agridat_data.html","id":"chaves-2023-et-al","dir":"Articles","previous_headings":"Papers","what":"Chaves 2023 et al","title":"Additional sources of agricultural data","text":"Analysis multi-harvest data mixed models: application Theobroma grandiflorum breeding https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.20995 Nice. Complete data R code. found FA3 best genetic covariances, AR1H best residual structure. Used FAST OP (Cullis) selection.","code":""},{"path":"/articles/agridat_data.html","id":"cleveland-m-a--and-john-m--hickey-selma-forni-2012-","dir":"Articles","previous_headings":"Papers","what":"Cleveland, M.A. and John M. Hickey, Selma Forni (2012).","title":"Additional sources of agricultural data","text":"Common Dataset Genomic Analysis Livestock Populations. G3, 2, 429-435. https://doi.org/10.1534/g3.111.001453 supplemental information paper contains data 3534 pigs high-density genotypes (50000 SNPs), pedigree including parents grandparents animals.","code":""},{"path":"/articles/agridat_data.html","id":"coelho-2021-et-al","dir":"Articles","previous_headings":"Papers","what":"Coelho 2021 et al","title":"Additional sources of agricultural data","text":"Accounting spatial trends multi-environment diallel analysis maize breeding https://doi.org/10.1371/journal.pone.0258473 78 hybrids diallel, 4 environments, 3 reps. Compared spatial non-spatial analyses.","code":""},{"path":"/articles/agridat_data.html","id":"daillant-spinnler-1996--relationships-between-perceived-sensory-properties-and-major-preference-directions-of-12-variaties-of-apples-from-the-southern-hemisphere-","dir":"Articles","previous_headings":"Papers","what":"Daillant-Spinnler (1996). Relationships between perceived sensory properties and major preference directions of 12 variaties of apples from the southern hemisphere.","title":"Additional sources of agricultural data","text":"Food Quality Preference, 7(2), 113-126. https://doi.org/10.1016/0950-3293(95)00043-7 data ClustVarLV::apples_sh$pref ClustVarLV::apples_sh$senso 12 apple varieties, 43 traits, 60 consumers","code":""},{"path":"/articles/agridat_data.html","id":"gregory-crowther-lambert-1932--the-interrelation-of-factors-controlling-the-production-of-cotton-under-irrigation-in-the-sudan-","dir":"Articles","previous_headings":"Papers","what":"Gregory, Crowther & Lambert (1932). The interrelation of factors controlling the production of cotton under irrigation in the Sudan.","title":"Additional sources of agricultural data","text":"Jour Agric Sci, 22, p. 617.","code":""},{"path":"/articles/agridat_data.html","id":"hedrick-1920-","dir":"Articles","previous_headings":"Papers","what":"Hedrick (1920).","title":"Additional sources of agricultural data","text":"Twenty years fertilizers apple orchard. https://books.google.com/books?hl=en&lr=&id=SqlJAAAAMAAJ&oi=fnd&pg=PA446 authors found significant differences fertilizer treatments.","code":""},{"path":"/articles/agridat_data.html","id":"meehan-gratton-2016-","dir":"Articles","previous_headings":"Papers","what":"Meehan & Gratton (2016).","title":"Additional sources of agricultural data","text":"Landscape View Agricultural Insecticide Use across Conterminous US 1997 2012. PLOS ONE, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166724 Supplemental material contains county-level data 4 years. Complete R-INLA code analysis.","code":""},{"path":"/articles/agridat_data.html","id":"monteverde-et-al","dir":"Articles","previous_headings":"Papers","what":"Monteverde et al","title":"Additional sources of agricultural data","text":"Integrating Molecular Markers Environmental Covariates Interpret Genotype Environment Interaction Rice (Oryza sativa L.) Grown Subtropical Areas https://doi.org/10.1534/g3.119.400064 https://gsajournals.figshare.com/articles/dataset/Supplemental_Material_for_Monteverde_et_al_2019/7685636 Supplemental information contains phenotypic data markers environmental covariates PLS analysis.","code":""},{"path":"/articles/agridat_data.html","id":"kenward-michael-g--1987-","dir":"Articles","previous_headings":"Papers","what":"Kenward, Michael G. (1987).","title":"Additional sources of agricultural data","text":"Method Comparing Profiles Repeated Measurements. Applied Statistics, 36, 296-308. ante-dependence model fit repeated measures cattle weight.","code":""},{"path":"/articles/agridat_data.html","id":"klumper-qaim-2015-","dir":"Articles","previous_headings":"Papers","what":"Klumper & Qaim (2015).","title":"Additional sources of agricultural data","text":"Meta-Analysis Impacts Genetically Modified Crops. https://doi.org/10.1371/journal.pone.0111629 Nice meta-analysis dataset. Published data include differences, standard-errors. See comments PLOS article peculiarities data.","code":""},{"path":"/articles/agridat_data.html","id":"lado-b--et-al--2013-","dir":"Articles","previous_headings":"Papers","what":"Lado, B. et al. (2013).","title":"Additional sources of agricultural data","text":"“Increased Genomic Prediction Accuracy Wheat Breeding Spatial Adjustment Field Trial Data”. G3, 3, 2105-2114. https://doi.org/10.1534/g3.113.007807 large haplotype dataset (83 MB) two-year phenotype data multiple traits.","code":""},{"path":"/articles/agridat_data.html","id":"oakey-cullis-thompson-2016","dir":"Articles","previous_headings":"Papers","what":"Oakey, Cullis, Thompson 2016","title":"Additional sources of agricultural data","text":"Genomic Selection Multi-environment Crop Trials https://www.g3journal.org/content/6/5/1313 http://www.g3journal.org/content/6/5/1313/suppl/DC1 648 genotypes planted pots yr 1, 856 lines yr 2, 639 common years. 7864 SNP markerks","code":""},{"path":"/articles/agridat_data.html","id":"peixoto-marco-antonio-et-al-2020","dir":"Articles","previous_headings":"Papers","what":"Peixoto, Marco Antonio et al (2020)","title":"Additional sources of agricultural data","text":"Random regression modeling yield genetic trajectories Jatropha curcas breeding. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244021 Repeated measurements six years. Data supplemental Word doc.","code":""},{"path":"/articles/agridat_data.html","id":"perez-valencia-2022-","dir":"Articles","previous_headings":"Papers","what":"Perez-Valencia (2022).","title":"Additional sources of agricultural data","text":"two‑stage approach spatio‑temporal analysis high‑throughput phenotyping data. https://doi.org/10.1038/s41598-022-06935-9 Time-series data individual plots field many genotypes.","code":""},{"path":"/articles/agridat_data.html","id":"roger-w--hexem-earl-o-heady-metin-caglar-1974","dir":"Articles","previous_headings":"Papers","what":"Roger W. Hexem, Earl O.Heady, Metin Caglar (1974)","title":"Additional sources of agricultural data","text":"compendium experimental data corn, wheat, cotton sugar beets grown selected sites western United States alternative production functions fitted data. Technical report: Center Agricultural Rural Development, Iowa State University. https://babel.hathitrust.org/cgi/pt?id=wu.89031116783;view=1up;seq=3 technical report provides data experiments corn, wheat, cotton & sugar beets, crop tested several locations two years, factorial structure irrigation nitrogen treatments, replications. Three polynomial functions fit data location (quadratic, square root, three-halves).","code":""},{"path":"/articles/agridat_data.html","id":"snedecor-george-and-e--s--haber-1946-","dir":"Articles","previous_headings":"Papers","what":"Snedecor, George and E. S. Haber (1946).","title":"Additional sources of agricultural data","text":"Statistical Methods Incomplete Experiment Perennial Crop. Biometrics Bulletin, 2, 61-67. https://www.jstor.org/stable/3001959 Harvest asparagus 10 years, three cutting dates per year, 6 blocks.","code":""},{"path":"/articles/agridat_data.html","id":"tanaka-takashi-x--t-","dir":"Articles","previous_headings":"Papers","what":"Tanaka, Takashi X. T.","title":"Additional sources of agricultural data","text":"Assessment design analysis frameworks -farm experimentation simulation study wheat yield Japan. https://github.com/takashit754/geostat Yield-monitor data 3 fields.","code":""},{"path":"/articles/agridat_data.html","id":"technow-frank-et-al--2014-","dir":"Articles","previous_headings":"Papers","what":"Technow, Frank, et al. (2014).","title":"Additional sources of agricultural data","text":"Genome Properties Prospects Genomic Prediction Hybrid Performance Breeding Program Maize. August 1, 2014 vol. 197 . 4 1343-1355. https://doi.org/10.1534/genetics.114.165860 Genotype phenotype data appears sommer package.","code":""},{"path":"/articles/agridat_data.html","id":"tian-ting-2015-","dir":"Articles","previous_headings":"Papers","what":"Tian, Ting (2015).","title":"Additional sources of agricultural data","text":"Application Multiple Imputation Missing Values Three-Way Three-Mode Multi-Environment Trial Data. https://doi.org/10.1371/journal.pone.0144370 Uses agridat::australia.soybean data one real dataset 4 traits identified. data code available.","code":""},{"path":"/articles/agridat_data.html","id":"randall-j--wisser-et-al--2011-","dir":"Articles","previous_headings":"Papers","what":"Randall J. Wisser et al. (2011).","title":"Additional sources of agricultural data","text":"Multivariate analysis maize disease resistances suggests pleiotropic genetic basis implicates GST gene. PNAS. https://doi.org/10.1073/pnas.1011739108 supplement contains genotype data, phenotype data.","code":""},{"path":"/articles/agridat_data.html","id":"rife-et-al--2018","dir":"Articles","previous_headings":"Papers","what":"Rife et al. (2018)","title":"Additional sources of agricultural data","text":"Genomic analysis prediction within US public collaborative winter wheat regional testing nursery. https://doi.org/10.5061/dryad.q968v83 Large phenotypic dataset 691 wheat lines, 33 years, 670 environments, 3-4 reps, 120000 datapoints. genotypic data included.","code":""},{"path":"/articles/agridat_data.html","id":"schmitz-carley-et-al-2018","dir":"Articles","previous_headings":"Papers","what":"Schmitz Carley et al (2018)","title":"Additional sources of agricultural data","text":"Genetic Covariance Environments Potato National Chip Processing Trial https://dl.sciencesocieties.org/publications/cs/articles/59/1/107 Supp 2 contains genomic data, easy way find phenotypic data.","code":""},{"path":"/articles/agridat_data.html","id":"van-der-voet-et-al--2017-","dir":"Articles","previous_headings":"Papers","what":"van der Voet et al. (2017).","title":"Additional sources of agricultural data","text":"Equivalence testing using existing reference data: example genetically modified conventional crops animal feeding studies. https://doi.org/10.1016/j.fct.2017.09.044 full datasets GRACE studies -E available : https://www.cadima.info/index.php/area/publicAnimalFeedingTrials CC license.","code":""},{"path":"/articles/agridat_data.html","id":"volpato-et-al-2024","dir":"Articles","previous_headings":"Papers","what":"Volpato et al (2024)","title":"Additional sources of agricultural data","text":"retrospective analysis historical data multi-environment trials dry bean ( Phaseolus vulgaris L.) Michigan. https://github.com/msudrybeanbreeding/DryBean_MultiEnvTrials Full dataset R code.","code":""},{"path":"/articles/agridat_data.html","id":"xavier-alencar-et-al--","dir":"Articles","previous_headings":"Papers","what":"Xavier, Alencar et al..","title":"Additional sources of agricultural data","text":"Genome-Wide Analysis Grain Yield Stability Environmental Interactions Multiparental Soybean Population. https://doi.org/10.1534/g3.117.300300 Data SoyNAM NAM packages.","code":""},{"path":"/articles/agridat_data.html","id":"yan-weikei-2002-","dir":"Articles","previous_headings":"Papers","what":"Yan, Weikei (2002).","title":"Additional sources of agricultural data","text":"Singular value partitioning biplots. Agron Journal. Winter wheat, 31 gen 8 loc. data different Yan’s earlier papers. Unfortunately, data given paper missing two rows.","code":""},{"path":"/articles/agridat_data.html","id":"r-packages-on-cran-github-etc-","dir":"Articles","previous_headings":"","what":"R packages on CRAN, Github, etc.","title":"Additional sources of agricultural data","text":"See also: https://cran.r-project.org/web/views/Agriculture.html","code":""},{"path":"/articles/agridat_data.html","id":"agml","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"AgML","title":"Additional sources of agricultural data","text":"https://github.com/Project-AgML/AgML Datasets agricultural machine learning image classification, semantic segmentation, object detection, etc.","code":""},{"path":"/articles/agridat_data.html","id":"agricensdata","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"agriCensData","title":"Additional sources of agricultural data","text":"https://github.com/OnofriAndreaPG/agriCensData Three datasets censored observations paper “Analyzing interval-censored data agricultural research: review examples software tips”.","code":""},{"path":"/articles/agridat_data.html","id":"agritutorial","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"agriTutorial","title":"Additional sources of agricultural data","text":"https://myaseen208.github.io/agriTutorial/ Five datasets used illustrate analyses.","code":""},{"path":"/articles/agridat_data.html","id":"agricolae","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"agricolae","title":"Additional sources of agricultural data","text":"assorted data functions analysis agricultural data.","code":""},{"path":"/articles/agridat_data.html","id":"agrobiodata","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"agroBioData","title":"Additional sources of agricultural data","text":"https://github.com/OnofriAndreaPG/agroBioData Datasets agriculture applied biology. Referenced blog: https://www.statforbiology.com/","code":""},{"path":"/articles/agridat_data.html","id":"aml---adaptive-mixed-lasso","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"aml - Adaptive Mixed LASSO","title":"Additional sources of agricultural data","text":"Data aml::wheat genetic phenotypic data wheat. Modest size.","code":""},{"path":"/articles/agridat_data.html","id":"bglr---bayesian-generalized-linear-regression-","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"BGLR - Bayesian Generalized Linear Regression.","title":"Additional sources of agricultural data","text":"matrix (pedigree) 499 genotypes 4 locations.","code":""},{"path":"/articles/agridat_data.html","id":"blr---bayesian-linear-regression-","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"BLR - Bayesian Linear Regression.","title":"Additional sources of agricultural data","text":"matrix (pedigree) 499 genotypes 4 locations.","code":""},{"path":"/articles/agridat_data.html","id":"bsagri","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"BSagri","title":"Additional sources of agricultural data","text":"Safety assessment agriculture trials","code":""},{"path":"/articles/agridat_data.html","id":"clustvarlv","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"ClustVarLV","title":"Additional sources of agricultural data","text":"Data apples_sh sensory attributes preference scores 12 apple varieties.","code":""},{"path":"/articles/agridat_data.html","id":"cropcc---climate-change-on-crops","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"cropcc - Climate change on crops","title":"Additional sources of agricultural data","text":"https://r-forge.r-project.org/projects/cropcc/","code":""},{"path":"/articles/agridat_data.html","id":"drc---dose-response-curves","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"drc - Dose response curves","title":"Additional sources of agricultural data","text":"nice herbicide dose response curves germination data mungbean, rice, wheat.","code":""},{"path":"/articles/agridat_data.html","id":"epiphy","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"epiphy","title":"Additional sources of agricultural data","text":"https://github.com/chgigot/epiphy Contains 10 historical datasets plant disease epidemics.","code":""},{"path":"/articles/agridat_data.html","id":"fw---finlay-wilkinson-regression","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"FW - Finlay-Wilkinson regression","title":"Additional sources of agricultural data","text":"https://github.com/lian0090/FW/ phenotype data marker data 599 wheat lines 4 environments.","code":""},{"path":"/articles/agridat_data.html","id":"ggenealogy","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"ggenealogy","title":"Additional sources of agricultural data","text":"https://doi.org/10.18637/jss.v089.i13 Data sbGeneal contains soybean pedigree 230 varieties.","code":""},{"path":"/articles/agridat_data.html","id":"grbase","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"gRbase","title":"Additional sources of agricultural data","text":"Data gRbase::carcass: thickness meat fat slaughter pigs","code":""},{"path":"/articles/agridat_data.html","id":"lmdiallel","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"lmDiallel","title":"Additional sources of agricultural data","text":"https://github.com/OnofriAndreaPG/lmDiallel/tree/master/data","code":""},{"path":"/articles/agridat_data.html","id":"lmtest","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"lmtest","title":"Additional sources of agricultural data","text":"Data lmtest::ChickEgg time series annual chicken egg production United States 1930-1983.","code":""},{"path":"/articles/agridat_data.html","id":"nada","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"NADA","title":"Additional sources of agricultural data","text":"Data Atra Recon contain measurements Atrazine water samples.","code":""},{"path":"/articles/agridat_data.html","id":"nlraa","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"nlraa","title":"Additional sources of agricultural data","text":"Miguez. Non-linear models agriculture. nlraa::sm = agridat::miguez.biomass Vignettes functions working (non)linear mixed models","code":""},{"path":"/articles/agridat_data.html","id":"nlme","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"nlme","title":"Additional sources of agricultural data","text":"nlme::Orange: Growth orange trees nlme::Soybean: Growth soybean plants. book “Nonlinear Models Repeated Measurement Data”.","code":""},{"path":"/articles/agridat_data.html","id":"ofpe---on-farm-precision-experiments","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"OFPE - On-Farm Precision Experiments","title":"Additional sources of agricultural data","text":"https://paulhegedus.github.io/OFPE-Website/ https://github.com/paulhegedus/OFPEDATA/","code":""},{"path":"/articles/agridat_data.html","id":"onfant-dataset","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"onfant.dataset","title":"Additional sources of agricultural data","text":"https://github.com/AnabelleLaurent/onfant.dataset","code":""},{"path":"/articles/agridat_data.html","id":"pbkrtest","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"pbkrtest","title":"Additional sources of agricultural data","text":"pbkrtest::beets Yield percent sugar split-plot experiment.","code":""},{"path":"/articles/agridat_data.html","id":"plantbreeding","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"plantbreeding","title":"Additional sources of agricultural data","text":"https://r-forge.r-project.org/projects/plantbreeding/","code":"Data: fulldial Data: linetester Data: peanut - same as agridat::kang.peanut"},{"path":"/articles/agridat_data.html","id":"sdaa---survey-data-and-analysis","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"SDaA - Survey Data and Analysis","title":"Additional sources of agricultural data","text":"package county-level data United States Census Agriculture, along vignette illustrate survey sampling analyses.","code":""},{"path":"/articles/agridat_data.html","id":"semipar","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"SemiPar","title":"Additional sources of agricultural data","text":"Data: SemiPar::onions agridat::ratkowski.onions","code":""},{"path":"/articles/agridat_data.html","id":"soildb","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"soilDB","title":"Additional sources of agricultural data","text":"https://ncss-tech.github.io/AQP/soilDB/soilDB-Intro.html Soil database interface.","code":""},{"path":"/articles/agridat_data.html","id":"sommer---solving-mixed-model-equations-in-r","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"sommer - Solving mixed model equations in R","title":"Additional sources of agricultural data","text":"Data: h2. Modest-sized GxE experiment potato Data: cornHybrid. Yield/PLTHT 100 hybrids 20 inbred * 20 inbred, 4 locs. Phenotype relationship matrix. Data: Data: RICE Data: FDdata taken agridat::bond.diallel Data:","code":"data(DT_wheat) # CIMMYT wheat data DT_wheat # 599 varieties, yield in 4 envts GT_wheat # 599 varieties, 1279 markers coded -1,1 data(DT_technow) # From http://www.genetics.org/content/197/4/1343.supplemental DT <- DT_technow # 1254 hybs, parents, GY=yield, GM=moisture Md <- Md_technow # 123 dent parents, 35478 markers Mf <- Mf_technow # 86 flint parents, 37478 markers Ad <- Ad_technow # 123 x 123 A matrix Af <- Af_technow # 86 x 85 A matrix"},{"path":"/articles/agridat_data.html","id":"soynam---soybean-nested-association-mapping","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"SoyNAM - Soybean nested association mapping","title":"Additional sources of agricultural data","text":"Dataset phenotype data 3 yr, 9 locations, 18 environments, 60 thousand observations height, maturity, lodging, moisture, protein, oil, fiber, seed size. 5000+ strains, 40 families. Data formatted analysis NAM package available following command: SoyNAM::ENV().","code":""},{"path":"/articles/agridat_data.html","id":"soyurt","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"SoyURT","title":"Additional sources of agricultural data","text":"https://github.com/mdkrause/SoyURT Large historical data yield trials Uniform Soybean Tests Northern States. Years 1989-2019, 63 locations, 4257 genotypes. package also contains soils weather data trial locations. Note: USDA published papers results : National Cotton Variety Tests, Uniform Soybean Tests Northern States, Uniform Soybean Tests Southern States : https://www.ars.usda.gov/southeast-area/stoneville-ms/crop-genetics-research/docs/","code":""},{"path":"/articles/agridat_data.html","id":"spdep","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"spdep","title":"Additional sources of agricultural data","text":"vignette ‘Problem Spatial Autocorrelation: forty years ’ examines agriculture Irish counties. See also data ade4::irishdata.","code":""},{"path":"/articles/agridat_data.html","id":"spurs","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"spuRs","title":"Additional sources of agricultural data","text":"Data: spuRs::trees data 107 trees cut cross sections volume calculated roughly 10-year increments. subset much-larger original data Guttenberg: https://archive.org/stream/wachstumundertra00gutt","code":""},{"path":"/articles/agridat_data.html","id":"statforbiology","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"StatForBiology","title":"Additional sources of agricultural data","text":"https://www.statforbiology.com/ Blog posts example analyses.","code":""},{"path":"/articles/agridat_data.html","id":"biometris---statgengxe","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"Biometris - statgenGxE","title":"Additional sources of agricultural data","text":"https://github.com/Biometris/statgenGxE https://biometris.github.io/statgenGxE/ AMMI, FW, GGE stability analyses.","code":""},{"path":"/articles/agridat_data.html","id":"biometris---statgengwas","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"Biometris - statgenGWAS","title":"Additional sources of agricultural data","text":"https://github.com/Biometris/statgenGWAS/ https://CRAN.R-project.org/package=statgenGWAS nice package full GxE data marker data 41722 loci 246 lines. 256 hybrids, 29 envts across 2 years, multi-trait (yield, silking, pltht, earht, etc). Includes worked example data : https://data.inra.fr/dataset.xhtml?persistentId=doi:10.15454/IASSTN publication: Millet 2016, Genome-Wide Analysis Yield Europe: Allelic Effects Vary Drought Heat Scenarios, https://academic.oup.com/plphys/article/172/2/749/6115953","code":""},{"path":"/articles/agridat_data.html","id":"biometris---statgensta","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"Biometris - statgenSTA","title":"Additional sources of agricultural data","text":"https://github.com/Biometris/statgenSTA/ https://CRAN.R-project.org/package=statgenSTA Analysis phenotypic data field experiments using SpATS, lme4, asreml.","code":""},{"path":"/articles/agridat_data.html","id":"st4gi---stat-for-genetic-improvement","dir":"Articles","previous_headings":"R packages on CRAN, Github, etc.","what":"st4gi - Stat for genetic improvement","title":"Additional sources of agricultural data","text":"https://github.com/reyzaguirre/st4gi","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"ars-oat-trials","dir":"Articles","previous_headings":"Web sites","what":"ARS oat trials","title":"Additional sources of agricultural data","text":"https://www.ars.usda.gov/Main/docs.htm?docid=8419&page=4","code":""},{"path":"/articles/agridat_data.html","id":"cimmyt-research-data","dir":"Articles","previous_headings":"Web sites","what":"CIMMYT Research Data","title":"Additional sources of agricultural data","text":"https://data.cimmyt.org/dataverse/cimmytdatadvn","code":""},{"path":[]},{"path":"/articles/agridat_data.html","id":"grain-genes","dir":"Articles","previous_headings":"Web sites","what":"Grain genes","title":"Additional sources of agricultural data","text":"https://wheat.pw.usda.gov/ggpages/HxT/ Harrington x TR306 Barley Mapping Population. genotype phenotype data comes Mapmaker, seems slightly non-standard format; 145 DH lines, 217 markers, 25 env, 1 rep. https://wheat.pw.usda.gov/ggpages/SxM/ . data agridat::steptoe.morex.","code":""},{"path":"/articles/agridat_data.html","id":"glten---a-network-of-long-term-trials-around-the-world","dir":"Articles","previous_headings":"Web sites","what":"GLTEN - A network of Long-Term trials around the world","title":"Additional sources of agricultural data","text":"https://glten.org/","code":""},{"path":"/articles/agridat_data.html","id":"ideals","dir":"Articles","previous_headings":"Web sites","what":"Ideals","title":"Additional sources of agricultural data","text":"https://www.ideals.illinois.edu/handle/2142/3528 Data File : Raw data ear analyzed year Illinois long-term selection experiment oil protein corn (1896-2004)","code":""},{"path":"/articles/agridat_data.html","id":"international-potato-center","dir":"Articles","previous_headings":"Web sites","what":"International Potato Center","title":"Additional sources of agricultural data","text":"https://data.cipotato.org/dataverse.xhtml","code":""},{"path":"/articles/agridat_data.html","id":"ilri-international-livestock-research-institute","dir":"Articles","previous_headings":"Web sites","what":"ILRI International Livestock Research Institute","title":"Additional sources of agricultural data","text":"Case study 4 nice diallel example sheep data. Available agridat::ilri.sheep","code":""},{"path":"/articles/agridat_data.html","id":"irri-biometrics-and-breeding-informatics","dir":"Articles","previous_headings":"Web sites","what":"IRRI Biometrics and Breeding Informatics","title":"Additional sources of agricultural data","text":"http://bbi.irri.org/products STAR, PBTools, CropStat. STAR user guide well-documented data (even using 2 agridat), PBTools user guide document data.","code":""},{"path":"/articles/agridat_data.html","id":"miappe-minimum-information-about-plant-phenotyping-experiments","dir":"Articles","previous_headings":"Web sites","what":"MIAPPE Minimum Information About Plant Phenotyping Experiments","title":"Additional sources of agricultural data","text":"https://www.miappe.org/ limited data.","code":""},{"path":"/articles/agridat_data.html","id":"rothamsted-electronic-archive","dir":"Articles","previous_headings":"Web sites","what":"Rothamsted Electronic Archive","title":"Additional sources of agricultural data","text":"http://www.era.rothamsted.ac.uk/index.php Data Broadbalk long-term experiments. Github draft data: https://github.com/Rothamsted-Ecoinformatics/YieldbookDatasetDrafts","code":""},{"path":"/articles/agridat_data.html","id":"rothamsted-documents-archive","dir":"Articles","previous_headings":"Web sites","what":"Rothamsted Documents Archive","title":"Additional sources of agricultural data","text":"http://www.era.rothamsted.ac.uk/eradoc/collections.php Annual reports Rothamsted 1908-1987. Many data, especially early years (WWII) data given ‘Classical Experiments’.","code":"Year, page 1908-1926 1926-1927 agridat::sawyer.multi.uniformity 1927-1928 agridat::sawyer.multi.uniformity 1929-1930 1931,143 agridat::yates.oats 1932 1933 1934,215-222 Sugar beet multi-environment trial with 3^3 fertilizer treatments at each site Roots, SugarPercent, SugarWeight, PlantNumber, Tops, Purity. 1935 1936,241 Similar to the 1934 experiment, but only gives the main effects, not the actual data. 1937-1939 1946-1955 1986"},{"path":"/articles/agridat_data.html","id":"yates-1937-the-design-and-analysis-of-factorial-experiments","dir":"Articles","previous_headings":"Web sites","what":"Yates (1937), The Design and analysis of factorial experiments","title":"Additional sources of agricultural data","text":"","code":"9 2x2x2, 4 rep 27 2x2x2x2x2 factorial 33 2x2x2 factorial in two 4x4 Latin Squares 42 3x3x3 factorial 59 3x2x2 factorial in 3 reps. See also page 39. 74 Split-plot agridat::yates.oats"},{"path":"/articles/agridat_data.html","id":"statistical-analysis-of-agricultural-experiments-with-r","dir":"Articles","previous_headings":"Web sites","what":"Statistical Analysis of Agricultural Experiments with R","title":"Additional sources of agricultural data","text":"rstats4ag.org (http included firewall problems). Datasets mixed models, ancova, dose response curves, competition.","code":""},{"path":"/articles/agridat_data.html","id":"syngenta-crop-challenge","dir":"Articles","previous_headings":"Web sites","what":"Syngenta Crop Challenge","title":"Additional sources of agricultural data","text":"https://www.ideaconnection.com/syngenta-crop-challenge/ Annual Kaggle-style competition sponsored Syngenta.","code":""},{"path":"/articles/agridat_data.html","id":"terra-ref","dir":"Articles","previous_headings":"Web sites","what":"Terra-Ref","title":"Additional sources of agricultural data","text":"https://terraref.org/ Sensor observations, plant phenotypes, derived traits, genetic genomic data. Beta version Nov 2018.","code":""},{"path":"/articles/agridat_data.html","id":"usda-national-agricultural-statistics-service","dir":"Articles","previous_headings":"Web sites","what":"USDA National Agricultural Statistics Service","title":"Additional sources of agricultural data","text":"https://www.nass.usda.gov https://quickstats.nass.usda.gov/ Group: Field Crops Commodity: Corn Category: Area Harvested, Yield Data Item: Corn grain Acres Harvested, Yield Bu/Ac Domain: Total Geography: State See agridat::nass.corn, nass.wheat, etc.","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Graphical Gems in the agridat Package","text":"exhibit agricultural data uses following packages: agridat, desplot, gge, HH, lattice, latticeExtra, mapproj, maps, reshape2.","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"potato-blight-incidence-over-space-and-time","dir":"Articles","previous_headings":"","what":"Potato blight incidence over space and time","title":"Graphical Gems in the agridat Package","text":"@lee2009random analyzed large dataset evaluate resistance potato varieties blight. data contains evaluations changing set varieties every two years, evaluated 5 blocks, repeatedly throughout growing season track progress disease. panel shows field map given date, separate row panels year. include field spatial trends model data? 1983, 20 varieties evaluated 5 blocks (shown colored numbers) throughout growing season disease resistance. Resistance scores start 9 varieties (shown panels). growing season progresses, ‘.HARDY’ variety succumbs quickly blight, ‘IWA’ succumbs steadily, ‘064.1’ resists blight near end season. view show differences blocks?","code":"## Loading required package: desplot ## Loading required package: latticeExtra ## Loading required package: lattice"},{"path":"/articles/agridat_graphical_gems.html","id":"an-informative-prior","dir":"Articles","previous_headings":"","what":"An informative prior","title":"Graphical Gems in the agridat Package","text":"@harrison2012bayesian used Bayesian approach model daidzein levels soybean samples. 18 previous publications, extracted published minimum maximum daidzein levels, number samples tested. line dotplot shows large, dark dots one published minimum maximum. small dots imputed using lognormal distribution. observed/imputed data used fit common lognormal distribution can used informative prior. common prior shown density top dotplot. think better use non-informative prior, informative prior?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"data-densities-for-a-binomial-glm","dir":"Articles","previous_headings":"","what":"Data densities for a binomial GLM","title":"Graphical Gems in the agridat Package","text":"@mead2002statistical present data germination seeds four temperatures (T1-T4) four chemical concentrations. 4*4=16 treatments, 50 seeds tested four reps. graphic, point one rep. blue line fitted curve GLM Temperature factor log concentration covariate. gray lines show central 95 percent binomial density position. display help understand logit link changing shape binomial density?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"verification-of-experiment-layout","dir":"Articles","previous_headings":"","what":"Verification of experiment layout","title":"Graphical Gems in the agridat Package","text":"@gomez1984statistical provide data experiment 3 reps, 6 genotypes, 3 levels nitrogen 2 planting dates. experiment layout putatively ‘’split strip-plot’’. verify design, desplot package used plotting design field experiments. design different ‘’split-split-plot’’ design?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"visualizing-main-effects-two-way-interactions","dir":"Articles","previous_headings":"","what":"Visualizing main effects, two-way interactions","title":"Graphical Gems in the agridat Package","text":"@heiberger2004statistical provide interesting way use lattice graphics visualize main effects (using boxplots) interactions (using interaction plots) data. Rice yield plotted versus replication, nitrogen, management type, genotype variety. Box plots show minor differences reps, increaing yield due nitrogen, high yield intensive management, large differences varieties. think interaction plots show interaction (lack parallelism)?","code":"## Loading required package: HH ## Loading required package: grid ## Loading required package: multcomp ## Loading required package: mvtnorm ## Loading required package: survival ## Loading required package: TH.data ## Loading required package: MASS ## ## Attaching package: 'TH.data' ## The following object is masked from 'package:MASS': ## ## geyser ## Loading required package: gridExtra ## ## Attaching package: 'HH' ## The following object is masked from 'package:base': ## ## is.R"},{"path":"/articles/agridat_graphical_gems.html","id":"d-yield-response-to-fertilizers","dir":"Articles","previous_headings":"","what":"3D yield response to fertilizers","title":"Graphical Gems in the agridat Package","text":"Note: image created manual manipulation rgl device. manual manipulation makes non-reproducible Rmd file. See example sinclair.clover data code. @sinclair1994sulphur examined clover yields function sulfur phosphorous fertilizer factorial-treatment experiment. @dodds1996bivariate modeled yield response using Mitzerlisch-like equation allows interacting curvature two dimensions xx yy: yield=α*(1+β*(σ+τ*xx+1)y)*(1+δ*(θ+ρ*yy+1)x) yield = \\alpha * \\left( 1 + \\beta *\\left(\\frac{\\sigma + \\tau*x}{x+1}\\right)^y\\right) * \\left( 1+\\delta*\\left(\\frac{\\theta + \\rho *y}{y+1}\\right)^x \\right) blue dots observed data, tan surface fitted surface drawn rgl package). decide optimal fertilizer levels?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"mosaic-plot-of-potato-damage-from-harvesting","dir":"Articles","previous_headings":"","what":"Mosaic plot of potato damage from harvesting","title":"Graphical Gems in the agridat Package","text":"@keen1997analysis looked damage potatoes caused lifting rods harvest. experiment, eight types lifting rods compared. Two energy levels, six genotypes three weight classes used. combinations treatments, 20 potato tubers rated undamaged (D1, yellow) severely damaged (D4, red). Counts per treatment shown mosaic plot. style lifting rods cause least/damage potatoes?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"yield-vs-covariate-for-latticebarley","dir":"Articles","previous_headings":"","what":"Yield vs covariate for lattice::barley","title":"Graphical Gems in the agridat Package","text":"@wright2013revisiting investigated lattice::barley data. original two years data extended 10 years (original source documents), supplemented weather covariates 6 locations 10 years. panel shows scatterplot regression average location yield verses weather covariate. Horizontal strips locations, vertical strips covariates: cdd = Cooling Degree Days, hdd = Heating Degree Days, precip = Precipitation). Higher values heating imply cooler weather. plotting symbol last digit year (1927-1936) location. barley yield better cooler warmer weather?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"gge-biplot","dir":"Articles","previous_headings":"","what":"GGE biplot","title":"Graphical Gems in the agridat Package","text":"@laffont2013genotype developed variation GGE (genotype plus genotype--environment) biplot include auxiliary information block/group environments. location classified one two mega-environments (colored). mosaic plots partition variation simultaneously principal component axis source (genotype, genotype--block, residual). genotypes best mega-environment?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"nebraska-farming-income-choropleth","dir":"Articles","previous_headings":"","what":"Nebraska farming income choropleth","title":"Graphical Gems in the agridat Package","text":"Red-Blue palette RColorBrewer package divergent palette light colors near middle scale. can cause problems missing values, appear white (technically, background). order increase visibility missing values, agridat package uses Red-Gray-Blue palette, gray color dark enough clearly distinguish missing values. outlier county (Butler) northeast Nebraska limit interpration spatial patterns data? counties different sizes, second graphic uses income rate per square mile. outlier, might smart use percentile break points, hides outlier. Instead, break points calculated using method called Fisher-Jenks. break points show outlier spatial patterns. now easy see northwest (Sandhills) Nebraska low farming income, especially crops. Counties missing data white, easily distinguished gray. farm incomes highest? ?","code":"## Loading required package: maps ## Loading required package: mapproj"},{"path":"/articles/agridat_graphical_gems.html","id":"las-rosas-yield-monitor","dir":"Articles","previous_headings":"","what":"Las Rosas yield monitor","title":"Graphical Gems in the agridat Package","text":"@anselin2004spatial @lambert2004comparison looked yield monitor data collected corn field Argentina 1999 2001, see yield affected field topography nitrogen fertilizer. figures show heatmaps yield year, also experiment design (colors reps, shades color nitrogen level, plotting character topography). year showed greater spatial variation yield?","code":""},{"path":"/articles/agridat_graphical_gems.html","id":"time-series-of-corn-yields-by-state","dir":"Articles","previous_headings":"","what":"Time series of corn yields by state","title":"Graphical Gems in the agridat Package","text":"National Agricultural Statistics Service tracks total number acres planted corn (crops) state U.S. large changes past century corn acreage selected states. states corn belt 1925? states corn belt 2000?","code":""},{"path":[]},{"path":"/articles/agridat_intro.html","id":"comments-on-the-package-purpose","dir":"Articles","previous_headings":"","what":"Comments on the package purpose","title":"Introduction to agridat","text":"project first begun early 2000s, electronic versions agricultural datasets hard find. Since , revolution availability datasets related agriculture. See vignette describes data sources. Box (1957) said, “hoped seen end obscene tribal habit practiced statisticians continually exhuming massaging dead data sets purpose life long since forgotten possibility anything useful result treatment.” Massaging dead data sets lead genetics released commercial use. value package : 1. Validating published analyses. 2. Providing data testing new analysis methods. 3. Illustrating (validating) use R packages. White van Evert (2008) present guidelines publication data. examples use asreml package since R tool fitting mixed models complex variance structures large datasets, best option modelling AR1xAR1 residual variance structures. Commercial use asreml requires license VSN. (Use search engine find latest version).","code":""},{"path":"/articles/agridat_intro.html","id":"comments-on-the-package-structure","dir":"Articles","previous_headings":"","what":"Comments on the package structure","title":"Introduction to agridat","text":"Many datasets appear electronic form first time. tremendous amount effort given curating process identifying datasets, extracting data source materials, checking data values, documenting data. effect, make data somewhat ‘computable’ (Wolfram 2017). original sources data use different words refer genotypes including accession, breed, cultivar, genotype, hybrid, line, progeny, stock, type, variety. consistency, datasets mostly use gen (genotype). Also consistency, row col usually used field coordinates. dataframes, block, rep, similar terms almost always coded like B1, B2, B3 instead 1, 2, 3. causes R treat data factor instead numeric covariate (good thing). Almost data presented ‘tidy’ dataframes observations rows variables columns. Although using data() necessary access data files, example sections include use data() devtools::run_examples() needs .","code":""},{"path":"/articles/agridat_intro.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Introduction to agridat","text":"G. E. P. Box (1957). Integration Techniques Process Development, Transactions American Society Quality Control. J. White Frits van Evert. (2008). Publishing Agronomic Data. Agronomy Journal, 100, 1396-1400. https://doi.org/10.2134/agronj2008.0080F Stephen Wolfram (2017). Launching Wolfram Data Repository: Data Publishing Really Works. https://writings.stephenwolfram.com/2017/04/launching--wolfram-data-repository-data-publishing--really-works/","code":""},{"path":[]},{"path":"/articles/agridat_mixed_model_example.html","id":"brms","dir":"Articles","previous_headings":"GLM (not mixed–no random plate)","what":"brms","title":"Example generalized linear mixed model analysis with different packages","text":"takes minute compile Stan program… Note, Emacs brms ends R process reason!","code":"if(require(brms)){ m1.brms <- brms::brm( germ|trials(n)~ gen*extract, data = dat, family = binomial, chains=3, iter=3000, warmup=1000) summary(m1.brms) # round( summary(m1.brms)$fixed[,1:4] , 2) # Estimate Est.Error l-95% CI u-95% CI # Intercept -0.42 0.18 -0.77 -0.06 # genO75 -0.14 0.22 -0.56 0.29 # extractcucumber 0.55 0.25 0.07 1.05 # genO75:extractcucumber 0.77 0.30 0.18 1.36 }"},{"path":"/articles/agridat_mixed_model_example.html","id":"glm","dir":"Articles","previous_headings":"GLM (not mixed–no random plate)","what":"glm","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"# ----- GLM. # family=binomial() fixes dispersion at 1 # family=quasibinomial() estimates dispersion, had larger std errors m1.glm <- glm(cbind(germ,n-germ) ~ gen*extract, data=dat, #family=\"binomial\", family=quasibinomial() ) summary(m1.glm) ## round(summary(m1.glm)$coef,2) ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -0.41 0.25 -1.64 0.12 ## genO75 -0.15 0.30 -0.48 0.64 ## extractcucumber 0.54 0.34 1.58 0.13 ## genO75:extractcucumber 0.78 0.42 1.86 0.08"},{"path":"/articles/agridat_mixed_model_example.html","id":"rstan","dir":"Articles","previous_headings":"GLM (not mixed–no random plate)","what":"rstan","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"# ----- Stan using pre-built models from rstanarm libs(tidyverse, rstan, rstanarm,bayesplot) set.seed(42) m1.stan <- stan_glm( cbind(germ,n-germ) ~ gen*extract, data=dat, family = binomial(link=\"logit\") ) summary(m1.stan) ## round(posterior_interval(m1.stan, prob=.90),3) # 5% 95% # (Intercept) -0.728 -0.115 # genO75 -0.506 0.243 # extractcucumber 0.133 0.947 # genO75:extractcucumber 0.255 1.267 libs(bayesplot) mcmc_areas(m1.stan, prob = 0.9) + ggtitle(\"Posterior distributions\", \"with medians and 95 pct intervals\")"},{"path":[]},{"path":"/articles/agridat_mixed_model_example.html","id":"asreml","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"asreml","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"if(require(asreml)){ m1.asreml <- asreml(germ ~ gen*extract, data=dat, random= ~ plate, family=asr_binomial(dispersion=1, total=n)) summary(m1.asreml) ## ## effect ## (Intercept) -0.47 ## gen_O73 0.00 ## gen_O75 -0.08 ## extract_bean 0.00 ## extract_cucumber 0.51 ## gen_O73:extract_bean 0.00 ## gen_O73:extract_cucumber 0.00 ## gen_O75:extract_bean 0.00 ## gen_O75:extract_cucumber 0.83 }"},{"path":"/articles/agridat_mixed_model_example.html","id":"massglmmpql","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"MASS::glmmPQL","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"# --- GLMM. Assumes Gaussian random effects libs(MASS) m1.glmm <- glmmPQL(cbind(germ, n-germ) ~ gen*extract, random= ~1|plate, family=binomial(), data=dat) summary(m1.glmm) ## round(summary(m1.glmm)$tTable,2) ## Value Std.Error DF t-value p-value ## (Intercept) -0.44 0.25 17 -1.80 0.09 ## genO75 -0.10 0.31 17 -0.34 0.74 ## extractcucumber 0.52 0.34 17 1.56 0.14 ## genO75:extractcucumber 0.80 0.42 17 1.88 0.08"},{"path":"/articles/agridat_mixed_model_example.html","id":"glmmtmb","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"glmmTMB","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"libs(glmmTMB) m1.glmmtmb <- glmmTMB(cbind(germ, n-germ) ~ gen*extract + (1|plate), data=dat, family=binomial) round(summary(m1.glmmtmb)$coefficients$cond , 2) ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -0.45 0.22 -2.03 0.04 ## genO75 -0.10 0.28 -0.35 0.73 ## extractcucumber 0.53 0.30 1.74 0.08 ## genO75:extractcucumber 0.81 0.38 2.11 0.04"},{"path":"/articles/agridat_mixed_model_example.html","id":"hglm","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"hglm","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"# ----- HGML package. Beta-binomial with beta-distributed random effects if(require(hglm)){ m1.hglm <- hglm(fixed= germ/n ~ I(gen==\"O75\")*extract, weights=n, data=dat, random=~1|plate, family=binomial(), rand.family=Beta(), fix.disp=1) summary(m1.hglm) # round(summary(m1.hglm)$FixCoefMat,2) ## Estimate Std. Error t-value Pr(>|t|) ## (Intercept) -0.47 0.24 -1.92 0.08 ## I(gen == \"O75\")TRUE -0.08 0.31 -0.25 0.81 ## extractcucumber 0.51 0.33 1.53 0.16 ## I(gen == \"O75\")TRUE:extractcucumber 0.83 0.43 1.92 0.08 }"},{"path":"/articles/agridat_mixed_model_example.html","id":"inla","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"INLA","title":"Example generalized linear mixed model analysis with different packages","text":"See: https://haakonbakka.bitbucket.io/btopic102.html","code":"if(require(INLA)){ #gen,extract are fixed. plate is a random effect #Priors for hyper parameters. See: inla.doc(\"pc.prec\") hyper1 = list(theta = list(prior=\"pc.prec\", param=c(1,0.01))) m1.inla = inla(germ ~ gen*extract + f(plate, model=\"iid\", hyper=hyper1), data=crowder.seeds, family=\"binomial\", Ntrials=n, control.family=list(control.link=list(model=\"logit\"))) round( summary(m1.inla)$fixed, 2) ## mean sd 0.025quant 0.5quant 0.975quant mode kld ## (Intercept) -0.47 0.24 -0.96 -0.46 0.00 -0.46 0 ## genO75 -0.08 0.31 -0.68 -0.09 0.54 -0.09 0 ## extractcucumber 0.53 0.33 -0.13 0.53 1.18 0.53 0 ## genO75:extractcucumber 0.82 0.43 -0.01 0.82 1.69 0.82 0 }"},{"path":"/articles/agridat_mixed_model_example.html","id":"rjags","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"rjags","title":"Example generalized linear mixed model analysis with different packages","text":"Requires JAGS installed.","code":"# JAGS/BUGS. See https://mathstat.helsinki.fi/openbugs/Examples/Seeds.html # Germination rate depends on p, which is a logit of a linear predictor # based on genotype and extract, plus random deviation to intercept # To match the output on the BUGS web page, use: dat$gen==\"O73\". # We use dat$gen==\"O75\" to compare with the parameterization above. jdat =list(germ = dat$germ, n = dat$n, root = as.numeric(dat$extract==\"cucumber\"), gen = as.numeric(dat$gen==\"O75\"), nobs = nrow(dat)) jinit = list(int = 0, genO75 = 0, extcuke = 0, g75ecuke = 0, tau = 10) # Use logical names (unlike BUGS documentation) mod.bug = \"model { for(i in 1:nobs) { germ[i] ~ dbin(p[i], n[i]) b[i] ~ dnorm(0.0, tau) logit(p[i]) <- int + genO75 * gen[i] + extcuke * root[i] + g75ecuke * gen[i] * root[i] + b[i] } int ~ dnorm(0.0, 1.0E-6) genO75 ~ dnorm(0.0, 1.0E-6) extcuke ~ dnorm(0.0, 1.0E-6) g75ecuke ~ dnorm(0.0, 1.0E-6) tau ~ dgamma(0.001, 0.001) sigma <- 1 / sqrt(tau) }\" libs(rjags) oo <- textConnection(mod.bug) j1 <- jags.model(oo, data=jdat, inits=jinit, n.chains=1) close(oo) c1 <- coda.samples(j1, c(\"int\",\"genO75\",\"g75ecuke\",\"extcuke\",\"sigma\"), n.iter=20000) summary(c1) # Medians are very similar to estimates from hglm # libs(lucid) # print(vc(c1),3) ## Mean SD 2.5% Median 97.5% ## extcuke 0.543 0.331 -0.118 0.542 1.2 ## g75ecuke 0.807 0.436 -0.0586 0.802 1.7 ## genO75 -0.0715 0.309 -0.665 -0.0806 0.581 ## int -0.479 0.241 -0.984 -0.473 -0.0299 ## sigma 0.289 0.142 0.0505 0.279 0.596 # Plot observed data with HPD intervals for germination probability c2 <- coda.samples(j1, c(\"p\"), n.iter=20000) hpd <- HPDinterval(c2)[[1]] med <- summary(c2, quantiles=.5)$quantiles fit <- data.frame(med, hpd) libs(latticeExtra) obs <- dotplot(1:21 ~ germ/n, dat, main=\"crowder.seeds\", ylab=\"plate\", col=as.numeric(dat$gen), pch=substring(dat$extract,1)) obs + segplot(1:21 ~ lower + upper, data=fit, centers=med)"},{"path":"/articles/agridat_mixed_model_example.html","id":"r2jags","dir":"Articles","previous_headings":"Generalized Linear Mixed Model","what":"R2jags","title":"Example generalized linear mixed model analysis with different packages","text":"","code":"libs(\"agridat\") libs(\"R2jags\") dat <- crowder.seeds # To match the output on the BUGS web page, use: dat$gen==\"O73\". # We use dat$gen==\"O75\" to compare with the parameterization above. jdat =list(germ = dat$germ, n = dat$n, root = as.numeric(dat$extract==\"cucumber\"), gen = as.numeric(dat$gen==\"O75\"), nobs = nrow(dat)) jinit = list(list(int = 0, genO75 = 0, extcuke = 0, g75ecuke = 0, tau = 10)) mod.bug = function() { for(i in 1:nobs) { germ[i] ~ dbin(p[i], n[i]) b[i] ~ dnorm(0.0, tau) logit(p[i]) <- int + genO75 * gen[i] + extcuke * root[i] + g75ecuke * gen[i] * root[i] + b[i] } int ~ dnorm(0.0, 1.0E-6) genO75 ~ dnorm(0.0, 1.0E-6) extcuke ~ dnorm(0.0, 1.0E-6) g75ecuke ~ dnorm(0.0, 1.0E-6) tau ~ dgamma(0.001, 0.001) sigma <- 1 / sqrt(tau) } parms <- c(\"int\",\"genO75\",\"g75ecuke\",\"extcuke\",\"sigma\") j1 <- jags(data=jdat, inits=jinit, parms, model.file=mod.bug, n.iter=20000, n.chains=1) print(j1) ## mu.vect sd.vect 2.5% 25% 50% 75% 97.5% ## extcuke 0.519 0.325 -0.140 0.325 0.531 0.728 1.158 ## g75ecuke 0.834 0.429 -0.019 0.552 0.821 1.101 1.710 ## genO75 -0.096 0.305 -0.670 -0.295 -0.115 0.089 0.552 ## int -0.461 0.236 -0.965 -0.603 -0.455 -0.312 0.016 ## sigma 0.255 0.148 0.033 0.140 0.240 0.352 0.572 ## deviance 103.319 7.489 90.019 98.010 102.770 108.689 117.288 traceplot(as.mcmc(j1)) densityplot(as.mcmc(j1)) HPDinterval(as.mcmc(j1)) }"},{"path":[]},{"path":"/articles/agridat_uniformity_data.html","id":"archive-org---2023-04-07","dir":"Articles","previous_headings":"Searches","what":"archive.org - 2023.04.07","title":"Notes on uniformity data","text":"“uniformity trial” “optimum size plots” “Optimum Size Shape Plots”","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"google","dir":"Articles","previous_headings":"Searches","what":"Google","title":"Notes on uniformity data","text":"“uniformity trial data” “optimum size plots” “Optimum Size Shape Plots” “plot shape size” “field plot technique”","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"hathitrust","dir":"Articles","previous_headings":"Searches","what":"HathiTrust","title":"Notes on uniformity data","text":"“blank experiment” “plot technic” “plot technique” “uniformity trial”","code":""},{"path":[]},{"path":"/articles/agridat_uniformity_data.html","id":"u--nebr","dir":"Articles","previous_headings":"ToDo","what":"U. 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M., Prajapati, B.H., Raja, K.R.V. Darji, V.B. (1994). Optimum plot size summer paddy Navsari zone Gujarat. Gujarat Agricultural University Research Journal, 19, 92-97. found. Vaidyanathan, M. (1934). method covariance applicable utilization previous crop records judging improved precision experiments. Ind. J. Agric. Sci., 4, 327-42. found. data 24 plots. TODO Weber, Charles Robert. 1941. Statistical analysis uniformity trial soybeans reference plot size shape comparison efficiency various experimental designs. Univ Illinois. Thesis. https://-share-uiu.primo.exlibrisgroup.com/permalink/01CARLI_UIU/gpjosq/alma99265257912205899 found. Physical copy Univ Illinois. TODO Wray, William Dean (1941). Applications Uniformity Trials. PhD, Cornell University. https://newcatalog.library.cornell.edu/catalog/302914 found. Physical copy Cornell. Yates, F., Vernon, . J. Nelson, S. W. (1964). example analysis uniformity trial data electronic computer. Empire Journal Experimental Agriculture. 32 (125). https://eurekamag.com/research/014/342/014342010.php found. Physical copy UNL Storage. TODO Yun, K.H.; Park, S.H.; Lee, Y.M. (1968). estimation optimum plot size, shape number replications yield performance trials rice. Res. Rep. Office rur. Dev, Suwon, 11: 1, 53-7. Bibl. 21 https://eurekamag.com/research/014/341/014341511.php Zuhlke, Thomas Albert (1968). Uniformity Trial Pisum Sativum Estimating Optimum Plot Size Shape, Replication Number, Relative Efficiency Lattice Designs. https://search.library.wisc.edu/catalog/999876004102121 found. Physical copy Univ Wisconsin Madison. TODO","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"reviewed-papers","dir":"Articles","previous_headings":"","what":"Reviewed papers","title":"Notes on uniformity data","text":"Afonja, Biyi (1968). Analysis Uniformity Trial Cassava. Experimental Agriculture, 4(2), 135-141. https://doi.org/:10.1017/S0014479700022468 Reviewed. data. Aftab--Islam, M. Ashfaq, M. Idrees Ahmed. (1984) Sizes shapes plots field plot experiments wheat uniformity trial data. Pakistan Journal Agricultural Sciences, Volume 21, Issue 3,4. https://www.tehqeeqat.com/english/articleDetails/36510 Reviewed. data. Ahring, R. M., Morrison, R. D., & Wilhite, M. L. (1959). Uniformity Trials Germination Switchgrass Seed 1. Agronomy Journal, 51(12), 734-737. Aly, .E.; Salem, S..; Shalaan, M.. (1978). Optimum plot size shape relative efficiency different designs yield trials rice Oryza sativa L. Alexandria Journal Agricultural Research 26(2): 317-326 https://eurekamag.com/research/000/711/000711675.php found. Ansari, M. . ., G. K. Sant (1943). Study Soil Heterogeneity Relation Size Shape Plots Wheat Field Raya (Muhra District). Ind. J. Agr. Sci, 13, 652-658. https://archive.org/details/.ernet.dli.2015.271748 agridat::ansari.wheat.uniformity Arny, . C. H. K. Hayes. (1918) Experiments field technic plot tests. J. Agric. Res., 15, 251-262. Reviewed. data. Assis, Janilson & Sousa, Roberto & Linhares, Paulo & Cardoso, Eudes & Rodrigues, Walter & Pereira, Joaquim & Sousa, Robson & Medeiros, Aline & Silva, Neurivan & Andrade, Anderson & Gomes, Geovanna & Santos, Mateus & Alves, Lunara. (2020). Optimum plot size field experiments sesame. Australian Journal Crop Science. 1957-1960. https://doi.org/10.21475/ajcs.20.14.12.2828. Reviewed. data. Awake, Girma Taye; Amsal Tarekegne; D. G. Tanner. (2000). “Estimation optimum plot dimensions replication number wheat experimentation Ethiopia.” African crop science journal 8.1 (2000): 11-23. https://repository.cimmyt.org/handle/10883/2321 Reviewed. data. Bailey, M. ., Trought, T. (1926). account experiments carried determine experimental error field trials cotton Egypt. Egypt Ministry Agriculture, Technical Science Service Bulletin 63, Min. Agriculture Egypt Technical Science Bulletin 63. https://www.google.com/books/edition/Bulletin/xBQlAQAAIAAJ?pg=PA46-IA205 agridat::bailey.cotton.uniformity Baker, G. . Huberty, MR Veihmeyer, FJ. (1952) uniformity trial unirrigated barley ten years’ duration. Agronomy Journal, 44, 267-270. https://doi.org/10.2134/agronj1952.00021962004400050011x agridat::baker.barley.uniformity Baker, G. .; R. E. Baker (1953). Strawberry Uniformity Yield Trials. Biometrics, 9, 412-421. https://doi.org/10.2307/3001713 agridat::baker.strawberry.uniformity Baker, G. . E. B. Roessler (1957). Implications uniformity trial small plots wheat. Hilgardia, 27, 183-188. https://hilgardia.ucanr.edu/Abstract/?=hilg.v27n05p183 agridat::baker.wheat.uniformity Bakke, Olaf Andreas. (1988). Tamanho e forma ótimos de parcelas em delineamentos experimentais. Dissertation. Escola Superior de Agricultura Luiz de Queiroz. https://teses.usp.br/teses/disponiveis/11/11134/tde-20181127-160559/pt-br.php Used Gomez rice uniformity. Bancroft, T. . et a1., (1948). Size Shape Plots Distribution Plot Yield Field Experiments Peanuts. Alabama Agricultural Experiment Station Progress Report, sec. 39. Table 4, page 6. http://hdl.handle.net/11200/1345 agridat::bancroft.peanut.uniformity Barber, Clarence W. (1914). Note Influence Shape Size Plot Tests Varieties Grain. Maine Agr. Expt. Sta. Bul. 226:76-84. 1914. https://www.google.com/books/edition/Annual_Report/QF84AQAAMAAJ Reviewed. data. Batchelor, L. D.; H. S. Reed. (1918). Relation variability yields fruit trees accuracy field trials. J. Agric. Res, 12, 245–283. https://books.google.com/books?id=Lil6AAAAMAAJ&lr&pg=PA245 agridat::batchelor.uniformity Bayoumi, Tarek Youssef; S El-Demardash. Effect water deficit soil variability, plot size, shape number replications chickpea. https://www.academia.edu/21549629/Effect_of_Water_Deficit_on_Soil_Variability_Plot_size_Shape_and_Number_of_Replications_for_Chickpea Reviewed. data. Beard, J.S. (1954). Investigations experimental plot technique black wattle. Empire Forestry Review, 33, 158-171. https://www.jstor.org/stable/42602653 Reviewed. data. Beattie, J. H. Victor R. Boswell E. T. Batten. (1936). Pot plant variation Virginia Peanuts. Proceedings American Society Horticultural Science, 34. https://archive.org/details/dli.ernet.26569/page/585/mode/2up?q=beattie Reviewed. data. Beckett, W.H.; Fletcher, S.R.B. (1929). uniformity trial maize. Gold Coast Dept Agric Bull 16: 222-226. https://babel.hathitrust.org/cgi/pt?id=coo.31924066682166&seq=470 Reviewed. Yield data 15 plots. Bhatt, Hitesh M. (1998). Plot technique Potato. AAU, Anand. https://krishikosh.egranth.ac./handle/1/5810043200 Reviewed. data. Bose, R. D. (1935). soil heterogeneity trials Pusa size shape experimental plots. Ind. J. Agric. Sci., 5, 579-608. Table 1 (p. 585), Table 4 (p. 589), Table 5 (p. 590). https://archive.org/details/.ernet.dli.2015.271739 agridat::bose.multi.uniformity Bose, S. S.; Ganguli, P. M., Mahalanobis, P. C. frequency distribution plot yields optimum size plots uniformity trial rice Assam. Indian J. Agric. Sci., 1936, 6 part 5, pp. 1107-22. https://archive.org/details/.ernet.dli.2015.271737/page/n1263/mode Reviewed. data. Bose, S. S.; Khanna, K L; Mahalanobis, P C Note optimum shape size plots sugarcane experiments Bihar. India Journal Agricultural Science 9, 807-816 https://hdl.handle.net/10263/1896 http://library.isical.ac.:8080/jspui/handle/10263/1896 Reviewed. data. Boyhan, George E.; David B. Langston; Albert C. Purvis; C. Randell Hill. (2003). Research determine suitable plot size number replications field trials sugarbeet. J. Amer. Soc. Hort. Sci., 128, 409-424. Reviewed. data. Compared Hatheway’s method Cochran Cox’s method, latter gave smaller number plots. Boyhan, George E., David B. Langston, Albert C. Purvis, C. Randell Hill. (2003). Optimum plot size number replications short-day onions yield, seedstem formation, number doubles, incidence foliar diseases. J. Amer. Soc. Hort. Sci., 128, 409-424. https://doi.org/10.21273/JASHS.128.3.0409 Reviewed. data. Boyhan, George E. (2013). Optimum Plot Size Number Replications Determining Watermelon Yield, Fruit Size, Fruit Firmness, Soluble Solids. HortScience, 48, 1200-1208. https://doi.org/10.21273/HORTSCI.48.9.1200 Reviewed. data. Bradley, P. L. (1941). study variation productivity number fixed plots field 2. Dissertation: University West Indies. Appendix 1a, 1b, 1c, 1d. https://uwispace.sta.uwi.edu/dspace/handle/2139/41259 agridat::bardley.multi.uniformity Brewer, . C. R. Mead (1986). Continuous Second Order Models Spatial Variation Application Efficiency Field Crop Experiments. Journal Royal Statistical Society. Series (General), 149(4), 314–348. See page 325. http://doi.org/10.2307/2981720 Reviewed. data. Used StVincent cotton data found Rothamsted. agridat::hutchinson.cotton.uniformity Brim, C. . D. D. Mason. (1959). Estimates Optimum Plot Size soybean Yield Trials. Agron. Journal, 51: pp. 331-335. https://doi.org/10.2134/agronj1959.00021962005100060008x Reviewed. data. Brown, .R. Morris, H.D. (1967), Estimation Optimum Plot Size Shape Grain Sorghum Yield Trials. Agron. J., 59: 576-577. https://doi.org/10.2134/agronj1967.00021962005900060026x Reviewed. data. Chaves, L. J.; J. B. Miranda Filho (1992). Plot size progeny selection maize (Zea maysL.). Theoretical Applied Genetics, 84, 963–970. https://doi.org/10.1007/bf00227411 Reviewed. data. Cheesman, E. E., Pound, F. J. Uniformity trials Cacao. Trop. Agric., 9, 277-88. Reviweed. data. Christidis, Basil G (1931). importance shape plots field experimentation. Journal Agricultural Science, 21, 14-37. Table VI, p. 28. https://doi.org/10.1017/S0021859600007942 agridat::christidis.wheat.uniformity Christidis, B. G. (1939). Variability Plots Various Shapes Affected Plot Orientation. Empire Journal Experimental Agriculture 7: 330-342. Table 1. agridat::christidis.cotton.uniformity G. Peter Y. Clarke Katia T. Steanova. (2011). Optimal design early-generation plant-breeding trials unreplicated partially replicated test lines. Aust. N. Z. Jour. Stat., 53, 461-480. Reviewed. data. Collison, R. C. J. D. Harlan. Technical bulletin 194. relationships soil properties performance Baldwin Greening Apple Trees. New York State Agricultural Experiment Station https://babel.hathitrust.org/cgi/pt?id=uiug.30112019767000 Reviewed. data trees. Collison, R. C. J. D. Harlan. Technical bulletin 126. Annual variation apple yields - possible cause. New York State Agricultural Experiment Station https://babel.hathitrust.org/cgi/pt?id=uiug.30112019766267 Reviewed. data. Conners, Helen Elizabeth (1951). Field plot techniques sweet potatoes obtained uniformity trial data. Master’s Thesis, Iowa State University. digital copy available. Reviewed. data. Coombs, G. E. J. Grantham (1916). Field Experiments Interpretation results. Agriculture Bulletin Federated Malay States, 7. https://www.google.com/books/edition/The_Agricultural_Bulletin_of_the_Federat/M2E4AQAAMAAJ agridat::coombs.rice.uniformity Cordeiro, Célia Maria Torres, João Eustáquio Cabral de Miranda, Jarbas Campos. “Tamanho de parcelas e número de repetições em experimento de batatas.” Pesquisa Agropecuária Brasileira 17.9 (1982): 1341-1348. Reviewed. data. Crews, Julian W., Jones, G.L. Mason, D.D. (1963). Field Plot Technique Studies Flue-Cured Tobacco. . Optimum Plot Size Shape. Agron. J., 55: 197-199. https://doi.org/10.2134/agronj1963.00021962005500020033x Reviewed. data. Damor, Bhavika. (2019). Comparison uniformity trial data experimental data plot technique. Department Agricultural Statistics, B. . College Agriculture, Anand Agricultural University. https://krishikosh.egranth.ac./handle/1/5810169594 Reviewed. used. data 3 uniformity trials, data just looks strange. Appendix , 500 numbers 9 “4” hundredths place. expect see 50. data collected non-kg weights converted kg. Appendix III, plots along left edge measured 3 decimals (0.993, 0.631), field measured 2 decimals. Weird. da Silva, Enedino Correa. (1974). Estudo tamanho e forma de parcelas para experimentos de soja (Plot size shape soybean yield trials). Pesquisa Agropecuaria Brasileira, Serie Agronomia, 9, 49-59. Table 3, page 52-53. agridat::dasilva.soybean.uniformity da Silva, Willerson Custódio; Mário Puiatti; Paulo Roberto Cecon; Leandro Roberto de Macedo; Tocio Sediyama. (2019) Estimation optimum experimental plot size taro culture. Ciência Rural, Santa Maria, v.49 https://doi.org/10.1590/0103-8478cr20180440 Reviewed. data. da Silva, Luiz Fernando de Oliveira; Katia Alves Campos; Augusto Ramalho de Morais; Franciane Diniz Cogo; Carolina Ruiz Zambon. Optimal plot size experiments radish. Revista Ceres 59(5):624-629 https://doi.org/10.1590/S0034-737X2012000500007 Reviewed. data. de Assis, J. P., de Sousa, R. P., Rodrigues, W. M., Linhares, P. C. F., Cardoso, E. de ., Soares Pereira, M. F., Araújo Paula, J. . de, & Oliveira, . da M. (2019). Optimum Size Shape Experimental Units Cassava Cropping. Journal Experimental Agriculture International, 31(6), 1–10. https://doi.org/10.9734/jeai/2019/v31i630090 Reviewed. data. de Sousa, Roberto Pequeno Assis, Janilson Pinheiro de Rodrigues, Walter Martins Linhares, Paulo César Ferreira Cardoso, Eudes de Almeida Pereira, Maria Francisca Soares Paula, José Aluisio de Araújo (2018). Optimum Plot Size Experimental Cassava Production. Journal Agricultural Science, 10, 231-237. Reviewed. data. https://doi.org/10.5539/jas.v10n10p231 de Sousa, Roberto Pequeno; Paulo Sérgio Lima e Silva; Janilson Pinheiro de Assis; Paulo Igor Barbosa e Silva; Júlio César DoVale. 2015. Optimum plot size experiments sunflower. Revista Ciencia Agronomica, 46, 170-175. https://doi.org/10.1590/S1806-66902015000100020 Reviewed. data. Precision increased plot sizes 5 m^2. Davies, J. Griffiths (1931). Experimental Error Yield Small Plots Natural Pasture. Council Scientific Industrial Research (Aust.) Bulletin 48. Table 1. agridat::davies.pasture.uniformity Day, James Westbay (1916). relation size, shape, number replications plats probable error field experimentation. Dissertation, University Missouri. Table 1, page 22. https://hdl.handle.net/10355/56391 agridat::day.wheat.uniformity Dorph-Petersen, K. 1949. Parcelfordeling markforsog. Tidsskrift Planteavl. 52, 111-175 https://dca.au.dk/publikationer/historiske/planteavl/ Reviewed. data. see anything looked like field 12x43 plots. Mentioned Kristensen (2003). Draper, Arlen D. (1959). Optimum plot size shape safflower yield tests. Dissertation. University Arizona. https://hdl.handle.net/10150/319371 agridat::draper.safflower.uniformity Eden, T. E. J. Maskell. (1928). influence soil heterogeneity growth yield successive crops. Jour Agricultural Science, 18, 163-185. https://archive.org/stream/.ernet.dli.2015.25895/2015.25895.Journal--Agricultural-Science-Vol-xviii-1928#page/n175 agridat::sawyer.multi.uniformity Eden, T. (1931). Studies yield tea. 1. experimental errors field experiments tea. Agricultural Science, 21, 547-573. https://doi.org/10.1017/S0021859600088511 agridat::eden.tea.uniformity Elliott, F. C., Darroch, J.G. Wang, H.L. (1952). Uniformity trials spring wheat. Agronomy Journal, 44, 524-528. https://doi.org/10.2134/agronj1952.00021962004400100005x Reviewed. data. England, F. (1968). Non-sward densities assessment yield Italian ryegrass: II. Convenient plot block size shape. Journal Agricultural Science, 70(02), 105. https://doi.org/10.1017/s0021859600010923 Reviewed. data. Ersboll, Annette. Spatial Temporal Variations Applications Agriculture. “Models action”, Proceedings seminar series 1995/1996. Reviewed. data. Facco, Giovani; Cargnelutti Filho Alberto; Mendonça Alves Bruna; et. al. (2017). Basic experimental unit plot sizes method maximum curvature coefficient variation sunn hemp. African journal agricultural research 12(6):415-423 https://doi.org/10.5897/AJAR2016.11814 Reviewed. data. Faria, Glaucia Amorim Lopes, Beatriz Garcia Peixoto, Ana Patrícia Bastos Ferreira, Antonio Flávio Arruda Maltoni, Kátia Luciene Pigari, Lucas Bernardo (2020). Experimental plot size passion fruit. Revista Brasileira de Fruticultura, 42. http://dx.doi.org/10.1590/0100-29452020125. Reviewed. data. Filho, Alberto; Cargnelutti; Marcos Toebe; Cláudia Burin; André Luis Fick; Gabriele Casarotto. (2011). Plot sizes uniformity assays turnip. Ciência Rural 41.9 (2011): 1517-1525. https://doi.org/10.1590/S0103-84782011005000119 Reviewed. data. Filho, Alberto Cargnelutti; Marcos Vinícius Loregian, Gabriel Elias Dumke, Felipe Manfio Somavilla, Samanta Luiza da Costa, Lucas Fillipin Osmari, Bruno Fillipin Osmari (2021). Optimal plot size buckwheat. https://doi.org/10.5433/1679-0359.2021v42n2p501 https://www.uel.br/revistas/uel/index.php/semagrarias/article/view/39448/29330 Reviewed. data Filho, Alberto Cargnelutti, Ismael Mario Marcio Neu, Valeria Escaio Bubans, Felipe Manfio Somafilla, Bruno Fillipin Osmari. (2022). Method estimating optimal plot size Black Oat, Common Vetch, Forage Turnip intercropping. http://dx.doi.org/10.1590/1983-21252022v35n425rc Reviewed. data. Fleming, .., Roger, T.H. Bancroft, T.. (1957). Field plot technique hybrid corn Alabama conditions. Agronomy Journal, 49, 1-4. https://doi.org/10.2134/agronj1957.00021962004900010001x Reviwed. data. Forster, Howard Carlyle Vasey, . J. (1928). Experimental error field trials Australia. Proceedings Royal Society Victoria. New series, 40, 70–80. https://www.biodiversitylibrary.org/page/54367272 Reviewed. agridat::forster.wheat.uniformity Frey, K.J. Baten, W.D. (1953). Optimum Plot Size Oat Yield Tests. Agron. J., 45: 502-504. https://doi.org/10.2134/agronj1953.00021962004500100012x Reviewed. data. Garber, RJ McIlvaine, TC Hoover, MM. (1926). study soil heterogeneity experiment plots. Jour Agr Res, 33, 255-268. Tables 3, 5. https://naldc.nal.usda.gov/download/IND43967148/PDF agridat::garber.multi.uniformity Garber, R. J. T. C. McIlvaine M. M. Hoover (1931). Method Laying Experimental Plats. Journal American Society Agronomy, 23, 286-298. https://archive.org/details/.ernet.dli.2015.229753/page/n299 agridat::garber.multi.uniformity Gardenhire, James H. (1949). Field Plot Technique Plant Characteristic Studies Varieties Castor Plants. Thesis, Okla. Agr Mech. Coll., Stillwater, Okla. https://shareok.org/handle/11244/43284 Reviewed. data. Garner, F. H.; Grantham, J.; Sanders, H. G. (1934). value covariance analysing field experimental data. Journal Agricultural Science, 24(2), 250–. https://doi:10.1017/s0021859600006626 George, M. V.; M. Sannamarappa (1984). Uniformity trial: Size, Shape, Direction Experimental Plots Turmeric. Proceedings Sixth Symposium Plantation Crops, p. 429. https://archive.org/details/.ernet.dli.2015.502074/page/n453 Reviewed. data. Gomez, K.. R. C. Alicbusan (1969). Estimation optimum plot size rice uniformity data. Philippine Agriculturist. 52, 586-601 https://www.google.com/books/edition/The_Philippine_Agriculturist/2irOAAAAMAAJ found, probably data used Gomez & Gomez (1984). Gomez, K.. Gomez, .. (1984). Statistical Procedures Agricultural Research. Wiley-Interscience. Page 481. agridat::gomez.rice.uniformity Gopalakrishna, S. (1992). Optimum plot size shape, block size shape relative efficiency designs field experiments navane (Setaria italica). Thesis. https://krishikosh.egranth.ac./handle/1/5810120871 Reviewed. data. Goulden, C. H. (1937). Efficiency field trials pseudo-factorial incomplete randomized block methods. Canadian Journal Research, 15. https://doi.org/10.1139/cjr37c-020 Reviewed. paper used uniformity trials papers (Batchelor, Wiebe, etc). Goulden, C. H. (1939). Methods statistical analysis, 1st ed. Page 18. https://archive.org/stream/methodsofstatist031744mbp version 20x20. full 48x48 data obtained Rothamsted. agridat::goulden.barley.uniformity Goyal, Manoj Kumar. (1998). Study uniformity trial wheat (Triticum aestivum L.). Thesis, Haryana Agricultural University. https://krishikosh.egranth.ac./handle/1/5810078304 . Reviewed. data. Guimarães, B. V. C., de Carvalho, . J., Aspiazú, ., da Silva, L. S., da Silva, R. R. P., Pimenta, . M. L., & Moura, M. M. . (2021). Optimal plot size experimentation common beans (Phaseolus vulgaris L.) northern region Minas Gerais, Brazil: Experimental plots. Revista de la Facultad de Ciencias Agrarias UNCuyo, 55-63, 1853-8665. https://doi.org/10.48162/rev.39.039 Reviewed. data. Guimarães, Bruno Vinícius Castro, et al. (2020). Optimal plot size experimental trials Opuntia cactus pear. Acta Scientiarum. Technology 42 (2020): e42579-e42579. https://doi.org/10.5539/jas.v11n18p206 Reviewed. data. Gupton, C.L. (1972). Estimates Optimum Plot Size Shape Uniformity Data Burley Tobacco (Nicotiana tabacum L.). Agron. J., 64, 678-682. https://doi.org/10.2134/agronj1972.00021962006400050038x Reviewed. data. Haapanen, Matti (1992). Effet plot size shape efficiency progeny tests. Silva Fennica, 26, 201-209. Reviewed. data. Handa, D. P.; Sreenath, P. R., & Rajpali, S. K. (1995). Uniformity trial lucerne grown fodder. Grass Forage Science, 50(3), 209–216. https://doi.org/10.1111/j.1365-2494.1995.tb02316.x Reviewed. data. Hansen, Niels Anton (1914). Prøvedyrkning paa Forsøgsstationen ved Aarslev. Tidsskrift landbrugets planteavl, Bind 21, page 553. Danish. https://dca.au.dk/publikationer/historiske/planteavl/ Reviewed. agridat::hansen.multi.uniformity Haque, H. N. Azad, N. K. Jha, R. N. Sing, S. N. (1988). Optimum Size Shape Plots Wheat. Annals Agricultural Research, 9, 165-170. https://eurekamag.com/research/001/901/001901913.php Reviewed. data. criterion selection optimum plot size CV point maximum curvature. Hariharan, V., Jacob Thomas, M. George, K.C. (1986). Optimum size shape plots field experiments brinjal. Agricultural Research Journal Kerala, 24(2), 189-194. https://krishikosh.egranth.ac./handle/1/5810100010 Reviewed. data. Haritonenko, Pavlo. Neue Präzisionsmethoden auf den Versuchsfeldern. Arbeiten der landw. Versuchsstation Iwanowskoje 1904-06, S. 159. Russian German summary. Original found. Data appears Roemer (1920). Harris, J Arthur Scofield, CS. (1920). Permanence differences plats experimental field. Jour. Agr. Res., 20, 335-356. https://naldc.nal.usda.gov/catalog/IND43966236 agridat::harris.multi.uniformity Harris, J. Arthur Scofield, CS. (1928). studies permanence differences plots experimental field. Jour. Agr. Res. 36, 15–40. https://naldc.nal.usda.gov/catalog/IND43967538 agridat::harris.multi.uniformity Harris, J.. 1920. Practical universality field heterogeneity factor influencing plot yields. Journal Agricultural Research, 19, 279–314. Page 296-297. https://books.google.com/books?id=jyEXAAAAYAAJ&pg=PA279 agridat::harris.multi.uniformity Hartman, J. D.; E. C. Stair (1942). Field plot technique studies tomatoes. Proceedings American Society Horticultural Science, 41, 315-320. https://archive.org/details/.ernet.dli.2015.240678 agridat::hartman.tomato.uniformity Hatheway, W. H., E. J. Williams. (1958). Efficient estimation relationship plot size variability crop yields. 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Tidsskrift Planteavls Specialserie. S 2021, 163 pp. https://dca.au.dk/publikationer/historiske/planteavlspecial/ data. obvious data found field 26x20 plots. Mentioned Kristensen (2003). Hidalgo, Eduardo Calero Calero (1965). Estudio Del Tamano Y Forma De La Parcela Experimental Para Ensayos De Campo En Frijol (Phaseolus vulgaris L.). Instituto Interamericano de Ciencias Agricolas de la OEA. https://www.google.com/books/edition/Estudio_Del_Tamano_Y_Forma_De_La_Parcela/rNIOAQAAIAAJ data. Spanish. Hodnett, G.E. Uniformity Trial Groundnuts. Journal Agricultural Science, 43, 323-328. https://doi.org/10.1017/S002185960005749X Reviewed. data. Holle, Michael. 1960. Plot technique field evaluation three characters lima bean. Master’s Thesis, Iowa State University. digital copy available. Reviweed. data. Holtsmark, G Larsen, BR (1905). Om Muligheder indskraenke de Fejl, som ved Markforsog betinges af Jordens Uensartethed. Tidsskrift Landbrugets Planteavl. 12, 330-351. (Danish) https://books.google.com/books?id=MdM0AQAAMAAJ&pg=PA330 https://dca.au.dk/publikationer/historiske/planteavl/ agridat::holtsmark.timothy.uniformity Hudson, H. G. (1939). Population studies wheat: 1. Sampling. J. Agric. Sci, 29, 76-109. https://doi.org/10.1017/s0021859600051571 Reviewed. data. researchers collected 7200 plots * 13 traits = 93,000 data points. Evaluation possible use “Hollerith equipment”. Humada-Gonzalez, G.G. (2013). Estimação tamanho otimo de parcela experimental em experimento com soja. Dissertation, Universidade Federal de Lavras. http://repositorio.ufla.br/jspui/handle/1/744 Reviewed. data published data agridat::dasilva.soybean.uniformity. Humada-Gonzalez, G.G.; Barbuio, R.; Cardozo, N.; MOREIRA, J.M.; Llanes Oviedo, L. Estimação tamanho otimo de parcela experimental em experimento com soja transgenica. : semana de iniciação cientifica e seminario integrado da Pos-graduação, 2018 Goias Brasil semana de iniciação cientifica e seminario integrado da Pos-graduação. 2018. Humada González G. G., Ramalho de Morais , Caballero Mendoza CA, Bortolini J, Rodrigues Liska G. (2018). Estimation Optimum Plot Size Experimentation Sweet Potato. Agrociencia Uruguay, 22(2):e13. https://agrocienciauruguay.uy/index.php/agrociencia/article/view/13 Reviewed. data. Idrees, Nadia; Muhammad Inayat Khan (2009). Design improvement using uniformity trials experimental data. Pak. J. Agri. Sci., Vol. 46(4), 2009. https://pakjas.com.pk Reviewed. data. Igue, Toshio; Ademar Espironelo, Heitor Cantarella, Erseni Joao Nelli. (1991). Tamanho e forma de parcela experimental para cana-de-acucar (Plot size shape sugar cane experiments). Bragantia, 50, 163-180. Appendix, page 169-170. https://doi.org/10.1590/S0006-87051991000100016 agridat::igue.sugarcane.uniformity Immer, F. R. (1932). Size shape plot relation field experiments sugar beets. Jour. Agr. Research, 44, 649–668. https://naldc.nal.usda.gov/download/IND43968078/PDF agridat::immer.sugarbeet.uniformity Immer, F. R. S. M. Raleigh (1933). studies size shape plot relation field experiments sugar beets. Journal Agricultural Research, 47, 591-598. https://naldc.nal.usda.gov/download/IND43968370/PDF agridat::immer.sugarbeet.uniformity Iyer, P. V. Krishna (1942). Studies wheat uniformity trial data. . Size shape experimental plots relative efficiency different layouts. Indian Journal Agricultural Science, 12, 240-262. Page 259-262. https://archive.org/stream/.ernet.dli.2015.7638/2015.7638.-Indian-Journal--Agricultural-Science-Vol-xii-1942#page/n267/mode/2up agridat::iyer.wheat.uniformity Jaggard, K. W. (1975). size shape plots sugar-beet experiments. Annals Applied Biology, 80(3), 351–357. https://doi.org/10.1111/j.1744-7348.1975.tb01641.x Reviewed. data. Jain, M. B. Studies techniques field trials range lands . Size, Shape arrangement plots. Jain, M. B. R. K. Bohra. Size Shape Plots Blocks field Experiments Lasiurus Sindicus. Reviewed. data. James, W. C., & Shih, C. S. (1973). Size Shape Plots Estimating Yield Losses Cereal Foliage Diseases. Experimental Agriculture, 9(01), 63. https://doi.org/10.1017/s0014479700023693 Reviewed. data. Jegorow, M. (1909). Zur Methodik des feldversuches. Russian Journ Expt Agric, 10, 502-520. https://www.google.com/books/edition/Journal_de_l_agriculture_experimentale/510jAQAAIAAJ?hl=en uniformity trial oats. agridat::jegorow.oats.uniformity Joachim, .W.R. 1935. uniformity trial coconuts. Trop. Agric. Mag. Ceylon. Agric. Soc. 85:198-207. https://eurekamag.com/research/013/298/013298524.php https://southasiacommons.net/artifacts/2343967/-tropical-agriculturist/3172871/ Reviewed. data, blocks different sizes. used. Johnson, .J. & Murphy, H.C. 1943. Lattice lattice square designs oat uniformity data varietal trials. J. Agric. Sci., 22, 366-372. https://doi.org/10.2134/agronj1943.00021962003500040004x data. contour map fertility. Jones, W.W.; T.W. Embleton; C.B. Cree. (1957). Number Replications Plot Sizes Required Reliable Evaluation Nutritional Studies Yield Relationships Citrus Avocado. American Society Horticultural Science, 69:208-216. Reviewed. data. Fruit yields much variable quality factors. Jones, Marcus; Marin Harbur, Ken J. Moore (2021). Automating Uniformity Trials Optimize Precision Agronomic Field Trials. Agronomy 2021, 11, 1254. https://doi.org/10.3390/agronomy11061254 agridat::jones.corn.uniformity Data 12x12 corn expt. Jua, Junagadh. Optimum plot size field experiments tomato – statistical investigation. Thesis, Junagadh Agricultural University. https://krishikosh.egranth.ac./handle/1/5810006490. Reviewed. data. Justesen, S. (1932). Influence Size Shape Plots Precision Field Experiments Potatoes. Journal Agricultural Science, 22(2), 366-372. doi:10.1017/S0021859600053685 https://doi.org/10.1017/S0021859600053685 Reviewed. data. Conclusion: Long, narrow plots efficient (unless competition rows). Kadam, B. S. Kadam; S. M. Patel. (1937). Studies Field-Plot Technique P. Typhoideum Rich. Empire Journal Experimental Agriculture, 5, 219-230. https://archive.org/details/.ernet.dli.2015.25282 agridat::kadam.millet.uniformity Kalamkar, R.J. (1932). Experimental Error Field-Plot Technique Potatoes. Journal Agricultural Science, 22, 373-385. https://doi.org/10.1017/S0021859600053697 agridat::kalamkar.potato.uniformity Kalamkar, R. J (1932). Study Sampling Technique Wheat. Journal Agricultural Science, Vol.22(4), pp.783-796. https://doi.org/10.1017/S0021859600054599 agridat::kalamkar.wheat.uniformity Katyal, Visay (1994). Relative efficiency neighbouring techniques block design field experiments wheat Sodic soils. J. Ind. Soc. Ag. Stat. 46, 231-234. Reviewed. data. Kaushik, L. S., R. P. Singh, T. P. Yadava. (1977). uniformity trial mustard [India]. Indian Journal Agricultural Sciences. Reviewed. data. Kavitha, B. (2010). Study optimum plot size optimum plot shape soybean crop. https://krishikosh.egranth.ac./handle/1/84302 Reviewed. data. Keller, Kenneth R. (1949). Uniformity Trial Hops, Humulus lupulus L., Increasing Precision Field Experiments. https://doi.org/10.2134/agronj1949.00021962004100080011x Reviewed. data. Keller, K.R. (1951). Relative Efficiency Rectangular Triple Rectangular Lattice Designs Using Hop Uniformity Trial Data. Agron. J., 43: 93-96. https://doi.org/10.2134/agronj1951.00021962004300020009x Reviewed. data. Kempton, R. . C. W. Howes (1981). use neighbouring plot values analysis variety trials. Applied Statistics, 30, 59–70. https://doi.org/10.2307/2346657 agridat::kempton.barley.uniformity Kerr, H. W. (1939). Notes plot technique. Proc. Internat. Soc. Sugarcane Technol. 6, 764-778. agridat::kerr.sugarcane.uniformity Khan, Abdur Rashid Jage Ram Dalal (1943). Optimum Size Shape Plots Brassica Experiments Punjab. Sankhyā: Indian Journal Statistics ,6, 3. Proceedings Indian Statistical Conference 1942 (1943), pp. 317-320 (4 pages). https://www.jstor.org/stable/25047782 agridat::khan.brassica.uniformity Khan, Mujahid; Ramesh Chander Hasija; Des Raj Aneja; Manish Kumar Sharma (2016). uniformity trial Indian mustard determination optimum size shape blocks. Journal Applied Natural Science 8 (3): 1589 - 1593. Reviewed. data. Khan, Mujahid & R. C. HASIJA NITIN TANWAR (2017). Optimum size shape plots based data uniformity trial Indian Mustard Haryana. MAUSAM, 68, 67-74. https://doi.org/10.54302/mausam.v68i1.434 Reviewed. data. Khin, San. 1950. Investigation relative costs rice experiments based efficiency designs. Dissertation: Imperial College Tropical Agriculture (ICTA). Appendix XV. https://uwispace.sta.uwi.edu/dspace/handle/2139/42396 agridat::khin.rice.uniformity Khurana, Alka. (1991). Study Uniformity Trial Soyabean. Thesis, Haryana Agricultural University. https://krishikosh.egranth.ac./handle/1/5810076525 Reviewed. data. Smith’s Law. Kiesselbach, Theodore . (1917). Studies Concerning Elimination Experimental Error Comparative Crop Tests. University Nebraska Agricultural Experiment Station Research Bulletin . 13. Pages 51-72. https://archive.org/details/StudiesConcerningTheEliminationOfExperimentalErrorInComparativeCrop https://digitalcommons.unl.edu/extensionhist/430/ agridat::kiesselbach.oats.uniformity Koch, E.J. Rigney, J.. (1951). Method Estimating Optimum Plot Size Experimental Data. Agron. J., 43: 17-21. https://doi.org/10.2134/agronj1951.00021962004300010005x Reviewed. data. Idea: footnote paper says paper part MS Thesis North Carolina State. unable find online. Kulkarni, R. K., Bose, S. S., Mahalanobis, P. C. (1936). influence shape size plots effective precision field experiments sorghum. Indian J. Agric. Sci., 6, 460-474. Appendix 1, page 172. https://archive.org/details/.ernet.dli.2015.271737 agridat::kulkarni.sorghum.uniformity Kristensen, R. K. (1925). Anlaeg og Opgoerelse af Markforsoeg. Tidsskrift landbrugets planteavl, Vol 31, 464-494. Fig 1, pg. 467. https://dca.au.dk/publikationer/historiske/planteavl/ agridat::kristensen.barley.uniformity Kristensen, K. (2003). Incomplete split-plots variety trials - based -designs. : Biuletyn Oceny Odmian, 31, pp. 7-17. Reviewed. data. Refers papers uniformity trials. Krysczun, Dionatan Ketzer; Lúcio, Alessandro Dal’Col; Sari, Bruno Giacomini; Diel, Maria Inês; Olivoto, Tiago; Santana, Cinthya Souza; Ubessi, Cassiane; Schabarum, Denison Esequiel (2018). Sample size, plot size number replications trials Solanum melongena L.. Scientia Horticulturae, 233(), 220–224. https://doi.org/10.1016/j.scienta.2018.01.044 Reviewed. data. Krysczun, Dionatan, Alessandro D. Lúcio, Bruno G. Sari, Maria . Diel, Tiago Olivoto, José . G. da Silva, Cinthya S. Santana, Patrícia J. Melo, & Sabrina M. Gomes. (2018). Size Uniformity Trial Affects Accuracy Plot Size Estimation Eggplant. Journal Agricultural Science; Vol. 10, . 11. https://www.researchgate.net/publication/328291020 Reviewed. data. Kuehl, R.O. Kittock, D.L. (1969). Estimate Optimum Plot Size Cotton Yield Trials. Agron. Journal, 61: 584-586. https://doi.org/10.2134/agronj1969.00021962006100040031x data. Kumar, Ajay. 1999. study uniformity trial sesame (Til) Sesamum indicum. https://krishikosh.egranth.ac./handle/1/5810078711. Reviewed. data. Kumar, Ajay; Kiran Kapoor; Gupta, S. C.; Hasija, R. C. Uniformity trial sesame Sesamum indicum. https://eurekamag.com/research/017/592/017592269.php found. find Kumar’s thesis, data. Lakhera, M.L.; M.. Ali (1996). Optimum plot size shape estimates sunflower yield trials. Journal Maharashtra Agricultural Universities 21(3): 350-353 Reviewed. data. Optimum plot size 20 units, 10 rows 2 m long. Lamb, J.., Dowdy, R.H., Anderson, J.L. Rehm, G.W. (1997). Spatial Temporal Stability Corn Grain Yields. Journal Production Agriculture, 10: 410-414. https://doi.org/10.2134/jpa1997.0410 Reviewed. data plots 5 years, scaled relative yield year, harvested area 20-foot long 60-foot long plot. Decided use. Lambert, Edmund B. (1934). Size arrangement plots yield tests cultivated mushrooms. Journal Agricultural Research, Vol 48, 971-980. https://naldc.nal.usda.gov/naldc/download.xhtml?id=IND43968493 Reviewed. used. Uniformity trial three locations mushrooms growth houses. Lander, P. E. et al. (1938). Soil Uniformity Trials Punjab . Ind. J. Agr. Sci. 8:271-307. agridat::lander.multi.uniformity Lavezo, André; Alberto Cargnelutti Filho, Cláudia Marques de Bem, Cláudia Burin, Jéssica Andiara Kleinpaul, Rafael Vieira Pezzini. Plot size number replications evaluate grain yield oat cultivars. Bragantia, Campinas, v. 76, n. 4, p.512-520, 2017. https://doi.org/10.1590/1678-4499.2016.410 Reviewed. data. Laycock, D. H. (1955). effect plot shape reducing errors tea experiments. Tropical Agriculture, 32, 107-114. agridat::laycock.tea.uniformity Lehmann, . Ninth Annual Report Agricultural Chemist Year 1907-08. Department Agriculture, Mysore State. [2nd-9th] Annual Report Agricultural Chemist. https://books.google.com/books?id=u_dHAAAAYAAJ agridat::lehmann.millet.uniformity Lessman, Koert James (1962). Comparisons methods testing grain yield sorghum. Iowa State University. Retrospective Theses Dissertations. Paper 2063. Appendix Table 17. https://dr.lib.iastate.edu/handle/20.500.12876/73575 agridat::lessman.sorghum.uniformity Lessman, K.J. Atkins, R.E. (1963). Optimum Plot Size Relative Efficiency Lattice Designs Grain Sorghum Yield Tests. Crop Science, 3: 477-481 https://doi.org/10.2135/cropsci1963.0011183X000300060006x Reviewed. See Lessman thesis data. agridat::lessman.sorghum.uniformity Li, HW Meng, CJ Liu, TN. 1936. Field Results Millet Breeding Experiment. Agronomy Journal, 28, 1-15. Table 1. DOI: 10.2134/agronj1936.00021962002800010001x agridat::li.millet.uniformity Ligon, L. L. (1930). Size Plat Number Replications Field Experiments Cotton. Agronomy Journal, 22, 689-699. https://doi.org/10.2134/agronj1930.00021962002200080003x Reviewed. uniformity test, rather ‘cultural’ test ‘varietal’ test. Liji, Kumari (1997). Optimum size plots coconut using multivariate techniques. Thesis, Kerala Agriculture Univ. Reviewed. data. Lizy, M.J. (1986). Uniformity trials colocasia. https://krishikosh.egranth.ac./handle/1/5810113181 Reviewed. data. Lizy, M. J.; K. C. George Jacob Thomas (1988). Optimum Size Shape Plots Colocasia. Agric. Res. J. Kerala, 25, 241-248. http://14.139.185.57:8080/jspui/bitstream/123456789/4103/1/25_2_241-248_0002-1628.pdf Reviewed. data. Lizy, M.J.; George, K.C.; Thomas, M.J. (1988). Optimum size shape plots colocasia (Colocasia esculenta L.) Agricultural Research Journal Kerala 25(2): 241-248 https://eurekamag.com/research/001/901/001901914.php Paper found, Lizy thesis found, data. Loesell, Clarence (1936). Size plot & number replications necessary varietal trials white pea beans. Thesis, Michigan State. https://d.lib.msu.edu/etd/5271 agridat::loesell.bean.uniformity Lohmor, Nishu (2015). Estimation optimum plot size, shape number replications sunflower (Helianthus annuus). Thesis. https://krishikosh.egranth.ac./handle/1/81261 Reviewed. data. Lohmor, Nishu; Mujahid Khan; Kiran Kapoor; Nitin Tanwar (2017). Study Optimum Block Size Shape Uniformity Trial Sunflower (Helianthus annuus). Advances Research, 9, 1-8. 2017. Reviewed. data. Lord, L. (1931). Uniformity Trial Irrigated Broadcast Rice. Journal Agricultural Science, 21(1), 178-188. https://doi.org/10.1017/S0021859600008029 agridat::lord.rice.uniformity Love, Harry (1937). Application Statistical Methods Agricultural Research. Commercial Press, Shanghai. Page 411. https://archive.org/details/.ernet.dli.2015.233346/page/n421 agridat::love.cotton.uniformity Love, H.H. W.T. Craig (1938). Investigations plot technic small grains. Cornell University, Memoir 214. https://catalog.hathitrust.org/Record/011481484 Reviewed. data. Described 3 trials 600 plots. Lorentz, Leandro Homrich; Alexandra Augusti Boligon; Lindolfo Storck; Alessandro Dal’Col Lúcio. (2010). Plot size experimental precision sunflower production. Sci. Agric. (Piracicaba, Braz.), v.67, n.4, p.408-413. Reviewed. data. Love, H. H. (1936). Uniformity Trials Useful?. Agronomy Journal, 28(3), 234. https://10.2134/agronj1936.00021962002800030007x Reviewed. data. Lúcio, . D. C., Nunes, L. F., Rego, F., & Pasini, M. P. (2016). Relations zero-inflated variables trials horticultural crops. Spanish Journal Agricultural Research, 14(2), 17. https://dialnet.unirioja.es/servlet/articulo?codigo=6802883 Reviewed. data. Lúcio, . D. C., & Benz, V. (2016). Accuracy estimates zucchini production related plot size number harvests. Ciência Rural, 47. https://doi.org/10.1590/0103-8478cr20160078 Reviewed. data. Lucyamma, Mathew. (1986). Standardization field plot technique cashew. Diss. Department Statistics, Kerala Agricultural University. https://krishikosh.egranth.ac./handle/1/5810142095 Reviewed. data. Page 34 details. 294 trees, 8 years (combined 4). Lyon, T.L. (1911). experiments estimate errors field plat tests. Proc. Amer. Soc. Agron, 3, 89-114. Table III. https://doi.org/10.2134/agronj1911.00021962000300010016x agridat::lyon.potato.uniformity MacDonald, D. Fielding, W. L. Ruston, D. F. (1939). Experimental methods cotton: . design plots variety trials. Journal Agricultural Science, 29, 35-47. http://dx.doi.org/10.1017/S0021859600051534 Reviewed. data. Two heatmaps fields. Magistad, O. C.; & C. . Farden (1934). Experimental Error Field Experiments Pineapples. Journal American Society Agronomy, 26, 631–643. agridat::magistad.pineapple.uniformity Masood, M Asif Irum Raza Muhammad Yaseen. 2012. Estimation optimum field plot size shape paddy yield trial. Pakistan J. Agric. Res., Vol. 25 . 4, 2012 agridat::masood.rice.uniformity McClelland, Chalmer Kirk (1926). determinations plat variability. Agronomy Journal, 18, 819-823. https://doi.org/10.2134/agronj1926.00021962001800090009x agridat::mcclelland.corn.uniformity McGuire, Judson U. (1954). Uniformity Data European Corn Borer, Pyrausta Nubilalis Hbn., Populations. [Ames, Ia.: s.n.], 1954. https://hdl.handle.net/2027/umn.31951000404095s Reviewed. publication data relating corn borer infestation thousands individual corn plants. used. Meier, V.D. Lessman, K.J. (1971). Estimation Optimum Field Plot Shape Size Testing Yield Crambe abyssinica Hochst. Crop Science, 11: 648-650. https://doi.org/10.2135/cropsci1971.0011183X001100050013x Reviewed. data. Idea: thesis Meier Perdue University. looked online found anything. Mercer, WB Hall, AD, (1911). experimental error field trials Journal Agricultural Science, 4, 107-132. Table 1. https://doi.org/10.1017/S002185960000160X agridat::mercer.mangold.uniformity agridat::mercer.wheat.uniformity Miller, John D., E. James Koch. (1962) “Plot Technique Study Birdsfoot Trefoil”. Agronomy Journal, 54, 95-97. https://acsess.onlinelibrary.wiley.com/doi/abs/10.2134/agronj1962.00021962005400020001x Reviewed. data. Mitscherlich, Alfred Franz Dühring. (1921). Über die Größe der Teilstücke bei Feldversuchen. Die Landwirtschaftlichen Versuchsstationen. Bd. XCVIII. 1921 p. 365-383. https://archive.org/details/dielandwirtschaf9819reun/page/364/mode/2up Reviewed. data given. Used datasets Roemer (1920). Miyaska, Susan C.; Charles E. McCulloch, Graham E. Fogg, James R. Hollyer. Optimum Plot Size Field Trials Taro (Colocasia esculenta). HortScience 48, 435-443. https://doi.org/10.21273/HORTSCI.48.4.435 Reviewed. data. Optimum plot size 18-21 plants (4.9 - 5.7 m^2). Modjeska, Janet aspects spatial analysis uniformity data. Inst. Statistics Mimeo Series 1345. Used data Batchelor & Reid. Modjeska, J. S., & Rawlings, J. O. (1983). Spatial Correlation Analysis Uniformity Data. Biometrics, 39(2), 373. https://doi.org/10.2307/2531010 Used data Batchelor & Reid. Mohammed, Ali Abulgasim. analysis autonormal moels correlation struture applied uniformity data. Institute Statistics Mimeograph Series . 1313. Reviewed. data. Montgomery, E. G. (1912). Variation Yield Methods Arranging Plats Secure Comparative Results. Twenty-Fifth Annual Report Agricultural Experiment Station Nebraska, 164-180. https://books.google.com/books?id=M-5BAQAAMAAJ&pg=RA4-PA164 agridat::montgomery.wheat.uniformity Montgomery, E. G. (1913). Experiments Wheat Breeding: Experimental Error Nursery Variation Nitrogen Yield. U.S. Dept Agriculture, Bureau Plant Industry, Bulletin 269. Figure 10, page 37. https://doi.org/10.5962/bhl.title.43602 agridat::montgomery.wheat.uniformity Moore, John F Darroch, JG. (1956). Field plot technique Blue Lake pole beans, bush beans, carrots, sweet corn, spring fall cauliflower. Washington Agricultural Experiment Stations, Institute Agricultural Sciences, State College Washington. https://babel.hathitrust.org/cgi/pt?id=uiug.30112019919072&view=1up&seq=33&skin=2021 agridat::moore.uniformity Mortensen, M. L. (1911). Die Technik der Feldversuche (Technique Field Experiments). Jahresbericht der Vereinigung für Angewandte Botanik, 9, 177-187. https://www.biodiversitylibrary.org/item/36059#page/573/mode/1up data. Muniya, Sejal ben. (2017). Plot size study mathematical approach. Thesis, Agricultural University Anand. https://krishikosh.egranth.ac./handle/1/5810115216 Reviewed. data. Murray, E. K. S. (1934). value uniformity trial field experimentation rubber. Journal Agricultural Science, 24(2), 177-184. https://doi.org/10.1017/s0021859600006572 Reviewed. used. 2 years 5x5 grid rubber trees. Based data archived Rothamsted, used time. see can get original data. Nagai, Violeta (1978). Tamanho da parcela e numero de repeticoes em experimentos com morangueiro (Plot size number repetitions experiments strawberry). Bragantia, 37, 71-81. Table 2, page 75. https://doi.org/10.1590/S0006-87051978000100009 agridat::nagai.strawberry.uniformity Nagardas, Motaka Ganapatlal (2006). Plot size study uniformity trial data durum wheat Bhal region. https://krishikosh.egranth.ac./handle/1/5810115906 Reviewed. data. Nair, Remesh B.; P. v. Prabhakaran (1983). Optimum size shape plots field experiments cashew. Agric Res J Kerrala, 21, 27-34. Reviewed. data. Nair, B. Gopakumaran Nair, (1984). Optimum plot size field experiments turmeric (Curcuma longa L) Thesis, Department Statistics, College Veterinary & Animal Sciences, Mannuthy. https://krishikosh.egranth.ac./handle/1/5810147397 agridat::nair.turmeric.uniformity Narain, R.; . Singh, (1940). note shape blocks field experiments. Ind. J. Agr. Sci., 10, 844-853. 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Estimates Optimum Plot Size Grain Sorghum Uniformity Trial Data. Technical bulletin, Kansas Agricultural Experiment Station. Page 17-20. https://babel.hathitrust.org/cgi/pt?id=uiug.30112019584322&view=1up&seq=21 agridat::stickler.sorghum.uniformity Stockberger, W. W. (1912). Study Individual Performance Hops. Journal Heredity, 1, 452-457. https://doi.org/10.1093/jhered/os-8.1.452 Reviewed. data. Stockem, J.E., Korontzis, G., Wilson, S.E. et al. (2021) Optimal Plot Dimensions Performance Testing Hybrid Potato Field. Potato Research, 65, 417–434. https://doi.org/10.1007/s11540-021-09526-9 Reviewed. data. Storck, Lindolfo (2010). Partial collection data potato yield experimental planning. Field Crops Research, 121, 286-290. https://doi.org/10.1016/j.fcr.2010.12.018. Reviewed. data. Storck, Lindolfo; Sidinei Lopes; Alessandro Dal’Col Lúcio; Alberto Cargnelutti Filho (2011). Optimum plot size number replications related selective precision. Ciência Rural 41(3):390-396. https://doi.org/10.1590/S0103-84782011000300005 https://www.researchgate.net/publication/270761907_Optimum_plot_size_and_number_of_replications_related_to_selective_precision data Strickland, . G. (1932). vine uniformity trial. Journal Agriculture, Victoria, 30, 584-593. Strickland, . G. (1935). Error horticultural experiments. Journal Agriculture, Victoria, 33, 408-416. agridat::strickland.apple.uniformity agridat::strickland.grape.uniformity agridat::strickland.peach.uniformity agridat::strickland.tomato.uniformity Strydom, G. J. (1966). Studies planning field experiments vegetable crops. S. Afr. J. Agric. Science 9, 183-194. https://hdl.handle.net/10520/AJA05858860_582 Reviewed. data. Summerby, R. (1923). Replication relation accuracy comparative crop tests. J. Soc Agron, 15. https://www.google.com/books/edition/Proceedings_of_the_American_Society_of_A/MWU4AQAAMAAJ Also: https://doi.org/10.2134/agronj1923.00021962001500050004x Reviewed. data. Data sent Rothamsted 1938. Two areas land, 100 links 505 links. One area Alaska Oats. Another area Huron wheat. fixme check can find Rothamsted papers. Summerby, R. (1925). Study Sizes Plats, Number Replications, Frequency Methods Using Check Plots Relation Accuracy Field Experiments. Jour. Amer. Soc, Agron. 17: 140-149. Reviewed. Data uniform. data-unused/summerby.oat.uniformity Summerby, R. (1934). value preliminary uniformity trials increasing precision field experiments. Macdonald College. https://books.google.com/books?id=6zlMAAAAYAAJ&pg=RA14-PA47 Reviewed. data 5x35 1x35 grid layouts. agridat::summerby.multi.uniformity. Swallow, William H.; Todd C. Wehner (1986). Optimum Plot Size Determination Application Cucumber Yield Trials. Euphytica, 35, 421-432. https://doi.org/10.1007/BF00021850 Reviewed. data. multiple-harvest yield trials, optimum plot sizes estimated 6.4 10.3 m^2. Swanson, .F. (1930). Variability Grain Sorghum Yields Influenced Size, Shape, Number Plats. Agron. J., 22: 833-838. https://doi.org/10.2134/agronj1930.00021962002200100002x Reviewed. data. Swearingin, M.L. Holt, D.. (1976). Using “Blank” trial teaching tool. Journal Agronomic Education, 5: 3-8. https://doi.org/10.2134/jae.1976.0003 Data 9 trials. trial RCBD 4 ‘varieties’ 4 reps. clear layout rows columns randomized. Taha, R.S. M.M. Shafik (2000). Relative precision incomplete block designs soybean uniformity trials optimum sample size. J. Agric. Sci. Mansoura Univ, 25, 5601-5610. Reviewed. data. Tartaglia, FdL, Lúcio, , Diel, MI, et al. Experimental Plan Tests Pea. Agronomy Journal. 2021; 113: 1394–1406. https://doi.org/10.1002/agj2.20575 Reviewed. data. Taylor, F.W. (1907-1909). size experimental plot field crops. Proceedings annual Society agronomy 1, 56-58. https://acsess.onlinelibrary.wiley.com/doi/10.2134/agronj1907-1909.00021962000100010014x Reviewed. data. Taylor, Howard Lewis (1951). effect plot shape experimental error. Master’s Thesis, Iowa State University. Reviewed. digital copy available. data given. Used data corn uniformity trial, oats uniformity, data Fairfield Smith. Smaller experimental errors found long narrow plots. Tedin, Olof (1931). Influence Systematic Plot Arrangement upon Estimate Error field Experiments. J. Agric. Science, 21, 191-208. https://doi.org/10.1017/S0021859600008613 Reviewed. data. Uses uniformity trials papers. Thomas, H.L. Abou-El-Fittouh, H.. (1968) Optimum Plot Size Number Replications Estimating Forage Yield Moisture Percentage. Agron. J., 60: 549-550. https://doi.org/10.2134/agronj1968.00021962006000050031x Reviewed. data. Thompson, Ross C. (1934). Size, Shape, Orientation Plots Number Replications Required Sweetpotato Field-Plot Experiments. J. Agric. Rsch., 5, 379-399. https://naldc.nal.usda.gov/catalog/IND43968506 Reviewed. data. Interesting paper experiment conducted 4 plots year/loc combinations. Toebe, Marcos, et al. (2020). Plot size number replications ryegrass experiments. Ciência Rural 50.1. https://doi.org/10.1590/0103-8478cr20190195 Reviewed. data. Toebe, Marcos, et. al. (2022). Plot size number replicates ryegrass experiments sowed rows. Pesquisa Agropecuária Brasileira, v.57, e02976, 2022. DOI: https://doi.org/10.1590/S1678-3921.pab2022.v57.02976. Reviewed. data. Torrie, J.H., Schmidt, D.R. Tenpas, G.H. (1963). Estimates Optimum Plot Size Shape Replicate Number Forage Yield Alfalfa-Bromegrass Mixtures. Agron. Journal, 55: 258-260. https://doi.org/10.2134/agronj1963.00021962005500030015x Tulaikow, N. (1913) Resultate einer mathematischen Bearbeitung von Ernteergebnissen. Russian Journal fur Exp Landw., 14, 88-113. Russian German summary. Two uniformity trials winter summer wheat. agridat::tulaikow.wheat.uniformity Vagholkar, B. P. ; Apte, V. N. ; Iyer, S. S. (1940). study plot size shape technique field experiments sugarcane. Indian Journal Agricultural Science 1940 Vol.10 pp.388-403 https://archive.org/details/.ernet.dli.2015.25316/page/n437/mode/2up Reviewed. data. Vallejo, R.L. & H. . Mendoza (1988). Determination optimum plot size adequate number replications evaluate potato seedling populations. VIIth Symposium International Society Tropical Root Crops, Gosier (Guadeloupe), 1-6 July 1985, Ed. INRA, Paris, 1988. http://www.istrc.org/images/Documents/Symposiums/Seventh/7th_symposium_proceedings_0081.pdf Reviewed. data. Vallejo, Roger L.; VHumberto . Mendoza. (1992). Plot Technique Studies Sweetpotato Yield Trials. J. AMER. Soc. HORT. SCI. 117(3):508-511. https://doi.org/10.21273/JASHS.117.3.508 Reviewed. data. Vargas-Rojas, Jorge Claudio. (2020). Size shape experimental unit yield trials Brachiaria , hybrid CIAT 3608. https://www.semanticscholar.org/paper/Size--shape---experimental-unit--yield--Vargas-Rojas/e9964eca6ac42471aa44bc45e84bc376b02daf09 Vishnaadevi, S.; K. Prabakaran, E. Subramanian, P. Arunachalam. (2019). Determination fertility gradient direction optimum plot shape paddy crop Madurai District. Green Farming, 10, 155-159. https://www.researchgate.net/publication/333892867 agridat::vishnaadevi.rice.uniformity Warren, J.. Mendez, . (1981). Block Size Orientation, Allowance Positional Effects, Field Experiments. Experimental Agriculture, 17, 17 - 24. https://doi.org/10.1017/S0014479700011182 Reviewed. Used dozen existing datasets. Wassom R.R. Kalton. (1953). Estimations Optimum Plot Size Using Data Bromegrass Uniformity Trials. Agricultural Experiment Station, Iowa State College, Bulletin 396, page 314-319. https://lib.dr.iastate.edu/ag_researchbulletins/32/ https://babel.hathitrust.org/cgi/pt?id=uiug.30112019570701&view=1up&seq=26&skin=2021 agridat::wassom.brome.uniformity Webster, C. 1939. note uniformity trial oil palms. Tropical Agriculture, 16, 15-19. data according Mendez. Westover, Kyle C. (1924). influence plot size replication experimental error field trials potatoes. Agricultural Experiment Station, West Virginia University, . 189. https://babel.hathitrust.org/cgi/pt?id=uiug.30112019921359&view=1up&seq=1 https://www.google.com/books/edition/The_Influence_of_Plot_Size_and_Replicati/Px6K44dYd1QC Reviewed. data. Wiebe, G.. 1935. Variation Correlation Grain Yield among 1,500 Wheat Nursery Plots. Journal Agricultural Research, 50, 331-357. https://naldc.nal.usda.gov/download/IND43968632/PDF agridat::wiebe.wheat.uniformity Wiedemann, Alfred Max. 1962. Estimation Optimum Plot Size Shape Use Safflower Yield Trails. Table 5. Graduate Theses Dissertations. Paper 3600. Table 5. https://digitalcommons.usu.edu/etd/3600/ agridat::wiedemann.safflower.uniformity Wiedemann, .M. Leininger, L.N. (1963), Estimation Optimum Plot Size Shape Safflower Yield Trials. Agron. J., 55: 222-225. https://doi.org/10.2134/agronj1963.00021962005500030004x Reviewed. See Wiedemann thesis data. agridat::wiedemann.safflower.uniformity Williams, ER Luckett, DJ. (1988). use uniformity data design analysis cotton barley variety trials. Australian Journal Agricultural Research, 39, 339-350. https://doi.org/10.1071/AR9880339 agridat::williams.barley.uniformity agridat::williams.cotton.uniformity Wilson, C. E. Study plots laid field II view obtaining plot-fertility data use future experiments plots, season 1940-41. Dissertation: University West Indies. Page 36-39. https://uwispace.sta.uwi.edu/dspace/handle/2139/43658 See also dissertation Bradley. agridat::bardley.multi.uniformity Wilson, Wendell W. (1970). Texas Vegetable Remote Sensing Study, 1969 Determination Optimum Plot Size Shape Estimation Carrot Yield. Research Development Branch Standards Research Division Statistical Reporting Service. https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/Yield_Reports/Texas%20Vegetable%20Remote%20Sensing%20Study%201969.pdf data found. Wood, T. B. F. J. M. Stratton. (1910). interpretation experimental results. J. Agric Sci 3, 417-440. Reviewed. data. Several plots distribution data. Wood, Ronald . (1972). Optimum Plot size winter wheat. Research & Development Branch, Standards Research Division Statistical Reporting Service. https://naldc.nal.usda.gov/catalog/27961 Reviewed. data. Wyatt, F. . (1927). Variation plot yields due soil heterogeneity. Scientific Agriculture, 7, 248-256. Table 1. https://doi.org/10.4141/sa-1927-0020 agridat::wyatt.multi.uniformity Yadav, Raj Pal. 1991. Spatial Correlation Analysis Uniformity Trial Data. College Basic Sciences Humanities Chaudhary Charan Singh Haryana Agricultural University Hisar. https://krishikosh.egranth.ac./handle/1/5810076755 Reviewed. data. Zandonadi, Cecília Uliana Viçosi, David Brunelli Fornazier, Maurício Lorenção Botacim, Luciana Aparecida Sousa, Douglas Gonzaga de Martinuzzo, Marx Bussular Alixandre, Fabiano Tristão Favarato, Luiz Fernando Krohling, Cesar Abel Guarçoni, Rogério Carvalho Alixandre, Ricardo Dias Fornazier, Maurício José (2022). Determining minimum size experimental plot evaluating field parameters Arabica coffee Research, Society Development, 11, 1-9. http://dx.doi.org/10.33448/rsd-v11i6.29384 Reviewed. data. Zhang, R . W. Warrick, D. E. Myers (1994). Heterogeneity, plot shape effect optimum plot size. Geoderma, 62, 183-197. https://doi.org/10.1016/0016-7061(94)90035-3 Reviewed. data. Oddly, appear use data Kuehl & Kittock, data paper. Zuber (1942). Relative Efficiency Incomplete Block Designs Using Corn Uniformity Trial Data. Agron Journal, 34, 30-47. https://doi.org/10.2134/agronj1942.00021962003400010004x Reviewed. Limited data, 3 reps 4x5. Zuhlke, Thomas . Gritton, E.T. (1969). Optimum Plot Size Shape Estimates Pea Yield Trials. Agron. J., 61: 905-908. https://doi.org/10.2134/agronj1969.00021962006100060023x Reviewed. data. paper based thesis W Wisconsin. Zuhlke, Thomas . Gritton, E.T. (1970). Relative Precision Different Experimental Designs Number Replications Pea Yield Trials. Agron J., 62, 61-64. https://doi.org/10.2134/agronj1970.00021962006200010020x Reviewed. data.","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section","dir":"Articles","previous_headings":"","what":"—————————————————————————","title":"Notes on uniformity data","text":"Roemer Note: Neyman (1935) says, “used data uniformity trials published T. Roemer (”Der Feldversuch, Berlin, 1920), suitable purpose, many rows columns. Cochran says Roemer: Sugarbeet: 96 plots 1916 1918 - table 1 & 3 Sugarbeet: 416 plots - table 4 Millet 105 plots (also Lehman) - table 13 Oats: 240 plots 8x30 - table 10 Ragi: 34 plots <—- fixme Timothy: 240 plots (also Holtsmark) - table 11 Summer wheat: 16x15=240 plots. Tulaikow Russian Journ Exp Agric , 14. (1913) - table 9 Winter wheat: 10x25=230 plots Tulaikow Russian Journ Exp Agric , 14. (1913) - table 8 Roemer (1920). Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ Page34. P 37 mean. | Tab | Orig qm | Mu | Wt | unit | note | |—–|———|——|—-|————-|————————————————————————-| | 1 | 6.8 | 21.7 | kg | | agridat::roemer.sugarbeet.uniformity (1916) | | 2 | 6.8 | | kg | | 1917 2x48=96 plots | | 3 | 6.8 | 23.5 | kg | | agridat::roemer.sugarbeet.uniformity (1918) | | 4 | 136.5 | 617 | pf | 1.33 x 22.5 | agridat::haritonenko.sugarbeet.uniformity | | 5 | 20.2 | 329 | lb | 1/10 acre | agridat::mercer.mangold.uniformity | | 6 | 8.1 | 395 | lb | 1/10 acre | agridat::mercer.wheat.uniformity | | 7 | 2.8 | 681 | g | | agridat::montgomery.wheat.uniformity 1909, 14x16 | | 8 | 4.6 | 700 | g | qm | agridat::tulaikow.wheat.uniformity (winter) | | 9 | 4.6 | 532 | g | qm | agridat::tulaikow.wheat.uniformity (summer) | | 10 | 4.6 | 203 | kg | qm | agridat::jegorow.oats.uniformity | | 11 | 6.3 | 17.8 | kg | 25 qm | agridat::holtsmark.timothy.uniformity (orig 5m x 5m) | | 12 | 404.7 | 286 | lb | 20.2 qm | todo 17x2 4 years Lehmann rice | | 13 | 404.7 | 167 | lb | 20.2 qm | agridat::lehmann.millet.uniformity (20 rows instead 22) | Roemer (1925) Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. https://www.google.com/books/edition/Arbeiten/6LJGAAAAYAAJ","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-1","dir":"Articles","previous_headings":"","what":"—————————————————————————","title":"Notes on uniformity data","text":"Roemer (1920). Note, based note Larsen (240 plot * 25 sq m = 6000 sq m / 60 = 100 sq m per “” appears “” “= 100m^2”) Page 27 1-3. Eigene Versuche mit Zuckerrüben, ausgeführt auf dem Neßthaler Zuchtfeld des Kaiser-Wilhelm-Institutes, Bromberg, den Jahren 1916, 1917 und 1918. 1916 und 1918 war die Versuchsfläche ein und dieselbe, 6,80 groß und den beiden Jahren mit Original Klein-Wanzlebener Zuckerrüben auf 30 X 40 cm bebaut. Vorfrucht für 1916 war Hafer, für 1918 Roggen; 1917 war eine andere Fläche, ebenfalls 6,80 groß, für den Versuch benußt; gesät wurden zwei verschiedene Zuchten von Strube, Schlanstedt. Beide Flächen sind von sehr gleichmäßiger Bodenbeschaffenheit. Bei der Fläche 1916 und 1918 machte sich im ersten Jahre bei den Reihen 31-33 eine geringe Stelle bemerkbar, die 1918 weit weniger Erscheinung trat. Die Bodenunterschiede sind allen drei Jahren geringer als die durch die Versuchstechnik bedingten Fehler. Translated: (Roemer) experiments sugar beets, carried Neßthal breeding field Kaiser Wilhelm Institute, Bromberg, years 1916, 1917 1918. 1916 1918 test area one , 6.80 large original years Klein-Wanzleben sugar beets cultivated 30 x 40 cm. previous crop 1916 oats, 1918 rye; 1917 another area, also 6.80 large, used experiment; Two different varieties Strube, Schlanstedt sown. areas uniform soil conditions. 1916 1918 area, small spot noticeable rows 31-33 first year, much less noticeable 1918. three years soil differences smaller errors caused experimental technology. Haritonenko (36), Versuch der landwirtschaftlichen Versuchsstation Iwanowskoje, Gouvernement Nowgorod. Versuchsfläche 5 ha 68 mit 416 Teilstücken zu 136,5 qm. Die Reihe weist erheblich geringeren Boden auf als die drei anderen Reihen. Translated: Haritonenko (36), experiment Ivanovskoye Agricultural Experimental Station, Novgorod Governorate. Test area 5 ha 68 416 sections (plots) 136.5 square meters. Row significantly less soil three rows. Haritonenko, Pavlo. Neue Präzisionsmethoden auf den Versuchsfeldern. Arbeiten der landw. Versuchsstation Iwanowskoje 1904-06, S. 159. Note: P. Haritonenko sometimes translated Pavlo/Pavel Kharitonenko. English: Pavel Kharitonenko Russian: Павел Иванович Харитоненко Russian name Journal: Журнал опытной агрономии Paper volume? Zhurnal opytnoĭ agronomii. Russisches journal fūr experimentelle landwirtschaft. Journal de l’agriculture experimentale v.5 (1904) https://babel.hathitrust.org/cgi/pt?id=uc1.b2907722&seq=770 1905 vol 6. Paper volume? https://babel.hathitrust.org/cgi/pt?id=uc1.b2907723&seq=8 List volumes . Missing 1906 issue 1-2. Searched found. https://catalog.hathitrust.org/Record/007918356","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-2","dir":"Articles","previous_headings":"—————————————————————————","what":"—-","title":"Notes on uniformity data","text":"Mercer und Hall (71), Mangold, Versuchsfeld Rothamsted. Versuchsfläche 40,40 mit 200 Teilstücken zu 20,2 qm (9,2×2,2 m). Das Feld ist weitgehend gleichmäßig, obwohl seine Qualität von Nord nach Süd abnimmt, wie aus den Mittelwerten zu ersehen ist. Trans: 5. Mercer Hall (71), Mangold, Rothamsted experimental field. Test area 40.40 200 sections 20.2 square meters (9.2 × 2.2 m). field largely uniform, although quality decreases north south, can seen averages. Mercer und Hall (71), Winterweizen, Versuchsfeld Rothamsted. Versuchsfläche 40,45 mit 500 Teilstücken zu 8,09 qm. Die Fläche ist weniger aus geglichen als jene von Versuch 5. Trans: 6. Mercer Hall (71), winter wheat, Rothamsted experimental field. Test area 40.45 500 sections 8.09 square meters. area less uniform experiment 5. Montgomery (86), Winterweizen, Versuchsfeld der Versuchsstation Nebraska, U. S. . Versuchsfläche 6 30 qm mit 224 Teilstücken zu 2,81 qm. Das Feld wird von unten nach oben hin besser, ist auch der rechten Hälfte ertragreicher als der linken, entspricht aber einem sehr ausgeglichenen Versuchsfelde der Praxis. Trans: 7. Montgomery (86), winter wheat, experimental field Nebraska Experimental Station, USA. Experimental area 6 30 square meters 224 sections 2.81 square meters. field gets better bottom top, also productive right half left, corresponds uniform experimental field practice. Tulaikow (131), Winterweizen, Versuchsfeld der landwirtschaftlichen Versuchsstation Besentschuk. Versuchsfläche 10 92 qm mit 240 Teilstücken zu 4,55 qm. Diese Fläche ist mäßig ausgeglichen. Translated: Tulaikow (131), Winter wheat, experimental field Besenchuk Agricultural Experimental Station. Test area 10 x 92 sqm 240 plots 4.55 sqm. area moderately uniform. Neyman cites: Tulaikow, Russian Journ Exp Agric 14 (1913). Number 2, p. 113 Tulaikow (131), Sommerweizen, genau wie bei Winterweizen, jedoch erscheint das Bild gleichmäßiger als jenes von Versuch 8. Translated: Tulaikow (131), spring wheat, winter wheat, picture appears uniform experiment 8. Jegerow (52), Hafer, Versuchsfeld der landwirtschaftlichen Versuchsstation Saumy (Gouvernement Charkow), Versuchsfläche 10 92 qm mit 240 Teilstücken zu 4,55 qm. Sehr gleichmäßiges Versuchsfeld. Translated: 10. Jegerow (52), oats, experimental field Saumy agricultural experimental station (Kharkov Governorate), experimental area 10 x 92 sqm 240 sections 4.55 sqm. even test field. Larsen (49), Timothyheu, Versuchsfläche 60 mit 240 Teilstücken zu 25 qm. Die Versuchsfläche wird von Ost nach West langsam etwas besser, und von Süd nach Nord nimmt die Ertragsfähigkeit ziemlich stark ab (-Streifen 1496 kg, B-Streifen 1449 kg, C-Streifen 1326 kg). Translated: Timothy hay, test area 60 240 sections 25 square meters. test area slowly improves slightly east west, south north yield decreases quite sharply (strip 1496 kg, B strip 1449 kg, C strip 1326 kg). Lehmann (61), Reis, Versuchsfeld Hebbal bei Benjalore, Indien. Versuchsfläche 68,68 mit 34 Teilstücken zu 20,2 qm, durch vier Jahre (1905-1908) fortgeführt. Dieser Versuch ist seines geringen Umfanges wegen nicht für alle zu handelnden Fragen brauchbar. Translated: Lehmann (61), Reis, Hebbal experimental field near Benjalore, India. Experimental area 68.68 34 sections 20.2 square meters, continued four years (1905-1908). small scope, experiment used questions addressed. 61 = Lehmann 7 9. Jahresbericht der Agriculturechemie 1905-1907. 13. 6th Lehmann (61), Hirse, Versuchsfeld Hebbal bei Benjalore. Versuchsfläche 21 21 qm mit 105 Teilstüden zu 20,2 qm, durch drei Jahre (1905-1907) fortgesezt, jedoch sind die Teilstücke durch Gebäude getrennt. Dieses Versuchsfeld ist aus kleinbäuerlichem Besit zusammengekauft, also früheren Jahren verschieden arbeitet und bestellt worden, es ist die ungleichmäßigste Fläche von allen 13 Versuchen. Translated: 13. Lehmann (61), millet, Hebbal test field near Benjalore. Experimental area 21 x 21 sqm 105 sections 20.2 sqm, continued three years (1905-1907), sections separated buildings. experimental field purchased small farmers property therefore worked cultivated differently previous years. uneven area 13 experiments. Die Originalerntezahlen finden sich den Tabellen des Anhanges, die die Lage der Teilstücke erkennen lassen. Es ließ sich infolge verschiedener Anlage der Versuche nicht ermöglichen, daß alle Versuche einheitlicher Anordnung verrechnet wurden. Die Ergebnisse sind übereinstimmend, daß die gezogenen Schlußfolgerungen große Sicherheit besigen. Trans: original harvest figures can found tables appendix, show location cuts. Due different design experiments, possible experiments calculated uniform manner. results consistent conclusions drawn certain. Untersuchungen über Beeinflussung der Genauigkeit. Trans: Studies influence accuracy. Hand der bezeichneten 13 Versuche ich nun im einzelnen nachweisen, welche Umstände und welchem Maße diese auf die Genauigkeit der Versuchsergeb nisse einwirken. Es können jeweils nur die Endzahlen der mühsamen und sehr umfangreichen Berechnungen gegeben werden, die ich selbst ausgeführt habe. Trans: Using 13 experiments mentioned, now want demonstrate detail circumstances extent influence accuracy test results. final numbers laborious extensive calculations carried can given.","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-3","dir":"Articles","previous_headings":"","what":"—————————————————————————","title":"Notes on uniformity data","text":"Ehrenberg, P. 1920. Versuch eines Beweises für die Anwendbarkeit der Wahrscheinlichkeitsrechnung bei Feldversuchen. Die Landwirtschaflichen versuchs-stationen, 95, 157-294 https://archive.org/details/dielandwirtschaf9519reun/page/156/ https://www.google.com/books/edition/Die_Landwirtschaftlichen_Versuchs_Statio/h9FGAAAAYAAJ?hl=en&gbpv=1&dq=bolatitz+jegorow&pg=RA1-PA160&printsec=frontcover Extensive datasets given 1 row text per plot! , field layout given??? Ehrenberg, Mitteilungen der Landwirtschaflichen Institute der Kgl. Universitat Breslau, 6, 21 (1910). Jegorow Tulaikow Lehmann ragi Lehmann Lehmann 1907 Montgomery Larsen Page 288-290 summarizes many studies refers paper: Landw. Versuchs-Stationen Bd. 87 (1915), S. 34. https://www.google.com/books/edition/Die_Landwirthschaftlichen_Versuchs_Stati/qFPbIBaHZKUC?hl=en&gbpv=1 # ————————————————————————— Vageler, H. (1919) Beziehung zwischen Parzellengrösse und Fehler der Einzelbeobachtung bei Felderversuchen (Relation size plats error detached observations field experiments). Journal für Landwirtschaft, 67, 97-108. https://www.google.com/books/edition/Journal_f%C3%BCr_Landwirtschaft/JHAZAQAAIAAJ?hl=en&gbpv=1&dq=Vageler+Beziehung+zwischen+parzellengr%C3%B6sse&pg=PA107&printsec=frontcover 128 plots roggen (rye) hafer (oats) kartoffeln (potatoes) wruken (rape/rutabega/kohlrabi?) layout? : https://archive.org/details/journalfurlandwi6719unse/page/96/mode/2up?q=ehrenberg+%22versuch+eines+beweises%22","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-4","dir":"Articles","previous_headings":"","what":"—————————————————————————","title":"Notes on uniformity data","text":"Gorski Mentioned Neyman (1935). Gorski, M. Stefaniow, M. (1917). Die Anwendbarkeit der Wahrscheinlichkeits-rechnung bei Feldversuchen. Die Landwirtschaflichen Versuchsstationen, 90, 225-240. https://babel.hathitrust.org/cgi/pt?id=coo.31924078248048&seq=241&q1=gorski Two experiments given, 200 300 plots, layout? Gorski & Stefaniow. (1917) Zastosowanie rachunku prawdopodobienstwa doswiadczen polowych Roczniki Nauk Rolniczych. searched google books, hathi, archive Roczniki journal https://catalog.hathitrust.org/Record/007913696 11-12 1924 17-18 1927 1917 vol 11 1923 vol 9 1930 vol 24 See also Cochran, # 52","code":""},{"path":"/articles/agridat_uniformity_data.html","id":"section-5","dir":"Articles","previous_headings":"","what":"—————————————————————————","title":"Notes on uniformity data","text":"Geisler 1958. Downloaded. Uniformity trial (?) page 269.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kevin Wright. Author, maintainer, copyright holder.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Wright K (2024). agridat: Agricultural Datasets. R package version 1.24, https://kwstat.github.io/agridat/.","code":"@Manual{, title = {agridat: Agricultural Datasets}, author = {Kevin Wright}, year = {2024}, note = {R package version 1.24}, url = {https://kwstat.github.io/agridat/}, }"},{"path":"/index.html","id":"agridat-","dir":"","previous_headings":"","what":"Agricultural Datasets","title":"Agricultural Datasets","text":"Homepage: https://kwstat.github.io/agridat Repository: https://github.com/kwstat/agridat agridat package provides extensive collection datasets agricultural experiments. datasets come books, papers, websites related agriculture. Example graphics analyses included. Data come small-plot trials, multi-environment trials, uniformity trials, yield monitors, . package tries make data FAIR: Findable, Accessible, Interoperable, Reusable.","code":""},{"path":"/index.html","id":"key-features","dir":"","previous_headings":"","what":"Key features","title":"Agricultural Datasets","text":"Thorough documentation. Examples (almost) every dataset.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Agricultural Datasets","text":"","code":"# Install the released version from CRAN: install.packages(\"agridat\") # Install the development version from GitHub: install.packages(\"devtools\") devtools::install_github(\"kwstat/agridat\")"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Agricultural Datasets","text":"","code":"library(agridat) ?agridat # list all datasets with keywords"},{"path":"/notes_agridat.html","id":null,"dir":"","previous_headings":"","what":"License note","title":"License note","text":"Substantial effort made contact authors papers published within past decades secure permission use data package. U.S., raw data generally subject copyright. See discussion. Data produced work United States government (including U.S. Department Agriculture) subject copyright. Creative Commons licenses can apply database, factual data.","code":""},{"path":"/notes_agridat.html","id":"cochran-uniformity-done","dir":"","previous_headings":"","what":"Cochran uniformity done","title":"License note","text":"evans.sugarcane.uniformity goulden.barley.uniformity ducker.groundnut.uniformity immer.sugarbeet.uniformity (1931) mckinstry.cotton.uniformity saunders.maize.uniformity smith.wheat.uniformity data made available special help staff Rothamsted Research Library. Murray, E. K. S. (1934). value uniformity trial field experimentation rubber. Journal Agricultural Science, 24(2), 177-184. https://doi.org/10.1017/s0021859600006572 Reviewed. used. 2 years 5x5 grid rubber trees. Based data archived Rothamsted, used time.","code":""},{"path":"/notes_agridat.html","id":"folder-1-genstat-data","dir":"","previous_headings":"","what":"Folder 1 Genstat data","title":"License note","text":"101 # 1925-1934 single rows, series E/F 102 # 1925-1934 single rows, series G 103_goulden_barley # done Grown Dominion, 1931 104_beckett_coconut # 1919-1928, 22 plots, nuts per plot. Cochran paper #17 105_panse_cotton # done (see also 509) 106_mckinstry_cotton # done Gatooma 480 plots 107_saunders_maize # done Maize, Potchefstroom 1929-30 108 # Field B2a, B5b 109_saunders_maize # done Maize, Potchefstroom 1928-1929 110_immer_sugarbeet # done U Minn 1931 111_evans_sugarcane # done Squared values 112_sayer_sugarcane # done Harpr Jhili 113_christidis_wheat # done 114_smith_wheat # done Fairfield Smith data","code":""},{"path":"/notes_agridat.html","id":"folder-2-data-received-since-publication","dir":"","previous_headings":"","what":"Folder 2 Data received since publication","title":"License note","text":"201 # cotton Wad Medani, Sudan 202 # Wad Medani, Cotton 24x8=192 203 # 3x20, 1935-1936, weight rotl 204 # Letter Parish Fisher, 205 # Oats 1923, Sudan Grass 1923, Wheat 1922 (10x26=260 plots) 206 # done hutchinson.cotton 207 # done Letter Finney Kilby 1943 208_ducker_groundnut # done Letter Killby Fisher, Ducker groundnut data 209 # 1938 Summerby Cochran, Oats, 1921 3x33, Wheat todo. Data. pdf. 210 # 1937 Letter Summerby Cochran, Cochran Summerby 211 # 1943 Letter Finney Wadley. Thanks data. 212 # 1934 Letter Wadley Finney. Ribes data. 213 # ignore China, 1x16 214 # omit - clove trees 600, 5 years, coordinates, East Africa 215 # omit - clove tree part 2 216 # omit - wireworm traps, plant trial 217 # LeClerg field expt 1937-1938 sugar beet U Minn 218 # LeClerg greenhouse 1933 219 # 1939 letter LeClerg Cochran, Narain Cochran, Cochran Westover, Eden Cochran","code":""},{"path":"/notes_agridat.html","id":"folder-3","dir":"","previous_headings":"","what":"Folder 3","title":"License note","text":"301_day_table # done data.","code":""},{"path":"/notes_agridat.html","id":"folder-4-uniformity-trials-1936-1938","dir":"","previous_headings":"","what":"Folder 4 Uniformity trials 1936-1938","title":"License note","text":"folder contains correspondence 401 402_pound 403 cheesman christidis collinson collison 404 405_day # Done. demandt vandyk 406 407 408 posthumus 409_konigsberger demandt trials 410_demandt $ done 411 hutchinson immer look parnell 412 413 kirk parnell macdonald 414 metzger parker pound 415 pound reynolds(cotton) richardson 416_saunders # done 417_saunders sayer swanson 418_swanson thompson 419 thompson notes 420 day westover wilcox","code":""},{"path":"/notes_agridat.html","id":"folder-5-uniformity-data","dir":"","previous_headings":"","what":"Folder 5 Uniformity data","title":"License note","text":"501_metzger # multi-year series 502_kansas 503 504_goulden_barley # done goulden.barley.uniformity 505_beckett_coconut # multi-year, Cochran #17 506 507_coffee # coffee 1934-1939, Cochran #18 508_mckinstry_cotton # done mckinstry.cotton.uniformity (correspondence, hand-written) 509_panse_cotton # done panse.cotton.uniformity (see also 109) 510_christidis_wheat # done christidis.wheat.uniformity 511_sayer_sugarcane # done sayer.sugarcane.uniformity 1932, 48 rows, 20 columns 513_hastings_oats_1911 # 514_immer_sugarbeet # done. 60 row, 10 col, 2nd year data 515_sugarcane # barbados sugarcane 516_macdonald_cotton # field B2a B5b. See folder 1, file 8 517_rothamsted 518_saunders_maize_28_29 # done potchefstroom 519_saunders_maize_29_30 # done potchefstroom","code":""},{"path":"/notes_agridat.html","id":"folder-6-ovs-heath-cotton-uniformity-1934-1935","dir":"","previous_headings":"","what":"Folder 6 OVS Heath cotton uniformity 1934-1935","title":"License note","text":"Decided use data. 2 cut dates. data dry matter highly variable. field notes bit cryptic suggest lack uniformity plants handled (cut morning, afternoon, etc). 601 7/1 pt 2, copy 1, row 1-84, col k-u 602 7/1 pt 2, copy 2, row 1-84, col k-u 603 4/2 pt 1, copy2, row 1-84, col -k 604 4/2 pt 1, copy1, row 1-84, col -k 605 4/2 pt 2, copy2, row 1-84, col l-v 606 7/1 pt 1, copy 2, row 1-84, col za-j 607 experiment details 608 7/1 pt 1, copy 1, row 1-84, col za-j 609 4/2 pt 2, copy1, row 1-84, col l-v","code":""},{"path":"/notes_agridat.html","id":"folder-7-yield-of-grain-per-foot-fairfield-smith","dir":"","previous_headings":"","what":"Folder 7 Yield of grain per foot, Fairfield smith","title":"License note","text":"701_smith_correspondence # done 702_smith_reference # done 703_smith_ears_copy_B # done 704_smith_grain_copy_B # done 705_smith_grain_copy_A # done 707_smith_ears_copy_A # done","code":""},{"path":"/notes_agridat.html","id":"folder-8-catalog-of-uniformity-data","dir":"","previous_headings":"","what":"Folder 8 Catalog of uniformity data","title":"License note","text":"801_cochran_notes_1 802_cochran_notes_2 803_cochran_notes_3 804_cochran_notes_4 800_cochran_notes_0 805_evans_sugarcane_letter # done evans.sugarcane.uniformity 806_evans_sugarcane_data # done evans.sugarcane.uniformity","code":""},{"path":"/notes_agridat.html","id":"folder-9-demandt-1931","dir":"","previous_headings":"","what":"Folder 9 Demandt 1931","title":"License note","text":"Decided use data. little contextual information data. 901_demandt_diagram 902_demandt_data","code":""},{"path":"/notes_agridat.html","id":"to-do","dir":"","previous_headings":"","what":"To do","title":"License note","text":"change theobald.covariate JAGS brms? Figure best way use jags JAGS code edwards.oats JAGS code lee.potatoblight JAGS code theobald.barley JAGS code besag.elbatan Note: R_MAX_NUM_DLLS=150 Rcmd check –run-dontrun release devtools::run_examples(run=FALSE, start=“butron.maize”) build_site(lazy=TRUE, run=TRUE) use Roxygen document data, complain data/*.txt files error messages like: Error: ‘uscrime’ exported object ‘namespace:agridat’ document lists notes data sources searched additional sources agricultural data. Although .md file, formatting best viewed plain text mode. Henry Wallace archive Univ Iowa Library http://www.lib.uiowa.edu/sc/location--hours/ Henry Wallace research papers http://aspace.lib.uiowa.edu/repositories/2/archival_objects/400608 500 Ear experiment. Might data “corn judge’s mind” paper http://aspace.lib.uiowa.edu/repositories/2/archival_objects/400615","code":""},{"path":"/notes_agridat.html","id":"wanted","dir":"","previous_headings":"","what":"wanted","title":"License note","text":"Yield-monitor data split-planter field Yield-monitor data strip trial. Jose Crossa papers http://repository.cimmyt.org/xmlui/handle/10883/1/browse?value=Crossa,%20J.&type=author Meta-r http://repository.cimmyt.org/xmlui/handle/10883/4130 Data http://repository.cimmyt.org/xmlui/handle/10883/4036 http://repository.cimmyt.org/xmlui/handle/10883/2976 http://repository.cimmyt.org/xmlui/handle/10883/1380 http://repository.cimmyt.org/xmlui/handle/10883/4128 http://repository.cimmyt.org/xmlui/handle/10883/4290 Review meta-analyses agronomy http://www6.versailles-grignon.inra.fr/agronomie/Meta-analysis--agronomy/References","code":""},{"path":"/notes_agridat.html","id":"papers","dir":"","previous_headings":"","what":"Papers","title":"License note","text":"Biplot/StdErr/ VanEeuwijk 1993 - Incorporating env info Biplot Bartkowiak GxE table GE folder: Crossa 1997","code":""},{"path":"/notes_agridat.html","id":"malosetti-2013","dir":"","previous_headings":"","what":"Malosetti 2013","title":"License note","text":"F2 data. Folder: GE http://www.frontiersin.org/Plant_Physiology/10.3389/fphys.2013.00044/abstract CC license:","code":""},{"path":"/notes_agridat.html","id":"perez","dir":"","previous_headings":"","what":"Perez","title":"License note","text":"Comparison Linear Non-parametric Regression Models Genome-Enabled Prediction Wheat https://www.scienceopen.com/document/vid/4017fb51-381c-4374-93aa-608423df4004;jsessionid=0TLjjSbaooSUk1y3JKd4nUeb.master:-app1-prd Data: http://www.g3journal.org/content/suppl/2012/12/05/2.12.1595.DC1 content CC license","code":""},{"path":"/notes_agridat.html","id":"extension","dir":"","previous_headings":"","what":"Extension","title":"License note","text":"https://plant-breeding-genomics.extension.org/plant-breeding--genomics-learning-lessons https://plant-breeding-genomics.extension.org/estimating-heritability--blups--traits-using-tomato-phenotypic-data/ https://plant-breeding-genomics.extension.org/genomic-relationships--gblup <— todo review https://plant-breeding-genomics.extension.org/rrblup-package--r--genomewide-selection/","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"/notes_agridat.html","id":"agronomy-journal","dir":"","previous_headings":"","what":"Agronomy Journal","title":"License note","text":"Skimmed Vol 1","code":""},{"path":"/notes_agridat.html","id":"the-american-statistician","dir":"","previous_headings":"","what":"The American Statistician","title":"License note","text":"Vol 1-13","code":""},{"path":"/notes_agridat.html","id":"biometrics-skimmed-1947-2006","dir":"","previous_headings":"","what":"Biometrics. Skimmed 1947-2006","title":"License note","text":"http://www.jstor.org/action/showPublication?journalCode=biometrics","code":""},{"path":"/notes_agridat.html","id":"the-empire-journal-of-experimental-agriculture","dir":"","previous_headings":"","what":"The Empire Journal of Experimental Agriculture","title":"License note","text":"http://archive.org Vol 3-5, 23-24 26","code":""},{"path":"/notes_agridat.html","id":"field-crops-research","dir":"","previous_headings":"","what":"Field Crops Research.","title":"License note","text":"http://www.sciencedirect.com/science/journal/03784290/157 Vol 1-40","code":""},{"path":"/notes_agridat.html","id":"iasri-newsletters","dir":"","previous_headings":"","what":"IASRI newsletters","title":"License note","text":"http://www.iasri.res./NewsLetters/nl.HTM","code":""},{"path":"/notes_agridat.html","id":"indian-journal-of-agricultural-science","dir":"","previous_headings":"","what":"Indian Journal of Agricultural Science","title":"License note","text":"","code":"Vol 1. Vol 2. Vol 3. https://archive.org/details/in.ernet.dli.2015.271738/page/n653/mode/2up 544 5 varieties, 2 blocks, 4 reps/block Vol 4. Vol 5. 579. agridat::bose.multi.uniformity Vol 6. https://archive.org/details/in.ernet.dli.2015.271737 34. 4-way factorial (3 gen, 5 date, 3 spacing, 3 pop) non-contiguous sub-plots. agridat::chakravertti.factorial 460. agridat::kulkarni.sorghum.uniformity 917. agridat::sayer.sugarcane.uniformity Vol 9. Vol 10. Vol 11. Vol 12. 240. Wheat uniformity trial. agridat::iyer.wheat.uniformity Vol 14. Vol 16. Vol 17. Vol 19."},{"path":"/notes_agridat.html","id":"jabes","dir":"","previous_headings":"","what":"JABES","title":"License note","text":"Vol 6.","code":""},{"path":"/notes_agridat.html","id":"journal-of-the-american-society-of-agronomy","dir":"","previous_headings":"","what":"Journal of the American Society of Agronomy","title":"License note","text":"Vol 23.","code":""},{"path":"/notes_agridat.html","id":"journal-of-the-indian-society-of-agricultural-statistics","dir":"","previous_headings":"","what":"Journal of the Indian Society of Agricultural Statistics","title":"License note","text":"http://www.isas.org./jsp/onlinejournal.jsp Skimmed: Vol 50-56","code":""},{"path":[]},{"path":"/notes_agridat.html","id":"jrssb-1940-1997","dir":"","previous_headings":"","what":"JRSSB 1940-1997","title":"License note","text":"http://www.jstor.org/action/showPublication?journalCode=jroyastatsocise4 Datasets 1998-2015 http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9868/homepage/seriesb_datasets.htm http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-985X/homepage/datasets_all_series.htm","code":""},{"path":"/notes_agridat.html","id":"jrssc-applied-statistics-datasets","dir":"","previous_headings":"","what":"JRSSC Applied Statistics datasets","title":"License note","text":"http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-985X/homepage/datasets_all_series.htm 1998-2015","code":""},{"path":"/notes_agridat.html","id":"tidsskrift-for-planteavl-1895-1992","dir":"","previous_headings":"","what":"Tidsskrift for Planteavl 1895-1992","title":"License note","text":"http://dca.au.dk/publikationer/historiske/planteavl/","code":""},{"path":[]},{"path":"/notes_agridat.html","id":"nebraska-agricultural-experiment-station-annual-report","dir":"","previous_headings":"","what":"Nebraska Agricultural Experiment Station Annual Report","title":"License note","text":"Vol 19-24, 1906-1911 https://books.google.com/books?id=HBlJAAAAMAAJ Vol 25, 1912 https://books.google.com/books?id=M-5BAQAAMAAJ","code":""},{"path":"/notes_agridat.html","id":"iowa-state-university-library-special-collections","dir":"","previous_headings":"","what":"Iowa State University Library Special Collections","title":"License note","text":"Helen Elizabeth Conners (1951). Field plot techniques sweet potatoes obtained uniformity trial data. Master’s Thesis. data given. Robert LeRoy Plaisted (1954). Field plot techniques estimating onion yields. Master’s Thesis. data given. Michael Holle. 1960. Plot technique field evaluation three characters lima bean. Master’s Thesis. data given. Howard Lewis Taylor (1951). effect plot shape experimental error. Master’s Thesis. data given. Used data corn uniformity trial, oats uniformity, data Fairfield Smith. Smaller experimental errors found long narrow plots.","code":""},{"path":[]},{"path":"/notes_agridat.html","id":"jack-weiss","dir":"","previous_headings":"","what":"Jack Weiss","title":"License note","text":"Ecol 563 Stat Meth Ecology http://www.unc.edu/courses/2010fall/ecol/563/001/ Env Studies 562 Stat Envt Science http://www.unc.edu/courses/2010spring/ecol/562/001/ Ecol 145 http://www.unc.edu/courses/2006spring/ecol/145/001/docs/lectures.htm","code":""},{"path":[]},{"path":"/notes_agridat.html","id":"applied-statistics-in-agriculture-conference","dir":"","previous_headings":"","what":"Applied Statistics in Agriculture Conference","title":"License note","text":"http://newprairiepress.org/agstatconference/ 1989-2014","code":""},{"path":"/notes_agridat.html","id":"computers-and-electronics-in-agriculture","dir":"","previous_headings":"","what":"Computers and Electronics in Agriculture.","title":"License note","text":"http://www.sciencedirect.com/science/journal/01681699/103 Vol 1-110 180-191","code":""},{"path":"/notes_agridat.html","id":"journal-of-agricultural-science","dir":"","previous_headings":"","what":"Journal of Agricultural Science","title":"License note","text":"https://www.cambridge.org/core/journals/journal--agricultural-science/-issues 1900-2016","code":""},{"path":"/notes_agridat.html","id":"experimental-agriculture","dir":"","previous_headings":"","what":"Experimental Agriculture","title":"License note","text":"https://www.cambridge.org/core/journals/experimental-agriculture 1965-2016","code":""},{"path":"/reference/aastveit.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Barley heights and environmental covariates in Norway — aastveit.barley","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"Average height 15 genotypes barley 9 years. Also 19 covariates 9 years.","code":""},{"path":"/reference/aastveit.barley.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"","code":"data(\"aastveit.barley.covs\") data(\"aastveit.barley.height\")"},{"path":"/reference/aastveit.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"'aastveit.barley.covs' dataframe 9 observations following 20 variables. year year R1 avg rainfall (mm/day) period 1 R2 avg rainfall (mm/day) period 2 R3 avg rainfall (mm/day) period 3 R4 avg rainfall (mm/day) period 4 R5 avg rainfall (mm/day) period 5 R6 avg rainfall (mm/day) period 6 S1 daily solar radiation (ca/cm^2) period 1 S2 daily solar radiation (ca/cm^2) period 2 S3 daily solar radiation (ca/cm^2) period 3 S4 daily solar radiation (ca/cm^2) period 4 S5 daily solar radiation (ca/cm^2) period 5 S6 daily solar radiation (ca/cm^2) period 6 ST sowing time, measured days April 1 T1 avg temp (deg Celsius) period 1 T2 avg temp (deg Celsius) period 2 T3 avg temp (deg Celsius) period 3 T4 avg temp (deg Celsius) period 4 T5 avg temp (deg Celsius) period 5 T6 avg temp (deg Celsius) period 6 'aastveit.barley.height' dataframe 135 observations following 3 variables. year year, 9 years spanning 1974 1982 gen genotype, 15 levels height height (cm)","code":""},{"path":"/reference/aastveit.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"Experiments conducted , Norway. height dataframe contains average plant height (cm) 15 varieties barley 9 years. growth season year divided eight periods sowing harvest. plant stop growing 20 days ear emergence, first 6 periods included . Used permission Harald Martens.","code":""},{"path":"/reference/aastveit.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"Aastveit, . H. Martens, H. (1986). ANOVA interactions interpreted partial least squares regression. Biometrics, 42, 829–844. https://doi.org/10.2307/2530697","code":""},{"path":"/reference/aastveit.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"J. Chadoeuf J. B. Denis (1991). Asymptotic variances multiplicative interaction model. J. App. Stat., 18, 331-353. https://doi.org/10.1080/02664769100000032","code":""},{"path":"/reference/aastveit.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Barley heights and environmental covariates in Norway — aastveit.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(\"aastveit.barley.covs\") data(\"aastveit.barley.height\") libs(reshape2, pls) # First, PCA of each matrix separately Z <- acast(aastveit.barley.height, year ~ gen, value.var=\"height\") Z <- sweep(Z, 1, rowMeans(Z)) Z <- sweep(Z, 2, colMeans(Z)) # Double-centered sum(Z^2)*4 # Total SS = 10165 sv <- svd(Z)$d round(100 * sv^2/sum(sv^2),1) # Prop of variance each axis # Aastveit Figure 1. PCA of height biplot(prcomp(Z), main=\"aastveit.barley - height\", cex=0.5) U <- aastveit.barley.covs rownames(U) <- U$year U$year <- NULL U <- scale(U) # Standardized covariates sv <- svd(U)$d # Proportion of variance on each axis round(100 * sv^2/sum(sv^2),1) # Now, PLS relating the two matrices m1 <- plsr(Z~U) loadings(m1) # Aastveit Fig 2a (genotypes), but rotated differently biplot(m1, which=\"y\", var.axes=TRUE) # Fig 2b, 2c (not rotated) biplot(m1, which=\"x\", var.axes=TRUE) # Adapted from section 7.4 of Turner & Firth, # \"Generalized nonlinear models in R: An overview of the gnm package\" # who in turn reproduce the analysis of Chadoeuf & Denis (1991), # \"Asymptotic variances for the multiplicative interaction model\" libs(gnm) dath <- aastveit.barley.height dath$year = factor(dath$year) set.seed(42) m2 <- gnm(height ~ year + gen + Mult(year, gen), data = dath) # Turner: \"To obtain parameterization of equation 1, in which sig_k is the # singular value for component k, the row and column scores must be constrained # so that the scores sum to zero and the squared scores sum to one. # These contrasts can be obtained using getContrasts\" gamma <- getContrasts(m2, pickCoef(m2, \"[.]y\"), ref = \"mean\", scaleWeights = \"unit\") delta <- getContrasts(m2, pickCoef(m2, \"[.]g\"), ref = \"mean\", scaleWeights = \"unit\") # estimate & std err gamma <- gamma$qvframe delta <- delta$qvframe # change sign of estimate gamma[,1] <- -1 * gamma[,1] delta[,1] <- -1 * delta[,1] # conf limits based on asymptotic normality, Chadoeuf table 8, p. 350, round(cbind(gamma[,1], gamma[, 1] + outer(gamma[, 2], c(-1.96, 1.96))) ,3) round(cbind(delta[,1], delta[, 1] + outer(delta[, 2], c(-1.96, 1.96))) ,3) } # }"},{"path":"/reference/acorsi.grayleafspot.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"Multi-environment trial evaluating 36 maize genotypes 9 locations","code":""},{"path":"/reference/acorsi.grayleafspot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"","code":"data(\"acorsi.grayleafspot\")"},{"path":"/reference/acorsi.grayleafspot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"data frame 324 observations following 3 variables. gen genotype, 36 levels env environment, 9 levels rep replicate, 2 levels y grey leaf spot severity","code":""},{"path":"/reference/acorsi.grayleafspot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"Experiments conducted 9 environments Brazil 2010-11. location RCB 2 reps. response variable percentage leaf area affected gray leaf spot within experimental unit (plot). Acorsi et al. use data illustrate fitting generalized AMMI model non-normal data.","code":""},{"path":"/reference/acorsi.grayleafspot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"C. R. L. Acorsi, T. . Guedes, M. M. D. Coan, R. J. B. Pinto, C. . Scapim, C. . P. Pacheco, P. E. O. Guimaraes, C. R. Casela. (2016). Applying generalized additive main effects multiplicative interaction model analysis maize genotypes resistant grey leaf spot. Journal Agricultural Science. https://doi.org/10.1017/S0021859616001015 Electronic data R code kindly provided Marlon Coan.","code":""},{"path":"/reference/acorsi.grayleafspot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"None","code":""},{"path":"/reference/acorsi.grayleafspot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial evaluating 36 maize genotypes in 9 locations — acorsi.grayleafspot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(acorsi.grayleafspot) dat <- acorsi.grayleafspot # Acorsi figure 2. Note: Acorsi used cell means op <- par(mfrow=c(2,1), mar=c(5,4,3,2)) libs(lattice) boxplot(y ~ env, dat, las=2, xlab=\"environment\", ylab=\"GLS severity\") title(\"acorsi.grayleafspot\") boxplot(y ~ gen, dat, las=2, xlab=\"genotype\", ylab=\"GLS severity\") par(op) # GLM models # glm main-effects model with logit u(1-u) and wedderburn u^2(1-u)^2 # variance functions # glm1 <- glm(y~ env/rep + gen + env, data=dat, family=quasibinomial) # glm2 <- glm(y~ env/rep + gen + env, data=dat, family=wedderburn) # plot(glm2, which=1); plot(glm2, which=2) # GAMMI models of Acorsi. See also section 7.4 of Turner # \"Generalized nonlinear models in R: An overview of the gnm package\" # full gnm model with wedderburn, seems to work libs(gnm) set.seed(1) gnm1 <- gnm(y ~ env/rep + env + gen + instances(Mult(env,gen),2), data=dat, family=wedderburn, iterMax =800) deviance(gnm1) # 433.8548 # summary(gnm1) # anova(gnm1, test =\"F\") # anodev, Acorsi table 4 ## Df Deviance Resid. Df Resid. Dev F Pr(>F) ## NULL 647 3355.5 ## env 8 1045.09 639 2310.4 68.4696 < 2.2e-16 *** ## env:rep 9 12.33 630 2298.1 0.7183 0.6923 ## gen 35 1176.23 595 1121.9 17.6142 < 2.2e-16 *** ## Mult(env, gen, inst = 1) 42 375.94 553 745.9 4.6915 < 2.2e-16 *** ## Mult(env, gen, inst = 2) 40 312.06 513 433.9 4.0889 3.712e-14 *** # maybe better, start simple and build up the model gnm2a <- gnm(y ~ env/rep + env + gen, data=dat, family=wedderburn, iterMax =800) # add first interaction term res2a <- residSVD(gnm2a, env, gen, 2) gnm2b <- update(gnm2a, . ~ . + Mult(env,gen,inst=1), start = c(coef(gnm2a), res2a[, 1])) deviance(gnm2b) # 692.19 # add second interaction term res2b <- residSVD(gnm2b, env, gen, 2) gnm2c <- update(gnm2b, . ~ . + Mult(env,gen,inst=1) + Mult(env,gen,inst=2), start = c(coef(gnm2a), res2a[, 1], res2b[,1])) deviance(gnm2c) # 433.8548 # anova(gnm2c) # weird error message # note, to build the ammi biplot, use the first column of res2a to get # axis 1, and the FIRST column of res2b to get axis 2. Slightly confusing emat <- cbind(res2a[1:9, 1], res2b[1:9, 1]) rownames(emat) <- gsub(\"fac1.\", \"\", rownames(emat)) gmat <- cbind(res2a[10:45, 1], res2b[10:45, 1]) rownames(gmat) <- gsub(\"fac2.\", \"\", rownames(gmat)) # match Acorsi figure 4 biplot(gmat, emat, xlim=c(-2.2, 2.2), ylim=c(-2.2, 2.2), expand=2, cex=0.5, xlab=\"Axis 1\", ylab=\"Axis 2\", main=\"acorsi.grayleafspot - GAMMI biplot\") } # }"},{"path":"/reference/adugna.sorghum.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","title":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","text":"Multi-environment trial sorghum 3 locations across 5 years","code":""},{"path":"/reference/adugna.sorghum.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","text":"data frame 289 observations following 6 variables. gen genotype, 28 levels trial trial, 2 levels env environment, 13 levels yield yield kg/ha year year, 2001-2005 loc location, 3 levels","code":""},{"path":"/reference/adugna.sorghum.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","text":"Sorghum yields 3 locations across 5 years. trials carried three locations dry, hot lowlands Ethiopia: Melkassa (39 deg 21 min E, 8 deg 24 min N) Mieso (39 deg 22 min E, 8 deg 41 min N) Kobo (39 deg 37 min E, 12 deg 09 min N) Trial 1 14 hybrids one open-pollinated variety. Trial 2 12 experimental lines. Used permission Asfaw Adugna.","code":""},{"path":"/reference/adugna.sorghum.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","text":"Asfaw Adugna (2008). Assessment yield stability sorghum using univariate multivariate statistical approaches. Hereditas, 145, 28–37. https://doi.org/10.1111/j.0018-0661.2008.2023.x","code":""},{"path":"/reference/adugna.sorghum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of sorghum at 3 locations across 5 years — adugna.sorghum","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(adugna.sorghum) dat <- adugna.sorghum libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(yield ~ env*gen, data=dat, main=\"adugna.sorghum gxe heatmap\", col.regions=redblue) # Genotype means match Adugna tapply(dat$yield, dat$gen, mean) # CV for each genotype. G1..G15 match, except for G2. # The table in Adugna scrambles the means for G16..G28 libs(reshape2) mat <- acast(dat, gen~env, value.var='yield') round(sqrt(apply(mat, 1, var, na.rm=TRUE)) / apply(mat, 1, mean, na.rm=TRUE) * 100,2) # Shukla stability. G1..G15 match Adugna. Can't match G16..G28. dat1 <- droplevels(subset(dat, trial==\"T1\")) mat1 <- acast(dat1, gen~env, value.var='yield') w <- mat1; k=15; n=8 # k=p gen, n=q env w <- sweep(w, 1, rowMeans(mat1, na.rm=TRUE)) w <- sweep(w, 2, colMeans(mat1, na.rm=TRUE)) w <- w + mean(mat1, na.rm=TRUE) w <- rowSums(w^2, na.rm=TRUE) sig2 <- k*w/((k-2)*(n-1)) - sum(w)/((k-1)*(k-2)*(n-1)) round(sig2/10000,1) # Genotypes in T1 are divided by 10000 } # }"},{"path":"/reference/agridat.html","id":null,"dir":"Reference","previous_headings":"","what":"Datasets from agricultural experiments — agridat","title":"Datasets from agricultural experiments — agridat","text":"package contains datasets publications relating agriculture, including field crops, tree crops, animal studies, others.","code":""},{"path":"/reference/agridat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Datasets from agricultural experiments — agridat","text":"use data, please cite agridat package original source data. Abbreviations '' column include: xy = coordinates, pls = partial least squares, rsm = response surface methodology, row-col = row-column design, ts = time series, Uniformity trials single genotype Yield monitor Animals Trees Field horticulture crops Time series Summaries: Diallel experiments: Factorial experiments: Multi-environment trials multi-genotype,loc,rep,year: Data markers: hadasch.lettuce.markers, steptoe.morex.geno Data pedigree: butron.maize","code":""},{"path":"/reference/agridat.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Datasets from agricultural experiments — agridat","text":"Kevin Wright, support many people granted permission include data package.","code":""},{"path":"/reference/agridat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Datasets from agricultural experiments — agridat","text":"J. White Frits van Evert. (2008). Publishing Agronomic Data. Agron J. 100, 1396-1400. https://doi.org/10.2134/agronj2008.0080F","code":""},{"path":"/reference/allcroft.lodging.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of cereal with lodging data — allcroft.lodging","title":"Multi-environment trial of cereal with lodging data — allcroft.lodging","text":"Percent lodging given 32 genotypes 7 environments.","code":""},{"path":"/reference/allcroft.lodging.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of cereal with lodging data — allcroft.lodging","text":"data frame 224 observations following 3 variables. env environment, 1-7 gen genotype, 1-32 y percent lodged","code":""},{"path":"/reference/allcroft.lodging.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of cereal with lodging data — allcroft.lodging","text":"data first year three-year study. Used permission Chris Glasbey.","code":""},{"path":"/reference/allcroft.lodging.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of cereal with lodging data — allcroft.lodging","text":"D. J. Allcroft C. . Glasbey, 2003. Analysis crop lodging using latent variable model. Journal Agricultural Science, 140, 383–393. https://doi.org/10.1017/S0021859603003332","code":""},{"path":"/reference/allcroft.lodging.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of cereal with lodging data — allcroft.lodging","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(allcroft.lodging) dat <- allcroft.lodging # Transformation dat$sy <- sqrt(dat$y) # Variety 4 has no lodging anywhere, so add a small amount dat[dat$env=='E5' & dat$gen=='G04',]$sy <- .01 libs(lattice) dotplot(env~y|gen, dat, as.table=TRUE, xlab=\"Percent lodged (by genotype)\", ylab=\"Variety\", main=\"allcroft.lodging\") # Tobit model libs(AER) m3 <- tobit(sy ~ 1 + gen + env, left=0, right=100, data=dat) # Table 2 trial/variety means preds <- expand.grid(gen=levels(dat$gen), env=levels(dat$env)) preds$pred <- predict(m3, newdata=preds) round(tapply(preds$pred, preds$gen, mean),2) round(tapply(preds$pred, preds$env, mean),2) } # }"},{"path":"/reference/alwan.lamb.html","id":null,"dir":"Reference","previous_headings":"","what":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"34 sheep sires, number lambs 5 foot shape classes.","code":""},{"path":"/reference/alwan.lamb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"","code":"data(\"alwan.lamb\")"},{"path":"/reference/alwan.lamb.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"data frame 340 observations following 11 variables. year numeric 1980/1981 breed breed PP, BRP, BR sex sex lamb M/F sire0 sire ID according Alwan shape sire ID according Gilmour count number lambs sire shape foot yr numeric contrast year b1 numeric contrast breeds b2 numeric contrast breeds b3 numeric contrast breeds","code":""},{"path":"/reference/alwan.lamb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"2513 lambs classified presence deformities feet. lambs represent offspring 34 sires, 5 strains, 2 years. variables yr, b1, b2, b3 numeric contrasts fixed effects defined paper Gilmour (1987) used SAS example. Gilmour explain reason particular contrasts. counts classes LF1, LF2, LF3 combined.","code":""},{"path":"/reference/alwan.lamb.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"Mohammed Alwan (1983). Studies flock mating performance Booroola merino crossbred ram lambs, foot conditions Booroola merino crossbreds Perendale sheep grazed hill country. Thesis, Massey University. https://hdl.handle.net/10179/5900 Appendix , II.","code":""},{"path":"/reference/alwan.lamb.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"Gilmour, Anderson, Rae (1987). Variance components underlying scale ordered multiple threshold categorical data using generalized linear mixed model. Journal Animal Breeding Genetics, 104, 149-155. https://doi.org/10.1111/j.1439-0388.1987.tb00117.x SAS/STAT(R) 9.2 Users Guide, Second Edition Example 38.11 Maximum Likelihood Proportional Odds Model Random Effects https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm","code":""},{"path":"/reference/alwan.lamb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. — alwan.lamb","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(alwan.lamb) dat <- alwan.lamb # merge LF1 LF2 LF3 class counts, and combine M/F dat$shape <- as.character(dat$shape) dat$shape <- ifelse(dat$shape==\"LF2\", \"LF3\", dat$shape) dat$shape <- ifelse(dat$shape==\"LF1\", \"LF3\", dat$shape) dat <- aggregate(count ~ year+breed+sire0+sire+shape+yr+b1+b2+b3, dat, FUN=sum) dat <- transform(dat, year=factor(year), breed=factor(breed), sire0=factor(sire0), sire=factor(sire)) # LF5 or LF3 first is a bit arbitary...affects the sign of the coefficients dat <- transform(dat, shape=ordered(shape, levels=c(\"LF5\",\"LF4\",\"LF3\"))) # View counts by year and breed libs(latticeExtra) dat2 <- aggregate(count ~ year+breed+shape, dat, FUN=sum) useOuterStrips(barchart(count ~ shape|year*breed, data=dat2, main=\"alwan.lamb\")) # Model used by Gilmour and SAS dat <- subset(dat, count > 0) libs(ordinal) m1 <- clmm(shape ~ yr + b1 + b2 + b3 + (1|sire), data=dat, weights=count, link=\"probit\", Hess=TRUE) summary(m1) # Very similar to Gilmour results ordinal::ranef(m1) # sign is opposite of SAS ## SAS var of sires .04849 ## Effect Shape Estimate Standard Error DF t Value Pr > |t| ## Intercept 1 0.3781 0.04907 29 7.71 <.0001 ## Intercept 2 1.6435 0.05930 29 27.72 <.0001 ## yr 0.1422 0.04834 2478 2.94 0.0033 ## b1 0.3781 0.07154 2478 5.28 <.0001 ## b2 0.3157 0.09709 2478 3.25 0.0012 ## b3 -0.09887 0.06508 2478 -1.52 0.1289 ## Gilmour results for probit analysis ## Int1 .370 +/- .052 ## Int2 1.603 +/- .061 ## Year -.139 +/- .052 ## B1 -.370 +/- .076 ## B2 -.304 +/- .103 ## B3 .098 +/- .070 # Plot random sire effects with intervals, similar to SAS example plot.random <- function(model, random.effect, ylim=NULL, xlab=\"\", main=\"\") { tab <- ordinal::ranef(model)[[random.effect]] tab <- data.frame(lab=rownames(tab), est=tab$\"(Intercept)\") tab <- transform(tab, lo = est - 1.96 * sqrt(model$condVar), hi = est + 1.96 * sqrt(model$condVar)) # sort by est, and return index ix <- order(tab$est) tab <- tab[ix,] if(is.null(ylim)) ylim <- range(c(tab$lo, tab$hi)) n <- nrow(tab) plot(1:n, tab$est, axes=FALSE, ylim=ylim, xlab=xlab, ylab=\"effect\", main=main, type=\"n\") text(1:n, tab$est, labels=substring(tab$lab,2) , cex=.75) axis(1) axis(2) segments(1:n, tab$lo, 1:n, tab$hi, col=\"gray30\") abline(h=c(-.5, -.25, 0, .25, .5), col=\"gray\") return(ix) } ix <- plot.random(m1, \"sire\") # foot-shape proportions for each sire, sorted by estimated sire effects # positive sire effects tend to have lower proportion of lambs in LF4 and LF5 tab <- prop.table(xtabs(count ~ sire+shape, dat), margin=1) tab <- tab[ix,] tab <- tab[nrow(tab):1,] # reverse the order lattice::barchart(tab, horizontal=FALSE, auto.key=TRUE, main=\"alwan.lamb\", xlab=\"Sire\", ylab=\"Proportion of lambs\", scales=list(x=list(rot=70)), par.settings = simpleTheme(col=c(\"yellow\",\"orange\",\"red\")) ) detach(\"package:ordinal\") # to avoid VarCorr clash with lme4 } # }"},{"path":"/reference/ansari.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — ansari.wheat.uniformity","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"Uniformity trial wheat India 1940.","code":""},{"path":"/reference/ansari.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"","code":"data(\"ansari.wheat.uniformity\")"},{"path":"/reference/ansari.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"data frame 768 observations following 3 variables. row row col column yield yield grain per plot, half-ounces","code":""},{"path":"/reference/ansari.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"experiment conducted Government Research Farm, Raya (Muttra District), rainy season 1939-40. \"Wheat sown area 180 ft. x 243 ft. 324 rows field average fertility. wheat 1938-39 rabi fallow 1939-40 kharif. seed sown behind desi plough rows 9 inches apart, length row 180 feet\". \"time harvest, 18 rows sides 10 feet end field discarded eliminate border effects area 160 feet x 216 feet 288 rows harvested small units, 2 feet 3 inches broad three rows 20 feet long. 96 units across rows eight units along rows. total number unit plots thus obtained 768. yield grain unit plot weighed recorded separately given appendix.\" Field width: 96 plots * 2.25 feet = 216 feet. Field length: 8 plots * 20 feet = 160 feet. Comment: seems strong cyclical patern fertility gradient. \"History field reveals explanation phenomenon, average field usually found farm selected trial.\"","code":""},{"path":"/reference/ansari.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"Ansari, M. . ., G. K. Sant (1943). Study Soil Heterogeneity Relation Size Shape Plots Wheat Field Raya (Muhra District). Ind. J. Agr. Sci, 13, 652-658. https://archive.org/details/.ernet.dli.2015.271748","code":""},{"path":"/reference/ansari.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"None","code":""},{"path":"/reference/ansari.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — ansari.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ansari.wheat.uniformity) dat <- ansari.wheat.uniformity # match Ansari figure 3 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=216/160, # true aspect main=\"ansari.wheat.uniformity\") } # }"},{"path":"/reference/arankacami.groundnut.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"Uniformity trial groundnut","code":""},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"","code":"data(\"arankacami.groundnut.uniformity\")"},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"data frame 96 observations following 3 variables. row row col column yield yield, kg/plot","code":""},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"year experiment unknown, 1995. Basic plot size 0.75 m (rows) x 4 m (columns).","code":""},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"Ira Arankacami, R. Rangaswamy. (1995). Text Book Agricultural Statistics. New Age International Publishers. Table 19.1. Page 237. https://www.google.com/books/edition/A_Text_Book_of_Agricultural_Statistics/QDLWE4oakSgC","code":""},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"None","code":""},{"path":"/reference/arankacami.groundnut.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of groundnut — arankacami.groundnut.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(arankacami.groundnut.uniformity) dat <- arankacami.groundnut.uniformity require(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(12*.75)/(8*4), main=\"arankacami.groundnut.uniformity\") } # }"},{"path":"/reference/archbold.apple.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-split plot experiment of apple trees — archbold.apple","title":"Split-split plot experiment of apple trees — archbold.apple","text":"Split-split plot experiment apple trees different spacing, root stock, cultivars.","code":""},{"path":"/reference/archbold.apple.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-split plot experiment of apple trees — archbold.apple","text":"data frame 120 observations following 10 variables. rep block, 5 levels row row pos position within row spacing spacing trees, 6,10,14 feet stock rootstock, 4 levels gen genotype, 2 levels yield yield total, kg/tree 1975-1979 trt treatment code","code":""},{"path":"/reference/archbold.apple.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-split plot experiment of apple trees — archbold.apple","text":"rep 1, 10-foot-spacing main plot split two non-contiguous pieces. also happened rep 4. analysis Cornelius Archbold, consider row x within-row-spacing distinct main plot. (Also true 14-foot row-spacing, even though 14-foot spacing plots contiguous.) treatment code defined 100 * spacing + 10 * stock + gen, stock=0,1,6,7 Seedling,MM111,MM106,M0007 gen=1,2 Redspur,Golden, respectively.","code":""},{"path":"/reference/archbold.apple.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-split plot experiment of apple trees — archbold.apple","text":"D Archbold G. R. Brown P. L. Cornelius. (1987). Rootstock -row spacing effects growth yield spur-type delicious Golden delicious apple. Journal American Society Horticultural Science, 112, 219-222.","code":""},{"path":"/reference/archbold.apple.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-split plot experiment of apple trees — archbold.apple","text":"Cornelius, PL Archbold, DD, 1989. Analysis split-split plot experiment missing data using mixed model equations. Applications Mixed Models Agriculture Related Disciplines. Pages 55-79.","code":""},{"path":"/reference/archbold.apple.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-split plot experiment of apple trees — archbold.apple","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(archbold.apple) dat <- archbold.apple # Define main plot and subplot dat <- transform(dat, rep=factor(rep), spacing=factor(spacing), trt=factor(trt), mp = factor(paste(row,spacing,sep=\"\")), sp = factor(paste(row,spacing,stock,sep=\"\"))) # Due to 'spacing', the plots are different sizes, but the following layout # shows the relative position of the plots and treatments. Note that the # 'spacing' treatments are not contiguous in some reps. libs(desplot) desplot(dat, spacing~row*pos, col=stock, cex=1, num=gen, # aspect unknown main=\"archbold.apple\") libs(lme4, lucid) m1 <- lmer(yield ~ -1 + trt + (1|rep/mp/sp), dat) vc(m1) # Variances/means on Cornelius, page 59 ## grp var1 var2 vcov sdcor ## sp:(mp:rep) (Intercept) 193.3 13.9 ## mp:rep (Intercept) 203.8 14.28 ## rep (Intercept) 197.3 14.05 ## Residual 1015 31.86 } # }"},{"path":"/reference/ars.earlywhitecorn96.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","title":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","text":"Multi-environment trial early white food corn 60 white hybrids.","code":""},{"path":"/reference/ars.earlywhitecorn96.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","text":"data frame 540 observations following 9 variables. loc location, 9 levels gen gen, 60 levels yield yield, bu/ac stand stand, percent rootlodge root lodging, percent stalklodge stalk lodging, percent earht ear height, inches flower days flower moisture moisture, percent","code":""},{"path":"/reference/ars.earlywhitecorn96.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","text":"Data average 3 replications. Yields measured plot converted bushels / acre adjusted 15.5 percent moisture. Stand expressed percentage optimum plant stand. Lodging expressed percentage total plants hybrid. Ear height measured soil level top ear leaf collar. Heights expressed inches. Days flowering number days planting mid-tassel mid-silk. Moisture grain measured harvest.","code":""},{"path":"/reference/ars.earlywhitecorn96.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","text":"L. Darrah, R. Lundquist, D. West, C. Poneleit, B. Barry, B. Zehr, . Bockholt, L. Maddux, K. Ziegler, P. Martin. (1996). White Food Corn 1996 Performance Tests. Agricultural Research Service Special Report 502.","code":""},{"path":"/reference/ars.earlywhitecorn96.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of early white food corn — ars.earlywhitecorn96","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ars.earlywhitecorn96) dat <- ars.earlywhitecorn96 libs(lattice) # These views emphasize differences between locations dotplot(gen~yield, dat, group=loc, auto.key=list(columns=3), main=\"ars.earlywhitecorn96\") ## dotplot(gen~stalklodge, dat, group=loc, auto.key=list(columns=3), ## main=\"ars.earlywhitecorn96\") splom(~dat[,3:9], group=dat$loc, auto.key=list(columns=3), main=\"ars.earlywhitecorn96\") # MANOVA m1 <- manova(cbind(yield,earht,moisture) ~ gen + loc, dat) m1 summary(m1) } # }"},{"path":"/reference/australia.soybean.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of soybean in Australia — australia.soybean","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"Yield traits 58 varieties soybeans, grown four locations across two years Australia. four-way data Year x Loc x Gen x Trait.","code":""},{"path":"/reference/australia.soybean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"data frame 464 observations following 10 variables. env environment, 8 levels, first character location last two characters year loc location year year gen genotype soybeans, 1-58 yield yield, metric tons / hectare height height (meters) lodging lodging size seed size, (millimeters) protein protein (percentage) oil oil (percentage)","code":""},{"path":"/reference/australia.soybean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"Measurement available four locations Queensland, Australia two consecutive years 1970, 1971. 58 different genotypes soybeans consisted 43 lines (40 local Australian selections cross, two parents, one used parent earlier trials) 15 lines 12 US. Lines 1-40 local Australian selections Mamloxi (CPI 172) Avoyelles (CPI 15939). Note data Basford Tukey book. values line 58 Nambour 1970 Redland Bay 1971 incorrectly listed page 477 20.490 15.070. 17.350 13.000, respectively. data set made available , values corrected. Used permission Kaye Basford, Pieter Kroonenberg.","code":""},{"path":"/reference/australia.soybean.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"Basford, K. E., Tukey, J. W. (1999). Graphical analysis multiresponse data illustrated plant breeding trial. Chapman Hall/CRC. Retrieved : https://three-mode.leidenuniv.nl/data/soybeaninf.htm","code":""},{"path":"/reference/australia.soybean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"K E Basford (1982). Use Multidimensional Scaling Analysing Multi-Attribute Genotype Response Across Environments, Aust J Agric Res, 33, 473–480. Kroonenberg, P. M., & Basford, K. E. B. (1989). investigation multi-attribute genotype response across environments using three-mode principal component analysis. Euphytica, 44, 109–123. Marcin Kozak (2010). Use parallel coordinate plots multi-response selection interesting genotypes. Communications Biometry Crop Science, 5, 83-95.","code":""},{"path":"/reference/australia.soybean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of soybean in Australia — australia.soybean","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(australia.soybean) dat <- australia.soybean libs(reshape2) dm <- melt(dat, id.var=c('env', 'year','loc','gen')) # Joint plot of genotypes & traits. Similar to Figure 1 of Kroonenberg 1989 dmat <- acast(dm, gen~variable, fun=mean) dmat <- scale(dmat) biplot(princomp(dmat), main=\"australia.soybean trait x gen biplot\", cex=.75) # Figure 1 of Kozak 2010, lines 44-58 libs(reshape2, lattice, latticeExtra) data(australia.soybean) dat <- australia.soybean dat <- melt(dat, id.var=c('env', 'year','loc','gen')) dat <- acast(dat, gen~variable, fun=mean) dat <- scale(dat) dat <- as.data.frame(dat)[,c(2:6,1)] dat$gen <- rownames(dat) # data for the graphic by Kozak dat2 <- dat[44:58,] dat3 <- subset(dat2, is.element(gen, c(\"G48\",\"G49\",\"G50\",\"G51\"))) parallelplot( ~ dat3[,1:6]|dat3$gen, main=\"australia.soybean\", as.table=TRUE, horiz=FALSE) + parallelplot( ~ dat2[,1:6], horiz=FALSE, col=\"gray80\") + parallelplot( ~ dat3[,1:6]|dat3$gen, as.table=TRUE, horiz=FALSE, lwd=2) } # }"},{"path":"/reference/bachmaier.nitrogen.html","id":null,"dir":"Reference","previous_headings":"","what":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"Trial wheat nitrogen fertilizer two fertility zones","code":""},{"path":"/reference/bachmaier.nitrogen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"","code":"data(\"bachmaier.nitrogen\")"},{"path":"/reference/bachmaier.nitrogen.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"data frame 88 observations following 3 variables. nitro nitrogen fertilizer, kg/ha yield wheat yield, Mg/ha zone fertility zone","code":""},{"path":"/reference/bachmaier.nitrogen.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"Data wheat fertilizer experiment Germany two yield zones. zone, design RCB 4 blocks 11 nitrogen levels. yield plot measured. Electronic data originally downloaded http://www.tec.wzw.tum.de/bachmaier/vino.zip (longer available).","code":""},{"path":"/reference/bachmaier.nitrogen.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"Bachmaier, Martin. 2009. Confidence Set X-Coordinate Quadratic Regression Model Given Gradient. Statistical Papers 50: 649–60. https://doi.org/10.1007/s00362-007-0104-1.","code":""},{"path":"/reference/bachmaier.nitrogen.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"Bachmaier, Martin. Test confidence set difference x-coordinates vertices two quadratic regression models. Stat Papers (2010) 51:285–296, https://doi.org/10.1007/s00362-008-0159-7","code":""},{"path":"/reference/bachmaier.nitrogen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trial of wheat with nitrogen fertilizer in two fertility zones — bachmaier.nitrogen","text":"","code":"library(agridat) data(bachmaier.nitrogen) dat <- bachmaier.nitrogen # Fit a quadratic model for the low-fertility zone dlow <- subset(dat, zone==\"low\") m1 <- lm(yield ~ nitro + I(nitro^2), dlow) # Slope of tangent line for economic optimum m <- .005454 # = (N 0.60 euro/kg) / (wheat 110 euro/Mg) # x-value of tangent point b1 <- coef(m1)[2] b2 <- coef(m1)[3] opt.bach <- (m-b1)/(2*b2) round(opt.bach, 0) #> nitro #> 199 # conf int for x value of tangent point round(vcovs <- vcov(m1), 7) #> (Intercept) nitro I(nitro^2) #> (Intercept) 0.1346512 -0.0016680 4.7e-06 #> nitro -0.0016680 0.0000295 -1.0e-07 #> I(nitro^2) 0.0000047 -0.0000001 0.0e+00 b1b1 <- vcovs[2,2] # estimated var of b1 b1b2 <- vcovs[2,3] # estimated cov of b1,b2 b2b2 <- vcovs[3,3] tval <- qt(1 - 0.05/2, nrow(dlow)-3) A <- b2^2 - b2b2 * tval^2 B <- (b1-m)*b2 - b1b2 * tval^2 C <- ((b1-m)^2 - b1b1 * tval^2)/4 D <- B^2 - 4*A*C x.lo <- -2*C / (B-sqrt(B^2-4*A*C)) x.hi <- (-B + sqrt(B^2-4*A*C))/(2*A) ci.bach <- c(x.lo, x.hi) round(ci.bach,0) # 95% CI 173,260 Matches Bachmaier #> nitro nitro #> 173 260 # Plot raw data, fitted quadratic, optimum, conf int plot(yield~nitro, dlow) p1 <- data.frame(nitro=seq(0,260, by=1)) p1$pred <- predict(m1, new=p1) lines(pred~nitro, p1) abline(v=opt.bach, col=\"blue\") abline(v=ci.bach, col=\"skyblue\") title(\"Economic optimum with 95 pct confidence interval\")"},{"path":"/reference/bailey.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"Uniformity trial cotton Egypt 1921-1923.","code":""},{"path":"/reference/bailey.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"","code":"data(\"bailey.cotton.uniformity\")"},{"path":"/reference/bailey.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"data frame 794 observations following 5 variables. row row ordinate col column ordinate yield yield, rotls year year loc location","code":""},{"path":"/reference/bailey.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"Two pickings taken. weights seeds cotton first second pickings totaled. Yields measured \"rotl\", \"order pound\". Layout Sakha Gemmeiza (page 9): Total area 4.86 feddans. bed 20 ridges 7 m , total dimension 15 m x 7 m. Add 1.5m irrigation channel. Center--center distances 15m x 8.5m. Charts 3 & 5 show yield \"Selected Average Plants\". data used . Chart 1: Sakha 1921, 8 x 20. Bed yield rotls. Length 20 ridges * .75 m = 15m. Width = 7m. Chart 2: Gemmeiza 1921, 8 x 20. Chart 3: Total S..P. yield grams. (used ) Chart 4: Gemmeiza 1922, 8 x 20. Chart 5: Total S..P. yield grams. (used ) Layout Giza (page 10) Beds 8 ridges 7 m , total dimension 6m x 7m. Add 1.5m irrigation channel. Center--center distance 6m x 8.5m Chart 6 - Giza 1921, 14 x 11 = 154 plots Chart 7 - Giza 1923, 20 x 8 = 160 plots Bailey said results Giza 1921 suitable reliability experiments. Data typed proofread KW 2023.01.11","code":""},{"path":"/reference/bailey.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"Bailey, M. ., Trought, T. (1926). account experiments carried determine experimental error field trials cotton Egypt. Egypt Ministry Agriculture, Technical Science Service Bulletin 63, Min. Agriculture Egypt Technical Science Bulletin 63. https://www.google.com/books/edition/Bulletin/xBQlAQAAIAAJ?pg=PA46-IA205","code":""},{"path":"/reference/bailey.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"None","code":""},{"path":"/reference/bailey.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton in Egypt — bailey.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bailey.cotton.uniformity) dat <- bailey.cotton.uniformity dat <- transform(dat, env=paste(year,loc)) # Data check. Matches Bailey 1926 Table 1. 28.13, , 46.02, 31.74, 13.52 libs(dplyr) # dat libs(desplot) desplot(dat, yield ~ col*row|env, main=\"bailey.cotton.uniformity\") # The yield scales are quite different at each loc, and the dimensions # are different, so plot each location separately. # Note: Bailey does not say if plots are 7x15 meters, or 15x7 meters. # The choices here seem most likely in our opinion. desplot(dat, yield ~ col*row, subset= env==\"1921 Sakha\", main=\"1921 Sakha\", aspect=(20*8.5)/(8*15)) desplot(dat, yield ~ col*row, subset= env==\"1921 Gemmeiza\", main=\"1921 Gemmeiza\", aspect=(20*8.5)/(8*15)) desplot(dat, yield ~ col*row, subset= env==\"1922 Gemmeiza\", main=\"1922 Gemmeiza\", aspect=(20*8.5)/(8*15)) desplot(dat, yield ~ col*row, subset= env==\"1921 Giza\", main=\"1921 Giza\", aspect=(11*6)/(14*8.5)) # 1923 Giza has alternately hi/lo yield rows. Not noticed by Bailey. desplot(dat, yield ~ col*row, subset= env==\"1923 Giza\", main=\"1923 Giza\", aspect=(20*6)/(8*8.5)) } # }"},{"path":"/reference/baker.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","title":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","text":"Uniformity trials barley Davis, California, 1925-1935, 10 years ground.","code":""},{"path":"/reference/baker.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","text":"data frame 570 observations following 4 variables. row row col column year year yield yield, pounds/acre","code":""},{"path":"/reference/baker.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","text":"Ten years uniformity trials sown ground. Baker (1952) shows map field, gravel subsoil extended upper right corner diagonally lower-center. part field lower yields 10-year average map. Plot 41 1928 missing. Field width: 19 plots = 827 ft Field length: 3 plots * 161 ft + 2 alleys * 15 feet = 513 ft","code":""},{"path":"/reference/baker.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","text":"Baker, GA Huberty, MR Veihmeyer, FJ. (1952) uniformity trial unirrigated barley ten years' duration. Agronomy Journal, 44, 267-270. https://doi.org/10.2134/agronj1952.00021962004400050011x","code":""},{"path":"/reference/baker.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of barley, 10 years on same ground — baker.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(baker.barley.uniformity) dat <- baker.barley.uniformity # Ten-year average dat2 <- aggregate(yield ~ row*col, data=dat, FUN=mean, na.rm=TRUE) libs(desplot) desplot(dat, yield~col*row|year, aspect = 513/827, # true aspect main=\"baker.barley.uniformity - heatmaps by year\") desplot(dat2, yield~col*row, aspect = 513/827, # true aspect main=\"baker.barley.uniformity - heatmap of 10-year average\") # Note low yield in upper right, slanting to left a bit due to sandy soil # as shown in Baker figure 1. # Baker fig 2, stdev vs mean dat3 <- aggregate(yield ~ row*col, data=dat, FUN=sd, na.rm=TRUE) plot(dat2$yield, dat3$yield, xlab=\"Mean yield\", ylab=\"Std Dev yield\", main=\"baker.barley.uniformity\") # Baker table 4, correlation of plots across years # libs(reshape2) # mat <- acast(dat, row+col~year) # round(cor(mat, use='pair'),2) } # }"},{"path":"/reference/baker.strawberry.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of strawberry — baker.strawberry.uniformity","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"Uniformity trial strawberry","code":""},{"path":"/reference/baker.strawberry.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"","code":"data(\"baker.strawberry.uniformity\")"},{"path":"/reference/baker.strawberry.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"data frame 700 observations following 4 variables. trial Factor trial row row ordinate col column ordinate yield yield per plant/plot grams","code":""},{"path":"/reference/baker.strawberry.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"Trial T1: 200 plants grown two double-row beds Davis, California, 1946. rows 1 foot apart. beds 42 inches apart. plants 10 inches apart within row, row consisting 50 plants. Field length: 50 plants * 10 inches = 500 inches. Field width: 12 + 42 + 12 = 66 inches. layout experiment Table 1 shows 4 columns. 12 inches column 1 column 2, 42 inches, 12 inches column 3 column 4. data R package, added 3 right two columns index values indicate layout. (3.5, want integer). Trial T2: 500 plants grown single beds. beds 30 inches apart. bed 50 plants long 10 inches plants. Field length: 50 plants * 10 = 500 . Field width: 10 beds * 30 = 300 .","code":""},{"path":"/reference/baker.strawberry.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"G. . Baker R. E. Baker (1953). Strawberry Uniformity Yield Trials. Biometrics, 9, 412-421. https://doi.org/10.2307/3001713","code":""},{"path":"/reference/baker.strawberry.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"None","code":""},{"path":"/reference/baker.strawberry.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of strawberry — baker.strawberry.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(baker.strawberry.uniformity) dat <- baker.strawberry.uniformity # Match mean and cv of Baker p 414. libs(dplyr) dat <- group_by(dat, trial) summarize(dat, mn=mean(yield), cv=sd(yield)/mean(yield)) libs(desplot) desplot(dat, yield ~ col*row, subset=trial==\"T1\", flip=TRUE, aspect=500/66, tick=TRUE, main=\"baker.strawberry.uniformity - trial T1\") desplot(dat, yield ~ col*row, subset=trial==\"T2\", flip=TRUE, aspect=500/300, tick=TRUE, main=\"baker.strawberry.uniformity - trial T2\") } # }"},{"path":"/reference/baker.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — baker.wheat.uniformity","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"Uniformity trial wheat","code":""},{"path":"/reference/baker.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"","code":"data(\"baker.wheat.uniformity\")"},{"path":"/reference/baker.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"data frame 225 observations following 3 variables. row row col col yield yield (grams)","code":""},{"path":"/reference/baker.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"Data collected 1939-1940. trial consists sixteen 40 ft. x 40 ft. blocks subdivided nine plots . data secured 1939-1940 White Federation wheat. design experiment square alleys 20 feet wide blocks. plots 10 feet long two guard rows side. Morning glories infested middle two columns blocks, uniformly blocks affected. data include missing values alleys field map approximately correct shape size. Field width: 4 blocks 40 feet + 3 alleys 20 feet = 220 feet. Field length: 4 blocks 40 feet + 3 alleys 20 feet = 220 feet.","code":""},{"path":"/reference/baker.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"G. . Baker, E. B. Roessler (1957). Implications uniformity trial small plots wheat. Hilgardia, 27, 183-188. https://hilgardia.ucanr.edu/Abstract/?=hilg.v27n05p183 https://doi.org/10.3733/hilg.v27n05p183","code":""},{"path":"/reference/baker.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"None","code":""},{"path":"/reference/baker.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — baker.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(baker.wheat.uniformity) dat <- baker.wheat.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=1, main=\"baker.wheat.uniformity\") } # }"},{"path":"/reference/bancroft.peanut.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of peanuts — bancroft.peanut.uniformity","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"Uniformity trial peanuts Alabama, 1946.","code":""},{"path":"/reference/bancroft.peanut.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"","code":"data(\"bancroft.peanut.uniformity\")"},{"path":"/reference/bancroft.peanut.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"data frame 216 observations following 5 variables. row row col column yield yield, pounds per plot block block","code":""},{"path":"/reference/bancroft.peanut.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"data obtained two parts field, located Wiregrass Substation, Headland, Alabama, USA. part 18 rows, 3 feet wide, 100 feet long. Plots harvested 1946. Green weights pounds recorded. plot 16.66 linear feet row 3 feet width, 50 sq feet. Field width: 6 plots * 16.66 feet = 100 feet Field length: 18 plots * 3 feet = 54 feet Conclusions: Based relative efficiencies, increasing size plot along row better across row. Narrow, rectangular plots efficient.","code":""},{"path":"/reference/bancroft.peanut.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"Bancroft, T. . et a1., (1948). Size Shape Plots Distribution Plot Yield Field Experiments Peanuts. Alabama Agricultural Experiment Station Progress Report, sec. 39. Table 4, page 6. https://aurora.auburn.edu/bitstream/handle/11200/1345/0477PROG.pdf;sequence=1","code":""},{"path":"/reference/bancroft.peanut.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"None","code":""},{"path":"/reference/bancroft.peanut.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of peanuts — bancroft.peanut.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bancroft.peanut.uniformity) dat <- bancroft.peanut.uniformity # match means Bancroft page 3 ## dat ## # A tibble: 2 x 2 ## block mn ## ## 1 B1 2.46 ## 2 B2 2.05 libs(desplot) desplot(dat, yield ~ col*row|block, flip=TRUE, aspect=(18*3)/(6*16.66), # true aspect main=\"bancroft.peanut.uniformity\") } # }"},{"path":"/reference/barrero.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize in Texas. — barrero.maize","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"Multi-environment trial maize Texas.","code":""},{"path":"/reference/barrero.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"","code":"data(\"barrero.maize\")"},{"path":"/reference/barrero.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"data frame 14568 observations following 15 variables. year year testing, 2000-2010 yor year release, 2000-2010 loc location, 16 places Texas env environment (year+loc), 107 levels rep replicate, 1-4 gen genotype, 847 levels daystoflower numeric plantheight plant height, cm earheight ear height, cm population plants per hectare lodged percent plants lodged moisture moisture percent testweight test weight kg/ha yield yield, Mt/ha","code":""},{"path":"/reference/barrero.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"large (14500 records), multi-year, multi-location, 10-trait dataset Texas AgriLife Corn Performance Trials. data 2-row plots approximately 36in wide 25 feet long. Barrero et al. used data estimate genetic gain maize hybrids 10-year period time. Used permission Seth Murray.","code":""},{"path":"/reference/barrero.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"Barrero, Ivan D. et al. (2013). multi-environment trial analysis shows slight grain yield improvement Texas commercial maize. Field Crops Research, 149, Pages 167-176. https://doi.org/10.1016/j.fcr.2013.04.017","code":""},{"path":"/reference/barrero.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"None.","code":""},{"path":"/reference/barrero.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize in Texas. — barrero.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(barrero.maize) dat <- barrero.maize library(lattice) bwplot(yield ~ factor(year)|loc, dat, main=\"barrero.maize - Yield trends by loc\", scales=list(x=list(rot=90))) # Table 6 of Barrero. Model equation 1. if(require(\"asreml\", quietly=TRUE)){ libs(dplyr,lucid) dat <- arrange(dat, env) dat <- mutate(dat, yearf=factor(year), env=factor(env), loc=factor(loc), gen=factor(gen), rep=factor(rep)) m1 <- asreml(yield ~ loc + yearf + loc:yearf, data=dat, random = ~ gen + rep:loc:yearf + gen:yearf + gen:loc + gen:loc:yearf, residual = ~ dsum( ~ units|env), workspace=\"500mb\") # Variance components for yield match Barrero table 6. lucid::vc(m1)[1:5,] ## effect component std.error z.ratio bound ## rep:loc:yearf 0.111 0.01092 10 P 0 ## gen 0.505 0.03988 13 P 0 ## gen:yearf 0.05157 0.01472 3.5 P 0 ## gen:loc 0.02283 0.0152 1.5 P 0.2 ## gen:loc:yearf 0.2068 0.01806 11 P 0 summary(vc(m1)[6:112,\"component\"]) # Means match last row of table 6 ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.1286 0.3577 0.5571 0.8330 1.0322 2.9867 } } # }"},{"path":"/reference/batchelor.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"Uniformity trials apples, lemons, oranges, walnuts, California & Utah, 1915-1918.","code":""},{"path":"/reference/batchelor.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"dataset following format row row col column yield yield per tree pounds","code":""},{"path":"/reference/batchelor.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"trees affected disease eliminated yield replaced average eight surrounding trees. following details Batchelor (1918). Jonathan Apples \"apple records obtained 10-year old Jonathan apple orchard located Providence, Utah. surface soil orchard uniform appearances except extreme eastern edge, percentage gravel increases slightly. trees planted 16 feet apart, east west, 30 feet apart north south.\" Note: orientation field given paper, fields paper north top, assumed true field well. Yields may 1916. Field width: 8 trees * 16 feet = 128 feet Field length: 28 rows * 30 feet = 840 feet Eureka Lemon lemon (Citrus limonia) tree yields obtained grove 364 23-year-old trees, located Upland, California. records extend October 1, 1915, October 1, 1916. grove consists 14 rows 23-year-old trees, extending north south, 26 trees row, planted 24 24 feet apart. grove presents uniform appearance consideration [paper]. land practically level, soil apparently uniform texture. records show grouping several low-yielding trees; yet field observation gives one impression grove whole remarkably uniform. Field width: 14 trees * 24 feet = 336 feet Field length: 26 trees * 24 feet = 624 feet Navel 1 Arlington records 1915-16 yields one thousand 24-year-old navel-orange trees near Arlington station, Riverside, California. grove consists 20 rows trees north south, 50 trees row, planted 22 22 feet. study records shows certain distinct high- low-yielding areas. northeast corner south end contain notably high-yielding trees. north two-thirds west side contains large number low-yielding trees. areas apparently correlated soil variation. Variations tree tree also occur, cause evident. variations, present every orchard, bring uncertainty results offield experiments. Field width: 20 trees * 22 feet = 440 feet Field length: 50 trees * 22 feet = 1100 feet Navel 2 Antelope navel-orange grove later referred Antelope Heights navels plantation 480 ten-yearold trees planted 22 22 feet, located Naranjo, California. yields 1916. general appearance trees gives visual impression uniformity greater comparison individual tree production substantiates. Field width: 15 trees * 22 feet = 330 feet Field length: 33 trees * 22 feet = 726 feet Valencia Orange Valencia orange grove composed 240 15-year-old trees, planted 21 feet 6 inches 22 feet 6 inches, located Villa Park, California. yields obtained 1916. Field width: 12 rows * 22 feet = 264 feet Field length: 20 rows * 22 feet = 440 feet Walnut walnut (Juglans regia) yields obtained seasons 1915 1916 24-year-old Santa Barbara softshell seedling grove, located Whittier, California. [Note, yields appear 1915 yields.] planting laid 10 trees wide 32 trees long, entirely surrounded additional walnut plantings, except part one side adjacent orange grove. trees planted square system, 50 feet apart. Field width: 10 trees * 50 feet = 500 feet Field length: 32 trees * 50 feet = 1600 feet","code":""},{"path":"/reference/batchelor.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"L. D. Batchelor H. S. Reed. (1918). Relation variability yields fruit trees accuracy field trials. J. Agric. Res, 12, 245–283. https://books.google.com/books?id=Lil6AAAAMAAJ&lr&pg=PA245","code":""},{"path":"/reference/batchelor.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667","code":""},{"path":"/reference/batchelor.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of apples, lemons, oranges, and walnuts — batchelor.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(desplot) # Apple data(batchelor.apple.uniformity) desplot(batchelor.apple.uniformity, yield~col*row, aspect=840/128, tick=TRUE, # true aspect main=\"batchelor.apple.uniformity\") # Lemon data(batchelor.lemon.uniformity) desplot(batchelor.lemon.uniformity, yield~col*row, aspect=624/336, # true aspect main=\"batchelor.lemon.uniformity\") # Navel1 (Arlington) data(batchelor.navel1.uniformity) desplot(batchelor.navel1.uniformity, yield~col*row, aspect=1100/440, # true aspect main=\"batchelor.navel1.uniformity - Arlington\") # Navel2 (Antelope) data(batchelor.navel2.uniformity) desplot(batchelor.navel2.uniformity, yield~col*row, aspect=726/330, # true aspect main=\"batchelor.navel2.uniformity - Antelope\") # Valencia data(batchelor.valencia.uniformity) desplot(batchelor.valencia.uniformity, yield~col*row, aspect=440/264, # true aspect main=\"batchelor.valencia.uniformity\") # Walnut data(batchelor.walnut.uniformity) desplot(batchelor.walnut.uniformity, yield~col*row, aspect=1600/500, # true aspect main=\"batchelor.walnut.uniformity\") } # }"},{"path":"/reference/battese.survey.html","id":null,"dir":"Reference","previous_headings":"","what":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"Survey satellite data corn soy areas Iowa","code":""},{"path":"/reference/battese.survey.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"","code":"data(\"battese.survey\")"},{"path":"/reference/battese.survey.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"data frame 37 observations following 9 variables. county county name segment sample segment number (within county) countysegs number segments county cornhect hectares corn segment soyhect hectares soy cornpix pixels corn segment soypix pixels soy cornmean county mean corn pixels per segment soymean county mean soy pixels per segment","code":""},{"path":"/reference/battese.survey.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"data 12 counties north-central Iowa 1978. USDA determined area soybeans 37 area sampling units (called 'segments'). segment one square mile (259 hectares). number pixels classified corn soybeans came Landsat images obtained Aug/Sep 1978. pixel represents approximately 0.45 hectares. Data originally compiled USDA. data also available R packages: 'rsae::landsat' 'JoSAE::landsat'.","code":""},{"path":"/reference/battese.survey.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"Battese, George E Harter, Rachel M Fuller, Wayne . (1988). error-components model prediction county crop areas using survey satellite data. Journal American Statistical Association, 83, 28-36. https://doi.org/10.2307/2288915 Battese (1982) preprint version. https://www.une.edu.au/__data/assets/pdf_file/0017/15542/emetwp15.pdf","code":""},{"path":"/reference/battese.survey.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"Pushpal K Mukhopadhyay Allen McDowell. (2011). Small Area Estimation Survey Data Analysis Using SAS Software SAS Global Forum 2011.","code":""},{"path":"/reference/battese.survey.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Survey and satellite data for corn and soy areas in Iowa — battese.survey","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(battese.survey) dat <- battese.survey # Battese fig 1 & 2. Corn plot shows outlier in Hardin county libs(lattice) dat <- dat[order(dat$cornpix),] xyplot(cornhect ~ cornpix, data=dat, group=county, type=c('p','l'), main=\"battese.survey\", xlab=\"Pixels of corn\", ylab=\"Hectares of corn\", auto.key=list(columns=3)) dat <- dat[order(dat$soypix),] xyplot(soyhect ~ soypix, data=dat, group=county, type=c('p','l'), main=\"battese.survey\", xlab=\"Pixels of soy\", ylab=\"Hectares of soy\", auto.key=list(columns=3)) libs(lme4, lucid) # Fit the models of Battese 1982, p.18. Results match m1 <- lmer(cornhect ~ 1 + cornpix + (1|county), data=dat) fixef(m1) ## (Intercept) cornpix ## 5.4661899 0.3878358 vc(m1) ## grp var1 var2 vcov sdcor ## county (Intercept) 62.83 7.926 ## Residual 290.4 17.04 m2 <- lmer(soyhect ~ 1 + soypix + (1|county), data=dat) fixef(m2) ## (Intercept) soypix ## -3.8223566 0.4756781 vc(m2) ## grp var1 var2 vcov sdcor ## county (Intercept) 239.2 15.47 ## Residual 180 13.42 # Predict for Humboldt county as in Battese 1982 table 2 5.4662+.3878*290.74 # 118.2152 # mu_i^0 5.4662+.3878*290.74+ -2.8744 # 115.3408 # mu_i^gamma (185.35+116.43)/2 # 150.89 # y_i bar # Survey regression estimator of Battese 1988 # Delete the outlier dat2 <- subset(dat, !(county==\"Hardin\" & soyhect < 30)) # Results match top-right of Battese 1988, p. 33 m3 <- lmer(cornhect ~ cornpix + soypix + (1|county), data=dat2) fixef(m3) ## (Intercept) cornpix soypix ## 51.0703979 0.3287217 -0.1345684 vc(m3) ## grp var1 var2 vcov sdcor ## county (Intercept) 140 11.83 ## Residual 147.3 12.14 m4 <- lmer(soyhect ~ cornpix + soypix + (1|county), data=dat2) fixef(m4) ## (Intercept) cornpix soypix ## -15.59027098 0.02717639 0.49439320 vc(m4) ## grp var1 var2 vcov sdcor ## county (Intercept) 247.5 15.73 ## Residual 190.5 13.8 } # }"},{"path":"/reference/beall.webworms.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"Counts webworms beet field, insecticide treatments.","code":""},{"path":"/reference/beall.webworms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"","code":"data(\"beall.webworms\")"},{"path":"/reference/beall.webworms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"data frame 1300 observations following 7 variables. row row col column y count webworms block block trt treatment spray spray treatment yes/lead lead treatment yes/","code":""},{"path":"/reference/beall.webworms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"beet webworm lays egg masses small 1 egg, seldom exceeding 5 eggs. larvae can move freely, usually mature plant hatch. plot contained 25 unit areas, 1 row 3 feet long. row width 22 inches. arrangement plots within blocks seems certain, arrangement blocks/treatments certain, since authors say \"since plots 5 units long 5 wide practicable combine groups 5 one direction \". Treatment 1 = None. Treatment 2 = Contact spray. Treatment 3 = Lead arsenate. Treatment 4 = spray, lead arsenate.","code":""},{"path":"/reference/beall.webworms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"Beall, Geoffrey (1940). fit significance contagious distributions applied observations larval insects. Ecology, 21, 460-474. Table 6. https://doi.org/10.2307/1930285","code":""},{"path":"/reference/beall.webworms.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"Michal Kosma et al. (2019). -dispersed count data crop agronomy research. Journal Agronomy Crop Science. https://doi.org/10.1111/jac.12333","code":""},{"path":"/reference/beall.webworms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts of webworms in a beet field, with insecticide treatments. — beall.webworms","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(beall.webworms) dat <- beall.webworms # Match Beall table 1 # with(dat, table(y,trt)) libs(lattice) histogram(~y|trt, data=dat, layout=c(1,4), as.table=TRUE, main=\"beall.webworms\") # Visualize Beall table 6. Block effects may exist, but barely. libs(desplot) grays <- colorRampPalette(c(\"white\",\"#252525\")) desplot(dat, y ~ col*row, col.regions=grays(10), at=0:10-0.5, out1=block, out2=trt, num=trt, flip=TRUE, # aspect unknown main=\"beall.webworms (count of worms)\") # Following plot suggests interaction is needed # with(dat, interaction.plot(spray, lead, y)) # Try the models of Kosma et al, Table 1. # Poisson model m1 <- glm(y ~ block + spray*lead, data=dat, family=\"poisson\") logLik(m1) # -1497.719 (df=16) # Negative binomial model # libs(MASS) # m2 <- glm.nb(y ~ block + spray*lead, data=dat) # logLik(m2) # -1478.341 (df=17) # # Conway=Maxwell-Poisson model (takes several minutes) # libs(spaMM) # # estimate nu parameter # m3 <- fitme(y ~ block + spray*lead, data=dat, family = COMPoisson()) # logLik(m3) # -1475.999 # # Kosma logLik(m3)=-1717 seems too big. Typo? Different model? } # }"},{"path":"/reference/beaven.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"Yields 8 barley varieties 1913.","code":""},{"path":"/reference/beaven.barley.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"","code":"data(\"beaven.barley\")"},{"path":"/reference/beaven.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"data frame 160 observations following 4 variables. row row col column gen genotype yield yield (grams)","code":""},{"path":"/reference/beaven.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"Eight races barley grown regular pattern plots. data prepared Richey (1926) text cleaner. plot planted 40 inches side, middle square 36 inches side harvested. Field width: 32 plots * 3 feet = 96 feet Field length: 5 plots * 3 feet = 15 feet","code":""},{"path":"/reference/beaven.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"Student. (1923). testing varieties cereals. Biometrika, 271-293. https://doi.org/10.1093/biomet/15.3-4.271","code":""},{"path":"/reference/beaven.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"Frederick D. Richey (1926). moving average basis measuring correlated variation agronomic experiments. Jour. Agr. Research, 32, 1161-1175.","code":""},{"path":"/reference/beaven.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yields of 8 barley varieties in 1913 as used by Student. — beaven.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(beaven.barley) dat <- beaven.barley # Match the means shown in Richey table IV tapply(dat$yield, dat$gen, mean) ## a b c d e f g h ## 298.080 300.710 318.685 295.260 306.410 276.475 304.605 271.820 # Compare to Student 1923, diagram I,II libs(desplot) desplot(dat, yield ~ col*row, aspect=15/96, # true aspect main=\"beaven.barley - variety trial\", text=gen) } # }"},{"path":"/reference/becker.chicken.html","id":null,"dir":"Reference","previous_headings":"","what":"Mating crosses of chickens — becker.chicken","title":"Mating crosses of chickens — becker.chicken","text":"Mating crosses chickens","code":""},{"path":"/reference/becker.chicken.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mating crosses of chickens — becker.chicken","text":"","code":"data(\"becker.chicken\")"},{"path":"/reference/becker.chicken.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mating crosses of chickens — becker.chicken","text":"data frame 45 observations following 3 variables. male male parent female female parent weight weight (g) 8 weeks","code":""},{"path":"/reference/becker.chicken.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mating crosses of chickens — becker.chicken","text":"large flock White Rock chickens, five male sires chosen mated three female dams, producing 3 female progeny. data body weights eight weeks age. Becker (1984) used data demonstrate calculation heritability.","code":""},{"path":"/reference/becker.chicken.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mating crosses of chickens — becker.chicken","text":"Walter . Becker (1984). Manual Quantitative Genetics, 4th ed. Page 83.","code":""},{"path":"/reference/becker.chicken.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mating crosses of chickens — becker.chicken","text":"None","code":""},{"path":"/reference/becker.chicken.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mating crosses of chickens — becker.chicken","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(becker.chicken) dat <- becker.chicken libs(lattice) dotplot(weight ~ female, data=dat, group=male, main=\"becker.chicken - progeny weight by M*F\", xlab=\"female parent\",ylab=\"progeny weight\", auto.key=list(columns=5)) # Sums match Becker # sum(dat$weight) # aggregate(weight ~ male + female, dat, FUN=sum) # Variance components libs(lme4,lucid) m1 <- lmer(weight ~ (1|male) + (1|female), data=dat) # vc(m1) ## grp var1 var2 vcov sdcor ## 1 female (Intercept) 1096 33.1 ## 2 male (Intercept) 776.8 27.87 ## 3 Residual 5524 74.32 # Calculate heritabilities # s2m <- 776 # variability for males # s2f <- 1095 # variability for females # s2w <- 5524 # variability within crosses # vp <- s2m + s2f + s2w # 7395 # 4*s2m/vp # .42 male heritability #4*s2f/vp # .59 female heritability } # }"},{"path":"/reference/beckett.maize.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"A uniformity trial of maize in Ghana. — beckett.maize.uniformity","title":"A uniformity trial of maize in Ghana. — beckett.maize.uniformity","text":"uniformity trial maize Ghana.","code":""},{"path":"/reference/beckett.maize.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A uniformity trial of maize in Ghana. — beckett.maize.uniformity","text":"","code":"data(\"beckett.maize.uniformity\")"},{"path":"/reference/beckett.maize.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A uniformity trial of maize in Ghana. — beckett.maize.uniformity","text":"data frame 83 observations following 8 variables. plot plot number row row ordinate col column ordinate germination germination count earnum number ears per plot earwt ear weight per plot yield grain yield per plot, pounds stalks number stalks per plot","code":""},{"path":"/reference/beckett.maize.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"A uniformity trial of maize in Ghana. — beckett.maize.uniformity","text":"Experiment Asuansi Experiment Station (Ghana). plot 1/40 acre, square shape, 33 x 33 feet. Seed sown 23 March 1928, four seeds per hole. Holes 3 feet 3 feet, giving 121 stands/hills per plot. Germination data collected 3-4 April. Counts stalks made 23 May, \"reliance attached figures\". Harvesting done 16-17 August. Due staff shortages, 15 plots chosen random measured ears yield. Field width: 6 plots * 33 feet = 198 feet Field length: 19 plots * 33 feet = 627 feet Data typed hand checked K.Wright 2024.12.06.","code":""},{"path":"/reference/beckett.maize.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A uniformity trial of maize in Ghana. — beckett.maize.uniformity","text":"Beckett, W.H.; Fletcher, S.R.B. (1929). uniformity trial maize. Gold Coast Dept Agric Bull 16: 222-226. https://babel.hathitrust.org/cgi/pt?id=coo.31924066682166&seq=470","code":""},{"path":"/reference/beckett.maize.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"A uniformity trial of maize in Ghana. — beckett.maize.uniformity","text":"None","code":""},{"path":"/reference/beckett.maize.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A uniformity trial of maize in Ghana. — beckett.maize.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ libs(agridat) data(beckett.maize.uniformity) dat <- beckett.maize.uniformity # QC. germination, earnum match published values. # stalks published value is 33091, but here 33101. (Data were hand-checked) colSums(dat) # Examine correlations. earwt,yield high cor pairs(dat[ , c(\"germination\",\"stalks\",\"earnum\",\"earwt\",\"yield\")]) libs(desplot) desplot(dat, germination ~ col*row, flip=TRUE, aspect=19/6, main=\"beckett.maize.uniformity - stalks\") } # }"},{"path":"/reference/belamkar.augmented.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"Multi-environment trial wheat Nebraska Augmented design","code":""},{"path":"/reference/belamkar.augmented.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"","code":"data(\"belamkar.augmented\")"},{"path":"/reference/belamkar.augmented.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"data frame 2700 observations following 9 variables. loc location rep replicate iblock incomplete block gen_new new genotype (1=yes, 0=) gen_check check genotype (0=) gen genotype name col column ordinate row row ordinate yield yield, bu/ac","code":""},{"path":"/reference/belamkar.augmented.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"experiment 8 locations 270 new, experimental lines (genotypes) 3 check lines. 10 incomplete blocks location. 2 replicate blocks Alliance 1 block locations. plot 3 m long 1.2 m wide. electronic data found supplement S4 downloaded https://doi.org/10.25387/g3.6249410 license data CC-4.0.","code":""},{"path":"/reference/belamkar.augmented.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"Vikas Belamkar, Mary J. Guttieri, Waseem Hussain, Diego Jarquín, Ibrahim El-basyoni, Jesse Poland, Aaron J. Lorenz, P. Stephen Baenziger (2018). Genomic Selection Preliminary Yield Trials Winter Wheat Breeding Program. G3 Genes|Genomes|Genetics, 8, Pages 2735–2747. https://doi.org/10.1534/g3.118.200415","code":""},{"path":"/reference/belamkar.augmented.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"data appear ASRtriala package: https://vsni.co.uk/free-software/asrtriala","code":""},{"path":"/reference/belamkar.augmented.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat with Augmented design — belamkar.augmented","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(belamkar.augmented) dat <- belamkar.augmented libs(desplot) desplot(dat, yield ~ col*row|loc, out1=rep, out2=iblock) # Experiment design showing check placement dat$gen_check <- factor(dat$gen_check) desplot(dat, gen_check ~ col*row|loc, out1=rep, out2=iblock, main=\"belamkar.augmented\") # Belamkar supplement S3 has R code for analysis if(require(\"asreml\", quietly=TRUE)){ library(asreml) # AR1xAR1 model to calculate BLUEs for a single loc d1 <- droplevels(subset(dat, loc==\"Lincoln\")) d1$colf <- factor(d1$col) d1$rowf <- factor(d1$row) d1$gen <- factor(d1$gen) d1$gen_check <- factor(d1$gen_check) d1 <- d1[order(d1$col),] d1 <- as.data.frame(d1) m1 <- asreml(fixed=yield ~ gen_check, data=d1, random = ~ gen_new:gen, residual = ~ar1(colf):ar1v(rowf) ) p1 <- predict(m1, classify=\"gen\") head(p1$pvals) } } # }"},{"path":"/reference/besag.bayesian.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of spring barley in United Kingdom — besag.bayesian","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"RCB experiment spring barley United Kingdom","code":""},{"path":"/reference/besag.bayesian.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"data frame 225 observations following 4 variables. col column (also blocking factor) row row yield yield gen variety/genotype","code":""},{"path":"/reference/besag.bayesian.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"RCB design, column one rep. Used permission David Higdon.","code":""},{"path":"/reference/besag.bayesian.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"Besag, J. E., Green, P. J., Higdon, D. Mengersen, K. (1995). Bayesian computation stochastic systems. Statistical Science, 10, 3-66. https://www.jstor.org/stable/2246224","code":""},{"path":"/reference/besag.bayesian.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"Davison, . C. 2003. Statistical Models. Cambridge University Press. Pages 534-535.","code":""},{"path":"/reference/besag.bayesian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of spring barley in United Kingdom — besag.bayesian","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.bayesian) dat <- besag.bayesian # Yield values were scaled to unit variance # var(dat$yield, na.rm=TRUE) # .999 # Besag Fig 2. Reverse row numbers to match Besag, Davison dat$rrow <- 76 - dat$row libs(lattice) xyplot(yield ~ rrow|col, dat, layout=c(1,3), type='s', xlab=\"row\", ylab=\"yield\", main=\"besag.bayesian\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml, lucid) # Use asreml to fit a model with AR1 gradient in rows dat <- transform(dat, cf=factor(col), rf=factor(rrow)) m1 <- asreml(yield ~ -1 + gen, data=dat, random= ~ rf) m1 <- update(m1, random= ~ ar1v(rf)) m1 <- update(m1) m1 <- update(m1) m1 <- update(m1) lucid::vc(m1) # Visualize trends, similar to Besag figure 2. # Need 'as.vector' because asreml uses a named vector dat$res <- unname(m1$resid) dat$geneff <- coef(m1)$fixed[as.numeric(dat$gen)] dat <- transform(dat, fert=yield-geneff-res) libs(lattice) xyplot(geneff ~ rrow|col, dat, layout=c(1,3), type='s', main=\"besag.bayesian - Variety effects\", ylim=c(5,15 )) xyplot(fert ~ rrow|col, dat, layout=c(1,3), type='s', main=\"besag.bayesian - Fertility\", ylim=c(-2,2)) xyplot(res ~ rrow|col, dat, layout=c(1,3), type='s', main=\"besag.bayesian - Residuals\", ylim=c(-4,4)) } } # }"},{"path":"/reference/besag.beans.html","id":null,"dir":"Reference","previous_headings":"","what":"Competition experiment in beans with height measurements — besag.beans","title":"Competition experiment in beans with height measurements — besag.beans","text":"Competition experiment beans height measurements","code":""},{"path":"/reference/besag.beans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Competition experiment in beans with height measurements — besag.beans","text":"","code":"data(\"besag.beans\")"},{"path":"/reference/besag.beans.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Competition experiment in beans with height measurements — besag.beans","text":"data frame 152 observations following 6 variables. gen genotype / variety height plot height, cm yield plot yield, g row row / block rep replicate factor col column","code":""},{"path":"/reference/besag.beans.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Competition experiment in beans with height measurements — besag.beans","text":"Field beans regular height grown beside shorter varieties. block, variety occurred left-side neighbor right-side neighbor every variety (including ). Border plots placed ends block. block 38 adjacent plots. plot one row, 3 meters long 50 cm spacing rows. gaps plots. Spacing plants 6.7 cm. Four blocks (rows) used, six replicates. Plot yield height recorded. Kempton Lockwood used models adjusted yield according difference height neighboring plots. Field length: 4 plots * 3m = 12m Field width: 38 plots * 0.5 m = 19m","code":""},{"path":"/reference/besag.beans.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Competition experiment in beans with height measurements — besag.beans","text":"Julian Besag Rob Kempton (1986). Statistical Analysis Field Experiments Using Neighbouring Plots. Biometrics, 42, 231-251. Table 6. https://doi.org/10.2307/2531047","code":""},{"path":"/reference/besag.beans.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Competition experiment in beans with height measurements — besag.beans","text":"Kempton, RA Lockwood, G. (1984). Inter-plot competition variety trials field beans (Vicia faba L.). Journal Agricultural Science, 103, 293–302.","code":""},{"path":"/reference/besag.beans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Competition experiment in beans with height measurements — besag.beans","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.beans) dat = besag.beans libs(desplot) desplot(dat, yield ~ col*row, aspect=12/19, out1=row, out2=rep, num=gen, cex=1, # true aspect main=\"besag.beans\") libs(reshape2) # Add a covariate = excess height of neighbors mat <- acast(dat, row~col, value.var='height') mat2 <- matrix(NA, nrow=4, ncol=38) mat2[,2:37] <- (mat[,1:36] + mat[,3:38] - 2*mat[,2:37]) dat2 <- melt(mat2) colnames(dat2) <- c('row','col','cov') dat <- merge(dat, dat2) # Drop border plots dat <- subset(dat, rep != 'R0') libs(lattice) # Plot yield vs neighbors height advantage xyplot(yield~cov, data=dat, group=gen, main=\"besag.beans\", xlab=\"Mean excess heights of neighbor plots\", auto.key=list(columns=3)) # Trial mean. mean(dat$yield) # 391 matches Kempton table 3 # Mean excess height of neighbors for each genotype # tapply(dat$cov, dat$gen, mean)/2 # Matches Kempton table 4 # Variety means, matches Kempton table 4 mean yield m1 <- lm(yield ~ -1 + gen, dat) coef(m1) # Full model used by Kempton, eqn 5. Not perfectly clear. # Appears to include rep term, perhaps within block dat$blk <- factor(dat$row) dat$blkrep <- factor(paste(dat$blk, dat$rep)) m2 <- lm(yield ~ -1 + gen + blkrep + cov, data=dat) coef(m2) # slope 'cov' = -.72, while Kempton says -.79 } # }"},{"path":"/reference/besag.checks.html","id":null,"dir":"Reference","previous_headings":"","what":"Check variety yields in winter wheat. — besag.checks","title":"Check variety yields in winter wheat. — besag.checks","text":"Check variety yields winter wheat.","code":""},{"path":"/reference/besag.checks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check variety yields in winter wheat. — besag.checks","text":"","code":"data(\"besag.checks\")"},{"path":"/reference/besag.checks.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Check variety yields in winter wheat. — besag.checks","text":"data frame 364 observations following 4 variables. yield yield, units 10g row row col column gen genotype/variety","code":""},{"path":"/reference/besag.checks.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check variety yields in winter wheat. — besag.checks","text":"data used Besag show spatial variation field experiment, Besag use data analysis. Yields winter wheat varieties (Bounty Huntsman) Plant Breeding Institute, Cambridge, 1980. data 'checks' genotypes larger variety trial. column checks, five columns new varieties. Repeat. Plot dimensions approx 1.5 4.5 metres Field length: 52 rows * 4.5 m = 234 m Field width: 31 columns * 1.5 m = 46.5 Electronic version data supplied David Clifford.","code":""},{"path":"/reference/besag.checks.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Check variety yields in winter wheat. — besag.checks","text":"Besag, J.E. & Kempton R.. (1986). Statistical analysis field experiments using neighbouring plots. Biometrics, 42, 231-251. https://doi.org/10.2307/2531047","code":""},{"path":"/reference/besag.checks.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Check variety yields in winter wheat. — besag.checks","text":"Kempton, Statistical Methods Plant Variety Evaluation, page 91–92","code":""},{"path":"/reference/besag.checks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check variety yields in winter wheat. — besag.checks","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.checks) dat <- besag.checks libs(desplot) desplot(dat, yield~col*row, num=gen, aspect=234/46.5, # true aspect main=\"besag.checks\") } # }"},{"path":"/reference/besag.elbatan.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"RCB experiment wheat, 50 varieties 3 blocks strong spatial trend.","code":""},{"path":"/reference/besag.elbatan.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"data frame 150 observations following 4 variables. yield yield wheat gen genotype, factor 50 levels col column/block row row","code":""},{"path":"/reference/besag.elbatan.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"RCB experiment wheat El Batan, Mexico. three single-column replicates 50 varieties replicate. Plot dimensions given Besag. Data retrieved https://web.archive.org/web/19991008143232/www.stat.duke.edu/~higdon/trials/elbatan.dat Used permission David Higdon.","code":""},{"path":"/reference/besag.elbatan.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"Julian Besag D Higdon, 1999. Bayesian Analysis Agricultural Field Experiments, Journal Royal Statistical Society: Series B,61, 691–746. Table 1. https://doi.org/10.1111/1467-9868.00201","code":""},{"path":"/reference/besag.elbatan.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"Wilkinson 1984. Besag & Seheult 1989.","code":""},{"path":"/reference/besag.elbatan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. — besag.elbatan","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.elbatan) dat <- besag.elbatan libs(desplot) desplot(dat, yield~col*row, num=gen, # aspect unknown main=\"besag.elbatan - wheat yields\") # Besag figure 1 library(lattice) xyplot(yield~row|col, dat, type=c('l'), layout=c(1,3), main=\"besag.elbatan wheat yields\") # RCB m1 <- lm(yield ~ 0 + gen + factor(col), dat) p1 <- coef(m1)[1:50] # Formerly used gam package, but as of R 3.1, Rcmd check --as-cran # is complaining # Calls: plot.gam ... model.matrix.gam -> predict -> predict.gam -> array # but it works perfectly in interactive mode !!! # Remove the FALSE to run the code below if(is.element(\"gam\", search())) detach(package:gam) libs(mgcv) m2 <- mgcv::gam(yield ~ -1 + gen + factor(col) + s(row), data=dat) plot(m2, residuals=TRUE, main=\"besag.elbatan\") pred <- cbind(dat, predict(m2, dat, type=\"terms\")) # Need to correct for the average loess effect, which is like # an overall intercept term. adjlo <- mean(pred$\"s(row)\") p2 <- coef(m2)[1:50] + adjlo # Compare estimates lims <- range(c(p1,p2)) plot(p1, p2, xlab=\"RCB prediction\", ylab=\"RCB with smooth trend (predicted)\", type='n', xlim=lims, ylim=lims, main=\"besag.elbatan\") text(p1, p2, 1:50, cex=.5) abline(0,1,col=\"gray\") } # }"},{"path":"/reference/besag.endive.html","id":null,"dir":"Reference","previous_headings":"","what":"Presence of footroot disease in an endive field — besag.endive","title":"Presence of footroot disease in an endive field — besag.endive","text":"Presence footroot disease endive field","code":""},{"path":"/reference/besag.endive.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Presence of footroot disease in an endive field — besag.endive","text":"data frame 2506 observations following 3 variables. col column row row disease plant diseased, Y=yes,N=","code":""},{"path":"/reference/besag.endive.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Presence of footroot disease in an endive field — besag.endive","text":"field endives, plant footrot, ? Data binary lattice 14 x 179 plants. Modeled autologistic distribution. assume endives single genotype. Besag (1978) may data taken 4 time points. data extracted Friel Pettitt. clear , , time point used. Friel give dimensions. Besag available.","code":""},{"path":"/reference/besag.endive.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Presence of footroot disease in an endive field — besag.endive","text":"J Besag (1978). Methods Statistical Analysis Spatial Data. Bulletin International Statistical Institute, 47, 77-92.","code":""},{"path":"/reference/besag.endive.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Presence of footroot disease in an endive field — besag.endive","text":"N Friel & . N Pettitt (2004). Likelihood Estimation Inference Autologistic Model. Journal Computational Graphical Statistics, 13:1, 232-246. https://doi.org/10.1198/1061860043029","code":""},{"path":"/reference/besag.endive.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Presence of footroot disease in an endive field — besag.endive","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.endive) dat <- besag.endive # Incidence map. Figure 2 of Friel and Pettitt libs(desplot) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) desplot(dat, disease~col*row, col.regions=grays(2), aspect = 0.5, # aspect unknown main=\"besag.endive - Disease incidence\") # Besag (2000) \"An Introduction to Markov Chain Monte Carlo\" suggested # that the autologistic model is not a very good fit for this data. # We try it anyway. No idea if this is correct or how to interpret... libs(ngspatial) A = adjacency.matrix(179,14) X = cbind(x=dat$col, y=dat$row) Z = as.numeric(dat$disease==\"Y\") m1 <- autologistic(Z ~ 0+X, A=A, control=list(confint=\"none\")) summary(m1) ## Coefficients: ## Estimate Lower Upper MCSE ## Xx -0.007824 NA NA NA ## Xy -0.144800 NA NA NA ## eta 0.806200 NA NA NA if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # Now try an AR1xAR1 model. dat2 <- transform(dat, xf=factor(col), yf=factor(row), pres=as.numeric(disease==\"Y\")) m2 <- asreml(pres ~ 1, data=dat2, resid = ~ar1(xf):ar1(yf)) # The 0/1 response is arbitrary, but there is some suggestion # of auto-correlation in the x (.17) and y (.10) directions, # suggesting the pattern is more 'patchy' than just random noise, # but is it meaningful? lucid::vc(m2) ## effect component std.error z.ratio bound ## xf:yf(R) 0.1301 0.003798 34 P 0 ## xf:yf!xf!cor 0.1699 0.01942 8.7 U 0 ## xf:yf!yf!cor 0.09842 0.02038 4.8 U 0 } } # }"},{"path":"/reference/besag.met.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn, incomplete-block design — besag.met","title":"Multi-environment trial of corn, incomplete-block design — besag.met","text":"Multi-environment trial corn, incomplete-block designlocation.","code":""},{"path":"/reference/besag.met.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn, incomplete-block design — besag.met","text":"data frame 1152 observations following 7 variables. county county row row col column rep rep block incomplete block yield yield gen genotype, 1-64","code":""},{"path":"/reference/besag.met.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn, incomplete-block design — besag.met","text":"Multi-environment trial 64 corn hybrids six counties North Carolina. location 3 replicates incomplete-block design 18x11 lattice plots whose length--width ratio 2:1. Note: original data, county 6 missing plots. data rows missing plot uses county/block/rep fill-row, sets genotype G01, sets yield missing. missing values added data asreml easily AR1xAR1 analysis using rectangular regions. location/panel : Field length: 18 rows * 2 units = 36 units. Field width: 11 plots * 1 unit = 11 units. Retrieved https://web.archive.org/web/19990505223413/www.stat.duke.edu/~higdon/trials/nc.dat Used permission David Higdon.","code":""},{"path":"/reference/besag.met.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn, incomplete-block design — besag.met","text":"Julian Besag D Higdon, 1999. Bayesian Analysis Agricultural Field Experiments, Journal Royal Statistical Society: Series B, 61, 691–746. Table 1. https://doi.org/10.1111/1467-9868.00201","code":""},{"path":"/reference/besag.met.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn, incomplete-block design — besag.met","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.met) dat <- besag.met libs(desplot) desplot(dat, yield ~ col*row|county, aspect=36/11, # true aspect out1=rep, out2=block, main=\"besag.met\") # Average reps datm <- aggregate(yield ~ county + gen, data=dat, FUN=mean) # Sections below fit heteroskedastic variance models (variance for each variety) # asreml takes 1 second, lme 73 seconds, SAS PROC MIXED 30 minutes # lme # libs(nlme) # m1l <- lme(yield ~ -1 + gen, data=datm, random=~1|county, # weights = varIdent(form=~ 1|gen)) # m1l$sigma^2 * c(1, coef(m1l$modelStruct$varStruct, unc = FALSE))^2 ## G02 G03 G04 G05 G06 G07 G08 ## 91.90 210.75 63.03 112.05 28.39 237.36 72.72 42.97 ## ... etc ... if(require(\"asreml\", quietly=TRUE)) { libs(asreml, lucid) # Average reps datm <- aggregate(yield ~ county + gen, data=dat, FUN=mean) # asreml Using 'rcov' ALWAYS requires sorting the data datm <- datm[order(datm$gen),] m1 <- asreml(yield ~ gen, data=datm, random = ~ county, residual = ~ dsum( ~ units|gen)) vc(m1)[1:7,] ## effect component std.error z.ratio bound ## county 1324 836.1 1.6 P 0.2 ## gen_G01!R 91.98 58.91 1.6 P 0.1 ## gen_G02!R 210.6 133.6 1.6 P 0.1 ## gen_G03!R 63.06 40.58 1.6 P 0.1 ## gen_G04!R 112.1 71.59 1.6 P 0.1 ## gen_G05!R 28.35 18.57 1.5 P 0.2 ## gen_G06!R 237.4 150.8 1.6 P 0 # We get the same results from asreml & lme # plot(m1$vparameters[-1], # m1l$sigma^2 * c(1, coef(m1l$modelStruct$varStruct, unc = FALSE))^2) # The following example shows how to construct a GxE biplot # from the FA2 model. dat <- besag.met dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$county, dat$xf, dat$yf), ] # First, AR1xAR1 m1 <- asreml(yield ~ county, data=dat, random = ~ gen:county, residual = ~ dsum( ~ ar1(xf):ar1(yf)|county)) # Add FA1 m2 <- update(m1, random=~gen:fa(county,1)) # rotate.FA=FALSE # FA2 m3 <- update(m2, random=~gen:fa(county,2)) asreml.options(extra=50) m3 <- update(m3, maxit=50) asreml.options(extra=0) # Use the loadings to make a biplot vars <- vc(m3) psi <- vars[grepl(\"!var$\", vars$effect), \"component\"] la1 <- vars[grepl(\"!fa1$\", vars$effect), \"component\"] la2 <- vars[grepl(\"!fa2$\", vars$effect), \"component\"] mat <- as.matrix(data.frame(psi, la1, la2)) # I tried using rotate.fa=FALSE, but it did not seem to # give orthogonal vectors. Rotate by hand. rot <- svd(mat[,-1])$v # rotation matrix lam <- mat[,-1] colnames(lam) <- c(\"load1\", \"load2\") co3 <- coef(m3)$random # Scores are the GxE coefficients ix1 <- grepl(\"_Comp1$\", rownames(co3)) ix2 <- grepl(\"_Comp2$\", rownames(co3)) sco <- matrix(c(co3[ix1], co3[ix2]), ncol=2, byrow=FALSE) sco <- sco dimnames(sco) <- list(levels(dat$gen) , c('load1','load2')) rownames(lam) <- levels(dat$county) sco[,1:2] <- -1 * sco[,1:2] lam[,1:2] <- -1 * lam[,1:2] biplot(sco, lam, cex=.5, main=\"FA2 coefficient biplot (asreml)\") # G variance matrix gvar <- lam # Now get predictions and make an ordinary biplot p3 <- predict(m3, data=dat, classify=\"county:gen\") p3 <- p3$pvals libs(\"gge\") bi3 <- gge(p3, predicted.value ~ gen*county, scale=FALSE) if(interactive()) dev.new() # Very similar to the coefficient biplot biplot(bi3, stand=FALSE, main=\"SVD biplot of FA2 predictions\") } } # }"},{"path":"/reference/besag.triticale.html","id":null,"dir":"Reference","previous_headings":"","what":"Four-way factorial agronomic experiment in triticale — besag.triticale","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"Four-way factorial agronomic experiment triticale","code":""},{"path":"/reference/besag.triticale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"","code":"data(\"besag.triticale\")"},{"path":"/reference/besag.triticale.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"data frame 54 observations following 7 variables. yield yield, g/m^2 row row col column gen genotype / variety, 3 levels rate seeding rate, kg/ha nitro nitrogen rate, kw/ha regulator growth regulator, 3 levels","code":""},{"path":"/reference/besag.triticale.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"Experiment conducted factorial yields triticale. Fully randomized. Plots 1.5m x 5.5m, orientation clear. Besag Kempton show accounting neighbors changes non-significant genotype differences significant differences.","code":""},{"path":"/reference/besag.triticale.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"Julian Besag Rob Kempton (1986). Statistical Analysis Field Experiments Using Neighbouring Plots. Biometrics, 42, 231-251. Table 2. https://doi.org/10.2307/2531047","code":""},{"path":"/reference/besag.triticale.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"None.","code":""},{"path":"/reference/besag.triticale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Four-way factorial agronomic experiment in triticale — besag.triticale","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(besag.triticale) dat <- besag.triticale dat <- transform(dat, rate=factor(rate), nitro=factor(nitro)) dat <- transform(dat, xf=factor(col), yf=factor(row)) libs(desplot) desplot(dat, yield ~ col*row, # aspect unknown main=\"besag.triticale\") # Besag & Kempton are not perfectly clear on the model, but # indicate that there was no evidence of any two-way interactions. # A reduced, main-effect model had genotype effects that were # \"close to significant\" at the five percent level. # The model below has p-value of gen at .04, so must be slightly # different than their model. m2 <- lm(yield ~ gen + rate + nitro + regulator + yf, data=dat) anova(m2) # Similar, but not exact, to Besag figure 5 dat$res <- resid(m2) libs(lattice) xyplot(res ~ col|as.character(row), data=dat, as.table=TRUE, type=\"s\", layout=c(1,3), main=\"besag.triticale\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml) # Besag uses an adjustment based on neighboring plots. # This analysis fits the standard AR1xAR1 residual model dat <- dat[order(dat$xf, dat$yf), ] m3 <- asreml(yield ~ gen + rate + nitro + regulator + gen:rate + gen:nitro + gen:regulator + rate:nitro + rate:regulator + nitro:regulator + yf, data=dat, resid = ~ ar1(xf):ar1(yf)) wald(m3) # Strongly significant gen, rate, regulator ## Df Sum of Sq Wald statistic Pr(Chisq) ## (Intercept) 1 1288255 103.971 < 2.2e-16 *** ## gen 2 903262 72.899 < 2.2e-16 *** ## rate 1 104774 8.456 0.003638 ** ## nitro 1 282 0.023 0.880139 ## regulator 2 231403 18.676 8.802e-05 *** ## yf 2 3788 0.306 0.858263 ## gen:rate 2 1364 0.110 0.946461 ## gen:nitro 2 30822 2.488 0.288289 ## gen:regulator 4 37269 3.008 0.556507 ## rate:nitro 1 1488 0.120 0.728954 ## rate:regulator 2 49296 3.979 0.136795 ## nitro:regulator 2 41019 3.311 0.191042 ## residual (MS) 12391 } } # }"},{"path":"/reference/blackman.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"Multi-environment trial wheat, conventional semi-dwarf varieties, 7 locs low/high fertilizer levels.","code":""},{"path":"/reference/blackman.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"data frame 168 observations following 5 variables. gen genotype loc location nitro nitrogen fertilizer, low/high yield yield (g/m^2) type type factor, conventional/semi-dwarf","code":""},{"path":"/reference/blackman.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"Conducted U.K. 1975. loc three reps, two nitrogen treatments. Locations Begbroke, Boxworth, Crafts Hill, Earith, Edinburgh, Fowlmere, Trumpington. two highest-yielding locations, Earith Edinburgh, yield _lower_ high-nitrogen treatment. Blackman et al. say \" seems probable effects development structure crop responsible reductions yield high nitrogen\".","code":""},{"path":"/reference/blackman.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"Blackman, JA Bingham, J. Davidson, JL (1978). Response semi-dwarf conventional winter wheat varieties application nitrogen fertilizer. Journal Agricultural Science, 90, 543–550. https://doi.org/10.1017/S0021859600056070","code":""},{"path":"/reference/blackman.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"Gower, J. Lubbe, S.G. Gardner, S. Le Roux, N. (2011). Understanding Biplots, Wiley.","code":""},{"path":"/reference/blackman.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat, conventional and semi-dwarf varieties — blackman.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(blackman.wheat) dat <- blackman.wheat libs(lattice) # Semi-dwarf generally higher yielding than conventional # bwplot(yield~type|loc,dat, main=\"blackman.wheat\") # Peculiar interaction--Ear/Edn locs have reverse nitro response dotplot(gen~yield|loc, dat, group=nitro, auto.key=TRUE, main=\"blackman.wheat: yield for low/high nitrogen\") # Height data from table 6 of Blackman. Height at Trumpington loc. # Shorter varieties have higher yields, greater response to nitro. heights <- data.frame(gen=c(\"Cap\", \"Dur\", \"Fun\", \"Hob\", \"Hun\", \"Kin\", \"Ran\", \"Spo\", \"T64\", \"T68\",\"T95\", \"Tem\"), ht=c(101,76,76,80,98,88,98,81,86,73,78,93)) dat$height <- heights$ht[match(dat$gen, heights$gen)] xyplot(yield~height|loc,dat,group=nitro,type=c('p','r'), main=\"blackman.wheat\", subset=loc==\"Tru\", auto.key=TRUE) libs(reshape2) # AMMI-style biplot Fig 6.4 of Gower 2011 dat$env <- factor(paste(dat$loc,dat$nitro,sep=\"-\")) datm <- acast(dat, gen~env, value.var='yield') datm <- sweep(datm, 1, rowMeans(datm)) datm <- sweep(datm, 2, colMeans(datm)) biplot(prcomp(datm), main=\"blackman.wheat AMMI-style biplot\") } # }"},{"path":"/reference/bliss.borers.html","id":null,"dir":"Reference","previous_headings":"","what":"Corn borer infestation under four treatments — bliss.borers","title":"Corn borer infestation under four treatments — bliss.borers","text":"Corn borer infestation four treatments","code":""},{"path":"/reference/bliss.borers.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Corn borer infestation under four treatments — bliss.borers","text":"data frame 48 observations following 3 variables. borers number borers per hill treat treatment factor freq frequency borer count","code":""},{"path":"/reference/bliss.borers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Corn borer infestation under four treatments — bliss.borers","text":"Four treatments control corn borers. Treatment 1 control. 15 blocks, treatment, 8 hills plants examined, number corn borers present recorded. data aggregated across blocks. Bliss mentions level infestation varied significantly blocks.","code":""},{"path":"/reference/bliss.borers.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Corn borer infestation under four treatments — bliss.borers","text":"C. Bliss R. . Fisher. (1953). Fitting Negative Binomial Distribution Biological Data. Biometrics, 9, 176–200. Table 3. https://doi.org/10.2307/3001850 Geoffrey Beall. 1940. Fit Significance Contagious Distributions Applied Observations Larval Insects. Ecology, 21, 460-474. Page 463. https://doi.org/10.2307/1930285","code":""},{"path":"/reference/bliss.borers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Corn borer infestation under four treatments — bliss.borers","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bliss.borers) dat <- bliss.borers # Add 0 frequencies dat0 <- expand.grid(borers=0:26, treat=c('T1','T2','T3','T4')) dat0 <- merge(dat0,dat, all=TRUE) dat0$freq[is.na(dat0$freq)] <- 0 # Expand to individual (non-aggregated) counts for each hill dd <- data.frame(borers = rep(dat0$borers, times=dat0$freq), treat = rep(dat0$treat, times=dat0$freq)) libs(lattice) histogram(~borers|treat, dd, type='count', breaks=0:27-.5, layout=c(1,4), main=\"bliss.borers\", xlab=\"Borers per hill\") libs(MASS) m1 <- glm.nb(borers~0+treat, data=dd) # Bliss, table 3, presents treatment means, which are matched by: exp(coef(m1)) # 4.033333 3.166667 1.483333 1.508333 # Bliss gives treatment values k = c(1.532,1.764,1.333,1.190). # The mean of these is 1.45, similar to this across-treatment estimate m1$theta # 1.47 # Plot observed and expected distributions for treatment 2 libs(latticeExtra) xx <- 0:26 yy <- dnbinom(0:26, mu=3.17, size=1.47)*120 # estimates are from glm.nb histogram(~borers, dd, type='count', subset=treat=='T2', main=\"bliss.borers - trt T2 observed and expected\", breaks=0:27-.5) + xyplot(yy~xx, col='navy', type='b') # \"Poissonness\"-type plot libs(vcd) dat2 <- droplevels(subset(dat, treat=='T2')) vcd::distplot(dat2$borers, type = \"nbinomial\", main=\"bliss.borers neg binomialness plot\") # Better way is a rootogram g1 <- vcd::goodfit(dat2$borers, \"nbinomial\") plot(g1, main=\"bliss.borers - Treatment 2\") } # }"},{"path":"/reference/bond.diallel.html","id":null,"dir":"Reference","previous_headings":"","what":"Diallel cross of winter beans — bond.diallel","title":"Diallel cross of winter beans — bond.diallel","text":"Diallel cross winter beans","code":""},{"path":"/reference/bond.diallel.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diallel cross of winter beans — bond.diallel","text":"data frame 36 observations following 3 variables. female female parent male male parent yield yield, grams/plot stems stems per plot nodes podded nodes per stem pods pods per podded node seeds seeds per pod weight weight (g) per 100 seeds height height (cm) April width width (cm) April flower mean flowering date May","code":""},{"path":"/reference/bond.diallel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diallel cross of winter beans — bond.diallel","text":"Yield grams/plot full diallel cross 6 inbred lines winter beans. Values means two years.","code":""},{"path":"/reference/bond.diallel.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Diallel cross of winter beans — bond.diallel","text":"D. . Bond (1966). Yield components yield diallel crosses inbred lines winter beans (Viciafaba). Journal Agricultural Science, 67, 325–336. https://doi.org/10.1017/S0021859600017329","code":""},{"path":"/reference/bond.diallel.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diallel cross of winter beans — bond.diallel","text":"Peter John, Statistical Design Analysis Experiments, p. 85.","code":""},{"path":"/reference/bond.diallel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diallel cross of winter beans — bond.diallel","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bond.diallel) dat <- bond.diallel # Because these data are means, we will not be able to reproduce # the anova table in Bond. More useful as a multivariate example. libs(corrgram) corrgram(dat[ , 3:11], main=\"bond.diallel\", lower=panel.pts) # Multivariate example from sommer package corrgram(dat[,c(\"stems\",\"pods\",\"seeds\")], lower=panel.pts, upper=panel.conf, main=\"bond.diallel\") libs(sommer) m1 <- mmer(cbind(stems,pods,seeds) ~ 1, random= ~ vs(female)+vs(male), rcov= ~ vs(units), dat) #### genetic variance covariance cov2cor(m1$sigma$`u:female`) cov2cor(m1$sigma$`u:male`) cov2cor(m1$sigma$`u:units`) } # }"},{"path":"/reference/bose.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"Uniformity trials barley, wheat, lentils India 1930-1932.","code":""},{"path":"/reference/bose.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"","code":"data(\"bose.multi.uniformity\")"},{"path":"/reference/bose.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"data frame 1170 observations following 5 variables. year year crop crop row row ordinate col column ordinate yield yield per plot grams","code":""},{"path":"/reference/bose.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"field 1/4 acre sown three consecutive years (beginning 1929-1930) barley, wheat, lentil. harvest, borders 3 feet east west 6 feet north south removed. field divided plots four feet square, harvested separately, measured grams. Fertility contours field somewhat similar across years, correlation values across years 0.45, 0.48, 0.21. Field width: 15 plots * 4 feet = 60 feet. Field length: 26 plots * 4 feet = 104 feet. Conclusions: \"experimental field may sensibly uniform one crop one season may another crop different season\" p. 592.","code":""},{"path":"/reference/bose.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"Bose, R. D. (1935). soil heterogeneity trials Pusa size shape experimental plots. Ind. J. Agric. Sci., 5, 579-608. Table 1 (p. 585), Table 4 (p. 589), Table 5 (p. 590). https://archive.org/details/.ernet.dli.2015.271739","code":""},{"path":"/reference/bose.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"Shaw (1935). Handbook Statistics Use Plant-Breeding Agricultural Problems, p. 149-170. https://krishikosh.egranth.ac./handle/1/21153","code":""},{"path":"/reference/bose.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of barley, wheat, lentils — bose.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bose.multi.uniformity) dat <- bose.multi.uniformity # match sum at bottom of Bose tables 1, 4, 5 # library(dplyr) # dat libs(desplot, dplyr) # Calculate percent of mean yield for each year dat <- group_by(dat, year) dat <- mutate(dat, pctyld = (yield-mean(yield))/mean(yield)) dat <- ungroup(dat) dat <- mutate(dat, year=as.character(year)) # Bose smoothed the data by averaging 2x3 plots together before drawing # contour maps. Heatmaps of raw data have similar structure to Bose Fig 1. desplot(dat, pctyld ~ col*row|year, tick=TRUE, flip=TRUE, aspect=(26)/(15), main=\"bose.multi.* - Percent of mean yield\") # contourplot() results need to be mentally flipped upside down # contourplot(pctyld ~ col*row|year, dat, # region=TRUE, as.table=TRUE, aspect=26/15) } # }"},{"path":"/reference/box.cork.html","id":null,"dir":"Reference","previous_headings":"","what":"Weight of cork samples on four sides of trees — box.cork","title":"Weight of cork samples on four sides of trees — box.cork","text":"cork data gives weights cork borings trunk 28 trees north (N), east (E), south (S) west (W) directions.","code":""},{"path":"/reference/box.cork.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weight of cork samples on four sides of trees — box.cork","text":"Data frame 28 observations following 5 variables. tree tree number dir direction N,E,S,W y weight cork deposit (centigrams), north direction","code":""},{"path":"/reference/box.cork.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Weight of cork samples on four sides of trees — box.cork","text":"C.R. Rao (1948). Tests significance multivariate analysis. Biometrika, 35, 58-79. https://doi.org/10.2307/2332629","code":""},{"path":"/reference/box.cork.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Weight of cork samples on four sides of trees — box.cork","text":"K.V. Mardia, J.T. Kent J.M. Bibby (1979) Multivariate Analysis, Academic Press. Russell D Wolfinger, (1996). Heterogeneous Variance: Covariance Structures Repeated Measures. Journal Agricultural, Biological, Environmental Statistics, 1, 205-230.","code":""},{"path":"/reference/box.cork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weight of cork samples on four sides of trees — box.cork","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(box.cork) dat <- box.cork libs(reshape2, lattice) dat2 <- acast(dat, tree ~ dir, value.var='y') splom(dat2, pscales=3, prepanel.limits = function(x) c(25,100), main=\"box.cork\", xlab=\"Cork yield on side of tree\", panel=function(x,y,...){ panel.splom(x,y,...) panel.abline(0,1,col=\"gray80\") }) ## Radial star plot, each tree is one line libs(plotrix) libs(reshape2) dat2 <- acast(dat, tree ~ dir, value.var='y') radial.plot(dat2, start=pi/2, rp.type='p', clockwise=TRUE, radial.lim=c(0,100), main=\"box.cork\", lwd=2, labels=c('North','East','South','West'), line.col=rep(c(\"royalblue\",\"red\",\"#009900\",\"dark orange\", \"#999999\",\"#a6761d\",\"deep pink\"), length=nrow(dat2))) if(require(\"asreml\", quietly=TRUE)) { libs(asreml, lucid) # Unstructured covariance dat$dir <- factor(dat$dir) dat$tree <- factor(dat$tree) dat <- dat[order(dat$tree, dat$dir), ] # Unstructured covariance matrix m1 <- asreml(y~dir, data=dat, residual = ~ tree:us(dir)) lucid::vc(m1) # Note: 'rcor' is a personal function to extract the correlations # into a matrix format # round(kw::rcor(m1)$dir, 2) # E N S W # E 219.93 223.75 229.06 171.37 # N 223.75 290.41 288.44 226.27 # S 229.06 288.44 350.00 259.54 # W 171.37 226.27 259.54 226.00 # Note: Wolfinger used a common diagonal variance # Factor Analytic with different specific variances # fixme: does not work with asreml4 # m2 <- update(m1, residual = ~tree:facv(dir,1)) # round(kw::rcor(m2)$dir, 2) # E N S W # E 219.94 209.46 232.85 182.27 # N 209.46 290.41 291.82 228.43 # S 232.85 291.82 349.99 253.94 # W 182.27 228.43 253.94 225.99 } } # }"},{"path":"/reference/bradley.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"Uniformity trial 4 crops land Trinidad.","code":""},{"path":"/reference/bradley.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"","code":"data(\"bradley.multi.uniformity\")"},{"path":"/reference/bradley.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"data frame 440 observations following 5 variables. row row col column yield yield, pounds per plot season season crop crop","code":""},{"path":"/reference/bradley.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"Experiments conducted Trinidad. Plots marked May 1939 Fields 1, 2, 3. Prior 1939 difficult obtain significant results land. Plots 1/40 acre , 33 feet square. Discard blocks ( rows) 7 feet plots (columns) 4 feet. roadways, gap 14 feet blocks 10 11 gap 10 feet plots E/F (call columns 5/6). Data collected 4 crops. Two crops poor germination omitted. Field width: 10 plots * 33 feet + 8 gaps * 4 feet + 1 gap * 10 = 372 feet Field length: 11 blocks (plots) * 33 feet + 9 gaps * 7 feet + 1 gap * 14 feet = 440 feet Crop 1. Woolly Pyrol. Crop cut flowering weighed pounds. Note, woolly pyrol appears bean also called black gram, phaseolus mungo. Crop 2. Woolly Pyrol. Crop cut flowering weighed pounds. Crop 3. Maize. Net weight cobs pounds. Source document also number cobs. Crop 4. Yams. Weights pounds. Source document weight 1/4 pound, rounded nearest pound. (Half pounds rounded nearest even pound.) Source document also number yams. Notes Bradley. edges field tended slightly higher yielding. Thought due heavier cultivation edges recieve (p. 18). plot row 9, col 7 (9G Bradley) higher yielding neighbors, thought site saman tree dug burned field plotted. Bits charcoal still soil. Bradley also examined soil samples selected plots looked nutrients, moisture, texture, etc. selected plots 4 high-yielding plots 4 low-yielding plots. Little difference observed. Unexpectedly, yams gave higher yield plots compaction.","code":""},{"path":"/reference/bradley.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"P. L. Bradley (1941). study variation productivity number fixed plots field 2. Dissertation: University West Indies. Appendix 1a, 1b, 1c, 1d. https://uwispace.sta.uwi.edu/items/e874561d-52e5-4e39-8416-ff8c1756049c https://hdl.handle.net/2139/41259 data repeated : C. E. Wilson. Study plots laid field II view obtaining plot-fertility data use future experiments plots, season 1940-41. Dissertation: University West Indies. Page 36-39. https://uwispace.sta.uwi.edu/dspace/handle/2139/43658","code":""},{"path":"/reference/bradley.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"None","code":""},{"path":"/reference/bradley.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of 4 crops on the same land — bradley.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bradley.multi.uniformity) dat <- bradley.multi.uniformity # figures similar to Bradley, pages 11-15 libs(desplot) desplot(dat, yield ~ col*row, subset=season==1, flip=TRUE, aspect=433/366, # true aspect (omits roadways) main=\"bradley.multi.uniformity - season 1, woolly pyrol\") desplot(dat, yield ~ col*row, subset=season==2, flip=TRUE, aspect=433/366, # true aspect (omits roadways) main=\"bradley.multi.uniformity - season 2, woolly pyrol\") desplot(dat, yield ~ col*row, subset=season==3, flip=TRUE, aspect=433/366, # true aspect (omits roadways) main=\"bradley.multi.uniformity - season 3, maize\") desplot(dat, yield ~ col*row, subset=season==4, flip=TRUE, aspect=433/366, # true aspect (omits roadways) main=\"bradley.multi.uniformity - season 4, yams\") dat1 <- subset(bradley.multi.uniformity, season==1) dat2 <- subset(bradley.multi.uniformity, season==2) dat3 <- subset(bradley.multi.uniformity, season==3) dat4 <- subset(bradley.multi.uniformity, season==4) # to combine plots across seasons, each yield value was converted to percent # of maximum yield in that season. Same as Bradley, page 17. dat1$percent <- dat1$yield / max(dat1$yield) * 100 dat2$percent <- dat2$yield / max(dat2$yield) * 100 dat3$percent <- dat3$yield / max(dat3$yield) * 100 dat4$percent <- dat4$yield / max(dat4$yield) * 100 # make sure data is in same order, then combine dat1 <- dat1[order(dat1$col, dat1$row),] dat2 <- dat2[order(dat2$col, dat2$row),] dat3 <- dat3[order(dat3$col, dat3$row),] dat4 <- dat4[order(dat4$col, dat4$row),] dat14 <- dat1[,c('row','col')] dat14$fertility <- dat1$percent + dat2$percent + dat3$percent + dat4$percent libs(desplot) desplot(dat14, fertility ~ col*row, tick=TRUE, flip=TRUE, aspect=433/366, # true aspect (omits roadways) main=\"bradley.multi.uniformity - fertility\") } # }"},{"path":"/reference/brandle.rape.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of rape in Manitoba — brandle.rape","title":"Multi-environment trial of rape in Manitoba — brandle.rape","text":"Rape seed yields 5 genotypes, 3 years, 9 locations.","code":""},{"path":"/reference/brandle.rape.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of rape in Manitoba — brandle.rape","text":"data frame 135 observations following 4 variables. gen genotype year year, numeric loc location, 9 levels yield yield, kg/ha","code":""},{"path":"/reference/brandle.rape.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of rape in Manitoba — brandle.rape","text":"yields mean 4 reps. Note, table 2 Brandle, value Triton 1985 Bagot shown 2355, 2555 match means reported paper. Used permission P. McVetty.","code":""},{"path":"/reference/brandle.rape.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of rape in Manitoba — brandle.rape","text":"Brandle, JE McVetty, PBE. (1988). Genotype x environment interaction stability analysis seed yield oilseed rape grown Manitoba. Canadian Journal Plant Science, 68, 381–388.","code":""},{"path":"/reference/brandle.rape.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of rape in Manitoba — brandle.rape","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(brandle.rape) dat <- brandle.rape libs(lattice) dotplot(gen~yield|loc, dat, group=year, auto.key=list(columns=3), main=\"brandle.rape, yields per location\", ylab=\"Genotype\") # Matches table 4 of Brandle # round(tapply(dat$yield, dat$gen, mean),0) # Brandle reports variance components: # sigma^2_gl: 9369 gy: 14027 g: 72632 resid: 150000 # Brandle analyzed rep-level data, so the residual variance is different. # The other components are matched by the following analysis. libs(lme4) libs(lucid) dat$year <- factor(dat$year) m1 <- lmer(yield ~ year + loc + year:loc + (1|gen) + (1|gen:loc) + (1|gen:year), data=dat) vc(m1) ## grp var1 var2 vcov sdcor ## gen:loc (Intercept) 9363 96.76 ## gen:year (Intercept) 14030 118.4 ## gen (Intercept) 72630 269.5 ## Residual 75010 273.9 } # }"},{"path":"/reference/brandt.switchback.html","id":null,"dir":"Reference","previous_headings":"","what":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":"Switchback experiment dairy cattle, milk yield two treatments","code":""},{"path":"/reference/brandt.switchback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":"","code":"data(\"brandt.switchback\")"},{"path":"/reference/brandt.switchback.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":"data frame 30 observations following 5 variables. group group: ,B cow cow, 10 levels trt treatment, 2 levels period period, 3 levels yield milk yield, pounds","code":""},{"path":"/reference/brandt.switchback.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":"experiment, 10 cows selected Iowa State College Holstein-Friesian herd divided two equal groups. Care taken groups nearly equal possible regard milk production, stage gestation, body weight, condition age. cows given 10 pounds timothy hay 30 pounds corn silage daily fed different grain mixtures. Treatment T1, , consisted feeding grain mixture 1 part corn cob meal 1 part ground oats, treatment T2 consisted feeding grain mixture 4 parts corn cob meal, 4 parts ground oats 3 parts gluten feed. three treatment periods covered 105 days – three periods 35 days . yields first 7 days period considered possible effect transition one treatment . data, together sums differences aid calculations incidental testing, given table 2. seems safe conclude inclusion gluten feed grain mixture fed timothy hay ration Holstein-Friesian cows increased production milk. average increase 21.7 pounds per cow 28-day period.","code":""},{"path":"/reference/brandt.switchback.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":".E. Brandt (1938). Tests Significance Reversal Switchback Trials Iowa State College, Agricultural Research Bulletins. Bulletin 234. Book 22. https://lib.dr.iastate.edu/ag_researchbulletins/22/","code":""},{"path":"/reference/brandt.switchback.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Switchback experiment on dairy cattle, milk yield for two treatments — brandt.switchback","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(brandt.switchback) dat <- brandt.switchback # In each period, treatment 2 is slightly higher # bwplot(yield~trt|period,dat, layout=c(3,1), main=\"brandt.switchback\", # xlab=\"Treatment\", ylab=\"Milk yield\") # Yield at period 2 (trt T2) is above the trend in group A, # below the trend (trt T1) in group B. # Equivalently, treatment T2 is above the trend line libs(lattice) xyplot(yield~period|group, data=dat, group=cow, type=c('l','r'), auto.key=list(columns=5), main=\"brandt.switchback\", xlab=\"Period. Group A: T1,T2,T1. Group B: T2,T1,T2\", ylab=\"Milk yield (observed and trend) per cow\") # Similar to Brandt Table 10 m1 <- aov(yield~period+group+cow:group+period:group, data=dat) anova(m1) } # }"},{"path":"/reference/bridges.cucumber.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","title":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","text":"Cucumber yields latin square design two locs.","code":""},{"path":"/reference/bridges.cucumber.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","text":"data frame 32 observations following 5 variables. loc location gen genotype/cultivar row row col column yield weight marketable fruit per plot","code":""},{"path":"/reference/bridges.cucumber.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","text":"Conducted Clemson University 1985. four cucumber cultivars grown latin square design Clemson, SC, Tifton, GA. Separate variances modeled location. Plot dimensions given. Bridges (1989) used data illustrate fitting heterogeneous mixed model. Used permission William Bridges.","code":""},{"path":"/reference/bridges.cucumber.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","text":"William Bridges (1989). Analysis plant breeding experiment heterogeneous variances using mixed model equations. Applications mixed models agriculture related disciplines, S. Coop. Ser. Bull, 45–51.","code":""},{"path":"/reference/bridges.cucumber.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of cucumbers in a latin square design — bridges.cucumber","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bridges.cucumber) dat <- bridges.cucumber dat <- transform(dat, rowf=factor(row), colf=factor(col)) libs(desplot) desplot(dat, yield~col*row|loc, # aspect unknown text=gen, cex=1, main=\"bridges.cucumber\") # Graphical inference test for heterogenous variances libs(nullabor) # Create a lineup of datasets fun <- null_permute(\"loc\") dat20 <- lineup(fun, dat, n=20, pos=9) # Now plot libs(lattice) bwplot(yield ~ loc|factor(.sample), dat20, main=\"bridges.cucumber - graphical inference\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) ## Random row/col/resid. Same as Bridges 1989, p. 147 m1 <- asreml(yield ~ 1 + gen + loc + loc:gen, random = ~ rowf:loc + colf:loc, data=dat) lucid::vc(m1) ## effect component std.error z.ratio bound ## rowf:loc 31.62 23.02 1.4 P 0 ## colf:loc 18.08 15.32 1.2 P 0 ## units(R) 31.48 12.85 2.4 P 0 ## Random row/col/resid at each loc. Matches p. 147 m2 <- asreml(yield ~ 1 + gen + loc + loc:gen, random = ~ at(loc):rowf + at(loc):colf, data=dat, resid = ~ dsum( ~ units|loc)) lucid::vc(m2) ## effect component std.error z.ratio bound ## at(loc, Clemson):rowf 32.32 36.58 0.88 P 0 ## at(loc, Tifton):rowf 30.92 28.63 1.1 P 0 ## at(loc, Clemson):colf 22.55 28.78 0.78 P 0 ## at(loc, Tifton):colf 13.62 14.59 0.93 P 0 ## loc_Clemson(R) 46.85 27.05 1.7 P 0 ## loc_Tifton(R) 16.11 9.299 1.7 P 0 predict(m2, data=dat, classify='loc:gen')$pvals ## loc gen predicted.value std.error status ## 1 Clemson Dasher 45.6 5.04 Estimable ## 2 Clemson Guardian 31.6 5.04 Estimable ## 3 Clemson Poinsett 21.4 5.04 Estimable ## 4 Clemson Sprint 26 5.04 Estimable ## 5 Tifton Dasher 50.5 3.89 Estimable ## 6 Tifton Guardian 38.7 3.89 Estimable ## 7 Tifton Poinsett 33 3.89 Estimable ## 8 Tifton Sprint 39.2 3.89 Estimable # Is a heterogeneous model justified? Maybe not. # m1$loglik ## -67.35585 # m2$loglik ## -66.35621 } } # }"},{"path":"/reference/broadbalk.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"Long term wheat yields Broadbalk fields Rothamsted.","code":""},{"path":"/reference/broadbalk.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"data frame 1258 observations following 4 variables. year year plot plot grain grain yield, tonnes straw straw yield, tonnes","code":""},{"path":"/reference/broadbalk.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"Note: data 1852-1925. can find recent data experiments Electronic Rothamsted Archive: https://www.era.rothamsted.ac.uk/ Rothamsted Experiment station conducted wheat experiments Broadbalk Fields beginning 1844 data yields grain straw collected 1852 1925. Ronald Fisher hired analyze data agricultural trials. Organic manures inorganic fertilizer treatments applied various combinations plots. N1 48kg, N1.5 72kg, N2 96kg, N4 192kg nitrogen. Electronic version data retrieved http://lib.stat.cmu.edu/datasets/Andrews/","code":""},{"path":"/reference/broadbalk.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"D.F. Andrews .M. Herzberg. 1985. \tData: Collection Problems Many Fields Student Research Worker. Springer.","code":""},{"path":"/reference/broadbalk.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"Broadbalk Winter Wheat Experiment. https://www.era.rothamsted.ac.uk/index.php?area=home&page=index&dataset=4","code":""},{"path":"/reference/broadbalk.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Long term wheat yields on Broadbalk fields at Rothamsted. — broadbalk.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(broadbalk.wheat) dat <- broadbalk.wheat libs(lattice) ## xyplot(grain~straw|plot, dat, type=c('p','smooth'), as.table=TRUE, ## main=\"broadbalk.wheat\") xyplot(grain~year|plot, dat, type=c('p','smooth'), as.table=TRUE, main=\"broadbalk.wheat\") # yields are decreasing # See the treatment descriptions to understand the patterns redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(grain~year*plot, dat, main=\"broadbalk.wheat: Grain\", col.regions=redblue) } # }"},{"path":"/reference/bryan.corn.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"Uniformity trial corn 3 locations Iowa.","code":""},{"path":"/reference/bryan.corn.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"","code":"data(\"bryan.corn.uniformity\")"},{"path":"/reference/bryan.corn.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"data frame 1728 observations following 4 variables. expt experiment (variety/orientation) row row col column yield yield, pounds per plot","code":""},{"path":"/reference/bryan.corn.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"Three varieties corn planted. experiment 48 rows, row 48 hills long, .65 acres. \"hill\" single hole possibly multiple seeds. Spacing hills sqrt(43560 sq ft * .64) / 48 = 3.5 feet. experiment code, K=Krug, =Iodent, M=McCulloch (varieties corn), 23=1923, 25=1925, E=East/West, N=North/South. experiment aggregated experimental units combining 8 hills, East/West direction also North/South direction. Thus, field represented twice data, \"E\" field name \"N\".","code":""},{"path":"/reference/bryan.corn.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"Arthur Bryan (1933). Factors Affecting Experimental Error Field Plot Tests Corn. Agricultural Experiment Station, Iowa State College. Tables 22-27. https://hdl.handle.net/2027/uiug.30112019568168","code":""},{"path":"/reference/bryan.corn.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"None","code":""},{"path":"/reference/bryan.corn.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of corn at 3 locations in Iowa. — bryan.corn.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(bryan.corn.uniformity) dat <- bryan.corn.uniformity libs(desplot) desplot(dat, yield ~ col*row|expt, main=\"bryan.corn.uniformity\", aspect=(48*3.5/(6*8*3.5)), # true aspect flip=TRUE, tick=TRUE) # CVs in Table 5, column 8 hills # libs(dplyr) # dat # summarize(cv=sd(yield)/mean(yield)*100) ## expt cv ## 1 K23E 10.9 ## 2 K23N 10.9 ## 3 I25E 16.3 ## 4 I25N 17.0 ## 5 M25E 16.2 ## 6 M25N 17.2 } # }"},{"path":"/reference/buntaran.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"Multi-environment trial wheat Sweden 2016.","code":""},{"path":"/reference/buntaran.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"","code":"data(\"buntaran.wheat\")"},{"path":"/reference/buntaran.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"data frame 1069 observations following 7 variables. zone Geographic zone: south, middle, north loc Location rep Block replicate (4) alpha Incomplete-block alpha design gen Genotype (cultivar) yield Dry matter yield, kg/ha","code":""},{"path":"/reference/buntaran.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"Dry matter yield wheat trials Sweden 2016. experiments location multi-rep incomplete blocks alpha design. Electronic data online supplement Buntaran (2020) also \"init\" package https://github.com/Flavjack/inti.","code":""},{"path":"/reference/buntaran.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"Buntaran, Harimurti et al. (2020). Cross-validation stagewise mixed-model analysis Swedish variety trials winter wheat spring barley. Crop Science, 60, 2221-2240. http://doi.org/10.1002/csc2.20177","code":""},{"path":"/reference/buntaran.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"None.","code":""},{"path":"/reference/buntaran.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat in Sweden in 2016. — buntaran.wheat","text":"","code":"if (FALSE) { # \\dontrun{ data(buntaran.wheat) library(agridat) dat <- buntaran.wheat library(lattice) bwplot(yield~loc|zone, dat, layout=c(1,3), scales=list(x=list(rot=90)), main=\"buntaran.wheat\") } # }"},{"path":"/reference/burgueno.alpha.html","id":null,"dir":"Reference","previous_headings":"","what":"Incomplete block alpha design — burgueno.alpha","title":"Incomplete block alpha design — burgueno.alpha","text":"Incomplete block alpha design","code":""},{"path":"/reference/burgueno.alpha.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incomplete block alpha design — burgueno.alpha","text":"","code":"data(\"burgueno.alpha\")"},{"path":"/reference/burgueno.alpha.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Incomplete block alpha design — burgueno.alpha","text":"data frame 48 observations following 6 variables. rep rep, 3 levels block block, 12 levels row row col column gen genotype, 16 levels yield yield","code":""},{"path":"/reference/burgueno.alpha.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Incomplete block alpha design — burgueno.alpha","text":"field experiment 3 reps, 4 blocks per rep, laid alpha design. plot size given. Electronic version data obtained CropStat software. Used permission Juan Burgueno.","code":""},{"path":"/reference/burgueno.alpha.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Incomplete block alpha design — burgueno.alpha","text":"J Burgueno, Cadena, J Crossa, M Banziger, Gilmour, B Cullis. 2000. User's guide spatial analysis field variety trials using ASREML. CIMMYT. https://books.google.com/books?id=PR_tYCFyLCYC&pg=PA1","code":""},{"path":"/reference/burgueno.alpha.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incomplete block alpha design — burgueno.alpha","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(burgueno.alpha) dat <- burgueno.alpha libs(desplot) desplot(dat, yield~col*row, out1=rep, out2=block, # aspect unknown text=gen, cex=1,shorten=\"none\", main='burgueno.alpha') libs(lme4,lucid) # Inc block model m0 <- lmer(yield ~ gen + (1|rep/block), data=dat) vc(m0) # Matches Burgueno p. 26 ## grp var1 var2 vcov sdcor ## block:rep (Intercept) 86900 294.8 ## rep (Intercept) 200900 448.2 ## Residual 133200 365 if(require(\"asreml\", quietly=TRUE)) { libs(asreml) dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf, dat$yf),] # Sequence of models on page 36 of Burgueno m1 <- asreml(yield ~ gen, data=dat) m1$loglik # -232.13 m2 <- asreml(yield ~ gen, data=dat, random = ~ rep) m2$loglik # -223.48 # Inc Block model m3 <- asreml(yield ~ gen, data=dat, random = ~ rep/block) m3$loglik # -221.42 m3$coef$fixed # Matches solution on p. 27 # AR1xAR1 model m4 <- asreml(yield ~ 1 + gen, data=dat, resid = ~ar1(xf):ar1(yf)) m4$loglik # -221.47 plot(varioGram(m4), main=\"burgueno.alpha\") # Figure 1 m5 <- asreml(yield ~ 1 + gen, data=dat, random= ~ yf, resid = ~ar1(xf):ar1(yf)) m5$loglik # -220.07 m6 <- asreml(yield ~ 1 + gen + pol(yf,-2), data=dat, resid = ~ar1(xf):ar1(yf)) m6$loglik # -204.64 m7 <- asreml(yield ~ 1 + gen + lin(yf), data=dat, random= ~ spl(yf), resid = ~ar1(xf):ar1(yf)) m7$loglik # -212.51 m8 <- asreml(yield ~ 1 + gen + lin(yf), data=dat, random= ~ spl(yf)) m8$loglik # -213.91 # Polynomial model with predictions m9 <- asreml(yield ~ 1 + gen + pol(yf,-2) + pol(xf,-2), data=dat, random= ~ spl(yf), resid = ~ar1(xf):ar1(yf)) m9 <- update(m9) m9$loglik # -191.44 vs -189.61 m10 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat, resid = ~ar1(xf):ar1(yf)) m10$loglik # -211.56 m11 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat, random= ~ spl(yf), resid = ~ar1(xf):ar1(yf)) m11$loglik # -208.90 m12 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat, random= ~ spl(yf)+spl(xf), resid = ~ar1(xf):ar1(yf)) m12$loglik # -206.82 m13 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat, random= ~ spl(yf)+spl(xf)) m13$loglik # -207.52 } } # }"},{"path":"/reference/burgueno.rowcol.html","id":null,"dir":"Reference","previous_headings":"","what":"Row-column design — burgueno.rowcol","title":"Row-column design — burgueno.rowcol","text":"Row-column design","code":""},{"path":"/reference/burgueno.rowcol.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row-column design — burgueno.rowcol","text":"","code":"data(\"burgueno.rowcol\")"},{"path":"/reference/burgueno.rowcol.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Row-column design — burgueno.rowcol","text":"data frame 128 observations following 5 variables. rep rep, 2 levels row row col column gen genotype, 64 levels yield yield, tons/ha","code":""},{"path":"/reference/burgueno.rowcol.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Row-column design — burgueno.rowcol","text":"field experiment two contiguous replicates 8 rows, 16 columns. plot size given. Electronic version data obtained CropStat software. Used permission Juan Burgueno.","code":""},{"path":"/reference/burgueno.rowcol.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Row-column design — burgueno.rowcol","text":"J Burgueno, Cadena, J Crossa, M Banziger, Gilmour, B Cullis (2000). User's guide spatial analysis field variety trials using ASREML. CIMMYT.","code":""},{"path":"/reference/burgueno.rowcol.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row-column design — burgueno.rowcol","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(burgueno.rowcol) dat <- burgueno.rowcol # Two contiguous reps in 8 rows, 16 columns libs(desplot) desplot(dat, yield ~ col*row, out1=rep, # aspect unknown text=gen, shorten=\"none\", cex=.75, main=\"burgueno.rowcol\") libs(lme4,lucid) # Random rep, row and col within rep # m1 <- lmer(yield ~ gen + (1|rep) + (1|rep:row) + (1|rep:col), data=dat) # vc(m1) # Match components of Burgueno p. 40 ## grp var1 var2 vcov sdcor ## rep:col (Intercept) 0.2189 0.4679 ## rep:row (Intercept) 0.1646 0.4057 ## rep (Intercept) 0.1916 0.4378 ## Residual 0.1796 0.4238 if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # AR1 x AR1 with linear row/col effects, random spline row/col dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf,dat$yf),] m2 <- asreml(yield ~ gen + lin(yf) + lin(xf), data=dat, random = ~ spl(yf) + spl(xf), resid = ~ ar1(xf):ar1(yf)) m2 <- update(m2) # More iterations # Scaling of spl components has changed in asreml from old versions lucid::vc(m2) # Match Burgueno p. 42 ## effect component std.error z.ratio bound ## spl(yf) 0.09077 0.08252 1.1 P 0 ## spl(xf) 0.08107 0.08209 0.99 P 0 ## xf:yf(R) 0.1482 0.03119 4.8 P 0 ## xf:yf!xf!cor 0.1152 0.2269 0.51 U 0.1 ## xf:yf!yf!cor 0.009467 0.2414 0.039 U 0.9 plot(varioGram(m2), main=\"burgueno.rowcol\") } } # }"},{"path":"/reference/burgueno.unreplicated.html","id":null,"dir":"Reference","previous_headings":"","what":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"Field experiment unreplicated genotypes plus one repeated check.","code":""},{"path":"/reference/burgueno.unreplicated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"","code":"data(\"burgueno.unreplicated\")"},{"path":"/reference/burgueno.unreplicated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"data frame 434 observations following 4 variables. gen genotype, 281 levels col column row row yield yield, tons/ha","code":""},{"path":"/reference/burgueno.unreplicated.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"field experiment 280 new genotypes. check genotype planted every 4th column. plot size given. Electronic version data obtained CropStat software. Used permission Juan Burgueno.","code":""},{"path":"/reference/burgueno.unreplicated.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"J Burgueno, Cadena, J Crossa, M Banziger, Gilmour, B Cullis (2000). User's guide spatial analysis field variety trials using ASREML. CIMMYT.","code":""},{"path":"/reference/burgueno.unreplicated.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Field experiment with unreplicated genotypes plus one repeated check. — burgueno.unreplicated","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(burgueno.unreplicated) dat <- burgueno.unreplicated # Define a 'check' variable for colors dat$check <- ifelse(dat$gen==\"G000\", 2, 1) # Every fourth column is the 'check' genotype libs(desplot) desplot(dat, yield ~ col*row, col=check, num=gen, #text=gen, cex=.3, # aspect unknown main=\"burgueno.unreplicated\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # AR1 x AR1 with random genotypes dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf,dat$yf),] m2 <- asreml(yield ~ 1, data=dat, random = ~ gen, resid = ~ ar1(xf):ar1(yf)) lucid::vc(m2) ## effect component std.error z.ratio bound ## gen 0.9122 0.127 7.2 P 0 ## xf:yf(R) 0.4993 0.05601 8.9 P 0 ## xf:yf!xf!cor -0.2431 0.09156 -2.7 U 0 ## xf:yf!yf!cor 0.1255 0.07057 1.8 U 0.1 # Note the strong saw-tooth pattern in the variogram. Seems to # be column effects. plot(varioGram(m2), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5), main=\"burgueno.unreplicated - AR1xAR1\") # libs(lattice) # Show how odd columns are high # bwplot(resid(m2) ~ col, data=dat, horizontal=FALSE) # Define an even/odd column factor as fixed effect # dat$oddcol <- factor(dat$col # The modulus operator throws a bug, so do it the hard way. dat$oddcol <- factor(dat$col - floor(dat$col / 2) *2 ) m3 <- update(m2, yield ~ 1 + oddcol) m3$loglik # Matches Burgueno table 3, line 3 plot(varioGram(m3), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5), main=\"burgueno.unreplicated - AR1xAR1 + Even/Odd\") # Much better-looking variogram } } # }"},{"path":"/reference/butron.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize with pedigrees — butron.maize","title":"Multi-environment trial of maize with pedigrees — butron.maize","text":"Maize yields multi-environment trial. Pedigree included.","code":""},{"path":"/reference/butron.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize with pedigrees — butron.maize","text":"data frame 245 observations following 5 variables. gen genotype male male parent female female parent env environment yield yield, Mg/ha","code":""},{"path":"/reference/butron.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize with pedigrees — butron.maize","text":"Ten inbreds crossed produce diallel without reciprocals. 45 F1 crosses evaluated along 4 checks triple-lattice 7x7 design. Pink stem borer infestation natural. Experiments performed 1995 1996 three sites northwestern Spain: Pontevedra (42 deg 24 min N, 8 deg 38 min W, 20 m sea), Pontecaldelas (42 deg 23 N, 8 min 32 W, 300 m sea), Ribadumia (42 deg 30 N, 8 min 46 W, 50 m sea). two-letter location code year concatenated define environment. average number larvae per plant environment: Used permission Ana Butron.","code":""},{"path":"/reference/butron.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize with pedigrees — butron.maize","text":"Butron, Velasco, P Ordas, Malvar, RA (2004). Yield evaluation maize cultivars across environments different levels pink stem borer infestation. Crop Science, 44, 741-747. https://doi.org/10.2135/cropsci2004.7410","code":""},{"path":"/reference/butron.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize with pedigrees — butron.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(butron.maize) dat <- butron.maize libs(reshape2) mat <- acast(dat, gen~env, value.var='yield') mat <- sweep(mat, 2, colMeans(mat)) mat.svd <- svd(mat) # Calculate PC1 and PC2 scores as in Table 4 of Butron # Comment out to keep Rcmd check from choking on ' # round(mat.svd$u[,1:2] biplot(princomp(mat), main=\"butron.maize\", cex=.7) # Figure 1 of Butron if(require(\"asreml\", quietly=TRUE)) { # Here we see if including pedigree information is helpful for a # multi-environment model # Including the pedigree provided little benefit # Create the pedigree ped <- dat[, c('gen','male','female')] ped <- ped[!duplicated(ped),] # remove duplicates unip <- unique(c(ped$male, ped$female)) # Unique parents unip <- unip[!is.na(unip)] # We have to define parents at the TOP of the pedigree ped <- rbind(data.frame(gen=c(\"Dent\",\"Flint\"), # genetic groups male=c(0,0), female=c(0,0)), data.frame(gen=c(\"A509\",\"A637\",\"A661\",\"CM105\",\"EP28\", \"EP31\",\"EP42\",\"F7\",\"PB60\",\"Z77016\"), male=rep(c('Dent','Flint'),each=5), female=rep(c('Dent','Flint'),each=5)), ped) ped[is.na(ped$male),'male'] <- 0 ped[is.na(ped$female),'female'] <- 0 libs(asreml) ped.ainv <- ainverse(ped) m0 <- asreml(yield ~ 1+env, data=dat, random = ~ gen) m1 <- asreml(yield ~ 1+env, random = ~ vm(gen, ped.ainv), data=dat) m2 <- update(m1, random = ~ idv(env):vm(gen, ped.ainv)) m3 <- update(m2, random = ~ diag(env):vm(gen, ped.ainv)) m4 <- update(m3, random = ~ fa(env,1):vm(gen, ped.ainv)) #summary(m0)$aic #summary(m4)$aic ## df AIC ## m0 2 229.4037 ## m1 2 213.2487 ## m2 2 290.6156 ## m3 6 296.8061 ## m4 11 218.1568 p0 <- predict(m0, data=dat, classify=\"gen\")$pvals p1 <- predict(m1, data=dat, classify=\"gen\")$pvals p1par <- p1[1:12,] # parents p1 <- p1[-c(1:12),] # remove parents # Careful! Need to manually sort the predictions p0 <- p0[order(as.character(p0$gen)),] p1 <- p1[order(as.character(p1$gen)),] # lims <- range(c(p0$pred, p1$pred)) * c(.95,1.05) lims <- c(6,8.25) # zoom in on the higher-yielding hybrids plot(p0$predicted.value, p1$predicted.value, pch=\"\", xlim=lims, ylim=lims, main=\"butron.maize\", xlab=\"BLUP w/o pedigree\", ylab=\"BLUP with pedigree\") abline(0,1,col=\"lightgray\") text(x=p0$predicted.value, y=p1$predicted.value, p0$gen, cex=.5, srt=-45) text(x=min(lims), y=p1par$predicted.value, p1par$gen, cex=.5, col=\"red\") round( cor(p0$predicted.value, p1$predicted.value), 3) # 0.994 # Including the pedigree provided very little change } } # }"},{"path":"/reference/byers.apple.html","id":null,"dir":"Reference","previous_headings":"","what":"Diameters of apples — byers.apple","title":"Diameters of apples — byers.apple","text":"Measurements diameters apples","code":""},{"path":"/reference/byers.apple.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diameters of apples — byers.apple","text":"data frame 480 observations following 6 variables. tree tree, 10 levels apple apple, 24 levels size size apple appleid unique id number apple time time period, 1-6 = (week/2) diameter diameter, inches","code":""},{"path":"/reference/byers.apple.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diameters of apples — byers.apple","text":"Experiment conducted Winchester Agricultural Experiment Station Virginia Polytechnic Institute State University. Twentyfive apples chosen ten apple trees. , 80 apples largest size class, 2.75 inches diameter greater. diameters apples recorded every two weeks 12-week period.","code":""},{"path":"/reference/byers.apple.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Diameters of apples — byers.apple","text":"Schabenberger, Oliver Francis J. Pierce. 2002. Contemporary Statistical Models Plant Soil Sciences. CRC Press, Boca Raton, FL.","code":""},{"path":"/reference/byers.apple.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diameters of apples — byers.apple","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(byers.apple) dat <- byers.apple libs(lattice) xyplot(diameter ~ time | factor(appleid), data=dat, type=c('p','l'), strip=strip.custom(par.strip.text=list(cex=.7)), main=\"byers.apple\") # Overall fixed linear trend, plus random intercept/slope deviations # for each apple. Observations within each apple are correlated. libs(nlme) libs(lucid) m1 <- lme(diameter ~ 1 + time, data=dat, random = ~ time|appleid, method='ML', cor = corAR1(0, form=~ time|appleid), na.action=na.omit) vc(m1) ## effect variance stddev corr ## (Intercept) 0.007354 0.08575 NA ## time 0.00003632 0.006027 0.83 ## Residual 0.0004555 0.02134 NA } # }"},{"path":"/reference/caribbean.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize with fertilization — caribbean.maize","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"Maize fertilization trial Antigua St. Vincent.","code":""},{"path":"/reference/caribbean.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"data frame 612 observations following 7 variables. isle island, 2 levels site site block block plot plot, numeric trt treatment factor combining N,P,K ears number ears harvested yield yield kilograms N nitrogen fertilizer level P phosphorous fertilizer level K potassium fertilizer level","code":""},{"path":"/reference/caribbean.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"Antigua coral island Caribbean sufficient level land experiments semi-arid climate, St. Vincent volcanic level areas uncommon, rainfall can seasonally heavy. 8-9 sites island. Plots 16 feet 18 feet. central area 12 feet 12 feet harvested recorded. number ears harvested recorded isle Antigua. actual amounts N, P, K given. 0, 1, 2, 3. digits treatment represent levels nitrogen, phosphorus, potassium fertilizer, respectively. TEAN site suffered damage goats plot 27, 35 36. LFAN site suffered damage cattle one boundary–plots 9, 18, 27, 36. Electronic version data retrieved http://lib.stat.cmu.edu/datasets/Andrews/ https://www2.stat.duke.edu/courses/Spring01/sta114/data/andrews.html","code":""},{"path":"/reference/caribbean.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"D.F. Andrews .M. Herzberg. 1985. \tData: Collection Problems Many Fields Student \tResearch Worker. Springer. Table 58.1 58.2.","code":""},{"path":"/reference/caribbean.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"Also DAAG package data sets antigua stVincent.","code":""},{"path":"/reference/caribbean.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize with fertilization — caribbean.maize","text":"","code":"library(agridat) data(caribbean.maize) dat <- caribbean.maize # Yield and ears are correlated libs(lattice) xyplot(yield~ears|site, dat, ylim=c(0,10), subset=isle==\"Antigua\", main=\"caribbean.maize - Antiqua\") # Some locs show large response to nitrogen (as expected), e.g. UISV, OOSV dotplot(trt~yield|site, data=dat, main=\"caribbean.maize treatment response\") # Show the strong N*site interaction with little benefit on Antiqua, but # a strong response on St.Vincent. dat <- transform(dat, env=paste(substring(isle,1,1),site,sep=\"-\")) bwplot(yield~N|env, dat, main=\"caribbean.maize\", xlab=\"nitrogen\")"},{"path":"/reference/carlson.germination.html","id":null,"dir":"Reference","previous_headings":"","what":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"Germination alfalfa seeds various salt concentrations","code":""},{"path":"/reference/carlson.germination.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"","code":"data(\"carlson.germination\")"},{"path":"/reference/carlson.germination.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"data frame 120 observations following 3 variables. gen genotype factor, 15 levels germ germination percent, 0-100 nacl salt concentration percent, 0-2","code":""},{"path":"/reference/carlson.germination.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"Data means averaged 5, 10, 15, 20 day counts. Germination expressed percent -salt control account differences germination among cultivars.","code":""},{"path":"/reference/carlson.germination.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"Carlson, JR Ditterline, RL Martin, JM Sands, DC Lund, RE. (1983). Alfalfa Seed Germination Antibiotic Agar Containing NaCl. Crop science, 23, 882-885. https://doi.org/10.2135/cropsci1983.0011183X002300050016x","code":""},{"path":"/reference/carlson.germination.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Germination of alfalfa seeds at various salt concentrations — carlson.germination","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(carlson.germination) dat <- carlson.germination dat$germ <- dat$germ/100 # Convert to percent # Separate response curve for each genotype. # Really, we should use a glmm with random int/slope for each genotype m1 <- glm(germ~ 0 + gen*nacl, data=dat, family=quasibinomial) # Plot data and fitted model libs(latticeExtra) newd <- data.frame(expand.grid(gen=levels(dat$gen), nacl=seq(0,2,length=100))) newd$pred <- predict(m1, newd, type=\"response\") xyplot(germ~nacl|gen, dat, as.table=TRUE, main=\"carlson.germination\", xlab=\"Percent NaCl\", ylab=\"Fraction germinated\") + xyplot(pred~nacl|gen, newd, type='l', grid=list(h=1,v=0)) # Calculate LD50 values. Note, Carlson et al used quadratics, not glm. # MASS::dose.p cannot handle multiple slopes, so do a separate fit for # each genotype. Results are vaguely similar to Carlson table 5. ## libs(MASS) ## for(ii in unique(dat$gen)){ ## cat(\"\\n\", ii, \"\\n\") ## mm <- glm(germ ~ 1 + nacl, data=dat, subset=gen==ii, family=quasibinomial(link=\"probit\")) ## print(dose.p(mm)) ## } ## Dose SE ## Anchor 1.445728 0.05750418 ## Apollo 1.305804 0.04951644 ## Baker 1.444153 0.07653989 ## Drylander 1.351201 0.03111795 ## Grimm 1.395735 0.04206377 } # }"},{"path":"/reference/carmer.density.html","id":null,"dir":"Reference","previous_headings":"","what":"Nonlinear maize yield-density model — carmer.density","title":"Nonlinear maize yield-density model — carmer.density","text":"Nonlinear maize yield-density model.","code":""},{"path":"/reference/carmer.density.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Nonlinear maize yield-density model — carmer.density","text":"data frame 32 observations following 3 variables. gen genotype/hybrid, 8 levels pop population (plants) yield yield, pounds per hill","code":""},{"path":"/reference/carmer.density.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Nonlinear maize yield-density model — carmer.density","text":"Eight single-cross hybrids experiment–Hy2xOh7 WF9xC103 included believed optimum yields relatively high low populations. Planted 1963. Plots thinned 2, 4, 6, 8 plants per hill, giving densities 8, 16, 24, 32 thousand plants per acre. Hills rows 40 inches apart. One hill = 1/4000 acre. Split-plot design 5 reps, density main plot subplot hybrid.","code":""},{"path":"/reference/carmer.density.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Nonlinear maize yield-density model — carmer.density","text":"S G Carmer J Jackobs (1965). Exponential Model Predicting Optimum Plant Density Maximum Corn Yield. Agronomy Journal, 57, 241–244. https://doi.org/10.2134/agronj1965.00021962005700030003x","code":""},{"path":"/reference/carmer.density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nonlinear maize yield-density model — carmer.density","text":"","code":"library(agridat) data(carmer.density) dat <- carmer.density dat$gen <- factor(dat$gen, levels=c('Hy2x0h7','WF9xC103','R61x187-2', 'WF9x38-11','WF9xB14','C103xB14', '0h43xB37','WF9xH60')) # Separate analysis for each hybrid # Model: y = x * a * k^x. Table 1 of Carmer and Jackobs. out <- data.frame(a=rep(NA,8), k=NA) preds <- NULL rownames(out) <- levels(dat$gen) newdat <- data.frame(pop=seq(2,8,by=.1)) for(i in levels(dat$gen)){ print(i) dati <- subset(dat, gen==i) mi <- nls(yield ~ pop * a * k^pop, data=dati, start=list(a=10,k=1)) out[i, ] <- mi$m$getPars() # Predicted values pi <- cbind(gen=i, newdat, pred= predict(mi, newdat=newdat)) preds <- rbind(preds, pi) } #> [1] \"Hy2x0h7\" #> [1] \"WF9xC103\" #> [1] \"R61x187-2\" #> [1] \"WF9x38-11\" #> [1] \"WF9xB14\" #> [1] \"C103xB14\" #> [1] \"0h43xB37\" #> [1] \"WF9xH60\" # Optimum plant density is -1/log(k) out$pop.opt <- -1/log(out$k) round(out, 3) #> a k pop.opt #> Hy2x0h7 0.782 0.865 6.875 #> WF9xC103 1.039 0.825 5.192 #> R61x187-2 0.998 0.798 4.441 #> WF9x38-11 1.042 0.825 5.203 #> WF9xB14 1.067 0.806 4.647 #> C103xB14 0.813 0.860 6.653 #> 0h43xB37 0.673 0.862 6.740 #> WF9xH60 0.858 0.854 6.358 ## a k pop.opt ## Hy2x0h7 0.782 0.865 6.875 ## WF9xC103 1.039 0.825 5.192 ## R61x187-2 0.998 0.798 4.441 ## WF9x38-11 1.042 0.825 5.203 ## WF9xB14 1.067 0.806 4.647 ## C103xB14 0.813 0.860 6.653 ## 0h43xB37 0.673 0.862 6.740 ## WF9xH60 0.858 0.854 6.358 # Fit an overall fixed-effect with random deviations for each hybrid. libs(nlme) m1 <- nlme(yield ~ pop * a * k^pop, fixed = a + k ~ 1, random = a + k ~ 1|gen, data=dat, start=c(a=10,k=1)) # summary(m1) # Random effect for 'a' probably not needed libs(latticeExtra) # Plot Data, fixed-effect prediction, random-effect prediction. pdat <- expand.grid(gen=levels(dat$gen), pop=seq(2,8,length=50)) pdat$pred <- predict(m1, pdat) pdat$predf <- predict(m1, pdat, level=0) xyplot(yield~pop|gen, dat, pch=16, as.table=TRUE, main=\"carmer.density models\", key=simpleKey(text=c(\"Data\", \"Fixed effect\",\"Random effect\"), col=c(\"blue\", \"red\",\"darkgreen\"), columns=3, points=FALSE)) + xyplot(predf~pop|gen, pdat, type='l', as.table=TRUE, col=\"red\") + xyplot(pred~pop|gen, pdat, type='l', col=\"darkgreen\", lwd=2)"},{"path":"/reference/cate.potassium.html","id":null,"dir":"Reference","previous_headings":"","what":"Relative cotton yield for different soil potassium concentrations — cate.potassium","title":"Relative cotton yield for different soil potassium concentrations — cate.potassium","text":"Relative cotton yield different soil potassium concentrations","code":""},{"path":"/reference/cate.potassium.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Relative cotton yield for different soil potassium concentrations — cate.potassium","text":"data frame 24 observations following 2 variables. yield Relative yield potassium Soil potassium, ppm","code":""},{"path":"/reference/cate.potassium.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Relative cotton yield for different soil potassium concentrations — cate.potassium","text":"Cate & Nelson used data determine minimum optimal amount soil potassium achieve maximum yield. Note, Fig 1 Cate & Nelson match data Table 2. sort appears points high-concentrations potassium shifted left truncation point. Also, calculations quite match results Table 1. Perhaps published data rounded?","code":""},{"path":"/reference/cate.potassium.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Relative cotton yield for different soil potassium concentrations — cate.potassium","text":"Cate, R.B. Nelson, L.. (1971). simple statistical procedure partitioning soil test correlation data two classes. Soil Science Society America Journal, 35, 658–660. https://doi.org/10.2136/sssaj1971.03615995003500040048x","code":""},{"path":"/reference/cate.potassium.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Relative cotton yield for different soil potassium concentrations — cate.potassium","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cate.potassium) dat <- cate.potassium names(dat) <- c('y','x') CateNelson <- function(dat){ dat <- dat[order(dat$x),] # Sort the data by x x <- dat$x y <- dat$y # Create a data.frame to store the results out <- data.frame(x=NA, mean1=NA, css1=NA, mean2=NA, css2=NA, r2=NA) css <- function(x) { var(x) * (length(x)-1) } tcss <- css(y) # Total corrected sum of squares for(i in 2:(length(y)-2)){ y1 <- y[1:i] y2 <- y[-(1:i)] out[i, 'x'] <- x[i] out[i, 'mean1'] <- mean(y1) out[i, 'mean2'] <- mean(y2) out[i, 'css1'] <- css1 <- css(y1) out[i, 'css2'] <- css2 <- css(y2) out[i, 'r2'] <- ( tcss - (css1+css2)) / tcss } return(out) } cn <- CateNelson(dat) ix <- which.max(cn$r2) with(dat, plot(y~x, ylim=c(0,110), xlab=\"Potassium\", ylab=\"Yield\")) title(\"cate.potassium - Cate-Nelson analysis\") abline(v=dat$x[ix], col=\"skyblue\") abline(h=(dat$y[ix] + dat$y[ix+1])/2, col=\"skyblue\") # another approach with similar results # https://joe.org/joe/2013october/tt1.php libs(\"rcompanion\") cateNelson(dat$x, dat$y, plotit=0) } # }"},{"path":"/reference/chakravertti.factorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"Factorial experiment rice, 3x5x3x3.","code":""},{"path":"/reference/chakravertti.factorial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"","code":"data(\"chakravertti.factorial\")"},{"path":"/reference/chakravertti.factorial.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"data frame 405 observations following 7 variables. block block/field yield yield date planting date, 5 levels gen genotype/variety, 3 levels treat treatment combination, 135 levels seeds number seeds per hole, 3 levels spacing spacing, inches, 3 levels","code":""},{"path":"/reference/chakravertti.factorial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"4 treatment factors: 3 Genotypes (varieties): Nehara, Bhasamanik, Bhasakalma 5 Planting dates: Jul 16, Aug 1, Aug 16, Sep 1, Sep 16 3 Spacings: 6 , 9 , 12 inches 3 Seedlings per hole: 1, 2, local method 3x5x3x3=135 treatment combinations. experiment divided 3 blocks (fields). Total 405 plots. \"plots sowing date within block grouped together, position occupied sowing date groups within Within blocks determined random. grouping together plots sewing date adopted facilitate cultural operations. reason, three varieties also laid compact rows. nine combinations spacings seedling numbers thrown random within combination date planting variety shown diagram.\" Note: diagram appears show treatment combinations, physical layout. Basically, date whole-plot effect, genotype sub-plot effect, 9 treatments (spacings * seedlings) completely randomized withing sub-plot effect.","code":""},{"path":"/reference/chakravertti.factorial.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"Chakravertti, S. C. S. S. Bose P. C. Mahalanobis (1936). complex experiment rice Chinsurah farm, Bengal, 1933-34. Indian Journal Agricultural Science, 6, 34-51. https://archive.org/details/.ernet.dli.2015.271737/page/n83/mode/2up","code":""},{"path":"/reference/chakravertti.factorial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"None","code":""},{"path":"/reference/chakravertti.factorial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial","text":"","code":"if (FALSE) { # \\dontrun{ libs(agridat) data(chakravertti.factorial) dat <- chakravertti.factorial # Simple means for each factor. Same as Chakravertti Table 3 group_by(dat, gen) group_by(dat, date) group_by(dat, spacing) group_by(dat, seeds) libs(HH) interaction2wt(yield ~ gen + date + spacing + seeds, data=dat, main=\"chakravertti.factorial\") # ANOVA matches Chakravertti table 2 # This has a very interesting error structure. # block:date is error term for date # block:date:gen is error term for gen and date:gen # Residual is error term for all other tests (not needed inside Error()) dat <- transform(dat,spacing=factor(spacing)) m2 <- aov(yield ~ block + date + gen + date:gen + spacing + seeds + seeds:spacing + date:seeds + date:spacing + gen:seeds + gen:spacing + date:gen:seeds + date:gen:spacing + date:seeds:spacing + gen:seeds:spacing + date:gen:seeds:spacing + Error(block/(date + date:gen)), data=dat) summary(m2) } # }"},{"path":"/reference/chinloy.fractionalfactorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"Fractional factorial sugarcane, 1/3 3^5 = 3x3x3x3x3.","code":""},{"path":"/reference/chinloy.fractionalfactorial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"","code":"data(\"chinloy.fractionalfactorial\")"},{"path":"/reference/chinloy.fractionalfactorial.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"data frame 81 observations following 10 variables. yield yield block block row row position col column position trt treatment code n nitrogen treatment, 3 levels 0, 1, 2 p phosphorous treatment, 3 levels 0, 1, 2 k potassium treatment, 3 levels 0, 1, 2 b bagasse treatment, 3 levels 0, 1, 2 m filter press mud treatment, 3 levels 0, 1, 2","code":""},{"path":"/reference/chinloy.fractionalfactorial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"experiment grown 1949 Worthy Park Estate Jamaica. 5 treatment factors: 3 Nitrogen levels: 0, 3, 6 hundred-weight per acre. 3 Phosphorous levels: 0, 4, 8 hundred-weight per acre. 3 Potassium (muriate potash) levels: 0, 1, 2 hundred-weight per acre. 3 Bagasse (applied pre-plant) levels: 0, 20, 40 tons per acre. 3 Filter press mud (applied pre-plant) levels: 0, 10, 20 tons per acre. plot 18 yards long 6 yards (3 rows) wide. Plots arranged nine columns nine, 2-yard space separating plots along rows two guard rows separating plots across rows. Field width: 6 yards * 9 plots + 4 yards * 8 gaps = 86 yards Field length: 18 yards * 9 plots + 2 yards * 8 gaps = 178 yards","code":""},{"path":"/reference/chinloy.fractionalfactorial.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"T. Chinloy, R. F. Innes D. J. Finney. (1953). example fractional replication experiment sugar cane manuring. Journ Agricultural Science, 43, 1-11. https://doi.org/10.1017/S0021859600044567","code":""},{"path":"/reference/chinloy.fractionalfactorial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"None","code":""},{"path":"/reference/chinloy.fractionalfactorial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 — chinloy.fractionalfactorial","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(chinloy.fractionalfactorial) dat <- chinloy.fractionalfactorial # Treatments are coded with levels 0,1,2. Make sure they are factors dat <- transform(dat, n=factor(n), p=factor(p), k=factor(k), b=factor(b), m=factor(m)) # Experiment layout libs(desplot) desplot(dat, yield ~ col*row, out1=block, text=trt, shorten=\"no\", cex=0.6, aspect=178/86, main=\"chinloy.fractionalfactorial\") # Main effect and some two-way interactions. These match Chinloy table 6. # Not sure how to code terms like p^2k=b^2m m1 <- aov(yield ~ block + n + p + k + b + m + n:p + n:k + n:b + n:m, dat) anova(m1) } # }"},{"path":"/reference/christidis.competition.html","id":null,"dir":"Reference","previous_headings":"","what":"Competition between varieties in cotton — christidis.competition","title":"Competition between varieties in cotton — christidis.competition","text":"Competition varieties cotton, measurements taken row.","code":""},{"path":"/reference/christidis.competition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Competition between varieties in cotton — christidis.competition","text":"","code":"data(\"christidis.competition\")"},{"path":"/reference/christidis.competition.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Competition between varieties in cotton — christidis.competition","text":"data frame 270 observations following 8 variables. plot plot plotrow row within plot block block row row, 1 row col column gen genotype yield yield, kg height height, cm","code":""},{"path":"/reference/christidis.competition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Competition between varieties in cotton — christidis.competition","text":"Nine genotypes/varieties cotton used variety test. plots 100 meters long 2.40 meters wide, plot 3 rows 0.80 meters apart. layout RCB 5 blocks, block 2 replicates every variety (original intention trying 2 seed treatments). row harvested/weighed separately. leaves plants dried fallen, mean height row measured. Christidis found significant competition varieties, due height differences. Crude analysis. TODO: Find better analysis data incorporates field trends competition effects, maybe including random effect border rows genotype pairs (neighbors)?","code":""},{"path":"/reference/christidis.competition.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Competition between varieties in cotton — christidis.competition","text":"Christidis, Basil G (1935). Intervarietal competition yield trials cotton. Journal Agricultural Science, 25, 231-237. Table 1. https://doi.org/10.1017/S0021859600009710","code":""},{"path":"/reference/christidis.competition.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Competition between varieties in cotton — christidis.competition","text":"None","code":""},{"path":"/reference/christidis.competition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Competition between varieties in cotton — christidis.competition","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(christidis.competition) dat <- christidis.competition # Match Christidis Table 2 means # aggregate(yield ~ gen, aggregate(yield ~ gen+plot, dat, sum), mean) # Each RCB block has 2 replicates of each genotype # with(dat, table(block,gen)) libs(lattice) # Tall plants yield more # xyplot(yield ~ height|gen, data=dat) # Huge yield variation across field. Also heterogeneous variance. xyplot(yield ~ col, dat, group=gen, auto.key=list(columns=5), main=\"christidis.competition\") libs(mgcv) if(is.element(\"package:gam\", search())) detach(\"package:gam\") # Simple non-competition model to remove main effects m1 <- gam(yield ~ gen + s(col), data=dat) p1 <- as.data.frame(predict(m1, type=\"terms\")) names(p1) <- c('geneff','coleff') dat2 <- cbind(dat, p1) dat2 <- transform(dat2, res=yield-geneff-coleff) libs(lattice) xyplot(res ~ col, data=dat2, group=gen, main=\"christidis.competition - residuals\") } # }"},{"path":"/reference/christidis.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton — christidis.cotton.uniformity","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"Uniformity trial cotton Greece, 1938","code":""},{"path":"/reference/christidis.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"","code":"data(\"christidis.cotton.uniformity\")"},{"path":"/reference/christidis.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"data frame 1024 observations following 4 variables. col column row row yield yield, kg/unit block block factor","code":""},{"path":"/reference/christidis.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"experiment conducted 1938 Sindos Greek Cotton Research Institute. block consisted 20 rows, 1 meter apart 66 meters long. Two rows side 1 meter end removed borders. row divided 4 meter-lengths harvested separately. 4 blocks, oriented 0, 30, 60, 90 degrees. block contained 16 rows, 64 meters long. Field width: 16 units * 4 m = 64 m Field depth: 16 rows * 1 m = 16 m","code":""},{"path":"/reference/christidis.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"Christidis, B. G. (1939). Variability Plots Various Shapes Affected Plot Orientation. Empire Journal Experimental Agriculture 7: 330-342. Table 1.","code":""},{"path":"/reference/christidis.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"None","code":""},{"path":"/reference/christidis.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton — christidis.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(christidis.cotton.uniformity) dat <- christidis.cotton.uniformity # Match the mean yields in table 2. Not sure why '16' is needed # sapply(split(dat$yield, dat$block), mean)*16 libs(desplot) dat$yld <- dat$yield/4*1000 # re-scale to match Christidis fig 1 desplot(dat, yld ~ col*row|block, flip=TRUE, aspect=(16)/(64), main=\"christidis.cotton.uniformity\") } # }"},{"path":"/reference/christidis.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — christidis.wheat.uniformity","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"Uniformity trial wheat Cambridge, UK 1931.","code":""},{"path":"/reference/christidis.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"","code":"data(\"christidis.wheat.uniformity\")"},{"path":"/reference/christidis.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"data frame 288 observations following 3 variables. row row col column yield yield","code":""},{"path":"/reference/christidis.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"Two blocks, 24 rows . block , 90-foot row divided 12 units, unit 7.5 feet long. Rows 8 inches wide. Field width: 12 units * 7.5 feet = 90 feet Field length: 24 rows * 8 inches = 16 feet","code":""},{"path":"/reference/christidis.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"Christidis, Basil G (1931). importance shape plots field experimentation. Journal Agricultural Science, 21, 14-37. Table VI, p. 28. https://dx.doi.org/10.1017/S0021859600007942","code":""},{"path":"/reference/christidis.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"None","code":""},{"path":"/reference/christidis.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — christidis.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(christidis.wheat.uniformity) dat <- christidis.wheat.uniformity # sum(dat$yield) # Matches Christidis libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=16/90, # true aspect main=\"christidis.wheat.uniformity\") } # }"},{"path":"/reference/cleveland.soil.html","id":null,"dir":"Reference","previous_headings":"","what":"Soil resistivity in a field — cleveland.soil","title":"Soil resistivity in a field — cleveland.soil","text":"Soil resistivity field","code":""},{"path":"/reference/cleveland.soil.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Soil resistivity in a field — cleveland.soil","text":"data frame 8641 observations following 5 variables. northing y ordinate easting x ordinate resistivity Soil resistivity, ohms .ns Indicator north/south track track Track number","code":""},{"path":"/reference/cleveland.soil.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Soil resistivity in a field — cleveland.soil","text":"Resistivity related soil salinity. Electronic version data retrieved http://lib.stat.cmu.edu/datasets/Andrews/ Cleaned version Luke Tierney https://homepage.stat.uiowa.edu/~luke/classes/248/examples/soil","code":""},{"path":"/reference/cleveland.soil.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Soil resistivity in a field — cleveland.soil","text":"William Cleveland, (1993). Visualizing Data.","code":""},{"path":"/reference/cleveland.soil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Soil resistivity in a field — cleveland.soil","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cleveland.soil) dat <- cleveland.soil # Similar to Cleveland fig 4.64 ## libs(latticeExtra) ## redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) ## levelplot(resistivity ~ easting + northing, data = dat, ## col.regions=redblue, ## panel=panel.levelplot.points, ## aspect=2.4, xlab= \"Easting (km)\", ylab= \"Northing (km)\", ## main=\"cleveland\") # 2D loess plot. Cleveland fig 4.68 sg1 <- expand.grid(easting = seq(.15, 1.410, by = .02), northing = seq(.150, 3.645, by = .02)) lo1 <- loess(resistivity~easting*northing, data=dat, span = 0.1, degree = 2) fit1 <- predict(lo1, sg1) libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(fit1 ~ sg1$easting * sg1$northing, col.regions=redblue, cuts = 9, aspect=2.4, xlab = \"Easting (km)\", ylab = \"Northing (km)\", main=\"cleveland.soil - 2D smooth of Resistivity\") # 3D loess plot with data overlaid libs(rgl) bg3d(color = \"white\") clear3d() points3d(dat$easting, dat$northing, dat$resistivity / 100, col = rep(\"gray50\", nrow(dat))) rgl::surface3d(seq(.15, 1.410, by = .02), seq(.150, 3.645, by = .02), fit1/100, alpha=0.9, col=rep(\"wheat\", length(fit1)), front=\"fill\", back=\"fill\") close3d() } # }"},{"path":"/reference/cochran.beets.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"Yield number plants sugarbeet fertilizer experiment.","code":""},{"path":"/reference/cochran.beets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"","code":"data(\"cochran.beets\")"},{"path":"/reference/cochran.beets.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"data frame 42 observations following 4 variables. fert fertilizer treatment block block yield yield, tons/acres plants number plants per plot","code":""},{"path":"/reference/cochran.beets.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"Yield (tons/acre) number beets per plot. Fertilizer treatments combine superphosphate (P), muriate potash (K), sodium nitrate (N).","code":""},{"path":"/reference/cochran.beets.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"George Snedecor (1946). Statisitcal Methods, 4th ed. Table 12.13, p. 332.","code":""},{"path":"/reference/cochran.beets.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"H. Fairfield Smith (1957). Interpretation Adjusted Treatment Means Regressions Analysis Covariance. Biometrics, 13, 282-308. https://doi.org/10.2307/2527917","code":""},{"path":"/reference/cochran.beets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield and number of plants in a sugarbeet fertilizer experiment — cochran.beets","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.beets) dat = cochran.beets # P has strong effect libs(lattice) xyplot(yield ~ plants|fert, dat, main=\"cochran.beets\") } # }"},{"path":"/reference/cochran.bib.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"Balanced incomplete block design corn","code":""},{"path":"/reference/cochran.bib.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"data frame 52 observations following 3 variables. loc location/block, 13 levels gen genotype/line, 13 levels yield yield, pounds/plot","code":""},{"path":"/reference/cochran.bib.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"Incomplete block design. loc/block 4 genotypes/lines. blocks planted different locations. Conducted 1943 North Carolina.","code":""},{"path":"/reference/cochran.bib.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"North Carolina Agricultural Experiment Station, United States Department Agriculture.","code":""},{"path":"/reference/cochran.bib.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"Cochran, W.G. Cox, G.M. (1957), Experimental Designs, 2nd ed., Wiley Sons, New York, p. 448.","code":""},{"path":"/reference/cochran.bib.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn, balanced incomplete block design — cochran.bib","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.bib) dat <- cochran.bib # Show the incomplete-block structure libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(yield~loc*gen, dat, col.regions=redblue, xlab=\"loc (block)\", main=\"cochran.bib - incomplete blocks\") with(dat, table(gen,loc)) rowSums(as.matrix(with(dat, table(gen,loc)))) colSums(as.matrix(with(dat, table(gen,loc)))) m1 = aov(yield ~ gen + Error(loc), data=dat) summary(m1) libs(nlme) m2 = lme(yield ~ -1 + gen, data=dat, random=~1|loc) } # }"},{"path":"/reference/cochran.crd.html","id":null,"dir":"Reference","previous_headings":"","what":"Potato scab infection with sulfur treatments — cochran.crd","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"Potato scab infection sulfur treatments","code":""},{"path":"/reference/cochran.crd.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"data frame 32 observations following 5 variables. inf infection percent trt treatment factor row row col column","code":""},{"path":"/reference/cochran.crd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"experiment conducted investigate effect sulfur controlling scab disease potatoes. seven treatments. Control, plus spring fall application 300, 600, 1200 pounds/acre sulfur. response variable infection percent surface area covered scab. completely randomized design used 8 replications control 4 replications treatments. Although original analysis show significant differences sulfur treatments, including polynomial trend model uncovered significant differences (Tamura, 1988).","code":""},{"path":"/reference/cochran.crd.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"W.G. Cochran G. Cox, 1957. Experimental Designs, 2nd ed. John Wiley, New York.","code":""},{"path":"/reference/cochran.crd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"Tamura, R.N. Nelson, L.. Naderman, G.C., (1988). investigation validity usefulness trend analysis field plot data. Agronomy Journal, 80, 712-718. https://doi.org/10.2134/agronj1988.00021962008000050003x","code":""},{"path":"/reference/cochran.crd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Potato scab infection with sulfur treatments — cochran.crd","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.crd) dat <- cochran.crd # Field plan libs(desplot) desplot(dat, inf~col*row, text=trt, cex=1, # aspect unknown main=\"cochran.crd\") # CRD anova. Table 6 of Tamura 1988 contrasts(dat$trt) <- cbind(c1=c(1,1,1,-6,1,1,1), # Control vs Sulf c2=c(-1,-1,-1,0,1,1,1)) # Fall vs Sp m1 <- aov(inf ~ trt, data=dat) anova(m1) summary(m1, split=list(trt=list(\"Control vs Sulf\"=1, \"Fall vs Spring\"=2))) # Quadratic polynomial for columns...slightly different than Tamura 1988 m2 <- aov(inf ~ trt + poly(col,2), data=dat) anova(m2) summary(m2, split=list(trt=list(\"Control vs Sulf\"=1, \"Fall vs Spring\"=2))) } # }"},{"path":"/reference/cochran.eelworms.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"Counts eelworms fumigant treatments","code":""},{"path":"/reference/cochran.eelworms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"data frame 48 observations following 7 variables. block block factor, 4 levels row row col column fumigant fumigant factor dose dose, Numeric 0,1,2. Maybe factor? initial count eelworms pre-treatment final count eelworms post-treatment grain grain yield pounds straw straw yield pounds weeds ratio weeds total oats","code":""},{"path":"/reference/cochran.eelworms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"soil fumigation experiment Spring Oats, conducted 1935. plot 30 links x 41.7 links, clear side plot specific length. Treatment codes: Con = Control, Chl = Chlorodinitrobenzen, Cym = Cymag, Car = Carbon Disulphide jelly, See = Seekay. Experiment conducted 1935 Rothamsted Experiment Station. early March 400 grams soil (4 x 100g) sampled number eelworm cysts counted. Fumigants added soil, oats sown later harvested. October, plots sampled final count cysts recorded. Rothamsted report concludes \"Car\" \"Cym\" produced higher yields, due partly nitrogen fumigant, \"Chl\" decreased yield. fumigants reduced weeds. crop 'unusually weedy'. \"Car\" \"See\" decreased number eelworm cysts soil. original data can found Rothamsted Report. report notes position blocks field slightly different shown. experiment plan shown Bailey (2008, p. 73), shows columns 9-11 shifted slightly upward. clear . Thanks U.Genschel identifying typo.","code":""},{"path":"/reference/cochran.eelworms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"Cochran Cox, 1950. Experimental Designs. Table 3.1.","code":""},{"path":"/reference/cochran.eelworms.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"R. . Bailey (2008). Design Comparative Experiments. Cambridge. Experiments Rothamsted (1936). Report 1935, Rothamsted Research. pp 174 - 193. https://doi.org/10.23637/ERADOC-1-67","code":""},{"path":"/reference/cochran.eelworms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts of eelworms before and after fumigant treatments — cochran.eelworms","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.eelworms) dat <- cochran.eelworms libs(lattice) splom(dat[ , 5:10], group=dat$fumigant, auto.key=TRUE, main=\"cochran.eelworms\") libs(desplot) desplot(dat, fumigant~col*row, text=dose, flip=TRUE, cex=2) # Very strong spatial trends desplot(dat, initial ~ col*row, flip=TRUE, # aspect unknown main=\"cochran.eelworms\") # final counts are strongly related to initial counts libs(lattice) xyplot(final~initial|factor(dose), data=dat, group=fumigant, main=\"cochran.eelworms - by dose (panel) & fumigant\", xlab=\"Initial worm count\", ylab=\"Final worm count\", auto.key=list(columns=5)) # One approach...log transform, use 'initial' as covariate, create 9 treatments dat <- transform(dat, trt=factor(paste0(fumigant, dose))) m1 <- aov(log(final) ~ block + trt + log(initial), data=dat) anova(m1) } # }"},{"path":"/reference/cochran.factorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"Factorial experiment beans, 2x2x2x2.","code":""},{"path":"/reference/cochran.factorial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"","code":"data(\"cochran.factorial\")"},{"path":"/reference/cochran.factorial.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"data frame 32 observations following 4 variables. rep rep factor block block factor trt treatment factor, 16 levels yield yield (pounds) d dung treatment, 2 levels n nitrogen treatment, 2 levels p phosphorous treatment, 2 levels k potassium treatment, 2 levels","code":""},{"path":"/reference/cochran.factorial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"Conducted Rothamsted Experiment Station 1936. 4 treatment factors: 2 d dung levels: None, 10 tons/acre. 2 n nitrochalk levels: None, 0.4 hundredweight nitrogen per acre. 2 p superphosphate levels: None, 0.6 hundredweight per acre 2 k muriate potash levels: None, 1 hundredweight K20 per acres. response variable yield beans.","code":""},{"path":"/reference/cochran.factorial.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"Cochran, W.G. Cox, G.M. (1957), Experimental Designs, 2nd ed., Wiley Sons, New York, p. 160.","code":""},{"path":"/reference/cochran.factorial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factorial experiment of beans, 2x2x2x2 — cochran.factorial","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.factorial) dat <- cochran.factorial # Ensure factors dat <- transform(dat, d=factor(d), n=factor(n), p=factor(p), k=factor(k)) # Cochran table 6.5. m1 <- lm(yield ~ rep * block + (d+n+p+k)^3, data=dat) anova(m1) libs(FrF2) aliases(m1) MEPlot(m1, select=3:6, main=\"cochran.factorial - main effects plot\") } # }"},{"path":"/reference/cochran.latin.html","id":null,"dir":"Reference","previous_headings":"","what":"Latin square design in wheat — cochran.latin","title":"Latin square design in wheat — cochran.latin","text":"Six wheat plots sampled six operators shoot heights measured. operators sampled plots six ordered sequences. dependent variate difference measured height true height plot.","code":""},{"path":"/reference/cochran.latin.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Latin square design in wheat — cochran.latin","text":"data frame 36 observations following 4 variables. row row col column operator operator factor diff difference measured height true height","code":""},{"path":"/reference/cochran.latin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latin square design in wheat — cochran.latin","text":"Cochran, W.G. Cox, G.M. (1957), Experimental Designs, 2nd ed., Wiley Sons, New York.","code":""},{"path":"/reference/cochran.latin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Latin square design in wheat — cochran.latin","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.latin) dat <- cochran.latin libs(desplot) desplot(dat, diff~col*row, text=operator, cex=1, # aspect unknown main=\"cochran.latin\") dat <- transform(dat, rf=factor(row), cf=factor(col)) aov.dat <- aov(diff ~ operator + Error(rf*cf), dat) summary(aov.dat) model.tables(aov.dat, type=\"means\") } # }"},{"path":"/reference/cochran.lattice.html","id":null,"dir":"Reference","previous_headings":"","what":"Balanced lattice experiment in cotton — cochran.lattice","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"Balanced lattice experiment cotton","code":""},{"path":"/reference/cochran.lattice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"","code":"data(\"cochran.lattice\")"},{"path":"/reference/cochran.lattice.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"data frame 80 observations following 5 variables. y percent affected flower buds rep replicate row row col column trt treatment factor","code":""},{"path":"/reference/cochran.lattice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"experiment balanced lattice square 16 treatments 4x4 layout 5 replicates. treatments applied cotton plants. plot ten rows wide 70 feet long (1/18 acre). (Estimated plot width 34.5 feet.) Data collected middle 4 rows. data percentages squares showing attack boll weevils. 'square' name given young flower bud. plot orientation clear.","code":""},{"path":"/reference/cochran.lattice.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"William G. Cochran, Gertrude M. Cox. Experimental Designs, 2nd Edition. Page 490. Originally : F. M. Wadley (1946). Incomplete block designs insect population problems. J. Economic Entomology, 38, 651–654.","code":""},{"path":"/reference/cochran.lattice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"Walter Federer. Combining Standard Block Analyses Spatial Analyses Random Effects Model. Cornell Univ Tech Report BU-1373-MA. https://hdl.handle.net/1813/31971","code":""},{"path":"/reference/cochran.lattice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Balanced lattice experiment in cotton — cochran.lattice","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.lattice) dat <- cochran.lattice libs(desplot) desplot(dat, y~row*col|rep, text=trt, # aspect unknown, should be 2 or .5 main=\"cochran.lattice\") # Random rep,row,column model often used by Federer libs(lme4) dat <- transform(dat, rowf=factor(row), colf=factor(col)) m1 <- lmer(y ~ trt + (1|rep) + (1|rep:row) + (1|rep:col), data=dat) summary(m1) } # }"},{"path":"/reference/cochran.wireworms.html","id":null,"dir":"Reference","previous_headings":"","what":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"Wireworms controlled fumigants latin square","code":""},{"path":"/reference/cochran.wireworms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"data frame 25 observations following 4 variables. row row col column trt fumigant treatment, 5 levels worms count wireworms per plot","code":""},{"path":"/reference/cochran.wireworms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"Plots approximately 22 cm 13 cm. Layout experiment latin square. number wireworms plot counted, following soil fumigation previous year.","code":""},{"path":"/reference/cochran.wireworms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"W. G. Cochran (1938). difficulties statistical analysis replicated experiments. Empire Journal Experimental Agriculture, 6, 157–175.","code":""},{"path":"/reference/cochran.wireworms.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"Ron Snee (1980). Graphical Display Means. American Statistician, 34, 195-199. https://www.jstor.org/stable/2684060 https://doi.org/10.1080/00031305.1980.10483028 W. Cochran (1940). analysis variance experimental errors follow Poisson binomial laws. Annals Mathematical Statistics, 11, 335-347. https://www.jstor.org/stable/2235680 G W Snedecor W G Cochran, 1980. Statistical Methods, Iowa State University Press. Page 288.","code":""},{"path":"/reference/cochran.wireworms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wireworms controlled by fumigants in a latin square — cochran.wireworms","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cochran.wireworms) dat <- cochran.wireworms libs(desplot) desplot(dat, worms ~ col*row, text=trt, cex=1, # aspect unknown main=\"cochran.wireworms\") # Trt K is effective, but not the others. Really, this says it all. libs(lattice) bwplot(worms ~ trt, dat, main=\"cochran.wireworms\", xlab=\"Treatment\") # Snedecor and Cochran do ANOVA on sqrt(x+1). dat <- transform(dat, rowf=factor(row), colf=factor(col)) m1 <- aov(sqrt(worms+1) ~ rowf + colf + trt, data=dat) anova(m1) # Instead of transforming, use glm m2 <- glm(worms ~ trt + rowf + colf, data=dat, family=\"poisson\") anova(m2) # GLM with random blocking. libs(lme4) m3 <- glmer(worms ~ -1 +trt +(1|rowf) +(1|colf), data=dat, family=\"poisson\") summary(m3) ## Fixed effects: ## Estimate Std. Error z value Pr(>|z|) ## trtK 0.1393 0.4275 0.326 0.745 ## trtM 1.7814 0.2226 8.002 1.22e-15 *** ## trtN 1.9028 0.2142 8.881 < 2e-16 *** ## trtO 1.7147 0.2275 7.537 4.80e-14 *** } # }"},{"path":"/reference/connolly.potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Potato yields in single-drill plots — connolly.potato","title":"Potato yields in single-drill plots — connolly.potato","text":"Potato yields single-drill plots","code":""},{"path":"/reference/connolly.potato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Potato yields in single-drill plots — connolly.potato","text":"","code":"data(\"connolly.potato\")"},{"path":"/reference/connolly.potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Potato yields in single-drill plots — connolly.potato","text":"data frame 80 observations following 6 variables. rep block gen variety row row col column yield yield, kg/ha matur maturity group","code":""},{"path":"/reference/connolly.potato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Potato yields in single-drill plots — connolly.potato","text":"Connolly et el use data illustrate yield can affected competition neighboring plots. data uses M1, M2, M3 maturity, Connolly et al use FE (first early), SE (second early) M (maincrop). trial 20 sections, independent row 20 drills. data four reps single-drill plots sections 1, 6, 11, 16. neighbor covariate plot defined average plots left right. drills edge trial, covariate average one neighboring plot yield section (.e. rep) mean. interesting fit model uses differences maturity plot neighbor actual covariate. https://doi.org/10.1111/j.1744-7348.1993.tb04099.x Used permission Iain Currie.","code":""},{"path":"/reference/connolly.potato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Potato yields in single-drill plots — connolly.potato","text":"Connolly, T Currie, ID Bradshaw, JE McNicol, JW. (1993). Inter-plot competition yield trials potatoes Solanum tuberosum L. single-drill plots. Annals Applied Biology, 123, 367-377.","code":""},{"path":"/reference/connolly.potato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Potato yields in single-drill plots — connolly.potato","text":"","code":"library(agridat) data(connolly.potato) dat <- connolly.potato # Field plan libs(desplot) desplot(dat, yield~col*row, out1=rep, # aspect unknown main=\"connolly.potato yields (reps not contiguous)\") # Later maturities are higher yielding libs(lattice) bwplot(yield~matur, dat, main=\"connolly.potato yield by maturity\") # Observed raw means. Matches Connolly table 2. mn <- aggregate(yield~gen, data=dat, FUN=mean) mn[rev(order(mn$yield)),] #> gen yield #> 8 V08 16.200 #> 19 V19 14.450 #> 10 V10 13.925 #> 12 V12 13.500 #> 7 V07 13.300 #> 20 V20 12.975 #> 14 V14 12.975 #> 6 V06 12.625 #> 11 V11 12.575 #> 16 V16 11.900 #> 3 V03 11.650 #> 9 V09 11.500 #> 1 V01 11.275 #> 18 V18 10.650 #> 2 V02 10.325 #> 17 V17 10.200 #> 15 V15 10.125 #> 13 V13 10.050 #> 4 V04 9.425 #> 5 V05 9.275 # Create a covariate which is the average of neighboring plot yields libs(reshape2) mat <- acast(dat, row~col, value.var='yield') mat2 <- matrix(NA, nrow=4, ncol=20) mat2[,2:19] <- (mat[ , 1:18] + mat[ , 3:20])/2 mat2[ , 1] <- (mat[ , 1] + apply(mat, 1, mean))/2 mat2[ , 20] <- (mat[ , 20] + apply(mat, 1, mean))/2 dat2 <- melt(mat2) colnames(dat2) <- c('row','col','cov') dat <- merge(dat, dat2) # xyplot(yield ~ cov, data=dat, type=c('p','r')) # Connolly et al fit a model with avg neighbor yield as a covariate m1 <- lm(yield ~ 0 + gen + rep + cov, data=dat) coef(m1)['cov'] # = -.303 (Connolly obtained -.31) #> cov #> -0.3030545 # Block names and effects bnm <- c(\"R1\",\"R2\",\"R3\",\"R4\") beff <- c(0, coef(m1)[c('repR2','repR3','repR4')]) # Variety names and effects vnm <- paste0(\"V\", formatC(1:20, width=2, flag='0')) veff <- coef(m1)[1:20] # Adjust yield for variety and block effects dat <- transform(dat, yadj = yield - beff[match(rep,bnm)] - veff[match(gen,vnm)]) # Similar to Connolly Fig 1. Point pattern doesn't quite match xyplot(yadj~cov, data=dat, type=c('p','r'), main=\"connolly.potato\", xlab=\"Avg yield of nearest neighbors\", ylab=\"Yield, adjusted for variety and block effects\")"},{"path":"/reference/coombs.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"Uniformity trial rice Malaysia","code":""},{"path":"/reference/coombs.rice.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"","code":"data(\"coombs.rice.uniformity\")"},{"path":"/reference/coombs.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"data frame 54 observations following 3 variables. row row col column yield yield gantangs per plot","code":""},{"path":"/reference/coombs.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"Estimated harvest date 1915 earlier. Field length, 18 plots * 1/2 chain. Field width, 3 plots * 1/2 chain.","code":""},{"path":"/reference/coombs.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"Coombs, G. E. J. Grantham (1916). Field Experiments Interpretation results. Agriculture Bulletin Federated Malay States, 7. https://www.google.com/books/edition/The_Agricultural_Bulletin_of_the_Federat/M2E4AQAAMAAJ","code":""},{"path":"/reference/coombs.rice.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"None","code":""},{"path":"/reference/coombs.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice in Malaysia — coombs.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(coombs.rice.uniformity) dat <- coombs.rice.uniformity # Data check. Matches Coombs 709.4 # sum(dat$yield) # There are an excess number of 12s and 14s in the yield libs(lattice) qqmath( ~ yield, dat) # weird libs(desplot) desplot(dat, yield ~ col*row, main=\"coombs.rice.uniformity\", flip=TRUE, aspect=(18 / 3)) } # }"},{"path":"/reference/cornelius.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"Maize yields 9 cultivars 20 locations.","code":""},{"path":"/reference/cornelius.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"","code":"data(\"cornelius.maize\")"},{"path":"/reference/cornelius.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"data frame 180 observations following 3 variables. env environment factor, 20 levels gen genotype/cultivar, 9 levels yield yield, kg/ha","code":""},{"path":"/reference/cornelius.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"Cell means (kg/hectare) CIMMYT EVT16B maize yield trial.","code":""},{"path":"/reference/cornelius.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"P L Cornelius J Crossa M S Seyedsadr. (1996). Statistical Tests Estimators Multiplicative Models Genotype--Environment Interaction. Book: Genotype--Environment Interaction. Pages 199-234.","code":""},{"path":"/reference/cornelius.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"Forkman, Johannes Piepho, Hans-Peter. (2014). Parametric bootstrap methods testing multiplicative terms GGE AMMI models. Biometrics, 70(3), 639-647. https://doi.org/10.1111/biom.12162","code":""},{"path":"/reference/cornelius.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize for 9 cultivars at 20 locations. — cornelius.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cornelius.maize) dat <- cornelius.maize # dotplot(gen~yield|env,dat) # We cannot compare genotype yields easily # Subtract environment mean from each observation libs(reshape2) mat <- acast(dat, gen~env) mat <- scale(mat, scale=FALSE) dat2 <- melt(mat) names(dat2) <- c('gen','env','yield') libs(lattice) bwplot(yield ~ gen, dat2, main=\"cornelius.maize - environment centered yields\") if(0){ # This reproduces the analysis of Forkman and Piepho. test.pc <- function(Y0, type=\"AMMI\", n.boot=10000, maxpc=6) { # Test the significance of Principal Components in GGE/AMMI # Singular value decomposition of centered/double-centered Y Y <- sweep(Y0, 1, rowMeans(Y0)) # subtract environment means if(type==\"AMMI\") { Y <- sweep(Y, 2, colMeans(Y0)) # subtract genotype means Y <- Y + mean(Y0) } lam <- svd(Y)$d # Observed value of test statistic. # t.obs[k] is the proportion of variance explained by the kth term out of # the k...M terms, e.g. t.obs[2] is lam[2]^2 / sum(lam[2:M]^2) t.obs <- { lam^2/rev(cumsum(rev(lam^2))) } [1:(M-1)] t.boot <- matrix(NA, nrow=n.boot, ncol=M-1) # Centering rows/columns reduces the rank by 1 in each direction. I <- if(type==\"AMMI\") nrow(Y0)-1 else nrow(Y0) J <- ncol(Y0)-1 M <- min(I, J) # rank of Y, maximum number of components M <- min(M, maxpc) # Optional step: No more than 5 components for(K in 0:(M-2)){ # 'K' multiplicative components in the svd for(bb in 1:n.boot){ E.b <- matrix(rnorm((I-K) * (J-K)), nrow = I-K, ncol = J-K) lam.b <- svd(E.b)$d t.boot[bb, K+1] <- lam.b[1]^2 / sum(lam.b^2) } } # P-value for each additional multiplicative term in the SVD. # P-value is the proportion of time bootstrap values exceed t.obs colMeans(t.boot > matrix(rep(t.obs, n.boot), nrow=n.boot, byrow=TRUE)) } dat <- cornelius.maize # Convert to matrix format libs(reshape2) dat <- acast(dat, env~gen, value.var='yield') ## R> test.pc(dat,\"AMMI\") ## [1] 0.0000 0.1505 0.2659 0.0456 0.1086 # Forkman: .00 .156 .272 .046 .111 ## R> test.pc(dat,\"GGE\") ## [1] 0.0000 0.2934 0.1513 0.0461 0.2817 # Forkman: .00 .296 .148 .047 .285 } } # }"},{"path":"/reference/corsten.interaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn — corsten.interaction","title":"Multi-environment trial of corn — corsten.interaction","text":"data yield (kg/acre) 20 genotypes corn 7 locations.","code":""},{"path":"/reference/corsten.interaction.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn — corsten.interaction","text":"data frame 140 observations following 3 variables. gen genotype, 20 levels loc location, 7 levels yield yield, kg/acre","code":""},{"path":"/reference/corsten.interaction.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn — corsten.interaction","text":"data used Corsten & Denis (1990) illustrate two-way clustering minimizing interaction sum squares. paper, labels location dendrogram slight typo. order loc labels shown 1 2 3 4 5 6 7. correct order loc labels 1 2 4 5 6 7 3. Used permission Jean-Baptiste Denis.","code":""},{"path":"/reference/corsten.interaction.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn — corsten.interaction","text":"L C Corsten J B Denis, (1990). Structuring Interaction Two-Way Tables Clustering. Biometrics, 46, 207–215. Table 1. https://doi.org/10.2307/2531644","code":""},{"path":"/reference/corsten.interaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn — corsten.interaction","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(corsten.interaction) dat <- corsten.interaction libs(reshape2) m1 <- melt(dat, measure.var='yield') dmat <- acast(m1, loc~gen) # Corsten (1990) uses this data to illustrate simultaneous row and # column clustering based on interaction sums-of-squares. # There is no (known) function in R to reproduce this analysis # (please contact the package maintainer if this is not true). # For comparison, the 'heatmap' function clusters the rows and # columns _independently_ of each other. heatmap(dmat, main=\"corsten.interaction\") } # }"},{"path":"/reference/cox.stripsplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"Strip-split-plot barley fertilizer, calcium, soil factors.","code":""},{"path":"/reference/cox.stripsplit.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"data frame 96 observations following 5 variables. rep replicate, 4 levels soil soil, 3 levels fert fertilizer, 4 levels calcium calcium, 2 levels yield yield winter barley","code":""},{"path":"/reference/cox.stripsplit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"Four different fertilizer treatments laid vertical strips, split subplots different levels calcium. Soil type stripped across split-plot experiment, entire experiment replicated three times. Sometimes called split-block design.","code":""},{"path":"/reference/cox.stripsplit.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"Comes notes Gertrude Cox . Rotti.","code":""},{"path":"/reference/cox.stripsplit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"SAS/STAT(R) 9.2 User's Guide, Second Edition. Example 23.5 Strip-Split Plot. https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_anova_sect030.htm","code":""},{"path":"/reference/cox.stripsplit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strip-split-plot of barley with fertilizer, calcium, and soil factors. — cox.stripsplit","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cox.stripsplit) dat <- cox.stripsplit # Raw means # aggregate(yield ~ calcium, data=dat, mean) # aggregate(yield ~ soil, data=dat, mean) # aggregate(yield ~ calcium, data=dat, mean) libs(HH) interaction2wt(yield ~ rep + soil + fert + calcium, dat, x.between=0, y.between=0, main=\"cox.stripsplit\") # Traditional AOV m1 <- aov(yield~ fert*calcium*soil + Error(rep/(fert+soil+calcium:fert+soil:fert)), data=dat) summary(m1) # With balanced data, the following are all basically identical libs(lme4) # The 'rep:soil:fert' term causes problems...so we drop it. m2 <- lmer(yield ~ fert*soil*calcium + (1|rep) + (1|rep:fert) + (1|rep:soil) + (1|rep:fert:calcium), data=dat) if(0){ # afex uses Kenword-Rogers approach for denominator d.f. libs(afex) mixed(yield ~ fert*soil*calcium + (1|rep) + (1|rep:fert) + (1|rep:soil) + (1|rep:fert:calcium) + (1|rep:soil:fert), data=dat, control=lmerControl(check.nobs.vs.rankZ=\"ignore\")) ## Effect stat ndf ddf F.scaling p.value ## 1 (Intercept) 1350.8113 1 3.0009 1 0.0000 ## 2 fert 3.5619 3 9.0000 1 0.0604 ## 3 soil 3.4659 2 6.0000 1 0.0999 ## 4 calcium 1.8835 1 12.0000 1 0.1950 ## 5 fert:soil 1.2735 6 18.0000 1 0.3179 ## 6 fert:calcium 4.4457 3 12.0000 1 0.0255 ## 7 soil:calcium 0.2494 2 24.0000 1 0.7813 ## 8 fert:soil:calcium 0.3504 6 24.0000 1 0.9027 } } # }"},{"path":"/reference/cramer.cucumber.html","id":null,"dir":"Reference","previous_headings":"","what":"Cucumber yields and quantitative traits — cramer.cucumber","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"Cucumber yields quantitative traits","code":""},{"path":"/reference/cramer.cucumber.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"","code":"data(\"cramer.cucumber\")"},{"path":"/reference/cramer.cucumber.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"data frame 24 observations following 9 variables. cycle cycle rep replicate plants plants per plot flowers number pistillate flowers branches number branches leaves number leaves totalfruit total fruit number culledfruit culled fruit number earlyfruit early fruit number","code":""},{"path":"/reference/cramer.cucumber.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"data used illustrate path analysis correlations phenotypic traits. Used permission Christopher Cramer.","code":""},{"path":"/reference/cramer.cucumber.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"Christopher S. Cramer, Todd C. Wehner, Sandra B. Donaghy. 1999. Path Coefficient Analysis Quantitative Traits. : Handbook Formulas Software Plant Geneticists Breeders, page 89.","code":""},{"path":"/reference/cramer.cucumber.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"Cramer, C. S., T. C. Wehner, S. B. Donaghy. 1999. PATHSAS: SAS computer program path coefficient analysis quantitative data. J. Hered, 90, 260-262 https://doi.org/10.1093/jhered/90.1.260","code":""},{"path":"/reference/cramer.cucumber.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cucumber yields and quantitative traits — cramer.cucumber","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cramer.cucumber) dat <- cramer.cucumber libs(lattice) splom(dat[3:9], group=dat$cycle, main=\"cramer.cucumber - traits by cycle\", auto.key=list(columns=3)) # derived traits dat <- transform(dat, marketable = totalfruit-culledfruit, branchesperplant = branches/plants, nodesperbranch = leaves/(branches+plants), femalenodes = flowers+totalfruit) dat <- transform(dat, perfenod = (femalenodes/leaves), fruitset = totalfruit/flowers, fruitperplant = totalfruit / plants, marketableperplant = marketable/plants, earlyperplant=earlyfruit/plants) # just use cycle 1 dat1 <- subset(dat, cycle==1) # define independent and dependent variables indep <- c(\"branchesperplant\", \"nodesperbranch\", \"perfenod\", \"fruitset\") dep0 <- \"fruitperplant\" dep <- c(\"marketable\",\"earlyperplant\") # standardize trait data for cycle 1 sdat <- data.frame(scale(dat1[1:8, c(indep,dep0,dep)])) # slopes for dep0 ~ indep X <- as.matrix(sdat[,indep]) Y <- as.matrix(sdat[,c(dep0)]) # estdep <- solve(t(X) estdep <- solve(crossprod(X), crossprod(X,Y)) estdep ## branchesperplant 0.7160269 ## nodesperbranch 0.3415537 ## perfenod 0.2316693 ## fruitset 0.2985557 # slopes for dep ~ dep0 X <- as.matrix(sdat[,dep0]) Y <- as.matrix(sdat[,c(dep)]) # estind2 <- solve(t(X) estind2 <- solve(crossprod(X), crossprod(X,Y)) estind2 ## marketable earlyperplant ## 0.97196 0.8828393 # correlation coefficients for indep variables corrind=cor(sdat[,indep]) round(corrind,2) ## branchesperplant nodesperbranch perfenod fruitset ## branchesperplant 1.00 0.52 -0.24 0.09 ## nodesperbranch 0.52 1.00 -0.44 0.14 ## perfenod -0.24 -0.44 1.00 0.04 ## fruitset 0.09 0.14 0.04 1.00 # Correlation coefficients for dependent variables corrdep=cor(sdat[,c(dep0, dep)]) round(corrdep,2) ## fruitperplant marketable earlyperplant ## fruitperplant 1.00 0.97 0.88 ## marketable 0.97 1.00 0.96 ## earlyperplant 0.88 0.96 1.00 result = corrind result = result*matrix(estdep,ncol=4,nrow=4,byrow=TRUE) round(result,2) # match SAS output columns 1-4 ## branchesperplant nodesperbranch perfenod fruitset ## branchesperplant 0.72 0.18 -0.06 0.03 ## nodesperbranch 0.37 0.34 -0.10 0.04 ## perfenod -0.17 -0.15 0.23 0.01 ## fruitset 0.07 0.05 0.01 0.30 resdep0 = rowSums(result) resdep <- cbind(resdep0,resdep0)*matrix(estind2, nrow=4,ncol=2,byrow=TRUE) colnames(resdep) <- dep # slightly different from SAS output last 2 columns round(cbind(fruitperplant=resdep0, round(resdep,2)),2) ## fruitperplant marketable earlyperplant ## branchesperplant 0.87 0.84 0.76 ## nodesperbranch 0.65 0.63 0.58 ## perfenod -0.08 -0.08 -0.07 ## fruitset 0.42 0.41 0.37 } # }"},{"path":"/reference/crampton.pig.html","id":null,"dir":"Reference","previous_headings":"","what":"Weight gain in pigs for different treatments — crampton.pig","title":"Weight gain in pigs for different treatments — crampton.pig","text":"Weight gain pigs different treatments, initial weight feed eaten covariates.","code":""},{"path":"/reference/crampton.pig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weight gain in pigs for different treatments — crampton.pig","text":"","code":"data(\"crampton.pig\")"},{"path":"/reference/crampton.pig.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weight gain in pigs for different treatments — crampton.pig","text":"data frame 50 observations following 5 variables. treatment feed treatment rep replicate weight1 initial weight feed feed eaten weight2 final weight","code":""},{"path":"/reference/crampton.pig.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Weight gain in pigs for different treatments — crampton.pig","text":"study effect initial weight feed eaten weight gaining ability pigs different feed treatments. data extracted Ostle. clear 'replicate' actually blocking replicate opposed repeated measurement. original source document needs consulted.","code":""},{"path":"/reference/crampton.pig.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Weight gain in pigs for different treatments — crampton.pig","text":"Crampton, EW Hopkins, JW. (1934). Use Method Partial Regression Analysis Comparative Feeding Trial Data, Part II. Journal Nutrition, 8, 113-123. https://doi.org/10.1093/jn/8.3.329","code":""},{"path":"/reference/crampton.pig.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Weight gain in pigs for different treatments — crampton.pig","text":"Bernard Ostle. Statistics Research, Page 458. https://archive.org/details/secondeditionsta001000mbp Goulden (1939). Methods Statistical Analysis, 1st ed. Page 256-259. https://archive.org/details/methodsofstatist031744mbp","code":""},{"path":"/reference/crampton.pig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weight gain in pigs for different treatments — crampton.pig","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(crampton.pig) dat <- crampton.pig dat <- transform(dat, gain=weight2-weight1) libs(lattice) # Trt 4 looks best xyplot(gain ~ feed, dat, group=treatment, type=c('p','r'), auto.key=list(columns=5), xlab=\"Feed eaten\", ylab=\"Weight gain\", main=\"crampton.pig\") # Basic Anova without covariates m1 <- lm(weight2 ~ treatment + rep, data=dat) anova(m1) # Add covariates m2 <- lm(weight2 ~ treatment + rep + weight1 + feed, data=dat) anova(m2) # Remove treatment, test this nested model for significant treatments m3 <- lm(weight2 ~ rep + weight1 + feed, data=dat) anova(m2,m3) # p-value .07. F=2.34 matches Ostle } # }"},{"path":"/reference/crossa.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"Wheat yields 18 genotypes 25 locations","code":""},{"path":"/reference/crossa.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"data frame 450 observations following 3 variables. loc location locgroup location group: Grp1-Grp2 gen genotype gengroup genotype group: W1, W2, W3 yield grain yield, tons/ha","code":""},{"path":"/reference/crossa.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"Grain yield 8th Elite Selection Wheat Yield Trial evaluate 18 bread wheat genotypes 25 locations 15 countries. Cross et al. used data cluster loctions 2 mega-environments clustered genotypes 3 wheat clusters. Locations Used permission Jose' Crossa.","code":""},{"path":"/reference/crossa.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"Crossa, J Fox, PN Pfeiffer, WH Rajaram, S Gauch Jr, HG. (1991). AMMI adjustment statistical analysis international wheat yield trial. Theoretical Applied Genetics, 81, 27–37. https://doi.org/10.1007/BF00226108","code":""},{"path":"/reference/crossa.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"Jean-Louis Laffont, Kevin Wright Mohamed Hanafi (2013). Genotype + Genotype x Block Environments (GGB) Biplots. Crop Science, 53, 2332-2341. https://doi.org/10.2135/cropsci2013.03.0178","code":""},{"path":"/reference/crossa.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat for 18 genotypes at 25 locations — crossa.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(crossa.wheat) dat <- crossa.wheat # AMMI biplot. Fig 3 of Crossa et al. libs(agricolae) m1 <- with(dat, AMMI(E=loc, G=gen, R=1, Y=yield)) b1 <- m1$biplot[,1:4] b1$PC1 <- -1 * b1$PC1 # Flip vertical plot(b1$yield, b1$PC1, cex=0.0, text(b1$yield, b1$PC1, cex=.5, labels=row.names(b1),col=\"brown\"), main=\"crossa.wheat AMMI biplot\", xlab=\"Average yield\", ylab=\"PC1\", frame=TRUE) mn <- mean(b1$yield) abline(h=0, v=mn, col='wheat') g1 <- subset(b1,type==\"GEN\") text(g1$yield, g1$PC1, rownames(g1), col=\"darkgreen\", cex=.5) e1 <- subset(b1,type==\"ENV\") arrows(mn, 0, 0.95*(e1$yield - mn) + mn, 0.95*e1$PC1, col= \"brown\", lwd=1.8,length=0.1) # GGB example library(agridat) data(crossa.wheat) dat2 <- crossa.wheat libs(gge) # Specify env.group as column in data frame m2 <- gge(dat2, yield~gen*loc, env.group=locgroup, gen.group=gengroup, scale=FALSE) biplot(m2, main=\"crossa.wheat - GGB biplot\") } # }"},{"path":"/reference/crowder.seeds.html","id":null,"dir":"Reference","previous_headings":"","what":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"Number Orobanche seeds tested/germinated two genotypes two treatments.","code":""},{"path":"/reference/crowder.seeds.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"plate Factor replication gen Factor genotype levels O73, O75 extract Factor extract bean, cucumber germ Number seeds germinated n Total number seeds tested","code":""},{"path":"/reference/crowder.seeds.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"Egyptian broomrape, orobanche aegyptiaca parasitic plant family. plants chlorophyll grow roots plants. seeds remain dormant soil certain compounds living plants stimulate germination. Two genotypes studied experiment, O. aegyptiaca 73 O. aegyptiaca 75. seeds brushed one two extracts prepared either bean plant cucmber plant. experimental design 2x2 factorial, 5 6 reps plates.","code":""},{"path":"/reference/crowder.seeds.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"Crowder, M.J., 1978. Beta-binomial anova proportions. Appl. Statist., 27, 34-37. https://doi.org/10.2307/2346223","code":""},{"path":"/reference/crowder.seeds.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"N. E. Breslow D. G. Clayton. 1993. Approximate inference generalized linear mixed models. Journal American Statistical Association, 88:9-25. https://doi.org/10.2307/2290687 Y. Lee J. . Nelder. 1996. Hierarchical generalized linear models discussion. J. R. Statist. Soc. B, 58:619-678.","code":""},{"path":"/reference/crowder.seeds.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Germination of Orobanche seeds for two genotypes and two treatments. — crowder.seeds","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(crowder.seeds) dat <- crowder.seeds m1.glm <- m1.glmm <- m1.glmmtmb <- m1.hglm <- NA # ----- Graphic libs(lattice) dotplot(germ/n~gen|extract, dat, main=\"crowder.seeds\") # --- GLMM. Assumes Gaussian random effects libs(MASS) m1.glmm <- glmmPQL(cbind(germ, n-germ) ~ gen*extract, random= ~1|plate, family=binomial(), data=dat) summary(m1.glmm) ## round(summary(m1.glmm)$tTable,2) ## Value Std.Error DF t-value p-value ## (Intercept) -0.44 0.25 17 -1.80 0.09 ## genO75 -0.10 0.31 17 -0.34 0.74 ## extractcucumber 0.52 0.34 17 1.56 0.14 ## genO75:extractcucumber 0.80 0.42 17 1.88 0.08 # ----- glmmTMB libs(glmmTMB) m1.glmmtmb <- glmmTMB(cbind(germ, n-germ) ~ gen*extract + (1|plate), data=dat, family=binomial) summary(m1.glmmtmb) ## round(summary(m1.glmmtmb)$coefficients$cond , 2) ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -0.45 0.22 -2.03 0.04 ## genO75 -0.10 0.28 -0.35 0.73 ## extractcucumber 0.53 0.30 1.74 0.08 ## genO75:extractcucumber 0.81 0.38 2.11 0.04 } # }"},{"path":"/reference/cullis.earlygen.html","id":null,"dir":"Reference","previous_headings":"","what":"Early generation variety trial in wheat — cullis.earlygen","title":"Early generation variety trial in wheat — cullis.earlygen","text":"Early generation variety trial wheat","code":""},{"path":"/reference/cullis.earlygen.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Early generation variety trial in wheat — cullis.earlygen","text":"data frame 670 observations following 5 variables. gen genotype factor row row col column entry entry (genotype) number yield yield plot, kg/ha weed weed score","code":""},{"path":"/reference/cullis.earlygen.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Early generation variety trial in wheat — cullis.earlygen","text":"data field experiment conducted Tullibigeal, New South Wales, Australia 1987-88. aim trials identify retain top (10-20 percent) lines testing. genotypes unreplicated, augmented genotypes. row, every 6th plot variety 526 = 'Kite'. Six varieties 527-532 randomly placed trial, 3 5 plots . plot 15m x 1.8m, \"oriented longest side rows\". 'weed' variable visual score 0 10 scale, 0 = weeds, 10 = 100 percent weeds. Cullis et al. (1989) presented analysis early generation variety trials included one-dimensional spatial analysis. , two-dimensional spatial analysis presented. Note: 'row' 'col' variables VSN link (switched compared paper Cullis et al.) Field width: 10 rows * 15 m = 150 m Field length: 67 plots * 1.8 m = 121 m orientation certain, alternative orientation field roughly 20m x 1000m, seems unlikely.","code":""},{"path":"/reference/cullis.earlygen.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Early generation variety trial in wheat — cullis.earlygen","text":"Brian R. Cullis, Warwick J. Lill, John . Fisher, Barbara J. Read Alan C. Gleeson (1989). New Procedure Analysis Early Generation Variety Trials. Journal Royal Statistical Society. Series C (Applied Statistics), 38, 361-375. https://doi.org/10.2307/2348066","code":""},{"path":"/reference/cullis.earlygen.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Early generation variety trial in wheat — cullis.earlygen","text":"Unreplicated early generation variety trial Wheat. https://www.vsni.co.uk/software/asreml/htmlhelp/asreml/xwheat.htm","code":""},{"path":"/reference/cullis.earlygen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Early generation variety trial in wheat — cullis.earlygen","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(cullis.earlygen) dat <- cullis.earlygen # Show field layout of checks. Cullis Table 1. dat$check <- ifelse(dat$entry < 8, dat$entry, NA) libs(desplot) desplot(dat, check ~ col*row, num=entry, cex=0.5, flip=TRUE, aspect=121/150, # true aspect main=\"cullis.earlygen (yield)\") desplot(dat, yield ~ col*row, num=\"check\", cex=0.5, flip=TRUE, aspect=121/150, # true aspect main=\"cullis.earlygen (yield)\") grays <- colorRampPalette(c(\"white\",\"#252525\")) desplot(dat, weed ~ col*row, at=0:6-0.5, col.regions=grays(7)[-1], flip=TRUE, aspect=121/150, # true aspect main=\"cullis.earlygen (weed)\") libs(lattice) bwplot(yield ~ as.character(weed), dat, horizontal=FALSE, xlab=\"Weed score\", main=\"cullis.earlygen\") # Moving Grid libs(mvngGrAd) shape <- list(c(1), c(1), c(1:4), c(1:4)) # sketchGrid(10,10,20,20,shapeCross=shape, layers=1, excludeCenter=TRUE) m0 <- movingGrid(rows=dat$row, columns=dat$col, obs=dat$yield, shapeCross=shape, layers=NULL) dat$mov.avg <- fitted(m0) if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # Start with the standard AR1xAR1 analysis dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf, dat$yf),] m2 <- asreml(yield ~ weed, data=dat, random= ~gen, resid = ~ ar1(xf):ar1(yf)) # Variogram suggests a polynomial trend m3 <- update(m2, fixed= yield~weed+pol(col,-1)) # Now add a nugget variance m4 <- update(m3, random= ~ gen + units) lucid::vc(m4) ## effect component std.error z.ratio bound ## gen 73780 10420 7.1 P 0 ## units 30440 8073 3.8 P 0.1 ## xf:yf(R) 54730 10630 5.1 P 0 ## xf:yf!xf!cor 0.38 0.115 3.3 U 0 ## xf:yf!yf!cor 0.84 0.045 19 U 0 ## # Predictions from models m3 and m4 are non-estimable. Why? ## # Use model m2 for predictions ## predict(m2, classify=\"gen\")$pvals ## ## gen predicted.value std.error status ## ## 1 Banks 2723.534 93.14719 Estimable ## ## 2 Eno008 2981.056 162.85241 Estimable ## ## 3 Eno009 2978.008 161.57129 Estimable ## ## 4 Eno010 2821.399 153.96943 Estimable ## ## 5 Eno011 2991.612 161.53507 Estimable ## # Compare AR1 with Moving Grid ## dat$ar1 <- fitted(m2) ## head(dat[ , c('yield','ar1','mov.avg')]) ## ## yield ar1 mg ## ## 1 2652 2467.980 2531.998 ## ## 11 3394 3071.681 3052.160 ## ## 21 3148 2826.188 2807.031 ## ## 31 3426 3026.985 3183.649 ## ## 41 3555 3070.102 3195.910 ## ## 51 3453 3006.352 3510.511 ## pairs(dat[ , c('yield','ar1','mg')]) } } # }"},{"path":"/reference/damesa.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"Incomplete-block experiment maize Ethiopia.","code":""},{"path":"/reference/damesa.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"","code":"data(\"damesa.maize\")"},{"path":"/reference/damesa.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"data frame 264 observations following 8 variables. site site, 4 levels rep replicate, 3 levels block incomplete block plot plot number gen genotype, 22 levels row row ordinate col column ordinate yield yield, t/ha","code":""},{"path":"/reference/damesa.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"experiment harvested 2012, evaluating drought-tolerant maize hybrids 4 sites Ethiopia. site, incomplete-block design used. Damesa et al use data compare single-stage two-stage analyses.","code":""},{"path":"/reference/damesa.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"Tigist Mideksa Damesa, Jens Möhring, Mosisa Worku, Hans-Peter Piepho (2017). One Step Time: Stage-Wise Analysis Series Experiments. Agronomy J, 109, 845-857. https://doi.org/10.2134/agronj2016.07.0395","code":""},{"path":"/reference/damesa.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"None","code":""},{"path":"/reference/damesa.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incomplete-block experiment of maize in Ethiopia. — damesa.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(damesa.maize) libs(desplot) desplot(damesa.maize, yield ~ col*row|site, main=\"damesa.maize\", out1=rep, out2=block, num=gen, cex=1) if(require(\"asreml\", quietly=TRUE)) { # Fit the single-stage model in Damesa libs(asreml,lucid) m0 <- asreml(data=damesa.maize, fixed = yield ~ gen, random = ~ site + gen:site + at(site):rep/block, residual = ~ dsum( ~ units|site) ) lucid::vc(m0) # match Damesa table 1 column 3 ## effect component std.error z.ratio bound ## at(site, S1):rep 0.08819 0.1814 0.49 P 0 ## at(site, S2):rep 1.383 1.426 0.97 P 0 ## at(site, S3):rep 0 NA NA B 0 ## at(site, S4):rep 0.01442 0.02602 0.55 P 0 ## site 10.45 8.604 1.2 P 0.1 ## gen:site 0.1054 0.05905 1.8 P 0.1 ## at(site, S1):rep:block 0.3312 0.3341 0.99 P 0 ## at(site, S2):rep:block 0.4747 0.1633 2.9 P 0 ## at(site, S3):rep:block 0 NA NA B 0 ## at(site, S4):rep:block 0.06954 0.04264 1.6 P 0 ## site_S1!R 1.346 0.3768 3.6 P 0 ## site_S2!R 0.1936 0.06628 2.9 P 0 ## site_S3!R 1.153 0.2349 4.9 P 0 ## site_S4!R 0.1112 0.03665 3 P 0 } } # }"},{"path":"/reference/darwin.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"Darwin's maize data crossed/inbred plant heights.","code":""},{"path":"/reference/darwin.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"data frame 30 observations following 4 variables. pot Pot factor, 4 levels pair Pair factor, 12 levels type Type factor, self-pollinated, cross-pollinated height Height, inches (measured 1/8 inch)","code":""},{"path":"/reference/darwin.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"Charles Darwin, 1876, reported data experiment conducted heights corn plants. seeds came parents, seeds produced self-fertilized parents seeds produced cross-fertilized parents. Pairs seeds planted pots. Darwin hypothesized cross-fertilization produced produced robust vigorous offspring. Darwin wrote, \"long doubted whether worth give measurements separate plant, decided , order may seen superiority crossed plants self-fertilised, commonly depend presence two three extra fine plants one side, poor plants side. Although several observers insisted general terms offspring intercrossed varieties superior either parent-form, precise measurements given;* met observations effects crossing self-fertilising individuals variety. Moreover, experiments kind require much time–mine continued eleven years–likely soon repeated.\" Darwin asked cousin Francis Galton help understanding data. Galton modern statistical methods approach problem said, \"doubt, making many tests, whether possible derive useful conclusions observations. least 50 plants case, order position deduce fair results\". Later, R. . Fisher used Darwin's data book design experiments showed t-test exhibits significant difference two groups.","code":""},{"path":"/reference/darwin.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"Darwin, C. R. 1876. effects cross self fertilisation vegetable kingdom. London: John Murray. Page 16. https://darwin-online.org.uk/converted/published/1881_Worms_F1357/1876_CrossandSelfFertilisation_F1249/1876_CrossandSelfFertilisation_F1249.html","code":""},{"path":"/reference/darwin.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"R. . Fisher, (1935) Design Experiments, Oliver Boyd. Page 30.","code":""},{"path":"/reference/darwin.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Darwin's maize data of crossed/inbred plant heights — darwin.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(darwin.maize) dat <- darwin.maize # Compare self-pollination with cross-pollination libs(lattice) bwplot(height~type, dat, main=\"darwin.maize\") libs(reshape2) dm <- melt(dat) d2 <- dcast(dm, pot+pair~type) d2$diff <- d2$cross-d2$self t.test(d2$diff) ## One Sample t-test ## t = 2.148, df = 14, p-value = 0.0497 ## alternative hypothesis: true mean is not equal to 0 ## 95 percent confidence interval: ## 0.003899165 5.229434169 } # }"},{"path":"/reference/dasilva.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize — dasilva.maize","title":"Multi-environment trial of maize — dasilva.maize","text":"Multi-environment trial maize 3 reps.","code":""},{"path":"/reference/dasilva.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize — dasilva.maize","text":"","code":"data(\"dasilva.maize\")"},{"path":"/reference/dasilva.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize — dasilva.maize","text":"data frame 1485 observations following 4 variables. env environment rep replicate block, 3 per env gen genotype yield yield (tons/hectare)","code":""},{"path":"/reference/dasilva.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize — dasilva.maize","text":"location 3 blocks. Block numbers unique across environments. NOTE! environment codes supplemental data file da Silva 2015 quite match environment codes paper, mostly 1. DaSilva Table 1 footnote \"Machado et al 2007\". reference appears : Machado et al. Estabilidade de producao de hibridos simples e duplos de milhooriundos de um mesmo conjunto genico. Bragantia, 67, 3. www.scielo.br/pdf/brag/v67n3/a10v67n3.pdf DaSilva Table 1, mean E1 10.803. appears copy mean row 1 Table 1 Machado. Using supplemental data paper, correct mean 8.685448.","code":""},{"path":"/reference/dasilva.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize — dasilva.maize","text":"Bayesian Shrinkage Approach AMMI Models. Carlos Pereira da Silva, Luciano Antonio de Oliveira, Joel Jorge Nuvunga, Andrezza Kellen Alves Pamplona, Marcio Balestre. Plos One. Supplemental material. https://doi.org/10.1371/journal.pone.0131414 Used via license: Creative Commons -SA.","code":""},{"path":"/reference/dasilva.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize — dasilva.maize","text":"J.J. Nuvunga, L.. Oliveira, .K.. Pamplona, C.P. Silva, R.R. Lima M. Balestre. Factor analysis using mixed models multi-environment trials different levels unbalancing. Genet. Mol. Res. 14.","code":""},{"path":"/reference/dasilva.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize — dasilva.maize","text":"","code":"library(agridat) data(dasilva.maize) dat <- dasilva.maize # Try to match Table 1 of da Silva 2015. # aggregate(yield ~ env, data=dat, FUN=mean) ## env yield ## 1 E1 6.211817 # match E2 in Table 1 ## 2 E2 4.549104 # E3 ## 3 E3 5.152254 # E4 ## 4 E4 6.245904 # E5 ## 5 E5 8.084609 # E6 ## 6 E6 13.191890 # E7 ## 7 E7 8.895721 # E8 ## 8 E8 8.685448 ## 9 E9 8.737089 # E9 # Unable to match CVs in Table 2, but who knows what they used # for residual variance. # aggregate(yield ~ env, data=dat, FUN=function(x) 100*sd(x)/mean(x)) # Match DaSilva supplement 2, ANOVA # m1 <- aov(yield ~ env + gen + rep:env + gen:env, dat) # anova(m1) ## Response: yield ## Df Sum Sq Mean Sq F value Pr(>F) ## env 8 8994.2 1124.28 964.1083 < 2.2e-16 *** ## gen 54 593.5 10.99 9.4247 < 2.2e-16 *** ## env:rep 18 57.5 3.19 2.7390 0.0001274 *** ## env:gen 432 938.1 2.17 1.8622 1.825e-15 *** ## Residuals 972 1133.5 1.17"},{"path":"/reference/dasilva.soybean.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of soybean — dasilva.soybean.uniformity","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"Uniformity trial soybean Brazil, 1970.","code":""},{"path":"/reference/dasilva.soybean.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"","code":"data(\"dasilva.soybean.uniformity\")"},{"path":"/reference/dasilva.soybean.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"data frame 1152 observations following 3 variables. row row col column yield yield, grams/plot","code":""},{"path":"/reference/dasilva.soybean.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"Field length: 48 rows * .6 m = 28.8 m Field width: 24 columns * .6 m = 14.4 m","code":""},{"path":"/reference/dasilva.soybean.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"Enedino Correa da Silva. (1974). Estudo tamanho e forma de parcelas para experimentos de soja (Plot size shape soybean yield trials). Pesquisa Agropecuaria Brasileira, Serie Agronomia, 9, 49-59. Table 3, page 52-53. https://seer.sct.embrapa.br/index.php/pab/article/view/17250","code":""},{"path":"/reference/dasilva.soybean.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"Humada-Gonzalez, G.G. (2013). Estimação tamanho otimo de parcela experimental em experimento com soja. Dissertation, Universidade Federal de Lavras. http://repositorio.ufla.br/jspui/handle/1/744","code":""},{"path":"/reference/dasilva.soybean.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of soybean — dasilva.soybean.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(dasilva.soybean.uniformity) dat <- dasilva.soybean.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=28.8/14.4, main=\"dasilva.soybean.uniformity\") } # }"},{"path":"/reference/davidian.soybean.html","id":null,"dir":"Reference","previous_headings":"","what":"Growth of soybean varieties in 3 years — davidian.soybean","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"Growth soybean varieties 3 years","code":""},{"path":"/reference/davidian.soybean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"","code":"data(\"davidian.soybean\")"},{"path":"/reference/davidian.soybean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"data frame 412 observations following 5 variables. plot plot code variety variety, F P year 1988-1990 day days planting weight weight soybean leaves","code":""},{"path":"/reference/davidian.soybean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"experiment compared growth patterns two genotypes soybean varieties: F=Forrest (commercial variety) P=Plant Introduction number 416937 (experimental variety). Data collected 3 consecutive years. start growing season, 16 plots seeded (8 variety). Data collected approximately weekly. timepoint, six plants randomly selected plot. leaves 6 plants weighed, average leaf weight per plant reported. (assume data collection destructive different plants sampled date). Note: data \"nlme::Soybean\" data.","code":""},{"path":"/reference/davidian.soybean.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"Marie Davidian D. M. Giltinan, (1995). Nonlinear Models Repeated Measurement Data. Chapman Hall, London. Electronic version retrieved https://www4.stat.ncsu.edu/~davidian/data/soybean.dat","code":""},{"path":"/reference/davidian.soybean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"Pinheiro, J. C. Bates, D. M. (2000). Mixed-Effects Models S S-PLUS. Springer, New York.","code":""},{"path":"/reference/davidian.soybean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Growth of soybean varieties in 3 years — davidian.soybean","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(davidian.soybean) dat <- davidian.soybean dat$year <- factor(dat$year) libs(lattice) xyplot(weight ~ day|variety*year, dat, group=plot, type='l', main=\"davidian.soybean\") # The only way to keep your sanity with nlme is to use groupedData objects # Well, maybe not. When I use \"devtools::run_examples\", # the \"groupedData\" function creates a dataframe with/within(?) an # environment, and then \"nlsList\" cannot find datg, even though # ls() shows datg is visible and head(datg) is fine. # Also works fine in interactive mode. It is driving me insane. # reid.grasses has the same problem # Use if(0){} to block this code from running. if(0){ libs(nlme) datg <- groupedData(weight ~ day|plot, dat) # separate fixed-effect model for each plot # 1988P6 gives unusual estimates m1 <- nlsList(SSlogis, data=datg, subset = plot != \"1988P6\") # plot(m1) # seems heterogeneous plot(intervals(m1), layout=c(3,1)) # clear year,variety effects in Asym # A = maximum, B = time of half A = steepness of curve # C = sharpness of curve (smaller = sharper curve) # switch to mixed effects m2 <- nlme(weight ~ A / (1+exp(-(day-B)/C)), data=datg, fixed=list(A ~ 1, B ~ 1, C ~ 1), random = A +B +C ~ 1, start=list(fixed = c(17,52,7.5))) # no list! # add covariates for A,B,C effects, correlation, weights # not necessarily best model, but it shows the syntax m3 <- nlme(weight ~ A / (1+exp(-(day-B)/C)), data=datg, fixed=list(A ~ variety + year, B ~ year, C ~ year), random = A +B +C ~ 1, start=list(fixed= c(19,0,0,0, 55,0,0, 8,0,0)), correlation = corAR1(form = ~ 1|plot), weights=varPower(), # really helps control=list(mxMaxIter=200)) plot(augPred(m3), layout=c(8,6), main=\"davidian.soybean - model 3\") } # end if(0) } # }"},{"path":"/reference/davies.pasture.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of pasture. — davies.pasture.uniformity","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"Uniformity trial pasture Australia.","code":""},{"path":"/reference/davies.pasture.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"","code":"data(\"davies.pasture.uniformity\")"},{"path":"/reference/davies.pasture.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"data frame 760 observations following 3 variables. row row col column yield yield per plot, grams","code":""},{"path":"/reference/davies.pasture.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"Conducted Waite Agricultural Research Institute 1928. rectangle 250 x 200 links selected, divided 1000 plots measuring 10 x 5 links, 1/2000th acre. Plots hand harvested herbage air-dried. Cutting began Tue, 25 Sep ended Sat, 29 Sep, time 760 plots harvested. Rain fell, harvesting ceased. minimum recommended plot size 150 square links. optimum recommended plot size 450 square links, 5 x 90 links size. Note, 4 digits hard read original document. Best estimates digits used yields affects plots. yields digitally watermarked extra .01 added yield value. botanical composition species clearly influenced total herbage. Field length: 40 plots * 5 links = 200 links Field width: 19 plots * 10 links = 190 links","code":""},{"path":"/reference/davies.pasture.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"J. Griffiths Davies (1931). Experimental Error Yield Small Plots Natural Pasture. Council Scientific Industrial Research (Aust.) Bulletin 48. Table 1.","code":""},{"path":"/reference/davies.pasture.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"None","code":""},{"path":"/reference/davies.pasture.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of pasture. — davies.pasture.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(davies.pasture.uniformity) dat <- davies.pasture.uniformity # range(dat$yield) # match Davies # mean(dat$yield) # 227.77, Davies has 221.7 # sd(dat$yield)/mean(dat$yield) # 33.9, Davies has 32.5 # libs(lattice) # qqmath( ~ yield, dat) # clearly non-normal, skewed right libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(40*5)/(19*10), # true aspect main=\"davies.pasture.uniformity\") } # }"},{"path":"/reference/day.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — day.wheat.uniformity","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"Uniformity trial wheat 1903 Missouri.","code":""},{"path":"/reference/day.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"","code":"data(\"day.wheat.uniformity\")"},{"path":"/reference/day.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"data frame 3090 observations following 4 variables. row row col col grain grain weight, grams per plot straw straw weight, grams per plot","code":""},{"path":"/reference/day.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"data Shelbina field Missouri Agricultural Experiment Station. field (plat) 1/4 acre area apparently uniform throughout. fall 1912, wheat drilled rows 8 inches apart, row 155 feet long. wheat harvested June, 5-foot segments. gross weight grain weight measured, straw weight calculated subtraction. Field width: 31 series * 5 feet = 155 feet Field length: 100 rows, 8 inches apart = 66.66 feet","code":""},{"path":"/reference/day.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"James Westbay Day (1916). relation size, shape, number replications plats probable error field experimentation. Dissertation, University Missouri. Table 1, page 22. https://hdl.handle.net/10355/56391","code":""},{"path":"/reference/day.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"James W. Day (1920). relation size, shape, number replications plats probable error field experimentation. Agronomy Journal, 12, 100-105. https://doi.org/10.2134/agronj1920.00021962001200030002x","code":""},{"path":"/reference/day.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — day.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(day.wheat.uniformity) dat <- day.wheat.uniformity libs(desplot) desplot(dat, grain~col*row, flip=TRUE, aspect=(100*8)/(155*12), # true aspect main=\"day.wheat.uniformity - grain yield\") # similar to Day table IV libs(lattice) xyplot(grain~straw, data=dat, main=\"day.wheat.uniformity\", type=c('p','r')) # cor(dat$grain, dat$straw) # .9498 # Day calculated 0.9416 libs(desplot) desplot(dat, straw~col*row, flip=TRUE, aspect=(100*8)/(155*12), # true aspect main=\"day.wheat.uniformity - straw yield\") # Day fig 2 coldat <- aggregate(grain~col, dat, sum) xyplot(grain ~ col, coldat, type='l', ylim=c(2500,6500)) dat$rowgroup <- round((dat$row +1)/3,0) rowdat <- aggregate(grain~rowgroup, dat, sum) xyplot(grain ~ rowgroup, rowdat, type='l', ylim=c(2500,6500)) } # }"},{"path":"/reference/denis.missing.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial with structured missing values — denis.missing","title":"Multi-environment trial with structured missing values — denis.missing","text":"Grain yield measured 5 genotypes 26 environments. Missing values non-random, structured.","code":""},{"path":"/reference/denis.missing.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial with structured missing values — denis.missing","text":"env environment, 26 levels gen genotype factor, 5 levels yield yield Used permission Jean-Baptists Denis.","code":""},{"path":"/reference/denis.missing.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial with structured missing values — denis.missing","text":"Denis, J. B. C P Baril, 1992, Sophisticated models numerous missing values: multiplicative interaction model example. Biul. Oceny Odmian, 24–25, 7–31.","code":""},{"path":"/reference/denis.missing.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial with structured missing values — denis.missing","text":"H P Piepho, (1999) Stability analysis using SAS system, Agron Journal, 91, 154–160. https://doi.og/10.2134/agronj1999.00021962009100010024x","code":""},{"path":"/reference/denis.missing.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial with structured missing values — denis.missing","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(denis.missing) dat <- denis.missing # view missingness structure libs(reshape2) acast(dat, env~gen, value.var='yield') libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(yield ~ gen*env, data=dat, col.regions=redblue, main=\"denis.missing - incidence heatmap\") # stability variance (Table 3 in Piepho) libs(nlme) m1 <- lme(yield ~ -1 + gen, data=dat, random= ~ 1|env, weights = varIdent(form= ~ 1|gen), na.action=na.omit) svar <- m1$sigma^2 * c(1, coef(m1$modelStruct$varStruct, unc = FALSE))^2 round(svar, 2) ## G5 G3 G1 G2 ## 39.25 22.95 54.36 12.17 23.77 } # }"},{"path":"/reference/denis.ryegrass.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"Plant strength perennial ryegrass France 21 genotypes 7 locations.","code":""},{"path":"/reference/denis.ryegrass.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"data frame 147 observations following 3 variables. gen genotype, 21 levels loc location, 7 levels strength average plant strength * 100","code":""},{"path":"/reference/denis.ryegrass.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"INRA conducted breeding trial western France 21 genotypes 7 locations. observed data 'strength' averaged 7-10 plants per plot three plots per location (adjusting blocking effects). plant scored scale 0-9. original data value 86.0 genotype G1 location L4–replaced additive estimated value 361.2 Gower Hand (1996).","code":""},{"path":"/reference/denis.ryegrass.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"Jean-Baptiste Denis John C. Gower, 1996. Asymptotic confidence regions biadditive models: interpreting genotype-environment interaction, Applied Statistics, 45, 479-493. https://doi.org/10.2307/2986069","code":""},{"path":"/reference/denis.ryegrass.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"Gower, J.C. Hand, D.J., 1996. Biplots. Chapman Hall.","code":""},{"path":"/reference/denis.ryegrass.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of perennial ryegrass in France — denis.ryegrass","text":"","code":"library(agridat) data(denis.ryegrass) dat <- denis.ryegrass # biplots (without ellipses) similar to Denis figure 1 libs(gge) #> #> Attaching package: ‘gge’ #> The following object is masked from ‘package:desplot’: #> #> RedGrayBlue m1 <- gge(dat, strength ~ gen*loc, scale=FALSE) biplot(m1, main=\"denis.ryegrass biplot\")"},{"path":"/reference/depalluel.sheep.html","id":null,"dir":"Reference","previous_headings":"","what":"Latin square of four breeds of sheep with four diets — depalluel.sheep","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"Latin square four breeds sheep four diets","code":""},{"path":"/reference/depalluel.sheep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"","code":"data(\"depalluel.sheep\")"},{"path":"/reference/depalluel.sheep.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"data frame 32 observations following 5 variables. food diet animal animal number breed sheep breed weight weight, pounds date months start","code":""},{"path":"/reference/depalluel.sheep.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"may earliest known Latin Square experiment. Four sheep four breeds randomized four feeds four slaughter dates. Sheep eat roots eat sheep eating corn, acre land produces roots corn. de Palleuel said: short, adopting use roots, instead corn, fattening sorts cattle, farmers neighborhood capital gain great profit , also much benefit public supplying great city resources, preventing sudden rise meat markets, often considerable.","code":""},{"path":"/reference/depalluel.sheep.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"M. Crette de Palluel (1788). advantage economy feeding sheep house roots. Annals Agriculture, 14, 133-139. https://books.google.com/books?id=LXIqAAAAYAAJ&pg=PA133","code":""},{"path":"/reference/depalluel.sheep.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"None","code":""},{"path":"/reference/depalluel.sheep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Latin square of four breeds of sheep with four diets — depalluel.sheep","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(depalluel.sheep) dat <- depalluel.sheep # Not the best view...weight gain is large in the first month, then slows down # and the linear line hides this fact libs(lattice) xyplot(weight ~ date|food, dat, group=animal, type='l', auto.key=list(columns=4), xlab=\"Months since start\", main=\"depalluel.sheep\") } # }"},{"path":"/reference/devries.pine.html","id":null,"dir":"Reference","previous_headings":"","what":"Graeco-Latin Square experiment in pine — devries.pine","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"Graeco-Latin Square experiment pine","code":""},{"path":"/reference/devries.pine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"","code":"data(\"devries.pine\")"},{"path":"/reference/devries.pine.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"data frame 36 observations following 6 variables. block block row row col column spacing spacing treatment thinning thinning treatment volume stem volume m^3/ha growth annual stem volume increment m^3/ha age 11","code":""},{"path":"/reference/devries.pine.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"Experiment conducted Caribbean Pine Coebiti Surinam (Long 55 28 30 W, Lat 5 18 5 N). Land cleared Jan 1965 planted May 1965. experimental plot 60m x 60m. Roads 10 m wide run rows. block thus 180m wide 200m deep. Data collected 40m x 40m plots center experimental unit. Plots thinned 1972 1975. two treatment factors (spacing, thinning) assigned Graeco-Latin Square design. Spacing: =2.5, B=3, C=3.5. Thinning: Z=low, M=medium, S=heavy. Field width: 4 blocks x 180 m = 720 m Field length: 1 block x 200 m = 200 m.","code":""},{"path":"/reference/devries.pine.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"P.G. De Vries, J.W. Hildebrand, N.R. De Graaf. (1978). Analysis 11 years growth carribbean pine replicated Graeco-Latin square spacing-thinning experiment Surinam. Page 46, 51. https://edepot.wur.nl/287590","code":""},{"path":"/reference/devries.pine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"None","code":""},{"path":"/reference/devries.pine.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graeco-Latin Square experiment in pine — devries.pine","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(devries.pine) dat <- devries.pine libs(desplot) desplot(dat, volume ~ col*row, main=\"devries.pine - expt design and tree volume\", col=spacing, num=thinning, cex=1, out1=block, aspect=200/720) libs(HH) HH::interaction2wt(volume ~ spacing+thinning, dat, main=\"devries.pine\") # ANOVA matches appendix 5 of DeVries m1 <- aov(volume ~ block + spacing + thinning + block:factor(row) + block:factor(col), data=dat) anova(m1) } # }"},{"path":"/reference/digby.jointregression.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat — digby.jointregression","title":"Multi-environment trial of wheat — digby.jointregression","text":"Yield 10 spring wheat varieties 17 locations 1976.","code":""},{"path":"/reference/digby.jointregression.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat — digby.jointregression","text":"data frame 134 observations following 3 variables. gen genotype, 10 levels env environment, 17 levels yield yield (t/ha)","code":""},{"path":"/reference/digby.jointregression.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat — digby.jointregression","text":"Yield 10 spring wheat varieties 17 locations 1976. Used illustrate modified joint regression.","code":""},{"path":"/reference/digby.jointregression.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat — digby.jointregression","text":"Digby, P.G.N. (1979). Modified joint regression analysis incomplete variety x environment data. Journal Agricultural Science, 93, 81-86. https://doi.org/10.1017/S0021859600086159","code":""},{"path":"/reference/digby.jointregression.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat — digby.jointregression","text":"Hans-Pieter Piepho, 1997. Analyzing Genotype-Environment Data Mixed-Models Multiplicative Terms. Biometrics, 53, 761-766. https://doi.org/10.2307/2533976 RJOINT procedure GenStat. https://www.vsni.co.uk/software/genstat/htmlhelp/server/RJOINT.htm","code":""},{"path":"/reference/digby.jointregression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat — digby.jointregression","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(digby.jointregression) dat <- digby.jointregression # Simple gen means, ignoring unbalanced data. # Matches Digby table 2, Unadjusted Mean round(tapply(dat$yield, dat$gen, mean),3) # Two-way model. Matches Digby table 2, Fitting Constants m00 <- lm(yield ~ 0 + gen + env, dat) round(coef(m00)[1:10]-2.756078+3.272,3) # Adjust intercept # genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10 # 3.272 3.268 4.051 3.724 3.641 3.195 3.232 3.268 3.749 3.179 n.gen <- nlevels(dat$gen) n.env <- nlevels(dat$env) # Estimate theta (env eff) m0 <- lm(yield ~ -1 + env + gen, dat) thetas <- coef(m0)[1:n.env] thetas <- thetas-mean(thetas) # center env effects # Add env effects to the data dat$theta <- thetas[match(paste(\"env\",dat$env,sep=\"\"), names(thetas))] # Initialize beta (gen slopes) at 1 betas <- rep(1, n.gen) done <- FALSE while(!done){ betas0 <- betas # M1: Fix thetas (env effects), estimate beta (gen slope) m1 <- lm(yield ~ -1 + gen + gen:theta, data=dat) betas <- coef(m1)[-c(1:n.gen)] dat$beta <- betas[match(paste(\"gen\",dat$gen,\":theta\",sep=\"\"), names(betas))] # print(betas) # M2: Fix betas (gen slopes), estimate theta (env slope) m2 <- lm(yield ~ env:beta + gen -1, data=dat) thetas <- coef(m2)[-c(1:n.gen)] thetas[is.na(thetas)] <- 0 # Change last coefficient from NA to 0 dat$theta <- thetas[match(paste(\"env\",dat$env,\":beta\",sep=\"\"), names(thetas))] # print(thetas) # Check convergence chg <- sum(((betas-betas0)/betas0)^2) cat(\"Relative change in betas\",chg,\"\\n\") if(chg < .0001) done <- TRUE } libs(lattice) xyplot(yield ~ theta|gen, data=dat, xlab=\"theta (environment effect)\", main=\"digby.jointregression - stability plot\") # Dibgy Table 2, modified joint regression # Genotype sensitivities (slopes) round(betas,3) # Match Digby table 2, Modified joint regression sensitivity # genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10 # 0.953 0.739 1.082 1.024 1.142 0.877 1.089 0.914 1.196 0.947 # Env effects. Match Digby table 3, Modified joint reg round(thetas,3)+1.164-.515 # Adjust intercept to match # envE01 envE02 envE03 envE04 envE05 envE06 envE07 envE08 envE09 envE10 # -0.515 -0.578 -0.990 -1.186 1.811 1.696 -1.096 0.046 0.057 0.825 # envE11 envE12 envE13 envE14 envE15 envE16 envE17 # -0.576 1.568 -0.779 -0.692 0.836 -1.080 0.649 # Using 'gnm' gives similar results. # libs(gnm) # m3 <- gnm(yield ~ gen + Mult(gen,env), data=dat) # slopes negated # round(coef(m3)[11:20],3) # Using 'mumm' gives similar results, though gen is random and the # coeffecients are shrunk toward 0 a bit. if(require(\"mumm\", quietly=TRUE)) { libs(mumm) m1 <- mumm(yield ~ -1 + env + mp(gen, env), dat) round(1 + ranef(m1)$`mp gen:env`,2) } } # }"},{"path":"/reference/diggle.cow.html","id":null,"dir":"Reference","previous_headings":"","what":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"Bodyweight cows 2--2 factorial experiment.","code":""},{"path":"/reference/diggle.cow.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"data frame 598 observations following 5 variables. animal Animal factor, 26 levels iron Factor levels Iron, NoIron infect Factor levels Infected, NonInfected weight Weight (rounded nearest 5) kilograms day Days birth","code":""},{"path":"/reference/diggle.cow.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"Diggle et al., 1994, pp. 100-101, consider experiment studied iron dosing (none/standard) micro-organism (infected non-infected) influence weight cows. Twenty-eight cows allocated 2--2 factorial design factors. calves inoculated tuberculosis six weeks age. six months, calves maintained supplemental iron diet 27 months. weight animal measured 23 times, unequally spaced. One cow died study data another cow removed.","code":""},{"path":"/reference/diggle.cow.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"Diggle, P. J., Liang, K.-Y., & Zeger, S. L. (1994). Analysis Longitudinal Data. Page 100-101. Retrieved Oct 2011 https://www.maths.lancs.ac.uk/~diggle/lda/Datasets/","code":""},{"path":"/reference/diggle.cow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"Lepper, AWD Lewis, VM, 1989. Effects altered dietary iron intake Mycobacterium paratuberculosis-infected dairy cattle: sequential observations growth, iron copper metabolism development paratuberculosis. Research veterinary science, 46, 289–296. Arunas P. Verbyla Brian R. Cullis Michael G. Kenward Sue J. Welham, (1999), analysis designed experiments longitudinal data using smoothing splines. Appl. Statist., 48, 269–311. SAS/STAT(R) 9.2 User's Guide, Second Edition. https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glimmix_sect018.htm","code":""},{"path":"/reference/diggle.cow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bodyweight of cows in a 2-by-2 factorial experiment — diggle.cow","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(diggle.cow) dat <- diggle.cow # Figure 1 of Verbyla 1999 libs(latticeExtra) useOuterStrips(xyplot(weight ~ day|iron*infect, dat, group=animal, type='b', cex=.5, main=\"diggle.cow\")) # Scaling dat <- transform(dat, time = (day-122)/10) if(require(\"asreml\", quietly=TRUE)) { libs(asreml, latticeExtra) ## # Smooth for each animal. No treatment effects. Similar to SAS Output 38.6.9 m1 <- asreml(weight ~ 1 + lin(time) + animal + animal:lin(time), data=dat, random = ~ animal:spl(time)) p1 <- predict(m1, data=dat, classify=\"animal:time\", design.points=list(time=seq(0,65.9, length=50))) p1 <- p1$pvals p1 <- merge(dat, p1, all=TRUE) # to get iron/infect merged in foo1 <- xyplot(weight ~ day|iron*infect, dat, group=animal, main=\"diggle.cow\") foo2 <- xyplot(predicted.value ~ day|iron*infect, p1, type='l', group=animal) print(foo1+foo2) } } # }"},{"path":"/reference/draper.safflower.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of safflower — draper.safflower.uniformity","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"Uniformity trial safflower Arizona 1958.","code":""},{"path":"/reference/draper.safflower.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"","code":"data(\"draper.safflower.uniformity\")"},{"path":"/reference/draper.safflower.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"data frame 640 observations following 4 variables. expt experiment row row col column yield yield per plot (grams)","code":""},{"path":"/reference/draper.safflower.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"Experiments conducted Agricultural Experiment Station Farm Eloy, Arizona. crop harvested July 1958. crop planted two rows 12 inches apart vegetable beds 40 inches center center. test, end ranges one row plots one side next alleys, plots gave estimates border effects. Experiment E4 (four foot test) Sandy streaks present field. Average yield 1487 lb/ac. diagonal fertility gradient field. Widening plot equally effective lengthening plot reduce variability. optimum plot size 1 bed wide, 24 feet long. Considering economic costs, optimum size 1 bed, 12 feet long. Field width: 16 beds * 3.33 feet = 53 feet Field length: 18 ranges * 4 feet = 72 feet Experiment E5 (five foot test) Average yield 2517 lb/ac, typical crop. Combining plots lengthwise effective widening plots, order reduce variability. optimum plot size 1 bed wide, 25 feet long. Considering economic costs, optimum size 1 bed, 18 feet long. Field width: 14 beds * 3.33 feet = 46.6 feet. Field length: 18 ranges * 5 feet = 90 feet. Data Table & B Draper, p. 53-56. Typed K.Wright.","code":""},{"path":"/reference/draper.safflower.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"Arlen D. Draper. (1959). Optimum plot size shape safflower yield tests. Dissertation. University Arizona. https://hdl.handle.net/10150/319371 Page 53-56.","code":""},{"path":"/reference/draper.safflower.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"None","code":""},{"path":"/reference/draper.safflower.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of safflower — draper.safflower.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(draper.safflower.uniformity) dat4 <- subset(draper.safflower.uniformity, expt==\"E4\") dat5 <- subset(draper.safflower.uniformity, expt==\"E5\") libs(desplot) desplot(dat4, yield~col*row, flip=TRUE, tick=TRUE, aspect=72/53, # true aspect main=\"draper.safflower.uniformity (four foot)\") desplot(dat5, yield~col*row, flip=TRUE, tick=TRUE, aspect=90/46, # true aspect main=\"draper.safflower.uniformity (five foot)\") # Draper appears to removed the border plots, but it is difficult to # match his results exactly dat4 <- subset(dat4, row>1 & row<20) dat4 <- subset(dat4, col>1 & col<17) dat5 <- subset(dat5, row>1 & row<20) dat5 <- subset(dat5, col<15) # Convert gm/plot to pounds/acre. Draper (p. 20) says 1487 pounds/acre mean(dat4$yield) / 453.592 / (3.33*4) * 43560 # 1472 lb/ac libs(agricolae) libs(reshape2) s4 <- index.smith(acast(dat4, row~col, value.var='yield'), main=\"draper.safflower.uniformity (four foot)\", col=\"red\")$uni s4 # match Draper table 2, p 22 ## s5 <- index.smith(acast(dat5, row~col, value.var='yield'), ## main=\"draper.safflower.uniformity (five foot)\", ## col=\"red\")$uni ## s5 # match Draper table 1, p 21 } # }"},{"path":"/reference/ducker.groundnut.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of groundnut — ducker.groundnut.uniformity","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"Uniformity trial groundnut.","code":""},{"path":"/reference/ducker.groundnut.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"","code":"data(\"ducker.groundnut.uniformity\")"},{"path":"/reference/ducker.groundnut.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"data frame 215 observations following 3 variables. row row ordinate col column ordinate yield yield, pounds per plot","code":""},{"path":"/reference/ducker.groundnut.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"experiment grown Nyasaland, Cotton Experiment Station, Domira Bay, 1942-43. 44x5 identical plots, 1/220 acre area. Single ridge plots one chain length, one yard apart. Two rows groundnuts planted per ridge, staggered 1 foot holes. Holes spaced 18 inches x 12 inches. Two seeds planted per hole. yield values pounds nuts shell. Field length: 5 plots, 22 yards = 110 yards. Field width: 44 plots, 1 yard = 44 yards. data made available special help staff Rothamsted Research Library. Data typed K.Wright checked hand.","code":""},{"path":"/reference/ducker.groundnut.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 2.","code":""},{"path":"/reference/ducker.groundnut.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"None","code":""},{"path":"/reference/ducker.groundnut.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of groundnut — ducker.groundnut.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ducker.groundnut.uniformity) dat <- ducker.groundnut.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=110/44, main=\"ducker.groundnut.uniformity\") } # }"},{"path":"/reference/durban.competition.html","id":null,"dir":"Reference","previous_headings":"","what":"Sugar beet yields with competition effects — durban.competition","title":"Sugar beet yields with competition effects — durban.competition","text":"Sugar beet yields competition effects","code":""},{"path":"/reference/durban.competition.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sugar beet yields with competition effects — durban.competition","text":"data frame 114 observations following 5 variables. gen Genotype factor, 36 levels plus Border col Column block Row/Block wheel Position relative wheel tracks yield Root yields, kg/plot","code":""},{"path":"/reference/durban.competition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sugar beet yields with competition effects — durban.competition","text":"sugar-beet trial conducted 1979. Single-row plots, 12 m long, 0.5 m rows. block made 36 genotypes laid side side. Guard/border plots end. Root yields collected. Wheel tracks located columns 1 2, columns 5 6, set six plots. genotype randomly allocated pair plots (1,6), (2,5), (3,4) across three reps. Wheel effect significant _this_ trial. Field width: 18m + 1m guard rows = 19m Field length: 3 blocks * 12m + 2*0.5m spacing = 37m Retrieved https://www.ma.hw.ac.uk/~iain/research/JAgSciData/data/Trial1.dat Used permission Iain Currie.","code":""},{"path":"/reference/durban.competition.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sugar beet yields with competition effects — durban.competition","text":"Durban, M., Currie, . R. Kempton, 2001. Adjusting fertility competition variety trials. J. Agricultural Science, 136, 129–140.","code":""},{"path":"/reference/durban.competition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sugar beet yields with competition effects — durban.competition","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(durban.competition) dat <- durban.competition # Check that genotypes were balanced across wheel tracks. with(dat, table(gen,wheel)) libs(desplot) desplot(dat, yield ~ col*block, out1=block, text=gen, col=wheel, aspect=37/19, # true aspect main=\"durban.competition\") # Calculate residual after removing block/genotype effects m1 <- lm(yield ~ gen + block, data=dat) dat$res <- resid(m1) ## desplot(dat, res ~ col*block, out1=block, text=gen, col=wheel, ## main=\"durban.competition - residuals\") # Calculate mean of neighboring plots dat$comp <- NA dat$comp[3:36] <- ( dat$yield[2:35] + dat$yield[4:37] ) / 2 dat$comp[41:74] <- ( dat$yield[40:73] + dat$yield[42:75] ) / 2 dat$comp[79:112] <- ( dat$yield[78:111] + dat$yield[80:113] ) / 2 # Demonstrate the competition effect # Competitor plots have low/high yield -> residuals are negative/positive libs(lattice) xyplot(res~comp, dat, type=c('p','r'), main=\"durban.competition\", xlab=\"Average yield of neighboring plots\", ylab=\"Residual\") } # }"},{"path":"/reference/durban.rowcol.html","id":null,"dir":"Reference","previous_headings":"","what":"Row-column experiment of spring barley, many varieties — durban.rowcol","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"Row-column experiment spring barley, many varieties","code":""},{"path":"/reference/durban.rowcol.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"data frame 544 observations following 5 variables. row row bed bed (column) rep rep, 2 levels gen genotype, 272 levels yield yield, tonnes/ha","code":""},{"path":"/reference/durban.rowcol.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"Spring barley variety trial 272 entries (260 new varieties, 12 control). Grown Scottish Crop Research Institute 1998. Row-column design 2 reps, 16 rows (north/south) 34 beds (east/west). land sloped downward row 16 row 1. Plot yields converted tonnes per hectare. Plot dimensions given. Used permission Maria Durban.","code":""},{"path":"/reference/durban.rowcol.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"Durban, Maria Hackett, Christine McNicol, James Newton, Adrian Thomas, William Currie, Iain. 2003. practical use semiparametric models field trials, Journal Agric Biological Envir Stats, 8, 48-66. https://doi.org/10.1198/1085711031265","code":""},{"path":"/reference/durban.rowcol.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"Edmondson, Rodney (2020). Multi-level Block Designs Comparative Experiments. J Agric, Biol, Env Stats. https://doi.org/10.1007/s13253-020-00416-0","code":""},{"path":"/reference/durban.rowcol.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row-column experiment of spring barley, many varieties — durban.rowcol","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(durban.rowcol) dat <- durban.rowcol libs(desplot) desplot(dat, yield~bed*row, out1=rep, num=gen, # aspect unknown main=\"durban.rowcol\") # Durban 2003 Figure 1 m10 <- lm(yield~gen, data=dat) dat$resid <- m10$resid ## libs(lattice) ## xyplot(resid~row, dat, type=c('p','smooth'), main=\"durban.rowcol\") ## xyplot(resid~bed, dat, type=c('p','smooth'), main=\"durban.rowcol\") # Figure 3 libs(lattice) xyplot(resid ~ bed|factor(row), data=dat, main=\"durban.rowcol\", type=c('p','smooth')) # Figure 5 - field trend # note, Durban used gam package like this # m1lo <- gam(yield ~ gen + lo(row, span=10/16) + lo(bed, span=9/34), data=dat) libs(mgcv) m1lo <- gam(yield ~ gen + s(row) + s(bed, k=5), data=dat) new1 <- expand.grid(row=unique(dat$row),bed=unique(dat$bed)) new1 <- cbind(new1, gen=\"G001\") p1lo <- predict(m1lo, newdata=new1) libs(lattice) wireframe(p1lo~row+bed, new1, aspect=c(1,.5), main=\"Field trend\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml) dat <- transform(dat, rowf=factor(row), bedf=factor(bed)) dat <- dat[order(dat$rowf, dat$bedf),] m1a1 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat, random=~spl(rowf) + spl(bedf) + units, family=asr_gaussian(dispersion=1)) m1a2 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat, random=~spl(rowf) + spl(bedf) + units, resid = ~ar1(rowf):ar1(bedf)) m1a2 <- update(m1a2) m1a3 <- asreml(yield~gen, data=dat, random=~units, resid = ~ar1(rowf):ar1(bedf)) # Figure 7 libs(lattice) v7a <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1a3$residuals) wireframe(gamma ~ x*y, v7a, aspect=c(1,.5)) # Fig 7a v7b <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1a2$residuals) wireframe(gamma ~ x*y, v7b, aspect=c(1,.5)) # Fig 7b v7c <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1lo$residuals) wireframe(gamma ~ x*y, v7c, aspect=c(1,.5)) # Fig 7c } } # }"},{"path":"/reference/durban.splitplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","title":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","text":"Split-plot experiment barley fungicide treatments","code":""},{"path":"/reference/durban.splitplot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","text":"data frame 560 observations following 6 variables. yield yield, tonnes/ha block block, 4 levels gen genotype, 70 levels fung fungicide, 2 levels row row bed bed (column)","code":""},{"path":"/reference/durban.splitplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","text":"Grown 1995-1996 Scottish Crop Research Institute. Split-plot design 4 blocks, 2 whole-plot fungicide treatments, 70 barley varieties variety mixes. Total area 10 rows (north/south) 56 beds (east/west). Used permission Maria Durban.","code":""},{"path":"/reference/durban.splitplot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","text":"Durban, Maria Hackett, Christine McNicol, James Newton, Adrian Thomas, William Currie, Iain. 2003. practical use semiparametric models field trials, Journal Agric Biological Envir Stats, 8, 48-66. https://doi.org/10.1198/1085711031265.","code":""},{"path":"/reference/durban.splitplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-plot experiment of barley with fungicide treatments — durban.splitplot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(durban.splitplot) dat <- durban.splitplot libs(desplot) desplot(dat, yield~bed*row, out1=block, out2=fung, num=gen, # aspect unknown main=\"durban.splitplot\") # Durban 2003, Figure 2 m20 <- lm(yield~gen + fung + gen:fung, data=dat) dat$resid <- m20$resid ## libs(lattice) ## xyplot(resid~row, dat, type=c('p','smooth'), main=\"durban.splitplot\") ## xyplot(resid~bed, dat, type=c('p','smooth'), main=\"durban.splitplot\") # Figure 4 doesn't quite match due to different break points libs(lattice) xyplot(resid ~ bed|factor(row), data=dat, main=\"durban.splitplot\", type=c('p','smooth')) # Figure 6 - field trend # note, Durban used gam package like this # m2lo <- gam(yield ~ gen*fung + lo(row, bed, span=.082), data=dat) libs(mgcv) m2lo <- gam(yield ~ gen*fung + s(row, bed,k=45), data=dat) new2 <- expand.grid(row=unique(dat$row), bed=unique(dat$bed)) new2 <- cbind(new2, gen=\"G01\", fung=\"F1\") p2lo <- predict(m2lo, newdata=new2) libs(lattice) wireframe(p2lo~row+bed, new2, aspect=c(1,.5), main=\"durban.splitplot - Field trend\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # Table 5, variance components. Table 6, F tests dat <- transform(dat, rowf=factor(row), bedf=factor(bed)) dat <- dat[order(dat$rowf, dat$bedf),] m2a2 <- asreml(yield ~ gen*fung, random=~block/fung+units, data=dat, resid =~ar1v(rowf):ar1(bedf)) m2a2 <- update(m2a2) lucid::vc(m2a2) ## effect component std.error z.ratio bound ## block 0 NA NA B NA ## block:fung 0.01206 0.01512 0.8 P 0 ## units 0.02463 0.002465 10 P 0 ## rowf:bedf(R) 1 NA NA F 0 ## rowf:bedf!rowf!cor 0.8836 0.03646 24 U 0 ## rowf:bedf!rowf!var 0.1261 0.04434 2.8 P 0 ## rowf:bedf!bedf!cor 0.9202 0.02846 32 U 0 wald(m2a2) } } # }"},{"path":"/reference/eden.nonnormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Height of barley plants in a study of non-normal data — eden.nonnormal","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"Height barley plants study non-normal data.","code":""},{"path":"/reference/eden.nonnormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"","code":"data(\"eden.nonnormal\")"},{"path":"/reference/eden.nonnormal.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"data frame 256 observations following 3 variables. pos position within block block block (numeric) height height wheat plant","code":""},{"path":"/reference/eden.nonnormal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"data used early example permutation test. Eden & Yates used data consider impact non-normal data validity hypothesis test assumes normality. concluded skew data negatively affect analysis variance. Grown Rothamsted. Eight blocks Yeoman II wheat. Sampling blocks quarter-meter rows, four times row. Rows selected random. Position within rows partly controlled make use whole length block. Plants ends sub-unit measured. Shoot height measured ground level auricle last expanded leaf.","code":""},{"path":"/reference/eden.nonnormal.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"T. Eden, F. Yates (1933). validity Fisher's z test applied actual example non-normal data. Journal Agric Science, 23, 6-17. https://doi.org/10.1017/S0021859600052862","code":""},{"path":"/reference/eden.nonnormal.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"Kenneth J. Berry, Paul W. Mielke, Jr., Janis E. Johnston Permutation Statistical Methods: Integrated Approach.","code":""},{"path":"/reference/eden.nonnormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Height of barley plants in a study of non-normal data — eden.nonnormal","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(eden.nonnormal) dat <- eden.nonnormal mean(dat$height) # 55.23 matches Eden table 1 # Eden figure 2 libs(dplyr, lattice) # Blocks had different means, so substract block mean from each datum dat <- group_by(dat, block) dat <- mutate(dat, blkmn=mean(height)) dat <- transform(dat, dev=height-blkmn) histogram( ~ dev, data=dat, breaks=seq(from=-40, to=30, by=2.5), xlab=\"Deviations from block means\", main=\"eden.nonnormal - heights skewed left\") # calculate skewness, permutation libs(dplyr, lattice, latticeExtra) # Eden table 1 # anova(aov(height ~ factor(block), data=dat)) # Eden table 2,3. Note, this may be a different definition of skewness # than is commonly used today (e.g. e1071::skewness). skew <- function(x){ n <- length(x) x <- x - mean(x) s1 = sum(x) s2 = sum(x^2) s3 = sum(x^3) k3=n/((n-1)*(n-2)) * s3 -3/n*s2*s1 + 2/n^2 * s1^3 return(k3) } # Negative values indicate data are skewed left dat <- group_by(dat, block) summarize(dat, s1=sum(height),s2=sum(height^2), mean2=var(height), k3=skew(height)) ## block s1 s2 mean2 k3 ## ## 1 1 1682.0 95929.5 242.56048 -1268.5210 ## 2 2 1858.0 111661.5 121.97984 -1751.9919 ## 3 3 1809.5 108966.8 214.36064 -3172.5284 ## 4 4 1912.0 121748.5 242.14516 -2548.2194 ## 5 5 1722.0 99026.5 205.20565 -559.0629 ## 6 6 1339.0 63077.0 227.36190 -801.2740 ## 7 7 1963.0 123052.5 84.99093 -713.2595 ## 8 8 1854.0 112366.0 159.67339 -1061.9919 # Another way to view skewness with qq plot. Panel 3 most skewed. qqmath( ~ dev|factor(block), data=dat, as.table=TRUE, ylab=\"Deviations from block means\", panel = function(x, ...) { panel.qqmathline(x, ...) panel.qqmath(x, ...) }) # Now, permutation test. # Eden: \"By a process of amalgamation the eight sets of 32 observations were # reduced to eight sets of four and the data treated as a potential # layout for a 32-plot trial\". dat2 <- transform(dat, grp = rep(1:4, each=8)) dat2 <- aggregate(height ~ grp+block, dat2, sum) dat2$trt <- rep(letters[1:4], 8) dat2$block <- factor(dat2$block) # Treatments were assigned at random 1000 times set.seed(54323) fobs <- rep(NA, 1000) for(i in 1:1000){ # randomize treatments within each block # trick from https://stackoverflow.com/questions/25085537 dat2$trt <- with(dat2, ave(trt, block, FUN = sample)) fobs[i] <- anova(aov(height ~ block + trt, dat2))[\"trt\",\"F value\"] } # F distribution with 3,21 deg freedom # Similar to Eden's figure 4, but on a different horizontal scale xval <- seq(from=0,to=max(fobs), length=50) yval <- df(xval, df1 = 3, df2 = 21) # Re-scale, 10 = max of historgram, 0.7 = max of density histogram( ~ fobs, breaks=xval, xlab=\"F value\", main=\"Observed (histogram) & theoretical (line) F values\") + xyplot((10/.7)* yval ~ xval, type=\"l\", lwd=2) } # }"},{"path":"/reference/eden.potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"Potato yields response potash nitrogen fertilizer. Data Fisher's 1929 paper Studies Crop Variation 6. different design used year.","code":""},{"path":"/reference/eden.potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"data frame 225 observations following 9 variables. year year/type factor yield yield, pounds per plot block block row row col column trt treatment factor nitro nitrogen fertilizer, cwt/acre potash potash fertilizer, cwt/acre ptype potash type","code":""},{"path":"/reference/eden.potato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"data interest show gradual development experimental designs agriculture. 1925/1926 potato variety Kerr's Pink. 1927 Arran Comrade. 1925a/1926a qualitative experiments, treatments O=None, S=Sulfate, M=Muriate, P=Potash manure salts. design Latin Square. 1925/1926b/1927 experiments RCB designs treatment codes defining amount type fertilizer used. Note: 't' treatment defined original paper.","code":""},{"path":"/reference/eden.potato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"T Eden R Fisher, 1929. Studies Crop Variation. VI. Experiments response potato potash nitrogen. Journal Agricultural Science, 19: 201-213.","code":""},{"path":"/reference/eden.potato.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667","code":""},{"path":"/reference/eden.potato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Potato yields in response to potash and nitrogen fertilizer — eden.potato","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(eden.potato) dat <- eden.potato # 1925 qualitative d5a <- subset(dat, year=='1925a') libs(desplot) desplot(d5a, trt~col*row, text=yield, cex=1, shorten='no', # aspect unknown main=\"eden.potato: 1925 qualitative\") anova(m5a <- aov(yield~trt+factor(row)+factor(col), d5a)) # table 2 # 1926 qualitative d6a <- subset(dat, year=='1926a') libs(desplot) desplot(d6a, trt~col*row, text=yield, cex=1, shorten='no', # aspect unknown main=\"eden.potato: 1926 qualitative\") anova(m6a <- aov(yield~trt+factor(row)+factor(col), d6a)) # table 4 # 1925 quantitative d5 <- subset(dat, year=='1925b') libs(desplot) desplot(d5, yield ~ col*row, out1=block, text=trt, cex=1, # aspect unknown main=\"eden.potato: 1925 quantitative\") # Trt 't' not defined, seems to be the same as 'a' libs(lattice) dotplot(trt~yield|block, d5, # aspect unknown main=\"eden.potato: 1925 quantitative\") anova(m5 <- aov(yield~trt+block, d5)) # table 6 # 1926 quantitative d6 <- subset(dat, year=='1926b') libs(desplot) desplot(d6, yield ~ col*row, out1=block, text=trt, cex=1, # aspect unknown main=\"eden.potato: 1926 quantitative\") anova(m6 <- aov(yield~trt+block, d6)) # table 7 # 1927 qualitative + quantitative d7 <- droplevels(subset(dat, year==1927)) libs(desplot) desplot(d7, yield ~ col*row, out1=block, text=trt, cex=1, col=ptype, # aspect unknown main=\"eden.potato: 1927 qualitative + quantitative\") # Table 8. Anova, mean yield tons / acre anova(m7 <- aov(yield~trt+block+ptype + ptype:potash, d7)) libs(reshape2) me7 <- melt(d7, measure.vars='yield') acast(me7, potash~nitro, fun=mean) * 40/2240 # English ton = 2240 pounds acast(me7, potash~ptype, fun=mean) * 40/2240 } # }"},{"path":"/reference/eden.tea.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of tea — eden.tea.uniformity","title":"Uniformity trial of tea — eden.tea.uniformity","text":"Uniformity trial tea Ceylon.","code":""},{"path":"/reference/eden.tea.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of tea — eden.tea.uniformity","text":"","code":"data(\"eden.tea.uniformity\")"},{"path":"/reference/eden.tea.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of tea — eden.tea.uniformity","text":"data frame 144 observations following 4 variables. entry entry number yield yield row row col column","code":""},{"path":"/reference/eden.tea.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of tea — eden.tea.uniformity","text":"Tea plucking Ceylon extended 20 Apr 1928 10 Dec 1929. 42 pluckings. clear units , paper mentions \"quarter pound\". field divided 144 plots 1/72 acre = 605 sq ft. plot contained 6 rows bushes, approximately 42 bushes. ( row thus 7 bushes). Plots row 12 high hillside, plots row 1 low hill. Note: assume plots roughly square: 6 rows 7 bushes. Field width: 12 plots * 24.6 feet = 295 feet Field length: 12 plots * 24.6 feet = 295 feet Data typed K.Wright. Although pdf paper crease across page hid digits, row column totals included paper allowed re-construction missing digits.","code":""},{"path":"/reference/eden.tea.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of tea — eden.tea.uniformity","text":"T. Eden. (1931). Studies yield tea. 1. experimental errors field experiments tea. Agricultural Science, 21, 547-573. https://doi.org/10.1017/S0021859600088511","code":""},{"path":"/reference/eden.tea.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of tea — eden.tea.uniformity","text":"None","code":""},{"path":"/reference/eden.tea.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of tea — eden.tea.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(eden.tea.uniformity) dat <- eden.tea.uniformity # sum(dat$yield) # 140050.6 matches total yield in appendix A # mean(dat$yield) # 972.574 match page 5554 m1 <- aov(yield ~ factor(entry) + factor(row) + factor(col), data=dat) summary(m1) libs(desplot) desplot(dat, yield ~ col*row, aspect=1, main=\"eden.tea.uniformity\") } # }"},{"path":"/reference/edwards.oats.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"Multi-environment trial oats 5 locations, 7 years, 3 replicates trial.","code":""},{"path":"/reference/edwards.oats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"","code":"data(\"edwards.oats\")"},{"path":"/reference/edwards.oats.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"data frame 3694 observations following 7 variables. eid Environment identification (factor) year Year loc Location name block Block gen Genotype name yield Yield testwt Test weight","code":""},{"path":"/reference/edwards.oats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"data comes breeding program, usual pattern (1) genotypes entering/leaving program (2) check genotypes remain throughout duration program. Experiments conducted Iowa State University Oat Variety Trial years 1997 2003. year 40 genotypes, 30 released checks 10 experimental lines. genotype appeared range 3 34 year-loc combinations. trials grown five locations Iowa: Ames, Nashua, Crawfordsville, Lewis, Sutherland. 1998 trial grown Sutherland. 3 blocks trial. Five genotypes removed data low yields (included ). environment identifaction values Edwards (2006) table 1. Electronic data supplied Jode Edwards.","code":""},{"path":"/reference/edwards.oats.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"Jode W. Edwards, Jean-Luc Jannink (2006). Bayesian Modeling Heterogeneous Error Genotype x Environment Interaction Variances. Crop Science, 46, 820-833. https://dx.doi.org/10.2135/cropsci2005.0164","code":""},{"path":"/reference/edwards.oats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"None","code":""},{"path":"/reference/edwards.oats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of oats in United States, 5 locations, 7 years. — edwards.oats","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(dplyr,lattice, reshape2, stringr) data(edwards.oats) dat <- edwards.oats dat$env <- paste0(dat$year,\".\",dat$loc) dat$eid <- factor(dat$eid) mat <- reshape2::acast(dat, env ~ gen, fun.aggregate=mean, value.var=\"yield\", na.rm=TRUE) lattice::levelplot(mat, aspect=\"m\", main=\"edwards.oats\", xlab=\"environment\", ylab=\"genotype\", scales=list(x=list(rot=90))) # Calculate BLUEs of gen/env effects m1 <- lm(yield ~ gen+eid, dat) gg <- coef(m1)[2:80] names(gg) <- stringr::str_replace(names(gg), \"gen\", \"\") gg <- c(0,gg) names(gg)[1] <- \"ACStewart\" ee <- coef(m1)[81:113] names(ee) <- stringr::str_replace(names(ee), \"eid\", \"\") ee <- c(0,ee) names(ee)[1] <- \"1\" # Subtract gen/env coefs from yield values dat2 <- dat dat2$gencoef <- gg[match(dat2$gen, names(gg))] dat2$envcoef <- ee[match(dat2$eid, names(ee))] dat2 <- dplyr::mutate(dat2, y = yield - gencoef - envcoef) # Calculate variance for each gen*env. Shape of the graph is vaguely # similar to Fig 2 of Edwards et al (2006), who used a Bayesian model dat2 <- group_by(dat2, gen, eid) dat2sum <- summarize(dat2, stddev = sd(y)) bwplot(stddev ~ eid, dat2sum) } # }"},{"path":"/reference/engelstad.nitro.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"Corn yield response nitrogen fertilizer single variety corn two locations five years","code":""},{"path":"/reference/engelstad.nitro.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"data frame 60 observations following 4 variables. loc location, 2 levels year year, 1962-1966 nitro nitrogen fertilizer kg/ha yield yield, quintals/ha","code":""},{"path":"/reference/engelstad.nitro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"Corn yield response nitrogen fertilizer single variety corn two locations Tennessee five years. yield data mean 9 replicates. original paper fits quadratic curves data. Schabenberger Pierce fit multiple models including linear plateau. example fits quadratic plateau one year/loc. original paper, 1965 1966 data Knoxville location used appeared response due nitrogen minimal 1965 nonexistant 1966. economic optimum can found setting tangent equal ratio (fertilizer price)/(grain price).","code":""},{"path":"/reference/engelstad.nitro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"Engelstad, OP Parks, WL. 1971. Variability Optimum N Rates Corn. Agronomy Journal, 63, 21–23.","code":""},{"path":"/reference/engelstad.nitro.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"Schabenberger, O. Pierce, F.J., 2002. Contemporary statistical models plant soil sciences, CRC. Page 254-259.","code":""},{"path":"/reference/engelstad.nitro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn with nitrogen fertilizer — engelstad.nitro","text":"","code":"library(agridat) data(engelstad.nitro) dat <- engelstad.nitro libs(latticeExtra) useOuterStrips(xyplot(yield ~ nitro | factor(year)*loc, dat, main=\"engelstad.nitro\")) # Fit a quadratic plateau model to one year/loc j62 <- droplevels(subset(dat, loc==\"Jackson\" & year==1962)) # ymax is maximum yield, M is the change point, k affects curvature m1 <- nls(yield ~ ymax*(nitro > M) + (ymax - (k/2) * (M-nitro)^2) * (nitro < M), data= j62, start=list(ymax=80, M=150, k=.01)) # Plot the raw data and model newdat <- data.frame(nitro=seq(0,max(dat$nitro))) p1 <- predict(m1, new=newdat) plot(yield ~ nitro, j62) lines(p1 ~ newdat$nitro, col=\"blue\") title(\"engelstad.nitro: quadratic plateau at Jackson 1962\") # Optimum nitro level ignoring prices = 225 coef(m1)['M'] #> M #> 225.3404 # Optimum nitro level using $0.11 for N cost, $1.15 for grain price = 140 # Set the first derivative equal to N/corn price, k(M-nitro)=.11/1.15 coef(m1)['M']-(.11/1.15)/coef(m1)['k'] #> M #> 140.7837"},{"path":"/reference/evans.sugarcane.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"Uniformity trial sugarcane Mauritius.","code":""},{"path":"/reference/evans.sugarcane.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"","code":"data(\"evans.sugarcane.uniformity\")"},{"path":"/reference/evans.sugarcane.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"data frame 710 observations following 3 variables. row row ordinate col column ordinate yield plot yield","code":""},{"path":"/reference/evans.sugarcane.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"field ratoon canes harvested 20-hole plots. Described letter Frank Yates written 21 May 1935. Field length: 5 plots x 50 feet (20 stools per plot; 30 inches stools) = 250 feet Field width: 142 plots x 5 feet = 710 feet data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/evans.sugarcane.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 8.","code":""},{"path":"/reference/evans.sugarcane.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"None.","code":""},{"path":"/reference/evans.sugarcane.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sugarcane — evans.sugarcane.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ data(evans.sugarcane.uniformity) dat <- evans.sugarcane.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(5*50)/(142*5), # true aspect main=\"evans.sugarcane.uniformity\") table( substring(dat$yield,3) ) # yields ending in 0,5 are much more common } # }"},{"path":"/reference/fan.stability.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize hybrids in China — fan.stability","title":"Multi-environment trial of maize hybrids in China — fan.stability","text":"Yield 13 hybrids, grown 10 locations across 2 years. Conducted Yunnan, China.","code":""},{"path":"/reference/fan.stability.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize hybrids in China — fan.stability","text":"data frame 260 observations following 5 variables. gen genotype maturity maturity, days year year loc location yield yield, Mg/ha","code":""},{"path":"/reference/fan.stability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize hybrids in China — fan.stability","text":"Data mean 3 reps. data used conduct stability analysis yield. Used permission Manjit Kang.","code":""},{"path":"/reference/fan.stability.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize hybrids in China — fan.stability","text":"Fan, X.M. Kang, M.S. Chen, H. Zhang, Y. Tan, J. Xu, C. (2007). Yield stability maize hybrids evaluated multi-environment trials Yunnan, China. Agronomy Journal, 99, 220-228. https://doi.org/10.2134/agronj2006.0144","code":""},{"path":"/reference/fan.stability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize hybrids in China — fan.stability","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(fan.stability) dat <- fan.stability dat$env <- factor(paste(dat$loc, dat$year, sep=\"\")) libs(lattice) dotplot(gen~yield|env, dat, main=\"fan.stability\") libs(reshape2, agricolae) dm <- acast(dat, gen~env, value.var='yield') # Use 0.464 as pooled error from ANOVA. Calculate yield mean/stability. stability.par(dm, rep=3, MSerror=0.464) # Table 5 of Fan et al. } # }"},{"path":"/reference/federer.diagcheck.html","id":null,"dir":"Reference","previous_headings":"","what":"Wheat experiment with diagonal checks — federer.diagcheck","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"Wheat experiment augmented two check varieties diagonal strips.","code":""},{"path":"/reference/federer.diagcheck.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"data frame 180 observations following 4 variables. row row col column gen genotype, 120 levels yield yield","code":""},{"path":"/reference/federer.diagcheck.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"experiment conducted Matthew Reynolds, CIMMYT. 180 plots field, 60 diagonal checks (G121 G122) 120 new varieties. Federer used data multiple papers illustrate use orthogonal polynomials model field trends related genetic effects. Note: Federer Wolfinger (2003) provide SAS program analysis data. However, SAS program used analyze data, results match results given Federer (1998) Federer Wolfinger (2003). differences slight, suggests typographical error presentation data. R code provides results consistent SAS code Federer & Wolfinger (2003) applied version data. Plot dimensions given.","code":""},{"path":"/reference/federer.diagcheck.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"Federer, Walter T. 1998. Recovery interblock, intergradient, intervariety information incomplete block lattice rectangle design experiments. Biometrics, 54, 471–481. https://doi.org/10.2307/3109756","code":""},{"path":"/reference/federer.diagcheck.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"Walter T Federer Russell D Wolfinger, 2003. Augmented Row-Column Design Trend Analysis, chapter 28 Handbook Formulas Software Plant Geneticists Breeders, Haworth Press.","code":""},{"path":"/reference/federer.diagcheck.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wheat experiment with diagonal checks — federer.diagcheck","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(federer.diagcheck) dat <- federer.diagcheck dat$check <- ifelse(dat$gen == \"G121\" | dat$gen==\"G122\", \"C\",\"N\") # Show the layout as in Federer 1998. libs(desplot) desplot(dat, yield ~ col*row, text=gen, show.key=FALSE, # aspect unknown shorten='no', col=check, cex=.8, col.text=c(\"yellow\",\"gray\"), main=\"federer.diagcheck\") # Now reproduce the analysis of Federer 2003. # Only to match SAS results dat$row <- 16 - dat$row dat <- dat[order(dat$col, dat$row), ] # Add row / column polynomials to the data. # The scaling factors sqrt() are arbitrary, but used to match SAS nr <- length(unique(dat$row)) nc <- length(unique(dat$col)) rpoly <- poly(dat$row, degree=10) * sqrt(nc) cpoly <- poly(dat$col, degree=10) * sqrt(nr) dat <- transform(dat, c1 = cpoly[,1], c2 = cpoly[,2], c3 = cpoly[,3], c4 = cpoly[,4], c6 = cpoly[,6], c8 = cpoly[,8], r1 = rpoly[,1], r2 = rpoly[,2], r3 = rpoly[,3], r4 = rpoly[,4], r8 = rpoly[,8], r10 = rpoly[,10]) dat$trtn <- ifelse(dat$gen == \"G121\" | dat$gen==\"G122\", dat$gen, \"G999\") dat$new <- ifelse(dat$gen == \"G121\" | dat$gen==\"G122\", \"N\", \"Y\") dat <- transform(dat, trtn=factor(trtn), new=factor(new)) m1 <- lm(yield ~ c1 + c2 + c3 + c4 + c6 + c8 + r1 + r2 + r4 + r8 + r10 + c1:r1 + c2:r1 + c3:r1 + gen, data = dat) # To get Type III SS use the following # libs(car) # car::Anova(m1, type=3) # Matches PROC GLM output ## Sum Sq Df F value Pr(>F) ## (Intercept) 538948 1 159.5804 3.103e-16 *** ## c1 13781 1 4.0806 0.0494940 * ## c2 51102 1 15.1312 0.0003354 *** ## c3 45735 1 13.5419 0.0006332 *** ## c4 24670 1 7.3048 0.0097349 ** ## ... # lmer libs(lme4,lucid) # \"group\" for all data dat$one <- factor(rep(1, nrow(dat))) # lmer with bobyqa (default) m2b <- lmer(yield ~ trtn + (0 + r1 + r2 + r4 + r8 + r10 + c1 + c2 + c3 + c4 + c6 + c8 + r1:c1 + r1:c2 + r1:c3 || one) + (1|new:gen), data = dat, control=lmerControl(check.nlev.gtr.1=\"ignore\")) vc(m2b) ## grp var1 var2 vcov sdcor ## new.gen (Intercept) 2869 53.57 ## one r1:c3 5532 74.37 ## one.1 r1:c2 58230 241.3 ## one.2 r1:c1 128000 357.8 ## one.3 c8 6456 80.35 ## one.4 c6 1400 37.41 ## one.5 c4 1792 42.33 ## one.6 c3 2549 50.49 ## one.7 c2 5942 77.08 ## one.8 c1 0 0 ## one.9 r10 1133 33.66 ## one.10 r8 1355 36.81 ## one.11 r4 2269 47.63 ## one.12 r2 241.8 15.55 ## one.13 r1 9200 95.92 ## Residual 4412 66.42 # lmer with Nelder_Mead gives 'wrong' results ## m2n <- lmer(yield ~ trtn + (0 + r1 + r2 + r4 + r8 + r10 + ## c1 + c2 + c3 + c4 + c6 + c8 + r1:c1 + r1:c2 + r1:c3 || one) + ## (1|new:gen) ## , data = dat, ## control=lmerControl(optimizer=\"Nelder_Mead\", ## check.nlev.gtr.1=\"ignore\")) ## vc(m2n) ## groups name variance stddev ## new.gen (Intercept) 3228 56.82 ## one r1:c3 7688 87.68 ## one.1 r1:c2 69750 264.1 ## one.2 r1:c1 107400 327.8 ## one.3 c8 6787 82.38 ## one.4 c6 1636 40.45 ## one.5 c4 12270 110.8 ## one.6 c3 2686 51.83 ## one.7 c2 7645 87.43 ## one.8 c1 0 0.0351 ## one.9 r10 1976 44.45 ## one.10 r8 1241 35.23 ## one.11 r4 2811 53.02 ## one.12 r2 928.2 30.47 ## one.13 r1 10360 101.8 ## Residual 4127 64.24 if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) m3 <- asreml(yield ~ -1 + trtn, data=dat, random = ~ r1 + r2 + r4 + r8 + r10 + c1 + c2 + c3 + c4 + c6 + c8 + r1:c1 + r1:c2 + r1:c3 + new:gen) ## coef(m3) ## # REML cultivar means. Very similar to Federer table 2. ## rev(sort(round(coef(m3)$fixed[3] + coef(m3)$random[137:256,],0))) ## ## gen_G060 gen_G021 gen_G011 gen_G099 gen_G002 ## ## 974 949 945 944 942 ## ## gen_G118 gen_G058 gen_G035 gen_G111 gen_G120 ## ## 938 937 937 933 932 ## ## gen_G046 gen_G061 gen_G082 gen_G038 gen_G090 ## ## 932 931 927 927 926 ## vc(m3) ## ## effect component std.error z.ratio constr ## ## r1!r1.var 9201 13720 0.67 pos ## ## r2!r2.var 241.7 1059 0.23 pos ## ## r4!r4.var 2269 3915 0.58 pos ## ## r8!r8.var 1355 2627 0.52 pos ## ## r10!r10.var 1133 2312 0.49 pos ## ## c1!c1.var 0.01 0 4.8 bound ## ## c2!c2.var 5942 8969 0.66 pos ## ## c3!c3.var 2549 4177 0.61 pos ## ## c4!c4.var 1792 3106 0.58 pos ## ## c6!c6.var 1400 2551 0.55 pos ## ## c8!c8.var 6456 9702 0.67 pos ## ## r1:c1!r1.var 128000 189700 0.67 pos ## ## r1:c2!r1.var 58230 90820 0.64 pos ## ## r1:c3!r1.var 5531 16550 0.33 pos ## ## new:gen!new.var 2869 1367 2.1 pos ## ## R!variance 4412 915 4.8 pos } } # }"},{"path":"/reference/federer.tobacco.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"RCB tobacco, height plants exposed radiation","code":""},{"path":"/reference/federer.tobacco.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"data frame 56 observations following 4 variables. row row block block, numeric dose radiation dose, roentgens height height 20 plants, cm","code":""},{"path":"/reference/federer.tobacco.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"experiment conducted 1951 described Federer (1954). treatment involved exposing tobacco seeds seven different doses radiation. seedlings transplanted field RCB experiment 7 treatments 8 blocks. physical layout experiment 8 rows 7 columns. Shortly plants transplanted field became apparent environmental gradient existed. response variable total height (centimeters) 20 plants.","code":""},{"path":"/reference/federer.tobacco.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"Walter T Federer C S Schlottfeldt, 1954. use covariance control gradients experiments. Biometrics, 10, 282–290. https://doi.org/10.2307/3001881","code":""},{"path":"/reference/federer.tobacco.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"R. D. Cook S. Weisberg (1999). Applied Regression Including Computing Graphics. Walter T Federer Russell D Wolfinger, 2003. PROC GLM PROC MIXED Codes Trend Analyses Row-Column Designed Experiments, Handbook Formulas Software Plant Geneticists Breeders, Haworth Press. Paul N Hinz, (1987). Nearest-Neighbor Analysis Practice, Iowa State Journal Research, 62, 199–217. https://lib.dr.iastate.edu/iowastatejournalofresearch/vol62/iss2/1","code":""},{"path":"/reference/federer.tobacco.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB of tobacco, height plants exposed to radiation — federer.tobacco","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(federer.tobacco) dat <- federer.tobacco # RCB analysis. Treatment factor not signficant. dat <- transform(dat, dosef=factor(dose), rowf=factor(row), blockf=factor(block)) m1 <- lm(height ~ blockf + dosef, data=dat) anova(m1) # RCB residuals show strong spatial trends libs(desplot) dat$resid <- resid(m1) desplot(dat, resid ~ row * block, # aspect unknown main=\"federer.tobacco\") # Row-column analysis. Treatment now significant m2 <- lm(height ~ rowf + blockf + dosef, data=dat) anova(m2) } # }"},{"path":"/reference/fisher.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"Multi-environment trial 5 barley varieties, 6 locations, 2 years","code":""},{"path":"/reference/fisher.barley.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"","code":"data(\"fisher.barley\")"},{"path":"/reference/fisher.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"data frame 60 observations following 4 variables. yield yield, bu/ac gen genotype/variety, 5 levels env environment/location, 2 levels year year, 1931/1932","code":""},{"path":"/reference/fisher.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"Trials 5 varieties barley conducted 6 stations Minnesota years 1931-1932. subset Immer's barley data. yield values totals 3 reps (Immer gave average yield 3 reps).","code":""},{"path":"/reference/fisher.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"Ronald Fisher (1935). Design Experiments.","code":""},{"path":"/reference/fisher.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"George Fernandez (1991). Analysis Genotype x Environment Interaction Stability Estimates. Hort Science, 26, 947-950. F. Yates & W. G. Cochran (1938). Analysis Groups Experiments. Journal Agricultural Science, 28, 556-580, table 1. https://doi.org/10.1017/S0021859600050978 G. K. Shukla, 1972. statistical aspects partitioning genotype-environmental components variability. Heredity, 29, 237-245. Table 1. https://doi.org/10.1038/hdy.1972.87","code":""},{"path":"/reference/fisher.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of 5 barley varieties, 6 locations, 2 years — fisher.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(fisher.barley) dat <- fisher.barley libs(dplyr,lattice) # Yates 1938 figure 1. Regression on env mean # Sum years within loc dat2 <- aggregate(yield ~ gen + env, data=dat, FUN=sum) # Avg within env emn <- aggregate(yield ~ env, data=dat2, FUN=mean) dat2$envmn <- emn$yield[match(dat2$env, emn$env)] xyplot(yield ~ envmn, dat2, group=gen, type=c('p','r'), main=\"fisher.barley - stability regression\", xlab=\"Environment total\", ylab=\"Variety mean\", auto.key=list(columns=3)) # calculate stability according to the sum-of-squares approach used by # Shukla (1972), eqn 11. match to Shukla, Table 4, M.S. column # also matches fernandez, table 3, stabvar column libs(dplyr) dat2 <- dat dat2 <- group_by(dat2, gen,env) dat2 <- summarize(dat2, yield=sum(yield)) # means across years dat2 <- group_by(dat2, env) dat2 <- mutate(dat2, envmn=mean(yield)) # env means dat2 <- group_by(dat2, gen) dat2 <- mutate(dat2, genmn=mean(yield)) # gen means dat2 <- ungroup(dat2) dat2 <- mutate(dat2, grandmn=mean(yield)) # grand mean # correction factor overall dat2 <- mutate(dat2, cf = sum((yield - genmn - envmn + grandmn)^2)) t=5; s=6 # t genotypes, s environments dat2 <- group_by(dat2, gen) dat2 <- mutate(dat2, ss=sum((yield-genmn-envmn+grandmn)^2)) # divide by 6 to scale down to plot-level dat2 <- mutate(dat2, sig2i = 1/((s-1)*(t-1)*(t-2)) * (t*(t-1)*ss-cf)/6) dat2[!duplicated(dat2$gen),c('gen','sig2i')] ## ## 1 Manchuria 25.87912 ## 2 Peatland 75.68001 ## 3 Svansota 19.59984 ## 4 Trebi 225.52866 ## 5 Velvet 22.73051 if(require(\"asreml\", quietly=TRUE)) { # mixed model approach gives similar results (but not identical) libs(asreml,lucid) dat2 <- dat dat2 <- dplyr::group_by(dat2, gen,env) dat2 <- dplyr::summarize(dat2, yield=sum(yield)) # means across years dat2 <- dplyr::arrange(dat2, gen) # G-side m1g <- asreml(yield ~ gen, data=dat2, random = ~ env + at(gen):units, family=asr_gaussian(dispersion=1.0)) m1g <- update(m1g) summary(m1g)$varcomp[-1,1:2]/6 # component std.error # at(gen, Manchuria):units 33.8145031 27.22721 # at(gen, Peatland):units 70.4489092 50.52680 # at(gen, Svansota):units 25.2728568 21.92919 # at(gen, Trebi):units 231.6981702 150.80464 # at(gen, Velvet):units 13.9325646 16.58571 # units!R 0.1666667 NA # R-side estimates = G-side estimate + 0.1666 (resid variance) m1r <- asreml(yield ~ gen, data=dat2, random = ~ env, residual = ~ dsum( ~ units|gen)) m1r <- update(m1r) summary(m1r)$varcomp[-1,1:2]/6 # component std.error # gen_Manchuria!R 34.00058 27.24871 # gen_Peatland!R 70.65501 50.58925 # gen_Svansota!R 25.42022 21.88606 # gen_Trebi!R 231.85846 150.78756 # gen_Velvet!R 14.08405 16.55558 } } # }"},{"path":"/reference/fisher.latin.html","id":null,"dir":"Reference","previous_headings":"","what":"Latin square experiment on mangolds — fisher.latin","title":"Latin square experiment on mangolds — fisher.latin","text":"Latin square experiment mangolds. Used R. . Fisher.","code":""},{"path":"/reference/fisher.latin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latin square experiment on mangolds — fisher.latin","text":"","code":"data(\"fisher.latin\")"},{"path":"/reference/fisher.latin.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Latin square experiment on mangolds — fisher.latin","text":"data frame 25 observations following 4 variables. trt treatment factor, 5 levels yield yield row row col column","code":""},{"path":"/reference/fisher.latin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Latin square experiment on mangolds — fisher.latin","text":"Yields root weights. Data originally collected Mercer Hall part uniformity trial. data data columns 1-5, rows 16-20, mercer.mangold.uniformity data package. Unsurprisingly, significant treatment differences.","code":""},{"path":"/reference/fisher.latin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latin square experiment on mangolds — fisher.latin","text":"Mercer, WB Hall, AD, 1911. experimental error field trials Journal Agricultural Science, 4, 107-132. Table 1. http::/doi.org/10.1017/S002185960000160X R. . Fisher. Statistical Methods Research Workers.","code":""},{"path":"/reference/fisher.latin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Latin square experiment on mangolds — fisher.latin","text":"","code":"library(agridat) data(fisher.latin) dat <- fisher.latin # Standard latin-square analysis m1 <- lm(yield ~ trt + factor(row) + factor(col), data=dat) anova(m1) #> Analysis of Variance Table #> #> Response: yield #> Df Sum Sq Mean Sq F value Pr(>F) #> trt 4 330.2 82.56 0.5647 0.692978 #> factor(row) 4 4240.2 1060.06 7.2511 0.003294 ** #> factor(col) 4 701.8 175.46 1.2002 0.360412 #> Residuals 12 1754.3 146.19 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"},{"path":"/reference/forster.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"Uniformity trial wheat Australia.","code":""},{"path":"/reference/forster.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"","code":"data(\"forster.wheat.uniformity\")"},{"path":"/reference/forster.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"data frame 160 observations following 3 variables. row row ordinate col column ordinate yield yield, ounces per plot","code":""},{"path":"/reference/forster.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"experiment repeat classic experiment Mercer Hall. Conducted State Research Farm, Werribee, Victoria, Australia. Planted 1926. Harvested 1927. acre land selected. plot one double-sown row. plot 30 x 20 links. whole experiment 300 x 320 links. Near west edge, strip damaged cart tracks excluded. field marked quarters one quarter subdivided harvested time. quarter cut 5 strips 8 plots. Field length: 16 plots * 20 links = 320 links (211 feet). Field width: 10 plots * 30 links = 300 links (197 feet). Note: clear strip \"yards wide\" omitted yet dimensions whole area still 300 x 320 links. Since omitted strip 1/3 width plot, (agridat authors) decided ignore omitted strip. electronic data manually typed source 2023-04-12. Summary statistics electronic data differ slightly summaries Forster, indicating possible typos rounding printed yield values paper. Values checked hand match paper.","code":""},{"path":"/reference/forster.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"Forster, H. C. (Howard Carlyle), - Vasey, . J. (1928). Experimental error field trials Australia. Proceedings Royal Society Victoria. New series, 40, 70–80. Table 1. https://www.biodiversitylibrary.org/page/54367272","code":""},{"path":"/reference/forster.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"None","code":""},{"path":"/reference/forster.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat in Australia. — forster.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ require(agridat) data(forster.wheat.uniformity) dat <- forster.wheat.uniformity mean(dat$yield) # 135.97 # Forster says 136.5 sd(dat$yield) # 10.68 # Forster says 10.9 # Compare to Forster table 3. Slight differences. table( cut(dat$yield, breaks = c(106,111,116,121,126,131,136,141, 146,151,156,161,166)+.5) ) # Forster has 5 plots in the 157-161 bin, but we show 6. # I filtered the data for this bin and verified our data # matches the layout in the paper. filter(dat, yield>156.5, yield<161.5) libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(16*20)/(10*30), # true aspect main=\"forster.wheat.uniformity\") } # }"},{"path":"/reference/foulley.calving.html","id":null,"dir":"Reference","previous_headings":"","what":"Calving difficulty by calf sex and age of dam — foulley.calving","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"Calving difficulty calf sex age dam","code":""},{"path":"/reference/foulley.calving.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"","code":"data(\"foulley.calving\")"},{"path":"/reference/foulley.calving.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"data frame 54 observations following 4 variables. sex calf gender age dam age factor, 9 levels score score birthing difficulty, S1 < S2 < S3 count count births category","code":""},{"path":"/reference/foulley.calving.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"data calving difficulty scores purebred US Simmental cows. raw data show greatest calving difficulty young dams male calves. Differences male/female calves decreased age dam. goodness fit can improved using scaling effect age dam. Note: paper Foulley Gianola '21943' count score 1, F, >8. data uses '20943' marginal totals data match marginal totals given paper. Used permission Jean-Louis Foulley.","code":""},{"path":"/reference/foulley.calving.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"JL Foulley, D Gianola (1996). Statistical Analysis Ordered Categorical Data via Structured Heteroskedastic Threshold Model. Genet Sel Evol, 28, 249–273. https://doi.org/10.1051/gse:19960304","code":""},{"path":"/reference/foulley.calving.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calving difficulty by calf sex and age of dam — foulley.calving","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(foulley.calving) dat <- foulley.calving ## Plot d2 <- transform(dat, age=ordered(age, levels=c(\"0.0-2.0\",\"2.0-2.5\",\"2.5-3.0\", \"3.0-3.5\",\"3.5-4.0\", \"4.0-4.5\",\"4.5-5.0\",\"5.0-8.0\",\"8.0+\")), score=ordered(score, levels=c('S1','S2','S3'))) libs(reshape2) d2 <- acast(dat, sex+age~score, value.var='count') d2 <- prop.table(d2, margin=1) libs(lattice) thm <- simpleTheme(col=c('skyblue','gray','pink')) barchart(d2, par.settings=thm, main=\"foulley.calving\", xlab=\"Frequency of calving difficulty\", ylab=\"Calf gender and dam age\", auto.key=list(columns=3, text=c(\"Easy\",\"Assited\",\"Difficult\"))) ## Ordinal multinomial model libs(ordinal) m2 <- clm(score ~ sex*age, data=dat, weights=count, link='probit') summary(m2) ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## sexM 0.500605 0.015178 32.982 < 2e-16 *** ## age2.0-2.5 -0.237643 0.013846 -17.163 < 2e-16 *** ## age2.5-3.0 -0.681648 0.018894 -36.077 < 2e-16 *** ## age3.0-3.5 -0.957138 0.018322 -52.241 < 2e-16 *** ## age3.5-4.0 -1.082520 0.024356 -44.446 < 2e-16 *** ## age4.0-4.5 -1.146834 0.022496 -50.981 < 2e-16 *** ## age4.5-5.0 -1.175312 0.028257 -41.594 < 2e-16 *** ## age5.0-8.0 -1.280587 0.016948 -75.559 < 2e-16 *** ## age8.0+ -1.323749 0.024079 -54.974 < 2e-16 *** ## sexM:age2.0-2.5 0.003035 0.019333 0.157 0.87527 ## sexM:age2.5-3.0 -0.076677 0.026106 -2.937 0.00331 ** ## sexM:age3.0-3.5 -0.080657 0.024635 -3.274 0.00106 ** ## sexM:age3.5-4.0 -0.135774 0.032927 -4.124 3.73e-05 *** ## sexM:age4.0-4.5 -0.124303 0.029819 -4.169 3.07e-05 *** ## sexM:age4.5-5.0 -0.198897 0.038309 -5.192 2.08e-07 *** ## sexM:age5.0-8.0 -0.135524 0.022804 -5.943 2.80e-09 *** ## sexM:age8.0+ -0.131033 0.031852 -4.114 3.89e-05 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Threshold coefficients: ## Estimate Std. Error z value ## S1|S2 0.82504 0.01083 76.15 ## S2|S3 1.52017 0.01138 133.62 ## Note 1.52017 - 0.82504 = 0.695 matches Foulley's '2-3' threshold estimate predict(m2) # probability of each category } # }"},{"path":"/reference/fox.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"Wheat yields 22 varieties 14 sites Australia","code":""},{"path":"/reference/fox.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"","code":"data(\"fox.wheat\")"},{"path":"/reference/fox.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"data frame 308 observations following 4 variables. gen genotype/variety factor, 22 levels site site factor, 14 levels yield yield, tonnes/ha state state Australia","code":""},{"path":"/reference/fox.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"1975 Interstate Wheat Variety trial Australia used RCB design 4 blocks, 22 varieties 14 sites. Wagga represented twice, trials sown May June. 22 varieties highly selected represent considerable genetic diversity four different groups. () University Sydney: Timson, Songlen, Gamenya. (ii) widely grown Mallee soils: Heron Halberd. (iii) late maturing varieties Victoria: Pinnacle, KL-21, JL-157. (iv) Mexican parentage: WW-15 Oxley.","code":""},{"path":"/reference/fox.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"Fox, P.N. Rathjen, .J. (1981). Relationships sites used interstate wheat variety trials. Australian Journal Agricultural Research, 32, 691-702. Electronic version supplied Jonathan Godfrey.","code":""},{"path":"/reference/fox.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat, 22 varieties at 14 sites in Australia — fox.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(fox.wheat) dat <- fox.wheat # Means of varieties. Slight differences from Fox and Rathjen suggest # they had more decimals of precision than shown. tapply(dat$yield, dat$gen, mean) # Calculate genotype means, merge into the data genm <- tapply(dat$yield, dat$gen, mean) dat$genm <- genm[match(dat$gen, names(genm))] # Calculate slopes for each site. Matches Fox, Table 2, Col A. m1 <- lm(yield~site+site:genm, data=dat) sort(round(coef(m1)[15:28],2), dec=TRUE) # Figure 1 of Fox libs(lattice) xyplot(yield~genm|state, data=dat, type=c('p','r'), group=site, auto.key=list(columns=4), main=\"fox.wheat\", xlab=\"Variety mean across all sites\", ylab=\"Variety yield at each site within states\") } # }"},{"path":"/reference/garber.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"Uniformity trials oat hay wheat grain, West Virginia Agricultural Experiment Station, 1923-1924, land.","code":""},{"path":"/reference/garber.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"data frame 270 observations following 4 variables. row row col column plot plot number year year crop crop yield yield (pounds bu/ac)","code":""},{"path":"/reference/garber.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"experiments conducted West Virginia Agricultural Experiment Station Maggie, West Virginia. Note, Garber et al (1926) Garber et al (1931) describe uniformity trials field, experimental plot numbers two papers different, indicating different parts field. data 1923 1924 given Garber (1926). data 1927, 1928, 1929 given Garber (1931). data given source papers relative deviations mean, converted absolute yields package. First paper: Garber (1926) plot 68 feet x 21 feet. discarding 3.5 foot border sides, harvested area 61 feet x 14 feet. plots laid double series 14-foot roadway plots. example, columns 1 & 2 side--side, 14 foot road, columns 3 & 4, 14 foot road, columns 5 & 6. Note: orientation plots (68x21) educated guess. orientation 21x68, field extremely narrow long. Field width: 6 plots * 68 feet + 14 ft/roadway * 2 = 436 feet Field length: 45 plots * 21 feet/plot = 945 feet Garber said: \"Plots 211 214, 261 264, [note, rows 11-14, columns 5-6] inclusive, eliminated study fact years ago straw stack stood vicinity...undoubtedly accounts relatively high yields plots 261 264, inclusive.\" 1923 oat hay, yield pounds per acre data oat hay given Table 5 mean-subtracted yields pounds per acre plot. oat yield row 22, column 5 given +59.7. obviously incorrect, since negative yields end '.7' positive yields ended '.3'. used -59.7 centered yield value added mean 1883.7 (p. 259) centered yields obtain absolute yields pounds per acre. 1924 wheat, yield bushels per acre data wheat given bushels per acre, expressed deviations mean yield (15.6 bu). added mean plot data. Second paper: Garber (1926) 1927 corn, 1928 oats, 1929 wheat field 10 plots wide, 84 plots tall. Field width: 10 plots * 68 feet + 4 roads * 14 feet = 736 feet. Field length: 84 plots * 21 feet + 3 roads * 14 feet = 1806 feet.","code":""},{"path":"/reference/garber.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"Garber, R.J. Mcllvaine, T.C. Hoover, M.M. (1926). study soil heterogeneity experiment plots. Jour Agr Res, 33, 255-268. Tables 3, 5. https://naldc.nal.usda.gov/download/IND43967148/PDF Garber, R. J. T. C. McIlvaine M. M. Hoover (1931). Method Laying Experimental Plats. Journal American Society Agronomy, 23, 286-298, https://archive.org/details/.ernet.dli.2015.229753/page/n299","code":""},{"path":"/reference/garber.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"None","code":""},{"path":"/reference/garber.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of oat hay and wheat grain — garber.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(garber.multi.uniformity) dat <- garber.multi.uniformity ## aggregate(yield~year, data=dat, FUN=mean) ## year yield ## 1 1923 1883.30741 ## 2 1924 15.58296 ## 3 1927 76.28965 ## 4 1928 32.81415 ## 5 1929 19.44650 libs(desplot) desplot(dat, yield ~ col*row, subset=year==1923, flip=TRUE, tick=TRUE, aspect=945/436, # true aspect main=\"garber.multi.uniformity 1923 oats\") desplot(dat, yield ~ col*row, subset=year==1924, flip=TRUE, tick=TRUE, aspect=945/436, # true aspect main=\"garber.multi.uniformity 1924 wheat\") desplot(dat, yield ~ col*row|year, subset=year >= 1927, flip=TRUE, tick=TRUE, aspect=1806/736, # true aspect main=\"garber.multi.uniformity 1927-1929\") # Correlation of same plots in 1923 vs 1924. Garber has 0.37 # cor(subset(dat, year==1923)$yield, # subset(dat, year==1924)$yield ) # .37 # Garber 1931 table 2 has .58, .20 # cor(subset(dat, year==1927)$yield, # subset(dat, year==1928)$yield, use=\"pair\" ) # .58 # cor(subset(dat, year==1927)$yield, # subset(dat, year==1929)$yield, use=\"pair\" ) # .19 } # }"},{"path":"/reference/gartner.corn.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield monitor data from a corn field in Minnesota — gartner.corn","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"Yield monitor data corn field Minnesota","code":""},{"path":"/reference/gartner.corn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"","code":"data(\"gartner.corn\")"},{"path":"/reference/gartner.corn.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"data frame 4949 observations following 8 variables. long longitude lat latitude mass grain mass flow per second, pounds time GPS time, seconds seconds seconds elapsed datum dist distance traveled datum, inches moist grain moisture, percent elev elevation, feet","code":""},{"path":"/reference/gartner.corn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"data collected 5 Nov 2011 corn field south Mankato, Minnesota, using combine-mounted yield monitor. https://www.google.com/maps/place/43.9237575,-93.9750632 harvested swath 12 rows wide = 360 inches. Timestamp 0 = 5 Nov 2011, 12:38:03 Central Time. Timestamp 16359 = 4.54 hours later. Yield calculated total dry weight (corrected 15.5 percent moisture), divided 56 pounds (get bushels), divided harvested area: drygrain = [massflow * seconds * (100-moisture) / (100-15.5)] / 56 harvested area = (distance * swath width) / 6272640 yield = drygrain / area","code":""},{"path":"/reference/gartner.corn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"University Minnesota Precision Agriculture Center. Retrieved 27 Aug 2015 https://web.archive.org/web/20100717003256/https://www.soils.umn.edu/academics/classes/soil4111/files/yield_a.xls Used via license: Creative Commons -SA 3.0.","code":""},{"path":"/reference/gartner.corn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"Suman Rakshit, Adrian Baddeley, Katia Stefanova, Karyn Reeves, Kefei Chen, Zhanglong Cao, Fiona Evans, Mark Gibberd (2020). Novel approach analysis spatially-varying treatment effects -farm experiments. Field Crops Research, 255, 15 September 2020, 107783. https://doi.org/10.1016/j.fcr.2020.107783","code":""},{"path":"/reference/gartner.corn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield monitor data from a corn field in Minnesota — gartner.corn","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gartner.corn) dat <- gartner.corn # Calculate yield from mass & moisture dat <- transform(dat, yield=(mass*seconds*(100-moist)/(100-15.5)/56)/(dist*360/6272640)) # Delete low yield outliers dat <- subset(dat, yield >50) # Group yield into 20 bins for red-gray-blue colors medy <- median(dat$yield) ncols <- 20 wwidth <- 150 brks <- seq(from = -wwidth/2, to=wwidth/2, length=ncols-1) brks <- c(-250, brks, 250) # 250 is safe..we cleaned data outside ?(50,450)? yldbrks <- brks + medy dat <- transform(dat, yldbin = as.numeric(cut(yield, breaks= yldbrks))) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) dat$yieldcolor = redblue(ncols)[dat$yldbin] # Polygons for soil map units # Go to: https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx # Click: Lat and Long. 43.924, -93.975 # Click the little AOI rectangle icon. Drag around the field # In the AOI Properties, enter the Name: Gartner # Click the tab Soil Map to see map unit symbols, names # Click: Download Soils Data. Click: Create Download Link. # Download the zip file and find the soilmu_a_aoi files. # Read shape files libs(sf) fname <- system.file(package=\"agridat\", \"files\", \"gartner.corn.shp\") shp <- sf::st_read( fname ) # Annotate soil map units. Coordinates chosen by hand. mulabs = data.frame( name=c(\"110\",\"319\",\"319\",\"230\",\"105C\",\"110\",\"211\",\"110\",\"211\",\"230\",\"105C\"), x = c(-93.97641, -93.97787, -93.97550, -93.97693, -93.97654, -93.97480, -93.97375, -93.978284, -93.977617, -93.976715, -93.975929), y = c(43.92185, 43.92290, 43.92358, 43.92445, 43.92532, 43.92553, 43.92568, 43.922163, 43.926427, 43.926993, 43.926631) ) mulabs = st_as_sf( mulabs, coords=c(\"x\",\"y\"), crs=4326) mulabs = st_transform(mulabs, 2264) # Trim top and bottom ends of the field dat <- subset(dat, lat < 43.925850 & lat > 43.921178) # Colored points for yield dat <- st_as_sf(dat, coords=c(\"long\",\"lat\"), crs=4326) libs(ggplot2) ggplot() + geom_sf(data=dat, aes(col=yieldcolor) ) + scale_color_identity() + geom_sf_label(data=mulabs, aes(label=name), cex=2) + geom_sf(data=shp[\"MUSYM\"], fill=\"transparent\") + ggtitle(\"gartner.corn\") + theme_classic() if(0){ # Draw a 3D surface. Clearly shows the low drainage area # Re-run the steps above up, stop before the \"Colored points\" line. libs(rgl) dat <- transform(dat, x=long-min(long), y=lat-min(lat), z=elev-min(elev)) clear3d() points3d(dat$x, dat$y, dat$z/50000, col=redblue(ncols)[dat$yldbin]) axes3d() title3d(xlab='x',ylab='y',zlab='elev') close3d() } } # }"},{"path":"/reference/gathmann.bt.html","id":null,"dir":"Reference","previous_headings":"","what":"Impact of Bt corn on non-target species — gathmann.bt","title":"Impact of Bt corn on non-target species — gathmann.bt","text":"Impact Bt corn non-target species","code":""},{"path":"/reference/gathmann.bt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Impact of Bt corn on non-target species — gathmann.bt","text":"data frame 16 observations following 3 variables. gen genotype/maize, Bt ISO thysan thysan abundance aranei aranei abundance","code":""},{"path":"/reference/gathmann.bt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Impact of Bt corn on non-target species — gathmann.bt","text":"experiment involved comparing Bt maize near-isogenic control variety. Species abundances measured Thysanoptera (thrips) Araneida (spiders) 8 different plots. response probably mean across repeated measurements. Used permission Achim Gathmann.","code":""},{"path":"/reference/gathmann.bt.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Impact of Bt corn on non-target species — gathmann.bt","text":"L. . Hothorn, 2005. Evaluation Bt-Maize Field Trials Proof Safety. https://www.seedtest.org/upload/cms/user/presentation7Hothorn.pdf","code":""},{"path":"/reference/gathmann.bt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Impact of Bt corn on non-target species — gathmann.bt","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gathmann.bt) dat <- gathmann.bt # EDA suggests Bt vs ISO is significant for thysan, not for aranei libs(lattice) libs(reshape2) d2 <- melt(dat, id.var='gen') bwplot(value ~ gen|variable, d2, main=\"gathmann.bt\", ylab=\"Insect abundance\", panel=function(x,y,...){ panel.xyplot(jitter(as.numeric(x)),y,...) panel.bwplot(x,y,...) }, scales=list(relation=\"free\")) if(0){ # ----- Parametric CI. Thysan significant, aranei not significant. libs(equivalence) th0 <- with(dat, tost(thysan[1:8], thysan[9:16], alpha=.05, paired=FALSE)) lapply(th0[c(\"estimate\",\"tost.interval\")], round, 2) # 14.28-8.72=5.56, (2.51, 8.59) # match Gathmann p. 11 ar0 <- with(dat, tost(aranei[1:8], aranei[9:16], alpha=.05, epsilon=.4)) lapply(ar0[c(\"estimate\",\"tost.interval\")], round, 2) # .57-.47=.10, (-0.19, 0.40) # match Gathmann p. 11 # ----- Non-parametric exact CI. Same result. libs(coin) th1 <- wilcox_test(thysan ~ gen, data=dat, conf.int=TRUE, conf.level=0.90) lapply(confint(th1), round, 2) # 6.36, (2.8, 9.2) # Match Gathmann p. 11 ar1 <- wilcox_test(aranei ~ gen, data=dat, conf.int=TRUE, conf.level=0.90) lapply(confint(ar1), round, 2) # .05 (-.2, .4) # ----- Log-transformed exact CI. Same result. th2 <- wilcox_test(log(thysan) ~ gen, data=dat, alternative=c(\"two.sided\"), conf.int=TRUE, conf.level=0.9) lapply(confint(th2), function(x) round(exp(x),2)) # 1.66, (1.38, 2.31) # Match Gathmann p 11 # ----- Log-transform doesn't work on aranei, but asinh(x/2) does ar2 <- wilcox_test(asinh(aranei/2) ~ gen, data=dat, alternative=c(\"two.sided\"), conf.int=TRUE, conf.level=0.9) lapply(confint(ar2), function(x) round(sinh(x)*2,1)) } } # }"},{"path":"/reference/gauch.soy.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"New York soybean yields, 1977 1988, 7 genotypes, 55 environments (9 loc, 12 years), 2-3 reps.","code":""},{"path":"/reference/gauch.soy.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"data frame 1454 observations following 4 variables. yield yield, kg/ha rep repeated measurement gen genotype, 7 levels env environment, 55 levels year year, 77-88 loc location, 10 levels","code":""},{"path":"/reference/gauch.soy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"Soybean yields 13 percent moisture 7 genotypes 55 environments 4 replicates. environments 2 3 replicates. experiment RCB design, plots missing many soybean varieties experiment. replications appear random order _NOT_ define blocks. Environment names combination first letter location name last two digits year. location codes : =Aurora, C=Chazy, D=Riverhead, E=Etna, G=Geneseo, =Ithica, L=Lockport, N=Canton, R=Romulus, V=Valatie. Plots 7.6 m long, four rows wide (middle two rows harvested). data widely used (various subsets) promote benefits AMMI (Additive Main Effects Multiplicative Interactions) analyses. gen x env means Table 1 (Zobel et al 1998) least-squares means (personal communication). Retrieved Sep 2011 https://www.microcomputerpower.com/matmodel/matmodelmatmodel_sample_.html Used permission Hugh Gauch.","code":""},{"path":"/reference/gauch.soy.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"Zobel, RW Wright, MJ Gauch Jr, HG. 1998. Statistical analysis yield trial. Agronomy journal, 80, 388-393. https://doi.org/10.2134/agronj1988.00021962008000030002x","code":""},{"path":"/reference/gauch.soy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"None","code":""},{"path":"/reference/gauch.soy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of soybeans in New York, 1977 to 1988 — gauch.soy","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gauch.soy) dat <- gauch.soy ## dat <- transform(dat, ## year = substring(env, 2), ## loc = substring(env, 1, 1)) # AMMI biplot libs(agricolae) # Figure 1 of Zobel et al 1988, means vs PC1 score dat2 <- droplevels(subset(dat, is.element(env, c(\"A77\",\"C77\",\"V77\", \"V78\",\"A79\",\"C79\",\"G79\",\"R79\",\"V79\",\"A80\",\"C80\",\"G80\",\"L80\",\"D80\", \"R80\",\"V80\",\"A81\",\"C81\",\"G81\",\"L81\",\"D81\",\"R81\",\"V81\",\"A82\",\"L82\", \"G82\",\"V82\",\"A83\",\"I83\",\"G83\",\"A84\",\"N84\",\"C84\",\"I84\",\"G84\")))) m2 <- with(dat2, AMMI(env, gen, rep, yield)) bip <- m2$biplot with(bip, plot(yield, PC1, type='n', main=\"gauch.soy -- AMMI biplot\")) with(bip, text(yield, PC1, rownames(bip), col=ifelse(bip$type==\"GEN\", \"darkgreen\", \"blue\"), cex=ifelse(bip$type==\"GEN\", 1.5, .75))) } # }"},{"path":"/reference/george.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-location/year breeding trial in California — george.wheat","title":"Multi-location/year breeding trial in California — george.wheat","text":"Multi-location/year breeding trial California","code":""},{"path":"/reference/george.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-location/year breeding trial in California — george.wheat","text":"","code":"data(\"george.wheat\")"},{"path":"/reference/george.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-location/year breeding trial in California — george.wheat","text":"data frame 13996 observations following 5 variables. gen genotype number year year loc location block block yield yield per plot","code":""},{"path":"/reference/george.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-location/year breeding trial in California — george.wheat","text":"nice example data breeding trial, check genotypes kepts whole experiment, genotypes enter leave breeding program. data highly unbalanced respect genotypes--environments. Results late-stage small-trials 211 genotypes wheat California, conducted 9 locations years 2004-2018. trial RCB 4 blocks. authors used data look GGE biplots across years concluded repeatable genotype--location patterns weak, therefore California cereal production region large, unstable, mega-environment. Data downloaded 2019-10-29 Dryad, https://doi.org/10.5061/dryad.bf8rt6b. Data public domain.","code":""},{"path":"/reference/george.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-location/year breeding trial in California — george.wheat","text":"Nicholas George Mark Lundy (2019). Quantifying Genotype x Environment Effects Long-Term Common Wheat Yield Trials Agroecologically Diverse Production Region. Crop Science, 59, 1960-1972. https://doi.org/10.2135/cropsci2019.01.0010","code":""},{"path":"/reference/george.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-location/year breeding trial in California — george.wheat","text":"None","code":""},{"path":"/reference/george.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-location/year breeding trial in California — george.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(lattice, reshape2) data(george.wheat) dat <- george.wheat dat$env <- paste0(dat$year, \".\", dat$loc) # average reps, cast to matrix mat <- reshape2::acast(dat, gen ~ env, value.var=\"yield\", fun=mean, na.rm=TRUE) lattice::levelplot(mat, aspect=\"m\", main=\"george.wheat\", xlab=\"genotype\", ylab=\"environment\", scales=list(x=list(cex=.3,rot=90),y=list(cex=.5))) } # }"},{"path":"/reference/giles.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Straw length and ear emergence for wheat genotypes. — giles.wheat","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"Straw length ear emergence wheat genotypes. Data unbalanced respect experiment year genotype.","code":""},{"path":"/reference/giles.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"","code":"data(\"giles.wheat\")"},{"path":"/reference/giles.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"data frame 247 observations following 4 variables. gen genotype. Note, numeric! env environment straw straw length emergence ear emergence, Julian date","code":""},{"path":"/reference/giles.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"Highly unbalanced data straw length ear emergence date wheat genotypes. 'genotype' column called 'Accession number' original data. genotypes chosen represent range variation trait. Julian date found preferable methods ( days sowing). Piepho (2003) fit bilinear model straw emergence data. similar Finlay-Wilkinson regression.","code":""},{"path":"/reference/giles.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"R. Giles (1990). Utilization unreplicated observations agronomic characters wheat germplasm collection. : Wheat Genetic Resources. Meeting Diverse Needs. Wiley, Chichester, U.K., pp.113-130.","code":""},{"path":"/reference/giles.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"Piepho, HP (2003). Model-based mean adjustment quantitative germplasm evaluation data. Genetic Resources Crop Evolution, 50, 281-290. https://doi.org/10.1023/:1023503900759","code":""},{"path":"/reference/giles.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Straw length and ear emergence for wheat genotypes. — giles.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(giles.wheat) dat <- giles.wheat dat <- transform(dat, gen=factor(gen)) dat_straw <- droplevels( subset(dat, !is.na(straw)) ) dat_emerg <- droplevels( subset(dat, !is.na(emergence)) ) # Traits are not related # with(dat, plot(straw~emergence)) # Show unbalancedness of data libs(lattice, reshape2) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(acast(dat_straw, env ~ gen, value.var='straw'), col.regions=redblue, scales=list(x=list(rot=90)), xlab=\"year\", ylab=\"genotype\", main=\"giles.wheat - straw length\") # ----- Analysis of straw length ----- libs(emmeans) # Mean across years. Matches Piepho Table 7 'Simple' m1 = lm(straw ~ gen, data=dat_straw) emmeans(m1, 'gen') # Simple two-way model. NOT the bi-additive model of Piepho. m2 = lm(straw ~ gen + env, data=dat_straw) emmeans(m2, 'gen') # Bi-additive model. Matches Piepho Table 6, rows (c) libs(gnm) m3 <- gnm(straw ~ env + Mult(gen,env), data=dat_straw) cbind(adjusted=round(fitted(m3),0), dat_straw) # ----- Analysis of Ear emergence ----- # Simple two-way model. m4 = lm(emergence ~ 1 + gen + env, data=dat_emerg) emmeans(m4, c('gen','env')) # Matches Piepho Table 9. rpws (c) emmeans(m4, 'gen') # Match Piepho table 10, Least Squares column } # }"},{"path":"/reference/gilmour.serpentine.html","id":null,"dir":"Reference","previous_headings":"","what":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"RCB experiment wheat South Australia, strong spatial variation serpentine row/column effects.","code":""},{"path":"/reference/gilmour.serpentine.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"data frame 330 observations following 5 variables. col column row row rep replicate factor, 3 levels gen wheat variety, 108 levels yield yield","code":""},{"path":"/reference/gilmour.serpentine.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"randomized complete block experiment. 108 varieties 3 reps. Plots 6 meters long, 0.75 meters wide, trimmed 4.2 meters lengths harvest. Trimming done spraying wheat herbicide. sprayer travelled serpentine pattern columns. trial sown serpentine manner planter seeds three rows time (Left, Middle, Right). Field width 15 columns * 6 m = 90 m Field length 22 plots * .75 m = 16.5 m Used permission Arthur Gilmour, turn permission Gil Hollamby.","code":""},{"path":"/reference/gilmour.serpentine.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"Arthur R Gilmour Brian R Cullis Arunas P Verbyla, 1997. Accounting natural extraneous variation analysis field experiments. Journal Agric Biol Env Statistics, 2, 269-293.","code":""},{"path":"/reference/gilmour.serpentine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"N. W. Galwey. 2014. Introduction Mixed Modelling: Beyond Regression Analysis Variance. Table 10.9","code":""},{"path":"/reference/gilmour.serpentine.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wheat yield in South Australia with serpentine row/col effects — gilmour.serpentine","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gilmour.serpentine) dat <- gilmour.serpentine libs(desplot) desplot(dat, yield~ col*row, num=gen, show.key=FALSE, out1=rep, aspect = 16.5/90, # true aspect main=\"gilmour.serpentine\") # Extreme field trend. Blocking insufficient--needs a spline/smoother # xyplot(yield~col, data=dat, main=\"gilmour.serpentine\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) dat <- transform(dat, rowf=factor(row), colf=factor(10*(col-8))) dat <- dat[order(dat$rowf, dat$colf), ] # Sort order needed by asreml # RCB m0 <- asreml(yield ~ gen, data=dat, random=~rep) # Add AR1 x AR1 m1 <- asreml(yield ~ gen, data=dat, resid = ~ar1(rowf):ar1(colf)) # Add spline m2 <- asreml(yield ~ gen + col, data=dat, random= ~ spl(col) + colf, resid = ~ar1(rowf):ar1(colf)) # Figure 4 shows serpentine spraying p2 <- predict(m2, data=dat, classify=\"colf\")$pvals plot(p2$predicted, type='b', xlab=\"column number\", ylab=\"BLUP\") # Define column code (due to serpentine spraying) # Rhelp doesn't like double-percent modulus symbol, so compute by hand dat <- transform(dat, colcode = factor(dat$col-floor((dat$col-1)/4)*4 -1)) m3 <- asreml(yield ~ gen + lin(colf) + colcode, data=dat, random= ~ colf + rowf + spl(colf), resid = ~ar1(rowf):ar1(colf)) # Figure 6 shows serpentine row effects p3 <- predict(m3, data=dat, classify=\"rowf\")$pvals plot(p3$predicted, type='l', xlab=\"row number\", ylab=\"BLUP\") text(1:22, p3$predicted, c('L','L','M','R','R','M','L','L', 'M','R','R','M','L','L','M','R','R','M','L','L','M','R')) # Define row code (due to serpentine planting). 1=middle, 2=left/right dat <- transform(dat, rowcode = factor(row)) levels(dat$rowcode) <- c('2','2','1','2','2','1','2','2','1', '2','2','1','2','2','1','2','2','1','2','2','1','2') m6 <- asreml(yield ~ gen + lin(colf) + colcode +rowcode, data=dat, random= ~ colf + rowf + spl(col), resid = ~ar1(rowf):ar1(colf)) plot(varioGram(m6), xlim=c(0:17), ylim=c(0,11), zlim=c(0,4000), main=\"gilmour.serpentine\") } } # }"},{"path":"/reference/gilmour.slatehall.html","id":null,"dir":"Reference","previous_headings":"","what":"Slate Hall Farm 1978 — gilmour.slatehall","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"Yields trial Slate Hall Farm 1978.","code":""},{"path":"/reference/gilmour.slatehall.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"data frame 150 observations following 5 variables. row row col column yield yield (grams/plot) gen genotype factor, 25 levels rep rep factor, 6 levels","code":""},{"path":"/reference/gilmour.slatehall.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"trial spring wheat Slate Hall Farm 1978. experiment balanced lattice 25 varieties 6 replicates. 'rep' labels arbitrary (rep labels appeared source data). row within rep incomplete block. plot size 1.5 meters 4 meters. Field width: 10 plots x 4 m = 40 m Field length: 15 plots x 1.5 meters = 22.5 m","code":""},{"path":"/reference/gilmour.slatehall.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"Arthur R Gilmour Brian R Cullis Arunas P Verbyla (1997). Accounting natural extraneous variation analysis field experiments. Journal Agricultural, Biological, Environmental Statistics, 2, 269-293. https://doi.org/10.2307/1400446","code":""},{"path":"/reference/gilmour.slatehall.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"None.","code":""},{"path":"/reference/gilmour.slatehall.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Slate Hall Farm 1978 — gilmour.slatehall","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gilmour.slatehall) dat <- gilmour.slatehall libs(desplot) desplot(dat, yield ~ col * row, aspect=22.5/40, num=gen, out1=rep, cex=1, main=\"gilmour.slatehall\") if(require(\"asreml\", quietly=TRUE)) { libs(asreml,lucid) # Model 4 of Gilmour et al 1997 dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf, dat$yf), ] m4 <- asreml(yield ~ gen + lin(row), data=dat, random = ~ dev(row) + dev(col), resid = ~ ar1(xf):ar1(yf)) # coef(m4)$fixed[1] # linear row # [1] 31.72252 # (sign switch due to row ordering) lucid::vc(m4) ## effect component std.error z.ratio bound ## dev(col) 2519 1959 1.3 P 0 ## dev(row) 20290 10260 2 P 0 ## xf:yf(R) 23950 4616 5.2 P 0 ## xf:yf!xf!cor 0.439 0.113 3.9 U 0 ## xf:yf!yf!cor 0.125 0.117 1.1 U 0 plot(varioGram(m4), main=\"gilmour.slatehall\") } } # }"},{"path":"/reference/gomez.fractionalfactorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","title":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","text":"Fractional factorial rice, 1/2 2^6 = 2x2x2x2x2x2. Two reps 2 blocks rep.","code":""},{"path":"/reference/gomez.fractionalfactorial.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","text":"data frame 64 observations following 6 variables. yield grain yield tons/ha rep replicate, 2 levels block block within rep, 2 levels trt treatment, levels (1) abcdef col column position field row row position field treatment, 2 levels b b treatment, 2 levels c c treatment, 2 levels d d treatment, 2 levels e e treatment, 2 levels f f treatment, 2 levels","code":""},{"path":"/reference/gomez.fractionalfactorial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","text":"Grain yield 2^6 fractional factorial experiment blocks 16 plots , two replications. Gomez inconsistencies. One example: Page 171: treatment (1) rep 1, block 2 rep 2, block 1. Page 172: treatment (1) Rep 1, block 1 rep 2, block 1. data uses layout shown page 171. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.fractionalfactorial.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 171-172.","code":""},{"path":"/reference/gomez.fractionalfactorial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 — gomez.fractionalfactorial","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.fractionalfactorial) dat <- gomez.fractionalfactorial # trt abcdef has the highest yield # Gomez, Figure 4.8 libs(desplot) desplot(dat, yield~col*row, # aspect unknown text=trt, shorten=\"none\", show.key=FALSE, cex=1, main=\"gomez.fractionalfactorial - treatment & yield\") # Ensure factors dat <- transform(dat, a=factor(a), b=factor(b), c=factor(c), d=factor(d), e=factor(e), f=factor(f) ) # Gomez table 4.24, trt SS totalled together. # Why didn't Gomez nest block within rep? m0 <- lm(yield ~ rep * block + trt, dat) anova(m0) # Gomez table 4.24, trt SS split apart m1 <- lm(yield ~ rep * block + (a+b+c+d+e+f)^3, dat) anova(m1) libs(FrF2) aliases(m1) MEPlot(m1, select=3:8, main=\"gomez.fractionalfactorial - main effects plot\") } # }"},{"path":"/reference/gomez.groupsplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Group balanced split-plot design in rice — gomez.groupsplit","title":"Group balanced split-plot design in rice — gomez.groupsplit","text":"Group balanced split-plot design rice","code":""},{"path":"/reference/gomez.groupsplit.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Group balanced split-plot design in rice — gomez.groupsplit","text":"data frame 270 observations following 7 variables. col column row row rep replicate factor, 3 levels fert fertilizer factor, 2 levels gen genotype factor, 45 levels group grouping (genotype) factor, 3 levels yield yield rice","code":""},{"path":"/reference/gomez.groupsplit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Group balanced split-plot design in rice — gomez.groupsplit","text":"Genotype group S1 less 105 days growth duration, S2 105-115 days growth duration, S3 115 days. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.groupsplit.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Group balanced split-plot design in rice — gomez.groupsplit","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 120.","code":""},{"path":"/reference/gomez.groupsplit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Group balanced split-plot design in rice — gomez.groupsplit","text":"","code":"library(agridat) data(gomez.groupsplit) dat <- gomez.groupsplit # Gomez figure 3.10. Obvious fert and group effects libs(desplot) desplot(dat, group ~ col*row, out1=rep, col=fert, text=gen, # aspect unknown main=\"gomez.groupsplit\") # Gomez table 3.19 (not partitioned by group) m1 <- aov(yield ~ fert*group + gen:group + fert:gen:group + Error(rep/fert/group), data=dat) summary(m1) #> #> Error: rep #> Df Sum Sq Mean Sq F value Pr(>F) #> Residuals 2 4.917 2.458 #> #> Error: rep:fert #> Df Sum Sq Mean Sq F value Pr(>F) #> fert 1 96.05 96.05 68.7 0.0142 * #> Residuals 2 2.80 1.40 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: rep:fert:group #> Df Sum Sq Mean Sq F value Pr(>F) #> group 2 4.259 2.1294 6.674 0.0197 * #> fert:group 2 0.628 0.3138 0.984 0.4150 #> Residuals 8 2.553 0.3191 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: Within #> Df Sum Sq Mean Sq F value Pr(>F) #> group:gen 42 20.494 0.4880 4.461 2.08e-12 *** #> fert:group:gen 42 4.093 0.0975 0.891 0.662 #> Residuals 168 18.378 0.1094 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"},{"path":"/reference/gomez.heterogeneity.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"RCB experiment rice, heterogeneity regressions","code":""},{"path":"/reference/gomez.heterogeneity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"","code":"data(\"gomez.heterogeneity\")"},{"path":"/reference/gomez.heterogeneity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"gen genotype yield yield kg/ha tillers tillers /hill","code":""},{"path":"/reference/gomez.heterogeneity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"experiment 3 genotypes examine relationship yield number tillers. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.heterogeneity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 377.","code":""},{"path":"/reference/gomez.heterogeneity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"None.","code":""},{"path":"/reference/gomez.heterogeneity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of rice, heterogeneity of regressions — gomez.heterogeneity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.heterogeneity) dat <- gomez.heterogeneity libs(lattice) xyplot(yield ~ tillers, dat, groups=gen, type=c(\"p\",\"r\"), main=\"gomez.heterogeneity\") } # }"},{"path":"/reference/gomez.heteroskedastic.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"RCB experiment rice, heteroskedastic varieties","code":""},{"path":"/reference/gomez.heteroskedastic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"","code":"data(\"gomez.heteroskedastic\")"},{"path":"/reference/gomez.heteroskedastic.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"data frame 105 observations following 4 variables. gen genotype group group genotypes rep replicate yield yield","code":""},{"path":"/reference/gomez.heteroskedastic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"RCB design three reps. Genotypes 1-15 hybrids, 16-32 parents, 33-35 checks. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.heteroskedastic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 310.","code":""},{"path":"/reference/gomez.heteroskedastic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"None.","code":""},{"path":"/reference/gomez.heteroskedastic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of rice, heteroskedastic varieties — gomez.heteroskedastic","text":"","code":"library(agridat) data(gomez.heteroskedastic) dat <- gomez.heteroskedastic # Fix the outlier as reported by Gomez p. 311 dat[dat$gen==\"G17\" & dat$rep==\"R2\",\"yield\"] <- 7.58 libs(lattice) bwplot(gen ~ yield, dat, group=as.numeric(dat$group), ylab=\"genotype\", main=\"gomez.heterogeneous\") # Match Gomez table 7.28 m1 <- lm(yield ~ rep + gen, data=dat) anova(m1) #> Analysis of Variance Table #> #> Response: yield #> Df Sum Sq Mean Sq F value Pr(>F) #> rep 2 3.306 1.65304 5.6164 0.005528 ** #> gen 34 40.020 1.17705 3.9992 5.806e-07 *** #> Residuals 68 20.014 0.29432 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ## Response: yield ## Df Sum Sq Mean Sq F value Pr(>F) ## rep 2 3.306 1.65304 5.6164 0.005528 ** ## gen 34 40.020 1.17705 3.9992 5.806e-07 *** ## Residuals 68 20.014 0.29432"},{"path":"/reference/gomez.multilocsplitplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot","title":"Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot","text":"Grain yield measured 3 locations 2 reps per location. Within rep, main plot 6 nitrogen fertilizer treatments sub plot 2 rice varieties.","code":""},{"path":"/reference/gomez.multilocsplitplot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot","text":"data frame 108 observations following 5 variables. loc location, 3 levels nitro nitrogen kg/ha rep replicate, 2 levels gen genotype, 2 levels yield yield, kg/ha Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.multilocsplitplot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 339.","code":""},{"path":"/reference/gomez.multilocsplitplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of rice, split-plot design — gomez.multilocsplitplot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.multilocsplitplot) dat <- gomez.multilocsplitplot dat$nf <- factor(dat$nitro) # Gomez figure 8.3 libs(lattice) xyplot(yield~nitro, dat, group=loc, type=c('p','smooth'), auto.key=TRUE, main=\"gomez.multilocsplitplot\") # AOV # Be careful to use the right stratum, 'nf' appears in both strata. # Still not quite the same as Gomez table 8.21 t1 <- terms(yield ~ loc * nf * gen + Error(loc:rep:nf), \"Error\", keep.order=TRUE) m1 <- aov(t1, data=dat) summary(m1) # F values are somewhat similar to Gomez Table 8.21 libs(lme4) m2 <- lmer(yield ~ loc*nf*gen + (1|loc/rep/nf), dat) anova(m2) ## Analysis of Variance Table ## Df Sum Sq Mean Sq F value ## loc 2 117942 58971 0.1525 ## nf 5 72841432 14568286 37.6777 ## gen 1 7557570 7557570 19.5460 ## loc:nf 10 10137188 1013719 2.6218 ## loc:gen 2 4270469 2135235 5.5223 ## nf:gen 5 1501767 300353 0.7768 ## loc:nf:gen 10 1502273 150227 0.3885 } # }"},{"path":"/reference/gomez.nitrogen.html","id":null,"dir":"Reference","previous_headings":"","what":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"Soil nitrogen three times eight fertilizer treatments","code":""},{"path":"/reference/gomez.nitrogen.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"data frame 96 observations following 4 variables. trt nitrogen treatment factor nitro soil nitrogen content, percent rep replicate stage growth stage, three periods","code":""},{"path":"/reference/gomez.nitrogen.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"Eight fertilizer treatments tested. Soil nitrogen content measured three times. P1 = 15 days post transplanting. P2 = 40 days post transplanting. P3 = panicle initiation. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.nitrogen.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 259.","code":""},{"path":"/reference/gomez.nitrogen.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"R-help mailing list, 9 May 2013. Data provided Cyril Lundrigan. Analysis method Rich Heiberger.","code":""},{"path":"/reference/gomez.nitrogen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Soil nitrogen at three times for eight fertilizer treatments — gomez.nitrogen","text":"","code":"library(agridat) data(gomez.nitrogen) dat <- gomez.nitrogen # Note the depletion of nitrogen over time (stage) libs(HH) #> Loading required package: grid #> Loading required package: multcomp #> Loading required package: mvtnorm #> Loading required package: survival #> Loading required package: TH.data #> Loading required package: MASS #> #> Attaching package: ‘TH.data’ #> The following object is masked from ‘package:MASS’: #> #> geyser #> Loading required package: gridExtra #> #> Attaching package: ‘HH’ #> The following object is masked from ‘package:base’: #> #> is.R interaction2wt(nitro ~ rep/trt + trt*stage, data=dat, x.between=0, y.between=0, main=\"gomez.nitrogen\") # Just the fertilizer profiles with(dat, interaction.plot(stage, trt, nitro, col=1:4, lty=1:3, main=\"gomez.nitrogen\", xlab=\"Soil nitrogen at three times for each treatment\")) # Gomez table 6.16 m1 <- aov(nitro ~ Error(rep/trt) + trt*stage, data=dat) summary(m1) #> #> Error: rep #> Df Sum Sq Mean Sq F value Pr(>F) #> Residuals 3 0.8457 0.2819 #> #> Error: rep:trt #> Df Sum Sq Mean Sq F value Pr(>F) #> trt 7 1.2658 0.18083 4.935 0.00201 ** #> Residuals 21 0.7695 0.03664 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: Within #> Df Sum Sq Mean Sq F value Pr(>F) #> stage 2 52.04 26.021 715.871 < 2e-16 *** #> trt:stage 14 3.57 0.255 7.008 1.53e-07 *** #> Residuals 48 1.74 0.036 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # Gomez table 6.18 # Treatment 1 2 3 4 5 6 7 8 cont <- cbind(\"T7 vs others\" = c( 1, 1, 1, 1, 1, 1,-7, 1), \"T8 vs others\" = c( 1, 1, 1, 1, 1, 1, 0,-6), \"T2,T5 vs others\" = c(-1, 2,-1,-1, 2,-1, 0, 0), \"T2 vs T5\" = c( 0, 1, 0, 0,-1, 0, 0, 0)) contrasts(dat$trt) <- cont contrasts(dat$trt) #> T7 vs others T8 vs others T2,T5 vs others T2 vs T5 #> T1 1 1 -1 0 -3.028130e-01 #> T2 1 1 2 1 8.326673e-17 #> T3 1 1 -1 0 -2.101031e-01 #> T4 1 1 -1 0 -3.487772e-01 #> T5 1 1 2 -1 2.775558e-17 #> T6 1 1 -1 0 8.616933e-01 #> T7 -7 0 0 0 -1.387779e-17 #> T8 1 -6 0 0 0.000000e+00 #> #> T1 -6.632738e-01 -4.673031e-01 #> T2 8.326673e-17 0.000000e+00 #> T3 -1.136421e-01 8.324315e-01 #> T4 7.387109e-01 -2.875078e-01 #> T5 8.326673e-17 0.000000e+00 #> T6 3.820501e-02 -7.762061e-02 #> T7 5.551115e-17 2.775558e-17 #> T8 5.551115e-17 5.551115e-17 m2 <- aov(nitro ~ Error(rep/trt) + trt*stage, data=dat) summary(m2, expand.split=FALSE, split=list(trt=list( \"T7 vs others\"=1, \"T8 vs others\"=2, \"T2,T5 vs others\"=3, \"T2 vs T5\"=4, rest=c(5,6,7)), \"trt:stage\"=list( \"(T7 vs others):P\"=c(1,8), \"(T8 vs others):P\"=c(2,9), \"(T2,T5 vs others):P\"=c(3,10), \"(T2 vs T5):P\"=c(4,11), \"rest:P\"=c(5,6,7,12,13,14)) )) #> #> Error: rep #> Df Sum Sq Mean Sq F value Pr(>F) #> Residuals 3 0.8457 0.2819 #> #> Error: rep:trt #> Df Sum Sq Mean Sq F value Pr(>F) #> trt 7 1.2658 0.1808 4.935 0.00201 ** #> trt: T7 vs others 1 0.3511 0.3511 9.581 0.00548 ** #> trt: T8 vs others 1 0.0455 0.0455 1.242 0.27761 #> trt: T2,T5 vs others 1 0.0228 0.0228 0.621 0.43952 #> trt: T2 vs T5 1 0.1176 0.1176 3.209 0.08764 . #> trt: rest 3 0.7289 0.2430 6.630 0.00252 ** #> Residuals 21 0.7695 0.0366 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: Within #> Df Sum Sq Mean Sq F value Pr(>F) #> stage 2 52.04 26.021 715.871 < 2e-16 *** #> trt:stage 14 3.57 0.255 7.008 1.53e-07 *** #> trt:stage: (T7 vs others):P 2 2.14 1.068 29.391 4.63e-09 *** #> trt:stage: (T8 vs others):P 2 0.54 0.268 7.373 0.001613 ** #> trt:stage: (T2,T5 vs others):P 2 0.64 0.321 8.843 0.000538 *** #> trt:stage: (T2 vs T5):P 2 0.02 0.011 0.298 0.743303 #> trt:stage: rest:P 6 0.23 0.038 1.051 0.404967 #> Residuals 48 1.74 0.036 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"},{"path":"/reference/gomez.nonnormal1.html","id":null,"dir":"Reference","previous_headings":"","what":"Insecticide treatment effectiveness — gomez.nonnormal1","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"Insecticide treatment effectiveness","code":""},{"path":"/reference/gomez.nonnormal1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"","code":"data(\"gomez.nonnormal1\")"},{"path":"/reference/gomez.nonnormal1.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"data frame 36 observations following 3 variables. trt insecticidal treatment rep replicate larvae number larvae","code":""},{"path":"/reference/gomez.nonnormal1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"Nine treatments (including control, T9) used four replicates. number living insect larvae recorded. data show signs non-normality, log transform used Gomez. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.nonnormal1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 300.","code":""},{"path":"/reference/gomez.nonnormal1.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"None.","code":""},{"path":"/reference/gomez.nonnormal1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Insecticide treatment effectiveness — gomez.nonnormal1","text":"","code":"library(agridat) data(gomez.nonnormal1) dat <- gomez.nonnormal1 # Gomez figure 7.3 ## libs(dplyr) ## dat2 <- dat %>% group_by(trt) ## dat2 <- summarize(dat2, mn=mean(larvae), rng=diff(range(larvae))) ## plot(rng ~ mn, data=dat2, ## xlab=\"mean number of larvae\", ylab=\"range of number of larvae\", ## main=\"gomez.nonnormal1\") # Because some of the original values are less than 10, # the transform used is log10(x+1) instead of log10(x). dat <- transform(dat, tlarvae=log10(larvae+1)) # QQ plots for raw/transformed data libs(reshape2, lattice) qqmath( ~ value|variable, data=melt(dat), main=\"gomez.nonnormal1 - raw/transformed QQ plot\", scales=list(relation=\"free\")) #> Using trt, rep as id variables # Gomez table 7.16 m1 <- lm(tlarvae ~ rep + trt, data=dat) anova(m1) #> Analysis of Variance Table #> #> Response: tlarvae #> Df Sum Sq Mean Sq F value Pr(>F) #> rep 3 0.9567 0.31889 3.6511 0.0267223 * #> trt 8 3.9823 0.49779 5.6995 0.0004092 *** #> Residuals 24 2.0961 0.08734 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ## Response: tlarvae ## Df Sum Sq Mean Sq F value Pr(>F) ## rep 3 0.9567 0.31889 3.6511 0.0267223 * ## trt 8 3.9823 0.49779 5.6995 0.0004092 *** ## Residuals 24 2.0961 0.08734"},{"path":"/reference/gomez.nonnormal2.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"RCB experiment rice, measuring white heads","code":""},{"path":"/reference/gomez.nonnormal2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"","code":"data(\"gomez.nonnormal2\")"},{"path":"/reference/gomez.nonnormal2.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"data frame 42 observations following 3 variables. gen genotype rep replicate white percentage white heads","code":""},{"path":"/reference/gomez.nonnormal2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"data percent white heads rice variety trial 14 varieties 3 reps. many values less 10, suggested data transformation sqrt(x+.5). Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.nonnormal2.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 300.","code":""},{"path":"/reference/gomez.nonnormal2.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"None.","code":""},{"path":"/reference/gomez.nonnormal2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of rice, measuring white heads — gomez.nonnormal2","text":"","code":"library(agridat) data(gomez.nonnormal2) dat <- gomez.nonnormal2 # Gomez suggested sqrt transform dat <- transform(dat, twhite = sqrt(white+.5)) # QQ plots for raw/transformed data libs(reshape2, lattice) qqmath( ~ value|variable, data=melt(dat), main=\"gomez.nonnormal2 - raw/transformed QQ plot\", scales=list(relation=\"free\")) #> Using gen, rep as id variables # Gomez anova table 7.21 m1 <- lm(twhite ~ rep + gen, data=dat) anova(m1) #> Analysis of Variance Table #> #> Response: twhite #> Df Sum Sq Mean Sq F value Pr(>F) #> rep 2 2.401 1.2004 1.9137 0.1678 #> gen 13 48.011 3.6931 5.8877 6.366e-05 *** #> Residuals 26 16.309 0.6273 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ## Response: twhite2 ## Df Sum Sq Mean Sq F value Pr(>F) ## rep 2 2.401 1.2004 1.9137 0.1678 ## gen 13 48.011 3.6931 5.8877 6.366e-05 *** ## Residuals 26 16.309 0.6273"},{"path":"/reference/gomez.nonnormal3.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"RCB experiment rice, 12 varieties leafhopper survival","code":""},{"path":"/reference/gomez.nonnormal3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"","code":"data(\"gomez.nonnormal3\")"},{"path":"/reference/gomez.nonnormal3.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"data frame 36 observations following 3 variables. gen genotype/variety rice rep replicate hoppers percentage surviving leafhoppers","code":""},{"path":"/reference/gomez.nonnormal3.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"rice variety, 75 leafhoppers caged percentage surviving insects determined. Gomez suggest replacing 0 values 1/(4*75) replacing 100 1-1/(4*75) 75 number insects. effect, means, example, (1/4)th insect survived. data percents, Gomez suggested using arcsin transformation. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.nonnormal3.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 307.","code":""},{"path":"/reference/gomez.nonnormal3.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"None.","code":""},{"path":"/reference/gomez.nonnormal3.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of rice, 12 varieties with leafhopper survival — gomez.nonnormal3","text":"","code":"library(agridat) data(gomez.nonnormal3) dat <- gomez.nonnormal3 # First, replace 0, 100 values dat$thoppers <- dat$hoppers dat <- transform(dat, thoppers=ifelse(thoppers==0, 1/(4*75), thoppers)) dat <- transform(dat, thoppers=ifelse(thoppers==100, 100-1/(4*75), thoppers)) # Arcsin transformation of percentage p converted to degrees # is arcsin(sqrt(p))/(pi/2)*90 dat <- transform(dat, thoppers=asin(sqrt(thoppers/100))/(pi/2)*90) # QQ plots for raw/transformed data libs(reshape2, lattice) qqmath( ~ value|variable, data=melt(dat), main=\"gomez.nonnormal3 - raw/transformed QQ plot\", scales=list(relation=\"free\")) #> Using gen, rep as id variables m1 <- lm(thoppers ~ gen, data=dat) anova(m1) # Match Gomez table 7.25 #> Analysis of Variance Table #> #> Response: thoppers #> Df Sum Sq Mean Sq F value Pr(>F) #> gen 11 16838.7 1530.79 16.502 1.316e-08 *** #> Residuals 24 2226.4 92.77 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ## Response: thoppers ## Df Sum Sq Mean Sq F value Pr(>F) ## gen 11 16838.7 1530.79 16.502 1.316e-08 *** ## Residuals 24 2226.4 92.77"},{"path":"/reference/gomez.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice — gomez.rice.uniformity","title":"Uniformity trial of rice — gomez.rice.uniformity","text":"Uniformity trial rice Philippines.","code":""},{"path":"/reference/gomez.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice — gomez.rice.uniformity","text":"data frame 648 observations following 3 variables. row row col column yield grain yield, grams/m^2","code":""},{"path":"/reference/gomez.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice — gomez.rice.uniformity","text":"area 20 meters 38 meters planted rice variety IR8. harvest, 1-meter border removed around field discarded. square meter (1 meter 1 meter) harvested weighed. Field width: 18 plots x 1 m = 18 m Field length: 38 plots x 1 m = 38 m Note Gomez published paper 1969 rice uniformity data four trials conducted 1968 dry wet seasons. likely data taken one four trials. Estimated harvest year 1968. \"Estimation optimum plot size rice uniformity data\". https://www.cabidigitallibrary.org/doi/full/10.5555/19711601105 Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice — gomez.rice.uniformity","text":"Gomez, K.. Gomez, .. (1984). Statistical Procedures Agricultural Research. Wiley-Interscience. Page 481.","code":""},{"path":"/reference/gomez.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice — gomez.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.rice.uniformity) dat <- gomez.rice.uniformity libs(desplot) # Raw data plot desplot(dat, yield ~ col*row, aspect=38/18, # true aspect main=\"gomez.rice.uniformity\") libs(desplot, reshape2) # 3x3 moving average. Gomez figure 12.1 dmat <- melt(dat, id.var=c('col','row')) dmat <- acast(dmat, row~col) m0 <- dmat cx <- 2:17 rx <- 2:35 dmat3 <- (m0[rx+1,cx+1]+m0[rx+1,cx]+m0[rx+1,cx-1]+ m0[rx,cx+1]+m0[rx,cx]+m0[rx,cx-1]+ m0[rx-1,cx+1]+m0[rx-1,cx]+m0[rx-1,cx-1])/9 dat3 <- melt(dmat3) desplot(dat3, value~Var2*Var1, aspect=38/18, at=c(576,637,695,753,811,870,927), main=\"gomez.rice.uniformity smoothed\") libs(agricolae) # Gomez table 12.4 tab <- index.smith(dmat, main=\"gomez.rice.uniformity\", col=\"red\")$uniformity tab <- data.frame(tab) ## # Gomez figure 12.2 ## op <- par(mar=c(5,4,4,4)+.1) ## m1 <- nls(Vx ~ 9041/Size^b, data=tab, start=list(b=1)) ## plot(Vx ~ Size, tab, xlab=\"Plot size, m^2\") ## lines(fitted(m1) ~ tab$Size, col='red') ## axis(4, at=tab$Vx, labels=tab$CV) ## mtext(\"CV\", 4, line=2) ## par(op) } # }"},{"path":"/reference/gomez.seedrate.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of rice, 6 densities — gomez.seedrate","title":"RCB experiment of rice, 6 densities — gomez.seedrate","text":"RCB experiment rice, 6 densities","code":""},{"path":"/reference/gomez.seedrate.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of rice, 6 densities — gomez.seedrate","text":"data frame 24 observations following 3 variables. rate kg seeds per hectare rep rep (block), four levels yield yield, kg/ha","code":""},{"path":"/reference/gomez.seedrate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of rice, 6 densities — gomez.seedrate","text":"Rice yield six different densities RCB design. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.seedrate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of rice, 6 densities — gomez.seedrate","text":"Gomez, K.. Gomez, .. 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 26.","code":""},{"path":"/reference/gomez.seedrate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of rice, 6 densities — gomez.seedrate","text":"","code":"library(agridat) data(gomez.seedrate) dat <- gomez.seedrate libs(lattice) xyplot(yield ~ rate, data=dat, group=rep, type='b', main=\"gomez.seedrate\", auto.key=list(columns=4)) # Quadratic response. Use raw polynomials so we can compute optimum m1 <- lm(yield ~ rep + poly(rate,2,raw=TRUE), dat) -coef(m1)[5]/(2*coef(m1)[6]) # Optimum is at 29 #> poly(rate, 2, raw = TRUE)1 #> 29.148 # Plot the model predictions libs(latticeExtra) newdat <- expand.grid(rep=levels(dat$rep), rate=seq(25,150)) newdat$pred <- predict(m1, newdat) p1 <- aggregate(pred ~ rate, newdat, mean) # average reps xyplot(yield ~ rate, data=dat, group=rep, type='b', main=\"gomez.seedrate (with model predictions)\", auto.key=list(columns=4)) + xyplot(pred ~ rate, p1, type='l', col='black', lwd=2)"},{"path":"/reference/gomez.splitplot.subsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","title":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","text":"Split-plot experiment rice, subsamples","code":""},{"path":"/reference/gomez.splitplot.subsample.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","text":"data frame 186 observations following 5 variables. time time factor, T1-T4 manage management, M1-M6 rep rep/block, R1-R3 sample subsample, S1-S2 height plant height (cm)","code":""},{"path":"/reference/gomez.splitplot.subsample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","text":"split-plot experiment three blocks. Whole-plot 'management', sub-plot 'time' application, two subsamples. data heights, measured two single-hill sampling units plot. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.splitplot.subsample.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 481.","code":""},{"path":"/reference/gomez.splitplot.subsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-plot experiment of rice, with subsamples — gomez.splitplot.subsample","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.splitplot.subsample) dat <- gomez.splitplot.subsample libs(HH) interaction2wt(height ~ rep + time + manage, data=dat, x.between=0, y.between=0, main=\"gomez.splitplot.subsample - plant height\") # Management totals, Gomez table 6.8 # tapply(dat$height, dat$manage, sum) # Gomez table 6.11 analysis of variance m1 <- aov(height ~ rep + manage + time + manage:time + Error(rep/manage/time), data=dat) summary(m1) ## Error: rep ## Df Sum Sq Mean Sq ## rep 2 2632 1316 ## Error: rep:manage ## Df Sum Sq Mean Sq F value Pr(>F) ## manage 7 1482 211.77 2.239 0.0944 . ## Residuals 14 1324 94.59 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Error: rep:manage:time ## Df Sum Sq Mean Sq F value Pr(>F) ## time 3 820.8 273.61 7.945 0.000211 *** ## manage:time 21 475.3 22.63 0.657 0.851793 ## Residuals 48 1653.1 34.44 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Error: Within ## Df Sum Sq Mean Sq F value Pr(>F) ## Residuals 96 167.4 1.744 } # }"},{"path":"/reference/gomez.splitsplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-split-plot experiment of rice — gomez.splitsplit","title":"Split-split-plot experiment of rice — gomez.splitsplit","text":"Grain yield three varieties rice grown split-split plot arrangement 3 reps, nitrogen level main plot, management practice sub-plot, rice variety sub-sub plot.","code":""},{"path":"/reference/gomez.splitsplit.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-split-plot experiment of rice — gomez.splitsplit","text":"data frame 135 observations following 7 variables. rep block, 3 levels nitro nitrogen fertilizer, kilograms/hectare management plot management gen genotype/variety rice yield yield col column position field row row position field Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.splitsplit.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-split-plot experiment of rice — gomez.splitsplit","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 143.","code":""},{"path":"/reference/gomez.splitsplit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-split-plot experiment of rice — gomez.splitsplit","text":"H. P. Piepho, R. N. Edmondson. (2018). tutorial statistical analysis factorial experiments qualitative quantitative treatment factor levels. Jour Agronomy Crop Science, 8, 1-27. https://doi.org/10.1111/jac.12267","code":""},{"path":"/reference/gomez.splitsplit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-split-plot experiment of rice — gomez.splitsplit","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.splitsplit) dat <- gomez.splitsplit dat$nf <- factor(dat$nitro) libs(desplot) desplot(dat, nf ~ col*row, # aspect unknown out1=rep, col=management, num=gen, cex=1, main=\"gomez.splitsplit\") desplot(dat, yield ~ col*row, # aspect unknown out1=rep, main=\"gomez.splitsplit\") libs(HH) position(dat$nf) <- c(0,50,80,110,140) interaction2wt(yield~rep+nf+management+gen, data=dat, main=\"gomez.splitsplit\", x.between=0, y.between=0, relation=list(x=\"free\", y=\"same\"), rot=c(90,0), xlab=\"\", par.strip.text.input=list(cex=.7)) # AOV. Gomez page 144-153 m0 <- aov(yield~ nf * management * gen + Error(rep/nf/management), data=dat) summary(m0) # Similar to Gomez, p. 153. } # }"},{"path":"/reference/gomez.stripplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Strip-plot experiment of rice — gomez.stripplot","title":"Strip-plot experiment of rice — gomez.stripplot","text":"strip-plot experiment three reps, variety horizontal strip nitrogen fertilizer vertical strip.","code":""},{"path":"/reference/gomez.stripplot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Strip-plot experiment of rice — gomez.stripplot","text":"yield Grain yield kg/ha rep Rep nitro Nitrogen fertilizer kg/ha gen Rice variety col column row row","code":""},{"path":"/reference/gomez.stripplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strip-plot experiment of rice — gomez.stripplot","text":"Note, subset 'gomez.stripsplitplot' data. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.stripplot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Strip-plot experiment of rice — gomez.stripplot","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 110.","code":""},{"path":"/reference/gomez.stripplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strip-plot experiment of rice — gomez.stripplot","text":"Jan Gertheiss (2014). ANOVA Factors Ordered Levels. J Agric Biological Environmental Stat, 19, 258-277.","code":""},{"path":"/reference/gomez.stripplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strip-plot experiment of rice — gomez.stripplot","text":"","code":"library(agridat) data(gomez.stripplot) dat <- gomez.stripplot # Gomez figure 3.7 libs(desplot) desplot(dat, gen ~ col*row, # aspect unknown out1=rep, out2=nitro, num=nitro, cex=1, main=\"gomez.stripplot\") # Gertheiss figure 1 # library(lattice) # dotplot(factor(nitro) ~ yield|gen, data=dat) # Gomez table 3.12 # tapply(dat$yield, dat$rep, sum) # tapply(dat$yield, dat$gen, sum) # tapply(dat$yield, dat$nitro, sum) # Gomez table 3.15. Anova table for strip-plot dat <- transform(dat, nf=factor(nitro)) m1 <- aov(yield ~ gen * nf + Error(rep + rep:gen + rep:nf), data=dat) summary(m1) #> #> Error: rep #> Df Sum Sq Mean Sq F value Pr(>F) #> Residuals 2 9220962 4610481 #> #> Error: rep:gen #> Df Sum Sq Mean Sq F value Pr(>F) #> gen 5 57100201 11420040 7.653 0.00337 ** #> Residuals 10 14922619 1492262 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: rep:nf #> Df Sum Sq Mean Sq F value Pr(>F) #> nf 2 50676061 25338031 34.07 0.00307 ** #> Residuals 4 2974908 743727 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Error: Within #> Df Sum Sq Mean Sq F value Pr(>F) #> gen:nf 10 23877979 2387798 5.801 0.000427 *** #> Residuals 20 8232917 411646 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ## Error: rep ## Df Sum Sq Mean Sq F value Pr(>F) ## Residuals 2 9220962 4610481 ## Error: rep:gen ## Df Sum Sq Mean Sq F value Pr(>F) ## gen 5 57100201 11420040 7.653 0.00337 ** ## Residuals 10 14922619 1492262 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Error: rep:nf ## Df Sum Sq Mean Sq F value Pr(>F) ## nf 2 50676061 25338031 34.07 0.00307 ** ## Residuals 4 2974908 743727 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Error: Within ## Df Sum Sq Mean Sq F value Pr(>F) ## gen:nf 10 23877979 2387798 5.801 0.000427 *** ## Residuals 20 8232917 411646 # More compact view ## libs(agricolae) ## with(dat, strip.plot(rep, nf, gen, yield)) ## Analysis of Variance Table ## Response: yield ## Df Sum Sq Mean Sq F value Pr(>F) ## rep 2 9220962 4610481 11.2001 0.0005453 *** ## nf 2 50676061 25338031 34.0690 0.0030746 ** ## Ea 4 2974908 743727 1.8067 0.1671590 ## gen 5 57100201 11420040 7.6528 0.0033722 ** ## Eb 10 14922619 1492262 3.6251 0.0068604 ** ## gen:nf 10 23877979 2387798 5.8006 0.0004271 *** ## Ec 20 8232917 411646 # Mixed-model version ## libs(lme4) ## m3 <- lmer(yield ~ gen * nf + (1|rep) + (1|rep:nf) + (1|rep:gen), data=dat) ## anova(m3) ## Analysis of Variance Table ## Df Sum Sq Mean Sq F value ## gen 5 15751300 3150260 7.6528 ## nf 2 28048730 14024365 34.0690 ## gen:nf 10 23877979 2387798 5.8006"},{"path":"/reference/gomez.stripsplitplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Strip-split-plot experiment of rice — gomez.stripsplitplot","title":"Strip-split-plot experiment of rice — gomez.stripsplitplot","text":"strip-split-plot experiment three reps, genotype horizontal strip, nitrogen fertilizer vertical strip, planting method subplot factor.","code":""},{"path":"/reference/gomez.stripsplitplot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Strip-split-plot experiment of rice — gomez.stripsplitplot","text":"yield grain yield kg/ha planting planting factor, P1=broadcast, P2=transplanted rep rep, 3 levels nitro nitrogen fertilizer, kg/ha gen genotype, G1 G6 col column row row","code":""},{"path":"/reference/gomez.stripsplitplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strip-split-plot experiment of rice — gomez.stripsplitplot","text":"Note, superset 'gomez.stripplot' data. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.stripsplitplot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Strip-split-plot experiment of rice — gomez.stripsplitplot","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 155.","code":""},{"path":"/reference/gomez.stripsplitplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strip-split-plot experiment of rice — gomez.stripsplitplot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.stripsplitplot) dat <- gomez.stripsplitplot # Layout libs(desplot) desplot(dat, gen ~ col*row, out1=rep, col=nitro, text=planting, cex=1, main=\"gomez.stripsplitplot\") # Gomez table 4.19, ANOVA of strip-split-plot design dat <- transform(dat, nf=factor(nitro)) m1 <- aov(yield ~ nf * gen * planting + Error(rep + rep:nf + rep:gen + rep:nf:gen), data=dat) summary(m1) # There is a noticeable linear trend along the y coordinate which may be # an artifact that blocking will remove, or may need to be modeled. # Note the outside values in the high-nitro boxplot. libs(\"HH\") interaction2wt(yield ~ nitro + gen + planting + row, dat, x.between=0, y.between=0, x.relation=\"free\") } # }"},{"path":"/reference/gomez.wetdry.html","id":null,"dir":"Reference","previous_headings":"","what":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"Rice yield wet & dry seasons nitrogen fertilizer treatments","code":""},{"path":"/reference/gomez.wetdry.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"data frame 96 observations following 4 variables. season season = wet/dry nitrogen nitrogen fertilizer kg/ha rep replicate yield grain yield, t/ha","code":""},{"path":"/reference/gomez.wetdry.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"Five nitrogen fertilizer treatments tested 2 seasons using 3 reps. Used permission Kwanchai Gomez.","code":""},{"path":"/reference/gomez.wetdry.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"Gomez, K.. Gomez, ... 1984, Statistical Procedures Agricultural Research. Wiley-Interscience. Page 318.","code":""},{"path":"/reference/gomez.wetdry.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"Rong-Cai Yang, Patricia Juskiw. (2011). Analysis covariance agronomy crop research. Canadian Journal Plant Science, 91:621-641. https://doi.org/10.4141/cjps2010-032","code":""},{"path":"/reference/gomez.wetdry.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rice yield in wet & dry seasons with nitrogen fertilizer treatments — gomez.wetdry","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gomez.wetdry) dat <- gomez.wetdry libs(lattice) foo1 <- xyplot(yield ~ nitrogen|season, data=dat, group=rep,type='l',auto.key=list(columns=3), ylab=\"yield in each season\", main=\"gomez.wetdry raw data & model\") # Yang & Juskiw fit a quadratic model with linear and quadratic # contrasts using non-equal intervals of nitrogen levels. # This example below omits the tedious contrasts libs(latticeExtra, lme4) m1 <-lmer(yield ~ season*poly(nitrogen, 2) + (1|season:rep), data=dat) pdat <- expand.grid(season=c('dry','wet'), nitrogen=seq(from=0,to=150,by=5)) pdat$pred <- predict(m1, newdata=pdat, re.form= ~ 0) foo1 + xyplot(pred ~ nitrogen|season, data=pdat, type='l',lwd=2,col=\"black\") # m2 <-lmer(yield ~ poly(nitrogen, 2) + (1|season:rep), data=dat) # anova(m1,m2) ## m2: yield ~ poly(nitrogen, 2) + (1 | season:rep) ## m1: yield ~ season * poly(nitrogen, 2) + (1 | season:rep) ## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) ## m2 5 86.418 93.424 -38.209 76.418 ## m1 8 64.216 75.425 -24.108 48.216 28.202 3 3.295e-06 *** } # }"},{"path":"/reference/gorski.oats.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of oats in Poland — gorski.oats.uniformity","title":"Uniformity trial of oats in Poland — gorski.oats.uniformity","text":"Uniformity trial oats Poland","code":""},{"path":"/reference/gorski.oats.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of oats in Poland — gorski.oats.uniformity","text":"","code":"data(\"gorski.oats.uniformity\")"},{"path":"/reference/gorski.oats.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of oats in Poland — gorski.oats.uniformity","text":"data frame 500 observations following 4 variables. row row ordinate col column ordinate yield yield, kg field field","code":""},{"path":"/reference/gorski.oats.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of oats in Poland — gorski.oats.uniformity","text":"authors Agricultural University Dublany near Lemberg. (Originally Poland, now Ukraine.) experiments carried fields Dublany estate. Gorski & Stefaniow (1917) show layout plots. say details given journal \"Roczniki nauk rolniczych\". (yet found) Kotowski (1924) shows layout plots. Field F1 loamy field sown early oats \"Rychlik mikulicki\". measured 200 plots 9 square meters harvested one day sickle. Two days later, harvest yield plot determined. unable determine harvest plots . 161, 163, 182, harvest 197 (instead 200) plots processed. Field width: 10 plots x 3 m = 30 m Field length: 20 plots x 3 m = 60 m Field F2 loess field used \"Ligowo\" oats 300 9 sq m plots. Data typed checked K.Wright 2024.12.11. Text translation via Google Translate. actual plot dimensions given, 3m 3m good approximation may exact.","code":""},{"path":"/reference/gorski.oats.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of oats in Poland — gorski.oats.uniformity","text":"Gorski, M. Stefaniow, M. (1917). Die Anwendbarkeit der Wahrscheinlichkeits-rechnung bei Feldversuchen. Die Landwirtschaflichen Versuchsstationen, 90, 225-240. https://www.google.com/books/edition/Die_Landwirtschaftlichen_versuchs_statio/qr8jAQAAMAAJ?hl=en&gbpv=1&pg=RA1-PA225","code":""},{"path":"/reference/gorski.oats.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of oats in Poland — gorski.oats.uniformity","text":"Kotowski, Feliks. (1924). criterion field homegenity value field trials. (English title). Roczniki Nauk Rolniczych, 11, 26-35. https://www.google.com/books/edition/Roczniki_nauk_rolniczych/mz0iAQAAIAAJ Polish version page 26. English abstract page 35.","code":""},{"path":"/reference/gorski.oats.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of oats in Poland — gorski.oats.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ libs(agridat) data(gorski.oats.uniformity) dat <- gorski.oats.uniformity libs(desplot) desplot(dat, yield~col*row, subset=field==\"F1\", flip=TRUE, tick=TRUE, aspect=(20)/(10), main=\"gorski.oats.uniformity - field F1\") desplot(dat, yield~col*row, subset=field==\"F2\", flip=TRUE, tick=TRUE, aspect=(20)/(15), main=\"gorski.oats.uniformity - field F2\") } # }"},{"path":"/reference/gotway.hessianfly.html","id":null,"dir":"Reference","previous_headings":"","what":"Hessian fly damage to wheat varieties — gotway.hessianfly","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"Hessian fly damage wheat varieties","code":""},{"path":"/reference/gotway.hessianfly.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"block block factor, 4 levels genotype factor, 16 wheat varieties lat latitude, numeric long longitude, numeric y number damaged plants n number total plants","code":""},{"path":"/reference/gotway.hessianfly.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"response binomial. plot square.","code":""},{"path":"/reference/gotway.hessianfly.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"C. . Gotway W. W. Stroup. Generalized Linear Model Approach Spatial Data Analysis Prediction Journal Agricultural, Biological, Environmental Statistics, 2, 157-178. https://doi.org/10.2307/1400401","code":""},{"path":"/reference/gotway.hessianfly.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"GLIMMIX procedure. https://www.ats.ucla.edu/stat/SAS/glimmix.pdf","code":""},{"path":"/reference/gotway.hessianfly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hessian fly damage to wheat varieties — gotway.hessianfly","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gotway.hessianfly) dat <- gotway.hessianfly dat$prop <- dat$y / dat$n libs(desplot) desplot(dat, prop~long*lat, aspect=1, # true aspect out1=block, num=gen, cex=.75, main=\"gotway.hessianfly\") # ---------------------------------------------------------------------------- # spaMM package example libs(spaMM) m1 = HLCor(cbind(y, n-y) ~ 1 + gen + (1|block) + Matern(1|long+lat), data=dat, family=binomial(), ranPars=list(nu=0.5, rho=1/.7)) summary(m1) fixef(m1) # The following line fails with \"Invalid graphics state\" # when trying to use pkgdown::build_site # filled.mapMM(m1) # ---------------------------------------------------------------------------- # Block random. See Glimmix manual, output 1.18. # Note: (Different parameterization) libs(lme4) l2 <- glmer(cbind(y, n-y) ~ gen + (1|block), data=dat, family=binomial, control=glmerControl(check.nlev.gtr.1=\"ignore\")) coef(l2) } # }"},{"path":"/reference/goulden.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of barley — goulden.barley.uniformity","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"Uniformity trial barley Canada","code":""},{"path":"/reference/goulden.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"data frame 400 observations following 3 variables. row row col column yield yield, grams per plot","code":""},{"path":"/reference/goulden.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"Yield (grams) 2304 square-yard plots barley grown field 48 yards side Dominion Rust Research Laboratory (Manitoba, Canada) 1931. field sown half density one direction, half-density perpendicular direction. letter Goulden Cochran, Goulden said: intended use yields study effect systematic arrangements also measure bias semi-Latin squares...correlation adjacent pairs plots high (0.5) difficult demonstrate bias satisfactory manner. Note: data Goulden (1939) subset 20 rows columns one corner field full dataset. Field width: 48 plots x 3 feet = 144 feet Field length: 48 plots x 3 feet = 144 feet data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/goulden.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 5.","code":""},{"path":"/reference/goulden.barley.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"C. H. Goulden, (1939). Methods statistical analysis, 1st ed. Page 18. https://archive.org/stream/methodsofstatist031744mbp Note: version 20 plots x 20 plots. Leonard, Warren Andrew Clark (1939). Field Plot Technique. Page 39. https://archive.org/stream/fieldplottechniq00leon Note: version 20 plots x 20 plots.","code":""},{"path":"/reference/goulden.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of barley — goulden.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(goulden.barley.uniformity) dat <- goulden.barley.uniformity libs(desplot) desplot(dat, yield ~ col*row, aspect=48/48, # true aspect main=\"goulden.barley.uniformity\") # Left skewed distribution. See LeClerg, Leonard, Clark hist(dat$yield, main=\"goulden.barley.uniformity\", breaks=c(21,40,59,78,97,116,135,154,173,192,211,230,249,268,287)+.5) } # }"},{"path":"/reference/goulden.eggs.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample of egg weights on 24 consecutive days — goulden.eggs","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"Sample egg weights 24 consecutive days","code":""},{"path":"/reference/goulden.eggs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"","code":"data(\"goulden.eggs\")"},{"path":"/reference/goulden.eggs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"data frame 240 observations following 2 variables. day day weight weight","code":""},{"path":"/reference/goulden.eggs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"Data weights 10 eggs taken random day 24 days. Day 1 Dec 10, Day 24 Jan 2. control chart standard deviations shows 4 values beyond upper limits. data reveals single, unusually large egg days. almost surely double-yolk eggs.","code":""},{"path":"/reference/goulden.eggs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"Cyrus H. Goulden (1952). Methods Statistical Analysis, 2nd ed. Page 425.","code":""},{"path":"/reference/goulden.eggs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"None.","code":""},{"path":"/reference/goulden.eggs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sample of egg weights on 24 consecutive days — goulden.eggs","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(goulden.eggs) dat <- goulden.eggs libs(qicharts) # Figure 19-4 of Goulden. (Goulden uses 1/n when calculating std dev) op <- par(mfrow=c(2,1)) qic(weight, x = day, data = dat, chart = 'xbar', main = 'goulden.eggs - Xbar chart', xlab = 'Date', ylab = 'Avg egg weight' ) qic(weight, x = day, data = dat, chart = 's', main = 'goulden.eggs - S chart', xlab = 'Date', ylab = 'Std dev egg weight' ) par(op) } # }"},{"path":"/reference/goulden.latin.html","id":null,"dir":"Reference","previous_headings":"","what":"Latin square experiment for testing fungicide — goulden.latin","title":"Latin square experiment for testing fungicide — goulden.latin","text":"Latin square experiment testing fungicide","code":""},{"path":"/reference/goulden.latin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latin square experiment for testing fungicide — goulden.latin","text":"","code":"data(\"goulden.latin\")"},{"path":"/reference/goulden.latin.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Latin square experiment for testing fungicide — goulden.latin","text":"data frame 25 observations following 4 variables. trt treatment factor, 5 levels yield yield row row col column","code":""},{"path":"/reference/goulden.latin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Latin square experiment for testing fungicide — goulden.latin","text":"Five treatments tested control stem rust wheat. Treatment codes descriptions: = Dusted rains. B = Dusted rains. C = Dusted week. D = Drifting, week. E = dusted.","code":""},{"path":"/reference/goulden.latin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latin square experiment for testing fungicide — goulden.latin","text":"Cyrus H. Goulden (1952). Methods Statistical Analysis, 2nd ed. Page 216.","code":""},{"path":"/reference/goulden.latin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Latin square experiment for testing fungicide — goulden.latin","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) library(agridat) data(goulden.latin) dat <- goulden.latin libs(desplot) desplot(dat, yield ~ col*row, text=trt, cex=1, # aspect unknown main=\"goulden.latin\") # Matches Goulden. m1 <- lm(yield~ trt + factor(row) + factor(col), data=dat) anova(m1) } # }"},{"path":"/reference/goulden.splitsplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-split-plot experiment of wheat — goulden.splitsplit","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"Split-split-plot experiment wheat","code":""},{"path":"/reference/goulden.splitsplit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"","code":"data(\"goulden.splitsplit\")"},{"path":"/reference/goulden.splitsplit.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"data frame 160 observations following 9 variables. row row col column yield yield inoc inoculate trt treatment number gen genotype dry dry/wet dust application dust dust treatment block block","code":""},{"path":"/reference/goulden.splitsplit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"interesting split-split plot experiment sub-plot treatments 2*5 factorial structure. experiment conducted 1932 experimental field Dominion Rust Research Laboratory. study designed determine effect incidence root rot, variety wheat, kinds dust seed treatment, method application dust, efficacy soil inoculation root-rot organism. field 4 blocks. block 2 whole plots genotypes. whole-plot 10 sub-plots 5 different kinds dust 2 methods application. sub-plot 2 sub-sub-plots, one inoculated soil one uninoculated soil.","code":""},{"path":"/reference/goulden.splitsplit.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"C. H. Goulden, (1939). Methods statistical analysis, 1st ed. Page 18. https://archive.org/stream/methodsofstatist031744mbp","code":""},{"path":"/reference/goulden.splitsplit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"None","code":""},{"path":"/reference/goulden.splitsplit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-split-plot experiment of wheat — goulden.splitsplit","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(goulden.splitsplit) dat <- goulden.splitsplit libs(desplot) ## Experiment design. Goulden p. 152-153 ## desplot(gen ~ col*row, data=dat, ## out1=block, out2=trt, text=dust, col=inoc, cex=1, ## main=\"goulden.splitsplit\") desplot(dat, yield ~ col*row, out1=block, out2=gen, col=inoc, num=trt, cex=1, main=\"goulden.splitsplit\") # Match Goulden table 40 m1 <- aov(yield ~ gen + dust + dry + dust:dry + gen:dust + gen:dry + gen:dust:dry + inoc + inoc:gen + inoc:dust + inoc:dry + inoc:dust:dry +inoc:gen:dust + inoc:gen:dry + Error(block/(gen+gen:dust:dry+gen:inoc:dry)), data=dat) summary(m1) } # }"},{"path":"/reference/graybill.heteroskedastic.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"Wheat varieties heteroskedastic yields","code":""},{"path":"/reference/graybill.heteroskedastic.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"data frame 52 observations following 3 variables. env environment, 13 levels gen genotype, 4 levels yield yield","code":""},{"path":"/reference/graybill.heteroskedastic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"Yield 4 varieties wheat 13 locations Oklahoma, USA. data used explore variability varieties.","code":""},{"path":"/reference/graybill.heteroskedastic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"F. . Graybill, 1954. Variance heterogeneity randomized block design, Biometrics, 10, 516-520.","code":""},{"path":"/reference/graybill.heteroskedastic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"Hans-Pieter Piepho, 1994. Missing observations analysis stability. Heredity, 72, 141–145. https://doi.org/10.1038/hdy.1994.20","code":""},{"path":"/reference/graybill.heteroskedastic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat varieties with heteroskedastic yields — graybill.heteroskedastic","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(graybill.heteroskedastic) dat <- graybill.heteroskedastic # Genotypes are obviously not homoscedastic boxplot(yield ~ gen, dat, main=\"graybill.heteroskedastic\") # Shukla stability variance of each genotype, same as Grubbs' estimate # Matches Piepho 1994 page 143. # Do not do this! Nowadays, use mixed models instead. libs(\"reshape2\") datm <- acast(dat, gen~env) w <- datm w <- sweep(w, 1, rowMeans(datm)) w <- sweep(w, 2, colMeans(datm)) w <- w + mean(datm) w <- rowSums(w^2) k=4; n=13 sig2 <- k*w/((k-2)*(n-1)) - sum(w)/((k-1)*(k-2)*(n-1)) ## sig2 ## G1 G2 G3 G4 ## 145.98 -14.14 75.15 18.25 var.shukla <- function(x,N){ # Estimate variance of shukla stability statistics # Piepho 1994 equation (5) K <- length(x) # num genotypes S <- outer(x,x) S1 <- diag(S) S2 <- rowSums(S) - S1 S[!upper.tri(S)] <- 0 # Make S upper triangular # The ith element of S3 is the sum of the upper triangular elements of S, # excluding the ith row and ith column S3 <- sum(S) - rowSums(S) - colSums(S) var.si2 <- 2*S1/(N-1) + 4/( (N-1)*(K-1)^2 ) * ( S2 + S3/(K-2)^2 ) return(var.si2) } # Set negative estimates to zero sig2[sig2<0] <- 0 # Variance of shukla stat. Match Piepho 1994, table 5, example 1 var.shukla(sig2,13) ## G1 G2 G3 G4 ## 4069.3296 138.9424 1423.0797 306.5270 } # }"},{"path":"/reference/gregory.cotton.html","id":null,"dir":"Reference","previous_headings":"","what":"Factorial experiment of cotton in Sudan. — gregory.cotton","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"Factorial experiment cotton Sudan.","code":""},{"path":"/reference/gregory.cotton.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"","code":"data(\"gregory.cotton\")"},{"path":"/reference/gregory.cotton.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"data frame 144 observations following 6 variables. yield yield year year nitrogen nitrogen level date sowing date water irrigation amount spacing spacing plants","code":""},{"path":"/reference/gregory.cotton.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"Experiment conducted Sudan Gezira Research Farm 1929-1930 1930-1931. effects yield four factors studied possible combinations. Sowing dates 1929: D1 = Jul 24, D2 = Aug 11, D3 = Sep 2, D4 = Sep 25. Spacing: S1 = 25 cm holes, S2 = 50 cm, S3 = 75 cm. usual spacing 50-70 cm. Irrigation: I1 = Light, I2 = Medium, I3 = Heavy. Nitrogen: N0 = None/Control, N1 = 600 rotls/feddan. year 4*3*2*2=72 treatments, replicated four times. means given . Gregory (1932) two interesting graphics: 1. radial bar plot 2. photographs 3D model treatment means.","code":""},{"path":"/reference/gregory.cotton.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"Gregory, FG Crowther, F Lambert, AR (1932). interrelation factors controlling production cotton irrigation Sudan. Journal Agricultural Science, 22, 617-638. Table 1, 10. https://doi.org/10.1017/S0021859600054137","code":""},{"path":"/reference/gregory.cotton.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"Paterson, D. Statistical Technique Agricultural Research, p. 211.","code":""},{"path":"/reference/gregory.cotton.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factorial experiment of cotton in Sudan. — gregory.cotton","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gregory.cotton) dat <- gregory.cotton # Main effect means, Gregory table 2 ## libs(dplyr) ## dat ## dat ## dat ## dat # Figure 2 of Gregory. Not recommended, but an interesting exercise. # https://stackoverflow.com/questions/13887365 if(FALSE){ libs(ggplot2) d1 <- subset(dat, year==\"Y1\") d1 <- transform(d1, grp=factor(paste(date,nitrogen,water,spacing))) d1 <- d1[order(d1$grp),] # for angles # Rotate labels on the left half 180 deg. First 18, last 18 labels d1$ang <- 90+seq(from=(360/nrow(d1))/1.5, to=(1.5*(360/nrow(d1)))-360, length.out=nrow(d1))+80 d1$ang[1:18] <- d1$ang[1:18] + 180 d1$ang[55:72] <- d1$ang[55:72] + 180 # Lables on left half to right-adjusted d1$hjust <- 0 d1$hjust[1:18] <- d1$hjust[55:72] <- 1 gg <- ggplot(d1, aes(x=grp,y=yield,fill=factor(spacing))) + geom_col() + guides(fill=FALSE) + # no legend for 'spacing' coord_polar(start=-pi/2) + # default is to start at top labs(title=\"gregory.cotton 1929\",x=\"\",y=\"\",label=\"\") + # The bar columns are centered on 1:72, subtract 0.5 to add radial axes geom_vline(xintercept = seq(1, 72, by=3)-0.5, color=\"gray\", size=.25) + geom_vline(xintercept = seq(1, 72, by=18)-0.5, size=1) + geom_vline(xintercept = seq(1, 72, by=9)-0.5, size=.5) + geom_hline(yintercept=c(1,2,3)) + geom_text(data=d1, aes(x=grp, y=max(yield), label=grp, angle=ang, hjust=hjust), size=2) + theme(panel.background=element_blank(), axis.title=element_blank(), panel.grid=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks=element_blank() ) print(gg) } } # }"},{"path":"/reference/grover.diallel.html","id":null,"dir":"Reference","previous_headings":"","what":"Diallel 6x6 — grover.diallel","title":"Diallel 6x6 — grover.diallel","text":"Diallel 6x6 4 blocks.","code":""},{"path":"/reference/grover.diallel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diallel 6x6 — grover.diallel","text":"","code":"data(\"grover.diallel\")"},{"path":"/reference/grover.diallel.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diallel 6x6 — grover.diallel","text":"data frame 144 observations following 5 variables. yield yield value rep character vector parent1 character vector parent2 character vector cross character vector","code":""},{"path":"/reference/grover.diallel.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diallel 6x6 — grover.diallel","text":"Yield 6x6 diallel 4 reps. Note: mean 2x2 cross slightly different Grover p. 252. appears unknown error one 4 reps data page 250.","code":""},{"path":"/reference/grover.diallel.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Diallel 6x6 — grover.diallel","text":"Grover, Deepak & Lajpat Rai (2010). Experimental Designing Data Analysis Agriculture Biology. Agrotech Publishing Academy. Page 85. https://archive.org/details/expldesnanddatanalinagblg00023","code":""},{"path":"/reference/grover.diallel.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diallel 6x6 — grover.diallel","text":"None","code":""},{"path":"/reference/grover.diallel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diallel 6x6 — grover.diallel","text":"","code":"if (FALSE) { # \\dontrun{ data(grover.diallel) dat <- grover.diallel anova(aov(yield ~ rep + cross, data=dat)) # These effects match the GCA and SCA values in Grover table 3, page 253. libs(lmDiallel) m2 <- lm.diallel(yield ~ parent1 + parent2, Block=rep, data=dat, fct=\"GRIFFING1\") library(multcomp) summary( glht(linfct=diallel.eff(m2), test=adjusted(type=\"none\")) ) ## Linear Hypotheses: ## Estimate Std. Error t value Pr(>|t|) ## Intercept == 0 93.0774 0.9050 102.851 <0.01 *** ## g_P1 == 0 1.4851 1.4309 1.038 1.0000 ## g_P2 == 0 -0.9911 1.4309 -0.693 1.0000 ## g_P3 == 0 2.2631 1.4309 1.582 0.9748 ## g_P4 == 0 5.4247 1.4309 3.791 0.0302 * ## g_P5 == 0 -4.2490 1.4309 -2.969 0.1972 ## g_P6 == 0 -3.9328 1.4309 -2.748 0.3008 ## ts_P1:P1 == 0 -10.4026 4.5249 -2.299 0.6014 ## ts_P1:P2 == 0 -9.7214 3.2629 -2.979 0.1933 ## ts_P1:P3 == 0 -0.4581 3.2629 -0.140 1.0000 ## ts_P1:P4 == 0 17.0428 3.2629 5.223 <0.01 *** ## ts_P1:P5 == 0 25.4765 3.2629 7.808 <0.01 *** ## ts_P1:P6 == 0 -21.9372 3.2629 -6.723 <0.01 *** ## ts_P2:P1 == 0 -9.7214 3.2629 -2.979 0.1928 ## ts_P2:P2 == 0 7.0899 4.5249 1.567 0.9773 } # }"},{"path":"/reference/grover.rcb.subsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Rice RCB with subsamples — grover.rcb.subsample","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"experiment rice 9 fertilizer treatments 4 blocks, 4 hills per plot.","code":""},{"path":"/reference/grover.rcb.subsample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"","code":"data(\"grover.rcb.subsample\")"},{"path":"/reference/grover.rcb.subsample.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"data frame 144 observations following 4 variables. tiller number tillers trt treatment factor block block factor unit subsample unit","code":""},{"path":"/reference/grover.rcb.subsample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"experiment rice 9 fertilizer treatments 4 blocks, 4 hills per plot. response variable tiller count (per hill). hills sampling units.","code":""},{"path":"/reference/grover.rcb.subsample.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"Grover, Deepak & Lajpat Rai (2010). Experimental Designing Data Analysis Agriculture Biology. Agrotech Publishing Academy. Page 85. https://archive.org/details/expldesnanddatanalinagblg00023","code":""},{"path":"/reference/grover.rcb.subsample.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"None.","code":""},{"path":"/reference/grover.rcb.subsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rice RCB with subsamples — grover.rcb.subsample","text":"","code":"if (FALSE) { # \\dontrun{ data(grover.rcb.subsample) # Fixed-effects ANOVA. Matches Grover page 86. anova(aov(tiller ~ block + trt + block:trt, data=grover.rcb.subsample)) ## Response: tiller ## Df Sum Sq Mean Sq F value Pr(>F) ## block 3 930 310.01 3.6918 0.01415 * ## trt 8 11816 1477.00 17.5891 < 2e-16 *** ## block:trt 24 4721 196.71 2.3425 0.00158 ** ## Residuals 108 9069 83.97 } # }"},{"path":"/reference/gumpertz.pepper.html","id":null,"dir":"Reference","previous_headings":"","what":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","title":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","text":"Phytophtera disease incidence pepper field","code":""},{"path":"/reference/gumpertz.pepper.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","text":"data frame 800 observations following 6 variables. field field factor, 2 levels row x ordinate quadrat y ordinate disease presence (Y) absence (N) disease water soil moisture percent leaf leaf assay count","code":""},{"path":"/reference/gumpertz.pepper.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","text":"field 20 rows 20 quadrates, 2 3 bell pepper plants per plot. plant wilted, dead, lesions, Phytophthora disease considered present plot. soil pathogen load assayed number leaf disks colonized five. field 2, pattern disease presence appears follow soil water content. field 1, obvious trends present. Gumpertz et al. model presence disease using soil moisture leaf assay covariates, using disease presence neighboring plots covariates autologistic model. Used permission Marcia Gumpertz. Research funded USDA.","code":""},{"path":"/reference/gumpertz.pepper.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","text":"Marcia L. Gumpertz; Jonathan M. Graham; Jean B. Ristaino (1997). Autologistic Model Spatial Pattern Phytophthora Epidemic Bell Pepper: Effects Soil Variables Disease Presence. Journal Agricultural, Biological, Environmental Statistics, Vol. 2, . 2., pp. 131-156.","code":""},{"path":"/reference/gumpertz.pepper.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Phytophtera disease incidence in a pepper field — gumpertz.pepper","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(gumpertz.pepper) dat <- gumpertz.pepper # Gumpertz deletes two outliers dat[ dat$field ==\"F1\" & dat$row==20 & dat$quadrat==10, 'water'] <- NA dat[ dat$field ==\"F2\" & dat$row==5 & dat$quadrat==4, 'water'] <- NA # Horizontal flip dat <- transform(dat, row=21-row) # Disease presence. Gumpertz fig 1a, 2a. libs(desplot) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) desplot(dat, disease ~ row*quadrat|field, col.regions=c('white','black'), aspect=1, # uncertain aspect main=\"gumpertz.pepper disease presence\", ) # Soil water. Gumpertz fig 1b, 2b desplot(dat, water ~ row*quadrat|field, col.regions=grays(5), aspect=1, # uncertain aspect at=c(5,7.5,10,12.5,15,18), main=\"gumpertz.pepper soil moisture\") # Leaf assay. Gumpertz fig 1c, 2c desplot(dat, leaf ~ row*quadrat|field, col.regions=grays(6), at=c(0,1,2,3,4,5,6)-.5, aspect=1, # uncertain aspect main=\"gumpertz.pepper leaf assay\", ) # Use the inner 16x16 grid of plots in field 2 dat2 <- droplevels(subset(dat, field==\"F2\" & !is.na(water) & row > 2 & row < 19 & quadrat > 2 & quadrat < 19)) m21 <- glm(disease ~ water + leaf, data=dat2, family=binomial) coef(m21) # These match Gumpertz et al table 4, model 1 ## (Intercept) water leaf ## -9.1019623 0.7059993 0.4603931 dat2$res21 <- resid(m21) if(0){ libs(desplot) desplot(dat2, res21 ~ row*quadrat, main=\"gumpertz.pepper field 2, model 1 residuals\") # Still shows obvious trends. Gumpertz et al add spatial covariates for # neighboring plots, but with only minor improvement in misclassification } } # }"},{"path":"/reference/hadasch.lettuce.html","id":null,"dir":"Reference","previous_headings":"","what":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"Lettuce resistance downy mildew resistance (marker data).","code":""},{"path":"/reference/hadasch.lettuce.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"","code":"data(\"hadasch.lettuce\")"},{"path":"/reference/hadasch.lettuce.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"data frame 703 observations following 4 variables. loc locations gen genotype rep replicate dmr downy mildew resistance","code":""},{"path":"/reference/hadasch.lettuce.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"biparental cross 95 recombinant inbred lines \"Salinas 88\" (susceptible) \"La Brillante\" (highly resistant downy mildew). 89 RILs evaluated field experiments performed 2010 2011 near Salinas, California. loc 2 3 rep RCB design. approximately 30 plants per plot. Plots scored 0 (disease) 5 (severe disease). authors used following model first-stage analysis compute adjusted means genotype: y = loc + gen + gen:loc + block:loc + error gen fixed terms random. adjusted means used response second stage: mn = 1 + Zu + error Z design matrix marker effects. error term fixed covariance matrix R first stage. Genotyping performed 95 SNPs 205 amplified fragment length polymporphism markers marker matrix M (89×300) provided. biallelic marker M(iw) ith genotype wth marker alleles A1 (.e. reference allele) A2 coded 1 A1,A1, -1 A2,A2 0 A1,A2 A2,A2. electronic version lettuce data licensed CC-4 downloaded 20 Feb 2021. https://figshare.com/articles/dataset/Lettuce_trial_phenotypic_and_marker_data_/8299493","code":""},{"path":"/reference/hadasch.lettuce.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"Hadasch, S., . Simko, R. J. Hayes, J. O. Ogutu, H.P. Piepho (2016). Comparing predictive abilities phenotypic marker-assisted selection methods biparental lettuce population. Plant Genome 9. https://doi.org/10.3835/plantgenome2015.03.0014","code":""},{"path":"/reference/hadasch.lettuce.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"Hayes, R. J., Galeano, C. H., Luo, Y., Antonise, R., & Simko, . (2014). Inheritance Decay Fresh-cut Lettuce Recombinant Inbred Line Population \"Salinas 88\" × \"La Brillante\". J. Amer. Soc. Hort. Sci., 139(4), 388-398. https://doi.org/10.21273/JASHS.139.4.388","code":""},{"path":"/reference/hadasch.lettuce.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lettuce resistance to downy mildew resistance (with marker data) — hadasch.lettuce","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hadasch.lettuce) data(hadasch.lettuce.markers) dat <- hadasch.lettuce datm <- hadasch.lettuce.markers libs(agridat) # loc 1 has 2 reps, loc 3 has higher dmr dotplot(dmr ~ factor(gen)|factor(loc), dat, group=rep, layout=c(1,3), main=\"hadasch.lettuce\") # kinship matrix # head( tcrossprod(as.matrix(datm[,-1])) ) if(require(\"asreml\", quietly=TRUE)){ libs(asreml) dat <- transform(dat, loc=factor(loc), gen=factor(gen), rep=factor(rep)) m1 <- asreml(dmr ~ 1 + gen, data=dat, random = ~ loc + gen:loc + rep:loc) p1 <- predict(m1, classify=\"gen\")$pvals } libs(sommer) m2 <- mmer(dmr ~ 0 + gen, data=dat, random = ~ loc + gen:loc + rep:loc) p2 <- coef(m2) head(p1) head(p2) } # }"},{"path":"/reference/hanks.sprinkler.html","id":null,"dir":"Reference","previous_headings":"","what":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"Three wheat varieties planted 3 blocks, line sprinkler crossing whole plots.","code":""},{"path":"/reference/hanks.sprinkler.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"data frame 108 observations following 7 variables. block block row row subplot column gen genotype, 3 levels yield yield (tons/ha) irr irrigation level, 1..6 dir direction sprinkler, N/S","code":""},{"path":"/reference/hanks.sprinkler.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"line-source sprinkler placed middle experiment (subplots 6 7). Subplots closest sprinkler receive irrigation. Subplots far sprinkler (near edges) lowest yields. One data value modified original (following example authors).","code":""},{"path":"/reference/hanks.sprinkler.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"Hanks, R.J., Sisson, D.V., Hurst, R.L, Hubbard K.G. (1980). Statistical Analysis Results Irrigation Experiments Using Line-Source Sprinkler System. Soil Science Society America Journal, 44, 886-888. https://doi.org/10.2136/sssaj1980.03615995004400040048x","code":""},{"path":"/reference/hanks.sprinkler.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"Johnson, D. E., Chaudhuri, U. N., Kanemasu, E. T. (1983). Statistical Analysis Line-Source Sprinkler Irrigation Experiments Nonrandomized Experiments Using Multivariate Methods. Soil Science Society American Journal, 47, 309-312. Stroup, W. W. (1989). Use Mixed Model Procedure Analyze Spatially Correlated Data: Example Applied Line-Source Sprinkler Irrigation Experiment. Applications Mixed Models Agriculture Related Disciplines, Southern Cooperative Series Bulletin . 343, 104-122. SAS Stat User's Guide. https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_mixed_sect038.htm","code":""},{"path":"/reference/hanks.sprinkler.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wheat yields in a line-source sprinkler experiment — hanks.sprinkler","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hanks.sprinkler) dat <- hanks.sprinkler # The line sprinkler is vertical between subplots 6 & 7 libs(desplot) desplot(dat, yield~subplot*row, out1=block, out2=irr, cex=1, # aspect unknown num=gen, main=\"hanks.sprinkler\") libs(lattice) xyplot(yield~subplot|block, dat, type=c('b'), group=gen, layout=c(1,3), auto.key=TRUE, main=\"hanks.sprinkler\", panel=function(x,y,...){ panel.xyplot(x,y,...) panel.abline(v=6.5, col='wheat') }) ## This is the model from the SAS documentation ## proc mixed; ## class block gen dir irr; ## model yield = gen|dir|irr@2; ## random block block*dir block*irr; ## repeated / type=toep(4) sub=block*gen r; if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) dat <- transform(dat, subf=factor(subplot), irrf=factor(irr)) dat <- dat[order(dat$block, dat$gen, dat$subplot),] # In asreml3, we can specify corb(subf, 3) # In asreml4, only corb(subf, 1) runs. corb(subf, 3) says: # Correlation structure is not positive definite m1 <- asreml(yield ~ gen + dir + irrf + gen:dir + gen:irrf + dir:irrf, data=dat, random= ~ block + block:dir + block:irrf, resid = ~ block:gen:corb(subf, 3)) lucid::vc(m1) ## effect component std.error z.ratio bound ## block 0.2195 0.2378 0.92 P 0.5 ## block:dir 0.01769 0.03156 0.56 P 0 ## block:irrf 0.03539 0.0362 0.98 P 0.1 ## block:gen:subf!R 0.2851 0.05088 5.6 P 0 ## block:gen:subf!subf!cor1 0.02829 0.1142 0.25 U 0.9 ## block:gen:subf!subf!cor2 0.004997 0.1278 0.039 U 9.5 ## block:gen:subf!subf!cor3 -0.3245 0.09044 -3.6 U 0.1 } } # }"},{"path":"/reference/hanover.whitepine.html","id":null,"dir":"Reference","previous_headings":"","what":"Mating crosses of white pine trees — hanover.whitepine","title":"Mating crosses of white pine trees — hanover.whitepine","text":"Mating crosses white pine trees","code":""},{"path":"/reference/hanover.whitepine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mating crosses of white pine trees — hanover.whitepine","text":"","code":"data(\"hanover.whitepine\")"},{"path":"/reference/hanover.whitepine.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mating crosses of white pine trees — hanover.whitepine","text":"data frame 112 observations following 4 variables. rep replicate female female parent male male parent length epicotyl length, cm","code":""},{"path":"/reference/hanover.whitepine.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mating crosses of white pine trees — hanover.whitepine","text":"Four male (pollen parent) White Pine trees mated seven female trees 2654 progeny grown four replications, one plot per mating replication. Parent trees sourced Idaho, USA. data plot means epicotyl length. Becker (1984) used data demonstrate calculation heritability.","code":""},{"path":"/reference/hanover.whitepine.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mating crosses of white pine trees — hanover.whitepine","text":"Hanover, James W Barnes, Burton V. (1962). Heritability height growth year-old western white pine. Proc Forest Genet Workshop. 22, 71–76. Walter . Becker (1984). Manual Quantitative Genetics, 4th ed. Page 83.","code":""},{"path":"/reference/hanover.whitepine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mating crosses of white pine trees — hanover.whitepine","text":"None","code":""},{"path":"/reference/hanover.whitepine.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mating crosses of white pine trees — hanover.whitepine","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hanover.whitepine) dat <- hanover.whitepine libs(lattice) # Relatively high male-female interaction in growth comared # to additive gene action. Response is more consistent within # male progeny than female progeny. # with(dat, interaction.plot(female, male, length)) # with(dat, interaction.plot(male, female, length)) bwplot(length ~ male|female, data=dat, main=\"hanover.whitepine - length for male:female crosses\", xlab=\"Male parent\", ylab=\"Epicotyl length\") # Progeny sums match Becker p 83 sum(dat$length) # 380.58 aggregate(length ~ female + male, data=dat, FUN=sum) # Sum of squares matches Becker p 85 m1 <- aov(length ~ rep + male + female + male:female, data=dat) anova(m1) # Variance components match Becker p. 85 libs(lme4) libs(lucid) m2 <- lmer(length ~ (1|rep) + (1|male) + (1|female) + (1|male:female), data=dat) #as.data.frame(lme4::VarCorr(m2)) vc(m2) ## grp var1 var2 vcov sdcor ## male:female (Intercept) 0.1369 0.3699 ## female (Intercept) 0.02094 0.1447 ## male (Intercept) 0.1204 0.3469 ## rep (Intercept) 0.01453 0.1205 ## Residual 0.2004 0.4477 # Becker used this value for variability between individuals, within plot s2w <- 1.109 # Calculating heritability for individual trees s2m <- .120 s2f <- .0209 s2mf <- .137 vp <- s2m + s2f + s2mf + s2w # variability of phenotypes = 1.3869 4*s2m / vp # heritability male 0.346 4*s2f / vp # heritability female 0.06 2*(s2m+s2f)/vp # heritability male+female .203 # As shown in the boxplot, heritability is stronger through the # males than through the females. } # }"},{"path":"/reference/hansen.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"Multi-year uniformity trial Denmark","code":""},{"path":"/reference/hansen.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"","code":"data(\"hansen.multi.uniformity\")"},{"path":"/reference/hansen.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"data frame 662 observations following 6 variables. field field name year year crop crop yield yield (percent mean) row row col column","code":""},{"path":"/reference/hansen.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"Uniformity trials carried 1906 1911 two fields Aarslev, Denmark. yield values expressed percent mean yield year. scale map Hansen shows \"Alen\" scale. See https://en.wikipedia.org/wiki/Alen_(unit_of_length) Danish alen = 62.77 cm. Field A2: Based map, field approximately 60 alen x 70 alen (38 m x 44 m), orientation field clear. Plots probably circa 7.4 m side. Divided 30 plots – 6 strips 5. crops grown : 1907 oats, 1908 rye, 1909 barley, 1910 mangolds, 1911 barley. Sanders said: appeared two printer errors paper. field A2 yields given 1908 add 3010 instead 3000: reference Fig. 6 given seemed indicate excess lay row 3 eventually decided reduce plots 3c 96 3f 84. Field E2: Field approximately 120 alen x 200 alen (76m x 125m). Plots probably circa 8-9m side. Divided 128 plots: 16 strips 8. Crops grown: 1906 oats, 1907 barley, 1908 seeds, 1909 rye. Sanders said, remarkable oscillation fertility across field E2 one direction, 1st, 3rd, ... 15th strips (columns) consistently giving much higher yields 2nd, 4th, ... 16th strips (columns). fact four years odd numbered strips gave total yield 27,817, compared 23,383 even numbered strips. oscillation apparently arose legacy old practice ploughing high ridges: tops ridges exhibited greater fertility borders furrows, soil worked former latter field leveled . meant site old furrows good depth rich soil, whilst shallow ridges . strips arranged cover site furrow ridge alternately, result noted . Sanders: order escape variation, table condensed taking 2 strips together (new strips included whole one old \"lands\") making 8 8 square. Sanders said: field E2 1908, column 10 sums 791 instead 786 shown: reference Fig. 13 indicated yield plot 10g probably 92 instead 97. version data package uses changes suggested Sanders. Data typed K.Wright.","code":""},{"path":"/reference/hansen.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"Hansen, Niels Anton (1914). Prøvedyrkning paa Forsøgsstationen ved Aarslev. Page 557 field A2. Page 562 field E2. https://dca.au.dk/publikationer/historiske/planteavl","code":""},{"path":"/reference/hansen.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"Eden, T. E. J. Maskell. (1928). influence soil heterogeneity growth yield successive crops. Journal Agricultural Science, 18, 163-185. https://archive.org/stream/.ernet.dli.2015.25895/2015.25895.Journal--Agricultural-Science-Vol-xviii-1928#page/n175 Sanders, H. G. 1930. note value uniformity trials subsequent experiments. Journal Agricultural Science. 20, 63-73. https://dx.doi.org/10.1017/S0021859600088626 https://repository.rothamsted.ac.uk/item/97039/-note---value--uniformity-trials--subsequent-experiments","code":""},{"path":"/reference/hansen.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-year uniformity trial in Denmark — hansen.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hansen.multi.uniformity) dat <- hansen.multi.uniformity # Field A2: Average across years libs(dplyr,reshape2) #dat # Field E2: Match column totals #dat # Heatmaps. Aspect ratio is an educated guess libs(dplyr, desplot) dat <- dat dat dat # Look at correlation of experimental unit plots across years libs(dplyr, reshape2, lattice) dat <- mutate(dat, plot=paste(row,col)) mat1 <- filter(dat, field==\"A2\") splom(mat1, main=\"hansen.multi.uniformity field A2\") mat2 <- filter(dat, field==\"E2\") splom(mat2, main=\"hansen.multi.uniformity field A2\") } # }"},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"Uniformity trial sugar beet Russia.","code":""},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"","code":"data(\"haritonenko.sugarbeet.uniformity\")"},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"data frame 416 observations following 3 variables. row Row ordinate col Column ordinate yield Yield pfund per plot","code":""},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"Roemer (1920) says: Haritonenko (36), experiment Ivanovskoye Agricultural Experimental Station, Novgorod Governorate. test area 5.68 ha 416 sections (plots) 136.5 square meters. Row 1 significantly less soil three rows. Based heatmap, 'Row 1' left column. Roemer p. 63 says: Table 4: Root yield pfund 30 quadratfaden (1.33 x 22.5). use 1 faden = 7 feet, : (1.33 faden * 7 feet) * (22.5 faden * 7 feet) * 416 plots = 609991 sq feet = 5.68 hectares, matches experiment description. 'pfund' (Germany pound) today defined 500g, 1920 might different, perhaps 467g??? Field width: 4 plots * (22.5 faden * 7 feet/faden) = 630 feet. Field length: 104 plots * (1.33 faden * 7 feet/faden) = 968 feet. Note: Cochran says plots 8 x 135 ft. seems based 1 faden = 6 feet, match total area 5.68 ha. Note: name Haritonenko sometimes translated English : Pavel Kharitonenko. data typed K.Wright Roemer (1920), table 4, p. 63.","code":""},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"Haritonenko, Pavlo. Neue Prazisionsmethoden auf den Versuchsfeldern. Arbeiten der landw. Versuchsstation Iwanowskoje 1904-06, S. 159. Russian German summary.","code":""},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"Neyman, J., & Iwaszkiewicz, K. (1935). Statistical problems agricultural experimentation. Supplement Journal Royal Statistical Society, 2(2), 107-180. Roemer, T. (1920). Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ","code":""},{"path":"/reference/haritonenko.sugarbeet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sugar beet — haritonenko.sugarbeet.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(haritonenko.sugarbeet.uniformity) dat <- haritonenko.sugarbeet.uniformity mean(dat$yield) # 615.68. # Roemer page 37 says 617 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(104*1.33*7)/(4*22.5*7), ticks=TRUE, main=\"haritonenko.sugarbeet.uniformity\") } # }"},{"path":"/reference/harris.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","title":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","text":"Uniformity trials multiple crops, Huntley Field Station, Montana, 1911-1925.","code":""},{"path":"/reference/harris.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","text":"data frame 1058 observations following 5 variables. series series (field coordinate) plot plot number (field ordinate) year year, 1911-1925 crop crop yield yield per plot (pounds)","code":""},{"path":"/reference/harris.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","text":"yields given Harris (1920) (Practical universality...) given quarter-plots. yields given Harris (1920) (Permanence ...) yields given Harris (1928) given single plots. Field width: 2 plots * 317 ft + 5 feet alley = 639 feet Field length: 23 plots * 23.3 feet = 536 feet yields given pound per plot. original data Harris (1920) 1911 sugarbeet yields tons/ac, (Harris 1920, table 3 footnote), converted pounds/plot purpose dataset. Harris (1928) shows map location page 16. Harris (1920): 1911: spring 1911 field laid 46 plots, measuring 23.5 317 feet containing 0.17 acre, arranged two parallel series 23 plots . two series plots separated merely temporary irrigation ditch. 1911 planted sugar beets. 1912: spring 1912 seeded alfalfa, one cutting harvested year. stand remained ground 1913 1914, entire field fall-plowed. 1913: Three cuttings made, third cutting lost heavy wind scattered mixed crop weighings various plots made. first cutting, designated alfalfa , made plots one-half original size. second cutting harvested plots one-quarter original size. 1914: first second cuttings 1914 weighed plots one-quarter original size–, 0.0425-acre plots– third cutting recorded plots one-third original size. furnish data alfalfa , II, III 1914. Total yields first second cuttings 1913 1914 first, second, third cuttings 1914 also considered. 1915: Ear corn. 1916: Ear corn. 1917: fields planted oats, records made grain, straw, total yield. 1918: Silage corn grown. 1919: land produced crop barley. 1920: Silage corn 1921 Alfalfa 1922 Alfalfa, cutting 3 1923 Alfalfa, cutting 1 3 1914 Alfalfa, cutting 2 3 Harris (1928): southeast corner Series II, east series, 80 feet main canal, southwest corner Series III 50 feet Ouster Coulee. main project canal carries normally irrigation season 400 second-feet water. water surface canal 4 feet high corner field. evident surface conditions, well borings made canal field, extensive seepage canal subsoil field. volume seepage larger recent years earlier years cropping experiments, probably canal bank worn away internal erosion, exposing stratum sandy subsoil underlies canal part field. Whereas earlier crops Series II better alfalfa, Series III better alfalfa later period. writers feel inclined suggest earlier experiments height water table harmful effect upon deep-rooted crop alfalfa. quite possible drier periods higher water table actually favored alfalfa growth Series II. higher water tables recent years probably deleterious influence, especially marked Series II, water apparently comes nearer surface Series III.","code":""},{"path":"/reference/harris.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","text":"Harris, J Arthur Scofield, CS. (1920). Permanence differences plats experimental field. Jour. Agr. Res, 20, 335-356. https://naldc.nal.usda.gov/catalog/IND43966236 https://www.google.com/books/edition/Journal_of_the_American_Society_of_Agron/Zwz0AAAAMAAJ?hl=en&gbpv=1&pg=PA257 data 1911-1919. Harris, J Arthur Scofield, CS. (1928). studies permanence differences plots experimental field. Jour. Agr. Res, 36, 15–40. https://naldc.nal.usda.gov/catalog/IND43967538 data 1920-1925.","code":""},{"path":"/reference/harris.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials with multiple crops, 15 years on the same land — harris.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(harris.multi.uniformity) dat <- harris.multi.uniformity # Combine year/crop into 'harvest' dat <- transform(dat, harv = factor(paste0(year,\".\",crop))) # Average yields. Harris 1928, table 2. aggregate(yield~harv, dat, mean) # Corrgram libs(reshape2,corrgram) mat <- acast(dat, series+plot~harv, value.var='yield') corrgram(mat, main=\"harris.multi.uniformity - correlation of crop yields\") # Compare to Harris 1928, table 4. More positive than negative correlations. # densityplot(as.vector(cor(mat)), xlab=\"correlations\", # main=\"harris.multi.uniformity\") # Standardize yields for each year mats <- scale(mat) # Melt and re-name columns so we can make field maps. Obvious spatial # patterns that persist over years d2 <- melt(mats) names(d2) <- c('ord','harv','yield') d2$series <- as.numeric(substring(d2$ord,1,1)) d2$plot <- as.numeric(substring(d2$ord,3)) # Series 2 is on the east side, so switch 2 and 3 for correct plotting d2$xord <- 5 - dat$series # Note that for alfalfa, higher-yielding plots in 1912-1914 were # lower-yielding in 1922-1923. # Heatmaps for individual year/harvest combinations libs(desplot) desplot(d2, yield ~ xord*plot|harv, aspect=536/639, flip=TRUE, # true aspect main=\"harris.multi.uniformity\") # Crude fertility map by averaging across years shows probable # sub-surface water effects agg <- aggregate(yield ~ xord + plot, data=d2, mean) desplot(agg, yield ~ xord + plot, aspect=536/639, # true aspect main=\"harris.multi.uniformity fertility\") } # }"},{"path":"/reference/harris.wateruse.html","id":null,"dir":"Reference","previous_headings":"","what":"Water use by horticultural trees — harris.wateruse","title":"Water use by horticultural trees — harris.wateruse","text":"Water use horticultural trees","code":""},{"path":"/reference/harris.wateruse.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Water use by horticultural trees — harris.wateruse","text":"data frame 1040 observations following 6 variables. species species factor, 2 levels age age factor, 2 levels tree tree factor, 40 (non-consecutive) levels day day, numeric water water use, numeric","code":""},{"path":"/reference/harris.wateruse.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Water use by horticultural trees — harris.wateruse","text":"Ten trees four groups (two species, two ages) assessed water usage, approximately every five days. Missing values included benefit asreml, needs 'balanced' data set due kronecker-like syntax R matrix. Used permission Roger Harris Virginia Polytechnic.","code":""},{"path":"/reference/harris.wateruse.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Water use by horticultural trees — harris.wateruse","text":"Schabenberger, Oliver Francis J. Pierce. 2002. Contemporary Statistical Models Plant Soil Sciences. CRC Press. Page 512.","code":""},{"path":"/reference/harris.wateruse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Water use by horticultural trees — harris.wateruse","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(harris.wateruse) dat <- harris.wateruse # Compare to Schabenberger & Pierce, fig 7.23 libs(latticeExtra) useOuterStrips(xyplot(water ~ day|species*age,dat, as.table=TRUE, group=tree, type=c('p','smooth'), main=\"harris.wateruse 2 species, 2 ages (10 trees each)\")) # Note that measurements on day 268 are all below the trend line and # thus considered outliers. Delete them. dat <- subset(dat, day!=268) # Schabenberger figure 7.24 xyplot(water ~ day|tree,dat, subset=age==\"A2\" & species==\"S2\", as.table=TRUE, type=c('p','smooth'), ylab=\"Water use profiles of individual trees\", main=\"harris.wateruse (Age 2, Species 2)\") # Rescale day for nicer output, and convergence issues, add quadratic term dat <- transform(dat, ti=day/100) dat <- transform(dat, ti2=ti*ti) # Start with a subgroup: age 2, species 2 d22 <- droplevels(subset(dat, age==\"A2\" & species==\"S2\")) # ----- Model 1, for subgroup A2,S2 # First, a fixed quadratic that is common to all trees, plus # a random quadratic deviation for each tree. ## Schabenberger, Output 7.26 ## proc mixed; ## class tree; ## model water = ti ti*ti / s; ## random intercept ti ti*ti/subject=tree; libs(nlme,lucid) ## We use pdDiag() to get uncorrelated random effects m1n <- lme(water ~ 1 + ti + ti2, data=d22, na.action=na.omit, random = list(tree=pdDiag(~1+ti+ti2))) # lucid::vc(m1n) ## effect variance stddev ## (Intercept) 0.2691 0.5188 ## ti 0 0.0000144 ## ti2 0 0.0000039 ## Residual 0.1472 0.3837 # Various other models with lme4 & asreml libs(lme4, lucid) m1l <- lmer(water ~ 1 + ti + ti2 + (1|tree) + (0+ti|tree) + (0+ti2|tree), data=d22) # lucid::vc(m1l) ## grp var1 var2 vcov sdcor ## tree (Intercept) 0.2691 0.5188 ## tree.1 ti 0 0 ## tree.2 ti2 0 0 ## Residual 0.1472 0.3837 # Once the overall quadratic trend has been removed, there is not # too much evidence for consecutive observations being correlated ## d22r <- subset(d22, !is.na(water)) ## d22r$res <- resid(m1n) ## xyplot(res ~ day|tree,d22r, ## as.table=TRUE, type=c('p','smooth'), ## ylab=\"residual\", ## main=\"harris.wateruse - Residuals of individual trees\") ## op <- par(mfrow=c(4,3)) ## tapply(d22r$res, d22r$tree, acf) ## par(op) # ----- Model 2, add correlation of consecutive measurements ## Schabenberger (page 516) adds correlation. ## Note how the fixed quadratic model is on the \"ti = day/100\" scale ## and the correlated observations are on the \"day\" scale. The ## only impact this has on the fitted model is to increase the ## correlation parameter by a factor of 100, which was likely ## done to get better convergence. ## proc mixed data=age2sp2; ## class tree; ## model water = ti ti*ti / s ; ## random intercept /subject=tree s; ## repeated /subject=tree type=sp(exp)(day); ## Same as SAS, use ti for quadratic, day for correlation m2l <- lme(water ~ 1 + ti + ti2, data=d22, random = ~ 1|tree, cor = corExp(form=~ day|tree), na.action=na.omit) m2l # Match output 7.27. Same fixef, ranef, variances, exp corr # lucid::vc(m2l) ## effect variance stddev ## (Intercept) 0.2656 0.5154 ## Residual 0.1541 0.3926 # --- ## Now use asreml. When I tried rcov=~tree:exp(ti), ## the estimated parameter value was on the 'boundary', i.e. 0. ## Changing rcov to the 'day' scale produced a sensible estimate ## that matched SAS. ## Note: SAS and asreml use different parameterizations for the correlation ## SAS uses exp(-d/phi) and asreml uses phi^d. ## SAS reports 3.79, asreml reports 0.77, and exp(-1/3.7945) = 0.7683274 ## Note: normally a quadratic would be included as 'pol(day,2)' if(require(\"asreml\", quietly=TRUE)){ libs(asreml) d22 <- d22[order(d22$tree, d22$day),] m2a <- asreml(water ~ 1 + ti + ti2, data=d22, random = ~ tree, residual=~tree:exp(day)) lucid::vc(m2a) ## effect component std.error z.ratio constr ## tree!tree.var 0.2656 0.1301 2 pos ## R!variance 0.1541 0.01611 9.6 pos ## R!day.pow 0.7683 0.04191 18 uncon } # ----- Model 3. Full model for all species/ages. Schabenberger p. 518 ## /* Continuous AR(1) autocorrelations included */ ## proc mixed data=wateruse; ## class age species tree; ## model water = age*species age*species*ti age*species*ti*ti / noint s; ## random intercept ti / subject=age*species*tree s; ## repeated / subject=age*species*tree type=sp(exp)(day); m3l <- lme(water ~ 0 + age:species + age:species:ti + age:species:ti2, data=dat, na.action=na.omit, random = list(tree=pdDiag(~1+ti)), cor = corExp(form=~ day|tree) ) m3l # Match Schabenberger output 7.27. Same fixef, ranef, variances, exp corr # lucid::vc(m3l) ## effect variance stddev ## (Intercept) 0.1549 0.3936 ## ti 0.02785 0.1669 ## Residual 0.16 0.4 # --- asreml if(require(\"asreml\", quietly=TRUE)){ dat <- dat[order(dat$tree,dat$day),] m3a <- asreml(water ~ 0 + age:species + age:species:ti + age:species:ti2, data=dat, random = ~ age:species:tree + age:species:tree:ti, residual = ~ tree:exp(day) ) # lucid::vc(m3a) # Note: day.pow = .8091 = exp(-1/4.7217) ## effect component std.error z.ratio constr ## age:species:tree!age.var 0.1549 0.07192 2.2 pos ## age:species:tree:ti!age.var 0.02785 0.01343 2.1 pos ## R!variance 0.16 0.008917 18 pos ## R!day.pow 0.8091 0.01581 51 uncon } } # }"},{"path":"/reference/harrison.priors.html","id":null,"dir":"Reference","previous_headings":"","what":"Ranges of analytes in soybean from other authors — harrison.priors","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"Ranges analytes soybean authors","code":""},{"path":"/reference/harrison.priors.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"data frame 80 observations following 5 variables. source Source document substance Analyte substance min minimum amount (numeric) max maximum analyte amount (numeric) number number substances","code":""},{"path":"/reference/harrison.priors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"Harrison et al. show construct informative Bayesian prior previously-published ranges concentration several analytes. units daidzein, genistein, glycitein micrograms per gram. raffinose stachyose units converted common 'percent' scale. author names 'source' variable shortened forms citations supplemental information Harrison et al.","code":""},{"path":"/reference/harrison.priors.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"Jay M. Harrison, Matthew L. Breeze, Kristina H. Berman, George G. Harrigan. 2013. Bayesian statistical approaches compositional analyses transgenic crops 2. Application validation informative prior distributions. Regulatory Toxicology Pharmacology, 65, 251-258. https://doi.org/10.1016/j.yrtph.2012.12.002 Data retrieved Supplemental Information source.","code":""},{"path":"/reference/harrison.priors.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"Jay M. Harrison, Derek Culp, George G. Harrigan. 2013. Bayesian MCMC analyses regulatory assessments safety food composition Proceedings 24th Conference Applied Statistics Agriculture (2012).","code":""},{"path":"/reference/harrison.priors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ranges of analytes in soybean from other authors — harrison.priors","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(harrison.priors) dat <- harrison.priors d1 <- subset(dat, substance==\"daidzein\") # Stack the data to 'tall' format and calculate empirical cdf d1t <- with(d1, data.frame(xx = c(min, max), yy=c(1/(number+1), number/(number+1)))) # Harrison 2012 Example 4: Common prior distribution # Harrison uses the minimum and maximum levels of daidzein from previous # studies as the first and last order statistics of a lognormal # distribution, and finds the best-fit lognormal distribution. m0 <- mean(log(d1t$xx)) # 6.37 s0 <- sd(log(d1t$xx)) # .833 mod <- nls(yy ~ plnorm(xx, meanlog, sdlog), data=d1t, start=list(meanlog=m0, sdlog=s0)) coef(mod) # Matches Harrison 2012 ## meanlog sdlog ## 6.4187829 0.6081558 plot(yy~xx, data=d1t, xlim=c(0,2000), ylim=c(0,1), main=\"harrison.priors - Common prior\", xlab=\"daidzein level\", ylab=\"CDF\") mlog <- coef(mod)[1] # 6.4 slog <- coef(mod)[2] # .61 xvals <- seq(0, 2000, length=100) lines(xvals, plnorm(xvals, meanlog=mlog, sdlog=slog)) d1a <- d1 d1a$source <- as.character(d1a$source) d1a[19,'source'] <- \"(All)\" # Add a blank row for the densitystrip d1 libs(latticeExtra) # Plot the range for each source, a density curve (with arbitary # vertical scale) for the common prior distribution, and a density # strip by stacking the individual bands and using transparency segplot(factor(source) ~ min+max, d1a, main=\"harrison.priors\",xlab=\"daidzein level\",ylab=\"source\") + xyplot(5000*dlnorm(xvals, mlog, slog)~xvals, type='l') + segplot(factor(rep(1,18)) ~ min+max, d1, 4, level=d1$number, col.regions=\"gray20\", alpha=.1) } # }"},{"path":"/reference/hartman.tomato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of tomato — hartman.tomato.uniformity","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"Uniformity trial tomato Indiana","code":""},{"path":"/reference/hartman.tomato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"","code":"data(\"hartman.tomato.uniformity\")"},{"path":"/reference/hartman.tomato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"data frame 384 observations following 3 variables. row row col column yield yield, pounds per plot","code":""},{"path":"/reference/hartman.tomato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"Grown Indiana 1941. column ordinates R package dataset quite exactly field due presence roads. Plants spaced 3 feet apart rows 6 feet apart, 330 feet long. row divided 3 sections 34 plants sparated strips 12 feet long provide roadways vehicles. row divided 4-plant plots, 8 plots section row one plant left guard end section. 49 plants missing 3072 total plants, ignored. Note, data given Table 1 Hartman 8-plant plots! Field width: 3 sections (34 plants * 3 feet) + 2 roads * 12 feet = 330 feet. Field length: 32 rows * 6 feet = 192 feet oriented page, plots , average, 330/12=27.5. feet wide, 6 feet tall. Discussion notes Hartman. Total yield 26001 pounds. Hartman says yield field 10.24 tons per acre, can verify: 26001 lb/field * (1/384 field/plot) * (1/(24*6) plot/ft2) * (43560 ft2/acre) * (1/2000 tons/lb) = 10.24 tons/acre rows top/bottom (north/south) intended guard rows, yields similar rows, suggesting competition rows exist. comparing varieties, 96*6 foot plots work well.","code":""},{"path":"/reference/hartman.tomato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"J. D. Hartman E. C. Stair (1942). Field Plot Technique Tomatoes. Proceedings American Society Horticultural Science, 41, 315-320. https://archive.org/details/.ernet.dli.2015.240678","code":""},{"path":"/reference/hartman.tomato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"None","code":""},{"path":"/reference/hartman.tomato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of tomato — hartman.tomato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hartman.tomato.uniformity) libs(desplot) desplot(hartman.tomato.uniformity, yield ~ col*row, flip=TRUE, tick=TRUE, aspect=192/330, # true aspect main=\"hartman.tomato.uniformity\") } # }"},{"path":"/reference/harvey.lsmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"Average daily gain 65 steers 3 lines, 9 sires.","code":""},{"path":"/reference/harvey.lsmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"","code":"data(\"harvey.lsmeans\")"},{"path":"/reference/harvey.lsmeans.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"data frame 65 observations following 7 variables. line line dam sire sire damage age class dam calf calf number weanage calf age weaning weight calf weight start feeding adg average daily gain","code":""},{"path":"/reference/harvey.lsmeans.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"average daily gain 'adg' 65 Hereford steers. calf age weaning initial weight beginning test feeding also given. steers fed length time feed lot. assumed calf unique dam twins repeat matings. Harvey (1960) one earliest papers presenting least squares means (lsmeans).","code":""},{"path":"/reference/harvey.lsmeans.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"Harvey, Walter R. (1960). Least-squares Analysis Data Unequal Subclass Numbers. Technical Report ARS 20-8. USDA, Agricultural Research Service. Page 101-102. Reprinted ARS H-4, 1975. https://archive.org/details/leastsquaresanal04harv","code":""},{"path":"/reference/harvey.lsmeans.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"Also appears 'dmm' package 'harv101.df' See package vignette complete analysis data.","code":""},{"path":"/reference/harvey.lsmeans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average daily gain of 65 steers for 3 lines, 9 sires. — harvey.lsmeans","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(harvey.lsmeans) dat = harvey.lsmeans libs(lattice) dotplot(adg ~ sire|line,dat, main=\"harvey.lsmeans\", xlab=\"sire\", ylab=\"average daily gain\") # Model suggested by Harvey on page 103 m0 <- lm(adg ~ 1 + line + sire + damage + line:damage + weanage + weight, data=dat) # Due to contrast settings, it can be hard to compare model coefficients to Harvey, # but note the slopes of the continuous covariates match Harvey p. 107, where his # b is weanage, d is weight # coef(m0) # weanage weight # -0.008154879 0.001970446 # A quick attempt to reproduce table 4 of Harvey, p. 109. Not right. # libs(emmeans) # emmeans(m0,c('line','sire','damage')) } # }"},{"path":"/reference/harville.lamb.html","id":null,"dir":"Reference","previous_headings":"","what":"Birth weight of lambs from different lines/sires — harville.lamb","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"Birth weight lambs different lines/sires","code":""},{"path":"/reference/harville.lamb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"","code":"data(\"harville.lamb\")"},{"path":"/reference/harville.lamb.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"data frame 62 observations following 4 variables. line genotype line number sire sire number damage dam age, class 1,2,3 weight lamb birth weight","code":""},{"path":"/reference/harville.lamb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"Weight birth 62 lambs. 5 distinct lines. sires multiple lambs. dam one lamb. age dam category: 1 (1-2 years), 2 (2-3 years) 3 (3 years). Note: Jiang, gives data table 1.2, small error. Jiang weight 9.0 sire 31, line 3, age 3. correct value 9.5.","code":""},{"path":"/reference/harville.lamb.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"David . Harville Alan P. Fenech (1985). Confidence Intervals Variance Ratio, Heritability, Unbalanced Mixed Linear Model. Biometrics, 41, 137-152. https://doi.org/10.2307/2530650","code":""},{"path":"/reference/harville.lamb.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"Jiming Jiang, Linear Generalized Linear Mixed Models Applications. Table 1.2. Andre . Khuri, Linear Model Methodology. Table 11.5. Page 368. https://books.google.com/books?id=UfDvCAAAQBAJ&pg=PA164 Daniel Gianola, Keith Hammond. Advances Statistical Methods Genetic Improvement Livestock. Table 8.1, page 165.","code":""},{"path":"/reference/harville.lamb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Birth weight of lambs from different lines/sires — harville.lamb","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(harville.lamb) dat <- harville.lamb dat <- transform(dat, line=factor(line), sire=factor(sire), damage=factor(damage)) library(lattice) bwplot(weight ~ line, dat, main=\"harville.lamb\", xlab=\"line\", ylab=\"birth weights\") if(0){ libs(lme4, lucid) m1 <- lmer(weight ~ -1 + line + damage + (1|sire), data=dat) summary(m1) vc(m1) # Khuri reports variances 0.5171, 2.9616 ## grp var1 var2 vcov sdcor ## sire (Intercept) 0.5171 0.7191 ## Residual 2.962 1.721 } } # }"},{"path":"/reference/hayman.tobacco.html","id":null,"dir":"Reference","previous_headings":"","what":"Diallel cross of Aztec tobacco — hayman.tobacco","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"Diallel cross Aztec tobacco 2 reps","code":""},{"path":"/reference/hayman.tobacco.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"year year block block factor, 2 levels male male parent, 8 levels female female parent day mean flowering time (days)","code":""},{"path":"/reference/hayman.tobacco.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"Data collected 1951 (Hayman 1954a) 1952 (Hayman 1954b). year 8 varieties Aztec tobacco, Nicotiana rustica L.. cross/self represented 10 progeny, two plots 5 plants . data mean flowering time per plot. Note, 1951 data published Hayman (1954a) Table 5 contain \"10 times mean flowering time\". data divided 10 comparable 1952 data. Hayman (1954b) says \"Table 2 lists...three characters diallel cross Nicotiana rustica varieties repeated three years.\" seems indicate varieties 1951 1952. Calculating GCA effects separately 1951 1952 comparing estimates shows highly correlated.","code":""},{"path":"/reference/hayman.tobacco.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"B. . Hayman (1954a). Analysis Variance Diallel Tables. Biometrics, 10, 235-244. Table 5, page 241. https://doi.org/10.2307/3001877 Hayman, B.. (1954b). theory analysis diallel crosses. Genetics, 39, 789-809. Table 3, page 805. https://www.genetics.org/content/39/6/789.full.pdf","code":""},{"path":"/reference/hayman.tobacco.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"# 1951 data Mohring, Melchinger, Piepho. (2011). REML-Based Diallel Analysis. Crop Science, 51, 470-478. # 1952 data C. Clark Cockerham B. S. Weir. (1977). Quadratic analyses reciprocal crosses. Biometrics, 33, 187-203. Appendix C. Andrea Onofri, Niccolo Terzaroli, Luigi Russi (2020). Linear models diallel crosses: review R functions. Theoretical Applied Genetics. https://doi.org/10.1007/s00122-020-03716-8","code":""},{"path":"/reference/hayman.tobacco.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diallel cross of Aztec tobacco — hayman.tobacco","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) # 1951 data. Fit the first REML model of Mohring 2011 Supplement. data(hayman.tobacco) dat1 <- subset(hayman.tobacco, year==1951) # Hayman's model # dat1 <- subset(hayman.tobacco, year==1951) # libs(lmDiallel) # m1 <- lm.diallel(day ~ male+female, Block=block, data=dat1, fct=\"HAYMAN2\") # anova(m1) # Similar to table 7 of Hayman 1954a ## Response: day ## Df Sum Sq Mean Sq F value Pr(>F) ## Block 1 1.42 1.42 0.3416 0.56100 ## Mean Dom. Dev. 1 307.97 307.97 73.8840 3.259e-12 *** ## GCA 7 2777.17 396.74 95.1805 < 2.2e-16 *** ## Dom. Dev. 7 341.53 48.79 11.7050 1.957e-09 *** ## SCA 20 372.89 18.64 4.4729 2.560e-06 *** ## RGCA 7 67.39 9.63 2.3097 0.03671 * ## RSCA 21 123.73 5.89 1.4135 0.14668 ## Residuals 63 262.60 # Griffing's model # https://www.statforbiology.com/2021/stat_met_diallel_griffing/ # dat1 <- subset(hayman.tobacco, year==1951) # libs(lmDiallel) # contrasts(dat1$block) <- \"contr.sum\" # dmod1 and dmod2 are the same model with different syntax # dmod1 <- lm(day ~ block + GCA(male, female) + tSCA(male, female) + # REC(male, female) , data = dat1) # dmod2 <- lm.diallel(day ~ male + female, Block=block, # data = dat1, fct = \"GRIFFING1\") # anova(dmod1) # anova(dmod2) ## Response: day ## Df Sum Sq Mean Sq F value Pr(>F) ## Block 1 1.42 1.42 0.3416 0.56100 ## GCA 7 2777.17 396.74 95.1805 < 2.2e-16 *** ## SCA 28 1022.38 36.51 8.7599 6.656e-13 *** ## Reciprocals 28 191.12 6.83 1.6375 0.05369 . ## Residuals 63 262.60 # Make a factor 'comb' in which G1xG2 is the same cross as G2xG1 dat1 <- transform(dat1, comb = ifelse(as.character(male) < as.character(female), paste0(male,female), paste0(female,male))) # 'dr' is the direction of the cross, 0 for self dat1$dr <- 1 dat1 <- transform(dat1, dr = ifelse(as.character(male) < as.character(female), -1, dr)) dat1 <- transform(dat1, dr = ifelse(as.character(male) == as.character(female), 0, dr)) # asreml r version 3 & 4 code for Mixed Griffing. # Mohring Table 2, column 2 (after dividing by 10^2) gives variances: # GCA 12.77, SCA 11.09, RSCA .65, Error 4.23. # Mohring Supplement ASREML code part1 model is: # y ~ mu r !r mother and(father) combination combination.dr # Note that the levels of 'male' and 'female' are the same, so the # and(female) term tells asreml to use the same levels (or, equivalently, # fix the correlation of the male/female levels to be 1. # The block effect is minimial and therefore ignored. ## libs(asreml, lucid) ## m1 <- asreml(day~1, data=dat1, ## random = ~ male + and(female) + comb + comb:dr) ## vc(m1) ## effect component std.error z.ratio con ## male!male.var 12.77 7.502 1.7 Positive ## comb!comb.var 11.11 3.353 3.3 Positive ## comb:dr!comb.var 0.6603 0.4926 1.3 Positive ## R!variance 4.185 0.7449 5.6 Positive # ---------- # 1952 data. Reproduce table 3 and figure 2 of Hayman 1954b. dat2 <- subset(hayman.tobacco, year==1952) # Does flowering date follow a gamma distn? Maybe. libs(lattice) densityplot(~day, data=dat2, main=\"hayman.tobacco\", xlab=\"flowering date\") d1 <- subset(dat2, block=='B1') d2 <- subset(dat2, block=='B2') libs(reshape2) m1 <- acast(d1, male~female, value.var='day') m2 <- acast(d2, male~female, value.var='day') mn1 <- (m1+t(m1))/2 mn2 <- (m2+t(m2))/2 # Variance and covariance of 'rth' offspring vr1 <- apply(mn1, 1, var) vr2 <- apply(mn2, 1, var) wr1 <- apply(mn1, 1, cov, diag(mn1)) wr2 <- apply(mn2, 1, cov, diag(mn2)) # Remove row names to prevent a mild warning rownames(mn1) <- rownames(mn2) <- NULL summ <- data.frame(rbind(mn1,mn2)) summ$block <- rep(c('B1','B2'), each=8) summ$vr <- c(vr1,vr2) summ$wr <- c(wr1,wr2) summ$male <- rep(1:8,2) # Vr and Wr match Hayman table 3 with(summ, plot(wr~vr, type='n', main=\"hayman.tobacco\")) with(summ, text(vr, wr, male)) # Match Hayman figure 2 abline(0,1,col=\"gray\") # Hayman notes that 1 and 3 do not lie along the line, # so modifies them and re-analyzes. } # }"},{"path":"/reference/hazell.vegetables.html","id":null,"dir":"Reference","previous_headings":"","what":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"Gross profit 4 vegetable crops 6 years","code":""},{"path":"/reference/hazell.vegetables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"","code":"data(\"hazell.vegetables\")"},{"path":"/reference/hazell.vegetables.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"data frame 6 observations following 5 variables. year year factor, 6 levels carrot Carrot profit, dollars/acre celery Celery profit, dollars/acre cucumber Cucumber profit, dollars/acre pepper Pepper profit, dollars/acre","code":""},{"path":"/reference/hazell.vegetables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"values table gross profits (loss) dollars per acre. criteria example (1) total acres < 200, (2) total labor < 10000, (3) crop rotation. example shows use linear programming maximize expected profit.","code":""},{"path":"/reference/hazell.vegetables.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"P.B.R. Hazell, (1971). linear alternative quadratic semivariance programming farm planning uncertainty. . J. Agric. Econ., 53, 53-62. https://doi.org/10.2307/3180297","code":""},{"path":"/reference/hazell.vegetables.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"Carlos Romero, Tahir Rehman. (2003). Multiple Criteria Analysis Agricultural Decisions. Elsevier.","code":""},{"path":"/reference/hazell.vegetables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gross profit for 4 vegetable crops in 6 years — hazell.vegetables","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hazell.vegetables) dat <- hazell.vegetables libs(lattice) xyplot(carrot+celery+cucumber+pepper ~ year,dat, ylab=\"yearly profit by crop\", type='b', auto.key=list(columns=4), panel.hline=0) # optimal strategy for planting crops (calculated below) dat2 <- apply(dat[,-1], 1, function(x) x*c(0, 27.5, 100, 72.5))/1000 colnames(dat2) <- rownames(dat) barplot(dat2, legend.text=c(\" 0 carrot\", \"27.5 celery\", \" 100 cucumber\", \"72.5 pepper\"), xlim=c(0,7), ylim=c(-5,120), col=c('orange','green','forestgreen','red'), xlab=\"year\", ylab=\"Gross profit, $1000\", main=\"hazell.vegetables - retrospective profit from optimal strategy\", args.legend=list(title=\"acres, crop\")) libs(linprog) # colMeans(dat[ , -1]) # 252.8333 442.6667 283.8333 515.8333 # cvec = avg across-years profit per acre for each crop cvec <- c(253, 443, 284, 516) # Maximize c'x for Ax=b A <- rbind(c(1,1,1,1), c(25,36,27,87), c(-1,1,-1,1)) colnames(A) <- names(cvec) <- c(\"carrot\",\"celery\",\"cucumber\",\"pepper\") rownames(A) <- c('land','labor','rotation') # bvec criteria = (1) total acres < 200, (2) total labor < 10000, # (3) crop rotation. bvec <- c(200,10000,0) const.dir <- c(\"<=\",\"<=\",\"<=\") m1 <- solveLP(cvec, bvec, A, maximum=TRUE, const.dir=const.dir, lpSolve=TRUE) # m1$solution # optimal number of acres for each crop # carrot celery cucumber pepper # 0.00000 27.45098 100.00000 72.54902 # Average income for this plan ## sum(cvec * m1$solution) ## [1] 77996.08 # Year-to-year income for this plan ## as.matrix(dat[,-1]) ## [,1] ## [1,] 80492.16 ## [2,] 80431.37 ## [3,] 81884.31 ## [4,] 106868.63 ## [5,] 37558.82 ## [6,] 80513.73 # optimum allocation that minimizes year-to-year income variability. # brute-force search # For generality, assume we have unequal probabilities for each year. probs <- c(.15, .20, .20, .15, .15, .15) # Randomly allocate crops to 200 acres, 100,000 times #set.seed(1) mat <- matrix(runif(4*100000), ncol=4) mat <- 200*sweep(mat, 1, rowSums(mat), \"/\") # each row is one strategy, showing profit for each of the six years # profit <- mat profit <- tcrossprod(mat, as.matrix(dat[,-1])) # Each row is profit, columns are years # calculate weighted variance using year probabilities wtvar <- apply(profit, 1, function(x) cov.wt(as.data.frame(x), wt=probs)$cov) # five best planting allocations that minimizes the weighted variance ix <- order(wtvar)[1:5] mat[ix,] ## carrot celery cucumber pepper ## [,1] [,2] [,3] [,4] ## [1,] 71.26439 28.09259 85.04644 15.59657 ## [2,] 72.04428 27.53299 84.29760 16.12512 ## [3,] 72.16332 27.35147 84.16669 16.31853 ## [4,] 72.14622 29.24590 84.12452 14.48335 ## [5,] 68.95226 27.39246 88.61828 15.03700 } # }"},{"path":"/reference/heady.fertilizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"Yield corn, alfalfa, clover two fertilizers","code":""},{"path":"/reference/heady.fertilizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"","code":"data(\"heady.fertilizer\")"},{"path":"/reference/heady.fertilizer.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"data frame 81 observations following 3 variables. crop crop rep replicate (block) P phosphorous, pounds/acre K potassium, pounds/acre N nitrogen, pounds/acre yield yield","code":""},{"path":"/reference/heady.fertilizer.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"Heady et al. fit two-variable semi-polynomial response surfaces crop. Clover alfalfa yields tons/acre. clover alfalfa experiments grown 1952. Corn yields given bu/acre. corn experiments grown 1952 1953. test plots used 1953 1952, fertilizer applied 1953–response yield due residual fertilizer 1952. experiments used incomplete factorial design. treatment combinations present.","code":""},{"path":"/reference/heady.fertilizer.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"Earl O. Heady, John T. Pesek, William G. Brown. (1955). Crop Response Surfaces Economic Optima Fertilizer Use. Agricultural Experiment Station, Iowa State College. Research bulletin 424. Pages 330-332. https://lib.dr.iastate.edu/cgi/viewcontent.cgi?filename=12&article=1032&context=ag_researchbulletins&type=additional","code":""},{"path":"/reference/heady.fertilizer.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"Pesek, John Heady, Earl O. 1956. two nutrient-response function determination economic optima rate grade fertilizer alfalfa. Soil Science Society America Journal, 20, 240-246. https://doi.org/10.2136/sssaj1956.03615995002000020025x","code":""},{"path":"/reference/heady.fertilizer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield of corn, alfalfa, clover with two fertilizers — heady.fertilizer","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(heady.fertilizer) dat <- heady.fertilizer libs(lattice) xyplot(yield ~ P|crop, data=dat, scales=list(relation=\"free\"), groups=factor(paste(dat$N,dat$K)), auto.key=list(columns=5), main=\"heady.fertilizer\", xlab=\"Phosphorous\") # Corn. Matches Heady, p. 292 d1 <- subset(dat, crop==\"corn\") m1 <- lm(yield ~ N + P + sqrt(N) + sqrt(P) + sqrt(N*P), data=d1) summary(m1) # Alfalfa. Matches Heady, p. 292. Also Pesek equation 3, p. 241 d2 <- subset(dat, crop==\"alfalfa\") m2 <- lm(yield ~ K + P + sqrt(K) + sqrt(P) + sqrt(K*P), data=d2) summary(m2) ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1.8735521 0.1222501 15.326 < 2e-16 *** ## K -0.0013943 0.0007371 -1.891 0.061237 . ## P -0.0050195 0.0007371 -6.810 5.74e-10 *** ## sqrt(K) 0.0617458 0.0160142 3.856 0.000196 *** ## sqrt(P) 0.1735383 0.0160142 10.837 < 2e-16 *** ## sqrt(K * P) -0.0014402 0.0007109 -2.026 0.045237 * # Clover. Matches Heady, p. 292. d3 <- subset(dat, crop==\"clover\") m3 <- lm(yield ~ P + sqrt(K) + sqrt(P) + sqrt(K*P), data=d3) summary(m3) # Corn with residual fertilizer. Matches Heady eq 56, p. 322. d4 <- subset(dat, crop==\"corn2\") m4 <- lm(yield ~ N + P + sqrt(N) + sqrt(P) + sqrt(N*P), data=d4) summary(m4) libs(rgl) with(d1, plot3d(N,P,yield)) with(d2, plot3d(K,P,yield)) with(d3, plot3d(K,P,yield)) with(d4, plot3d(N,P,yield)) # Mostly linear in both N and P close3d() } # }"},{"path":"/reference/heath.cabbage.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cabbage. — heath.cabbage.uniformity","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"Uniformity trial cabbage.","code":""},{"path":"/reference/heath.cabbage.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"","code":"data(\"heath.cabbage.uniformity\")"},{"path":"/reference/heath.cabbage.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"data frame 48 observations following 3 variables. yield pounds per plot col column row row","code":""},{"path":"/reference/heath.cabbage.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"Heath says plot .011 acres. acre 43560 sq ft, plot 479.16 sq feet, rounds 480 sq feet. Heath Figure 3-1 correctly shaped, plot approximately 12 feet x 40 feet = 480 sq ft. plot \"350\" plants. Harvested 1958.","code":""},{"path":"/reference/heath.cabbage.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"O.V.S. Heath (1970). Investigation Experiment. Fig. 3-1, p. 50. https://archive.org/details/investigationbye0000heat","code":""},{"path":"/reference/heath.cabbage.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"None.","code":""},{"path":"/reference/heath.cabbage.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cabbage. — heath.cabbage.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(heath.cabbage.uniformity) dat <- heath.cabbage.uniformity # Heath Fig 3-1, p. 50 libs(desplot) desplot(dat, yield ~ col*row, aspect=(8*12)/(6*40), main=\"heath.cabbage.uniformity\") } # }"},{"path":"/reference/heath.raddish.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of radish — heath.radish.uniformity","title":"Uniformity trial of radish — heath.radish.uniformity","text":"Uniformity trial radish four containers.","code":""},{"path":"/reference/heath.raddish.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of radish — heath.radish.uniformity","text":"","code":"data(\"heath.radish.uniformity\")"},{"path":"/reference/heath.raddish.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of radish — heath.radish.uniformity","text":"data frame 400 observations following 4 variables. row row col column block block yield weight per plant, grams","code":""},{"path":"/reference/heath.raddish.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of radish — heath.radish.uniformity","text":"Weight 399 radish plants grown 1 inch x 1 inch spacing four plastic basins. Seed wetted 1968-02-15, planted 1968-02-17, harvested 1968-03-26. Heath said, large plants round edges...one important source variation might competition light.","code":""},{"path":"/reference/heath.raddish.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of radish — heath.radish.uniformity","text":"O.V.S. Heath (1970). Investigation Experiment. Table 1, p 24-25. https://archive.org/details/investigationbye0000heat","code":""},{"path":"/reference/heath.raddish.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of radish — heath.radish.uniformity","text":"None","code":""},{"path":"/reference/heath.raddish.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of radish — heath.radish.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ require(agridat) data(heath.radish.uniformity) dat <- heath.radish.uniformity libs(desplot, dplyr) desplot(dat, yield ~ col*row|block, aspect=1, main=\"heath.radish.uniformity\") # Indicator for border/interior plants dat <- mutate(dat, inner = row > 1 & row < 10 & col > 1 & col < 10) # Heath has 5.80 and 9.63 (we assume this is a typo of 9.36) dat <- group_by(dat, inner) summarize(dat, mean=mean(yield, na.rm=TRUE)) # Interior plots are significantly lower yielding anova(aov(yield ~ block + inner, dat)) # lattice::bwplot(yield ~ inner, dat, horiz=0) # similar to Heath fig 2-2 # lattice::histogram( ~ yield|inner, dat, layout=c(1,2), n=20) } # }"},{"path":"/reference/henderson.milkfat.html","id":null,"dir":"Reference","previous_headings":"","what":"Milk fat yields for a single cow — henderson.milkfat","title":"Milk fat yields for a single cow — henderson.milkfat","text":"Average daily fat yields (kg/day) milk single cow 35 weeks.","code":""},{"path":"/reference/henderson.milkfat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Milk fat yields for a single cow — henderson.milkfat","text":"data frame 35 observations following 2 variables. week week, numeric yield yield, kg/day","code":""},{"path":"/reference/henderson.milkfat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Milk fat yields for a single cow — henderson.milkfat","text":"Charles McCulloch. Workshop Generalized Linear Mixed Models. Used permission Charles McCulloch Harold Henderson.","code":""},{"path":"/reference/henderson.milkfat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Milk fat yields for a single cow — henderson.milkfat","text":"None.","code":""},{"path":"/reference/henderson.milkfat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Milk fat yields for a single cow — henderson.milkfat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(henderson.milkfat) dat <- henderson.milkfat plot(yield~week, data=dat, cex = 0.8, ylim=c(0,.9), main=\"henderson.milkfat\", xlab = \"Week\", ylab = \"Fat yield (kg/day)\") # Yield ~ a * t^b * exp(g*t) # where t is time m1 <- nls(yield ~ alpha * week^beta * exp(gamma * week), data=dat, start=list(alpha=.1, beta=.1, gamma=.1)) # Or, take logs and fit a linear model # log(yield) ~ log(alpha) + beta*log(t) + gamma*t m2 <- lm(log(yield) ~ 1 + log(week) + week, dat) # Or, use glm and a link to do the transform m3 <- glm(yield ~ 1 + log(week) + week, quasi(link = \"log\"), dat) # Note: m2 has E[log(y)] = log(alpha) + beta*log(t) + gamma*t # and m3 has log(E[y]) = log(alpha) + beta*log(t) + gamma*t # Generalized additive models libs(\"mgcv\") m4 <- gam(log(yield) ~ s(week), gaussian, dat) m5 <- gam(yield ~ s(week), quasi(link = \"log\"), dat) # Model predictions pdat <- data.frame(week = seq(1, 35, by = 0.1)) pdat <- transform(pdat, p1 = predict(m1, pdat), p2 = exp(predict(m2, pdat)), # back transform p3 = predict(m3, pdat, type=\"resp\"), # response scale p4 = exp(predict(m4, pdat)), p5 = predict(m5, pdat, type=\"response\")) # Compare fits with(pdat, { lines(week, p1) lines(week, p2, col = \"red\", lty=\"dotted\") lines(week, p3, col = \"red\", lty=\"dashed\") lines(week, p4, col = \"blue\", lty = \"dashed\") lines(week, p5, col = \"blue\") }) legend(\"topright\", c(\"obs\", \"lm, log-transformed\", \"glm, log-link\", \"gam, log-transformed\", \"gam, log-link\"), lty = c(\"solid\", \"dotted\", \"dashed\", \"dashed\", \"solid\"), col = c(\"black\", \"red\", \"red\", \"blue\", \"blue\"), cex = 0.8, bty = \"n\") } # }"},{"path":"/reference/hernandez.nitrogen.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","title":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","text":"Corn response nitrogen fertilizer 5 sites.","code":""},{"path":"/reference/hernandez.nitrogen.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","text":"data frame 136 observations following 5 variables. site site factor, 5 levels loc location name rep rep, 4 levels nitro nitrogen, kg/ha yield yield, Mg/ha","code":""},{"path":"/reference/hernandez.nitrogen.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","text":"Experiment conducted 2006 5 sites Minnesota.","code":""},{"path":"/reference/hernandez.nitrogen.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","text":"Hernandez, J.. Mulla, D.J. 2008. Estimating uncertainty economically optimum fertilizer rates, Agronomy Journal, 100, 1221-1229. https://doi.org/10.2134/agronj2007.0273 Electronic data kindly supplied Jose Hernandez.","code":""},{"path":"/reference/hernandez.nitrogen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn with nitrogen fertilizer at 5 sites. — hernandez.nitrogen","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hernandez.nitrogen) dat <- hernandez.nitrogen cprice <- 118.1 # $118.1/Mg or $3/bu nprice <- 0.6615 # $0.66/kg N or $0.30/lb N # Hernandez optimized yield with a constraint on the ratio of the prices. # Simpler to just calculate the income and optimize that. dat <- transform(dat, inc = yield * cprice - nitro * nprice) libs(lattice) xyplot(inc ~ nitro|site, dat, groups=rep, auto.key=list(columns=4), xlab=\"nitrogen\", ylab=\"income\", main=\"hernandez.nitrogen\") # Site 5 only dat1 <- subset(dat, site=='S5') # When we optimize on income, a simple quadratic model works just fine, # and matches the results of the nls model below. # Note, 'poly(nitro)' gives weird coefs lm1 <- lm(inc ~ 1 + nitro + I(nitro^2), data=dat1) c1 <- coef(lm1) -c1[2] / (2*c1[3]) ## nitro ## 191.7198 # Optimum nitrogen is 192 for site 5 # Use the delta method to get a conf int libs(\"car\") del1 <- deltaMethod(lm1, \"-b1/(2*b2)\", parameterNames= paste(\"b\", 0:2, sep=\"\")) # Simple Wald-type conf int for optimum del1$Est + c(-1,1) * del1$SE * qt(1-.1/2, nrow(dat1)-length(coef(lm1))) ## 118.9329 264.5067 # Nonlinear regression # Reparameterize b0 + b1*x + b2*x^2 using th2 = -b1/2b2 so that th2 is optimum nls1 <- nls(inc ~ th11- (2*th2*th12)*nitro + th12*nitro^2, data = dat1, start = list(th11 = 5, th2 = 150, th12 =-0.1),) summary(nls1) # Wald conf int wald <- function(object, alpha=0.1){ nobs <- length(resid(object)) npar <- length(coef(object)) est <- coef(object) stderr <- summary(object)$parameters[,2] tval <- qt(1-alpha/2, nobs-npar) ci <- cbind(est - tval * stderr, est + tval * stderr) colnames(ci) <- paste(round(100*c(alpha/2, 1-alpha/2), 1), \"pct\", sep= \"\") return(ci) } round(wald(nls1),2) ## 5 ## th11 936.44 1081.93 ## th2 118.93 264.51 # th2 is the optimum ## th12 -0.03 -0.01 # Likelihood conf int libs(MASS) round(confint(nls1, \"th2\", level = 0.9),2) ## 5 ## 147.96 401.65 # Bootstrap conf int libs(boot) dat1$fit <- fitted(nls1) bootfun <- function(rs, i) { # bootstrap the residuals dat1$y <- dat1$fit + rs[i] coef(nls(y ~ th11- (2*th2*th12)*nitro + th12*nitro^2, dat1, start = coef(nls1) )) } res1 <- scale(resid(nls1), scale = FALSE) # remove the mean. Why? It is close to 0. set.seed(5) # Sometime the bootstrap fails, but this seed works boot1 <- boot(res1, bootfun, R = 500) boot.ci(boot1, index = 2, type = c(\"perc\"), conf = 0.9) ## Level Percentile ## 90 } # }"},{"path":"/reference/hessling.argentina.html","id":null,"dir":"Reference","previous_headings":"","what":"Relation between wheat yield and weather in Argentina — hessling.argentina","title":"Relation between wheat yield and weather in Argentina — hessling.argentina","text":"Relation wheat yield weather Argentina","code":""},{"path":"/reference/hessling.argentina.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Relation between wheat yield and weather in Argentina — hessling.argentina","text":"data frame 30 observations following 15 variables. yield average yield, kg/ha year year p05 precipitation (mm) May p06 precip June p07 precip July p08 precip August p09 precip Septempber p10 precip October p11 precip November p12 precip December t06 june temperature deviation normal, deg Celsius t07 july temp deviation t08 august temp deviation t09 september temp deviation t10 october temp deviation t11 november temp deviation","code":""},{"path":"/reference/hessling.argentina.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Relation between wheat yield and weather in Argentina — hessling.argentina","text":"Argentina wheat typically sown May August. Harvest begins November December.","code":""},{"path":"/reference/hessling.argentina.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Relation between wheat yield and weather in Argentina — hessling.argentina","text":"N. . Hessling, 1922. Relations weather yield wheat Argentine republic, Monthly Weather Review, 50, 302-308. https://doi.org/10.1175/1520-0493(1922)50<302:RBTWAT>2.0.CO;2","code":""},{"path":"/reference/hessling.argentina.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Relation between wheat yield and weather in Argentina — hessling.argentina","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hessling.argentina) dat <- hessling.argentina # Fig 1 of Hessling. Use avg Aug-Nov temp to predict yield dat <- transform(dat, avetmp=(t08+t09+t10+t11)/4) # Avg temp m0 <- lm(yield ~ avetmp, dat) plot(yield~year, dat, ylim=c(100,1500), type='l', main=\"hessling.argentina: observed (black) and predicted yield (blue)\") lines(fitted(m0)~year, dat, col=\"blue\") # A modern, PLS approach libs(pls) yld <- dat[,\"yield\",drop=FALSE] yld <- as.matrix(sweep(yld, 2, colMeans(yld))) cov <- dat[,c(\"p06\",\"p07\",\"p08\",\"p09\",\"p10\",\"p11\", \"t08\",\"t09\",\"t10\",\"t11\")] cov <- as.matrix(scale(cov)) m2 <- plsr(yld~cov) # biplot(m2, which=\"x\", var.axes=TRUE, main=\"hessling.argentina\") libs(corrgram) corrgram(dat, main=\"hessling.argentina - correlations of yield and covariates\") } # }"},{"path":"/reference/hildebrand.systems.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"Maize yields four cropping systems 14 -farm trials.","code":""},{"path":"/reference/hildebrand.systems.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"data frame 56 observations following 4 variables. village village, 2 levels farm farm, 14 levels system cropping system yield yield, t/ha","code":""},{"path":"/reference/hildebrand.systems.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"Yields 14 -farm trials Phalombe Project region south-eastern Malawi. farms located near two different villages. farm, four different cropping systems tested. systems : LM = Local Maize, LMF = Local Maize Fertilizer, CCA = Improved Composite, CCAF = Improved Composite Fertilizer.","code":""},{"path":"/reference/hildebrand.systems.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"P. E. Hildebrand, 1984. Modified Stability Analysis Farmer Managed, -Farm Trials. Agronomy Journal, 76, 271–274. https://doi.org/10.2134/agronj1984.00021962007600020023x","code":""},{"path":"/reference/hildebrand.systems.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"H. P. Piepho, 1998. Methods Comparing Yield Stability Cropping Systems. Journal Agronomy Crop Science, 180, 193–213. https://doi.org/10.1111/j.1439-037X.1998.tb00526.x","code":""},{"path":"/reference/hildebrand.systems.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize for four cropping systems — hildebrand.systems","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hildebrand.systems) dat <- hildebrand.systems # Piepho 1998 Fig 1 libs(lattice) dotplot(yield ~ system, dat, groups=village, auto.key=TRUE, main=\"hildebrand.systems\", xlab=\"cropping system by village\") # Plot of risk of 'failure' of System 2 vs System 1 s11 = .30; s22 <- .92; s12 = .34 mu1 = 1.35; mu2 = 2.70 lambda <- seq(from=0, to=5, length=20) system1 <- pnorm((lambda-mu1)/sqrt(s11)) system2 <- pnorm((lambda-mu2)/sqrt(s22)) # A simpler view plot(lambda, system1, type=\"l\", xlim=c(0,5), ylim=c(0,1), xlab=\"Yield level\", ylab=\"Prob(yield < level)\", main=\"hildebrand.systems - risk of failure for each system\") lines(lambda, system2, col=\"red\") # Prob of system 1 outperforming system 2. Table 8 pnorm((mu1-mu2)/sqrt(s11+s22-2*s12)) # .0331 # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Environmental variance model, unstructured correlations dat <- dat[order(dat$system, dat$farm),] m1 <- asreml(yield ~ system, data=dat, resid = ~us(system):farm) # Means, table 5 ## predict(m1, data=dat, classify=\"system\")$pvals ## system pred.value std.error est.stat ## CCA 1.164 0.2816 Estimable ## CCAF 2.657 0.3747 Estimable ## LM 1.35 0.1463 Estimable ## LMF 2.7 0.2561 Estimable # Variances, table 5 # lucid::vc(m1)[c(2,4,7,11),] ## effect component std.error z.ratio constr ## R!system.CCA:CCA 1.11 0.4354 2.5 pos ## R!system.CCAF:CCAF 1.966 0.771 2.5 pos ## R!system.LM:LM 0.2996 0.1175 2.5 pos ## R!system.LMF:LMF 0.9185 0.3603 2.5 pos # Stability variance model m2 <- asreml(yield ~ system, data=dat, random = ~ farm, resid = ~ dsum( ~ units|system)) m2 <- update(m2) # predict(m2, data=dat, classify=\"system\")$pvals ## # Variances, table 6 # lucid::vc(m2) ## effect component std.error z.ratio bound ## farm 0.2998 0.1187 2.5 P 0 ## system_CCA!R 0.4133 0.1699 2.4 P 0 ## system_CCAF!R 1.265 0.5152 2.5 P 0 ## system_LM!R 0.0003805 0.05538 0.0069 P 1.5 ## system_LMF!R 0.5294 0.2295 2.3 P 0 } } # }"},{"path":"/reference/holland.arthropods.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"Counts arthropods grid-sampled wheat field","code":""},{"path":"/reference/holland.arthropods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"","code":"data(\"holland.arthropods\")"},{"path":"/reference/holland.arthropods.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"data frame 63 observations following 8 variables. row row col column n.brevicollis species counts linyphiidae species counts collembola species counts carabidae species counts lycosidae species counts weedcover percent weed cover","code":""},{"path":"/reference/holland.arthropods.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"Arthropods sampled 30m x 30m grid wheat field near Wimborne, Dorest, UK 6 dates Jun/Jul 1996. Arthropod counts aggregated across 6 dates. Holland et al. used SADIE (Spatial Analysis Distance Indices) look spatial patterns. Significant patterns found N. brevicollis, Carabidae, Lycosidae. Lycosidae counts also significantly associated weed cover. Used permission John Holland.","code":""},{"path":"/reference/holland.arthropods.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"Holland J. M., Perry J. N., Winder, L. (1999). within-field spatial temporal distribution arthropods within winter wheat. Bulletin Entomological Research, 89: 499-513. Figure 3 (large grid 1996). https://doi.org/10.1017/S0007485399000656","code":""},{"path":"/reference/holland.arthropods.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts of arthropods in a grid-sampled wheat field — holland.arthropods","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(holland.arthropods) dat <- holland.arthropods # use log count to make it possible to have same scale for insects libs(reshape2, lattice) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) dat2 <- melt(dat, id.var=c('row','col')) contourplot(log(value) ~ col*row|variable, dat2, col.regions=grays(7), region=TRUE, main=\"holland.arthropods - log counts in winter wheat\") if(0){ # individual species libs(lattice) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) contourplot(linyphiidae ~ col*row, dat, at=c(0,40,80,120,160,200), region=TRUE, col.regions=grays(5), main=\"holland.arthropods - linyphiidae counts in winter wheat\") contourplot(n.brevicollis ~ col*row, dat, region=TRUE) contourplot(linyphiidae~ col*row, dat, region=TRUE) contourplot(collembola ~ col*row, dat, region=TRUE) contourplot(carabidae ~ col*row, dat, region=TRUE) contourplot(lycosidae ~ col*row, dat, region=TRUE) contourplot(weedcover ~ col*row, dat, region=TRUE) } } # }"},{"path":"/reference/holshouser.splitstrip.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-strip-plot of soybeans — holshouser.splitstrip","title":"Split-strip-plot of soybeans — holshouser.splitstrip","text":"Split-strip-plot soybeans","code":""},{"path":"/reference/holshouser.splitstrip.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-strip-plot of soybeans — holshouser.splitstrip","text":"data frame 160 observations following 8 variables. block block factor, 4 levels plot plot number cultivar cultivar factor, 4 levels spacing row spacing pop population (thousand per acre) yield yield row row col column","code":""},{"path":"/reference/holshouser.splitstrip.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-strip-plot of soybeans — holshouser.splitstrip","text":"Within block, cultivars whole plots. Withing whole plots, spacing applied strips vertically, population applied strips horizontally. Used permission David Holshouser Virginia Polytechnic.","code":""},{"path":"/reference/holshouser.splitstrip.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-strip-plot of soybeans — holshouser.splitstrip","text":"Schabenberger, Oliver Francis J. Pierce. 2002. Contemporary Statistical Models Plant Soil Sciences CRC Press, Boca Raton, FL. Page 493.","code":""},{"path":"/reference/holshouser.splitstrip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-strip-plot of soybeans — holshouser.splitstrip","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(holshouser.splitstrip) dat <- holshouser.splitstrip dat$spacing <- factor(dat$spacing) dat$pop <- factor(dat$pop) # Experiment layout and field trends libs(desplot) desplot(dat, yield ~ col*row, out1=block, # unknown aspect main=\"holshouser.splitstrip\") desplot(dat, spacing ~ col*row, out1=block, out2=cultivar, # unknown aspect col=cultivar, text=pop, cex=.8, shorten='none', col.regions=c('wheat','white'), main=\"holshouser.splitstrip experiment design\") # Overall main effects and interactions libs(HH) interaction2wt(yield~cultivar*spacing*pop, dat, x.between=0, y.between=0, main=\"holshouser.splitstrip\") ## Schabenberger's SAS model, page 497 ## proc mixed data=splitstripplot; ## class block cultivar pop spacing; ## model yield = cultivar spacing spacing*cultivar pop pop*cultivar ## spacing*pop spacing*pop*cultivar / ddfm=satterth; ## random block block*cultivar block*cultivar*spacing block*cultivar*pop; ## run; ## Now lme4. This design has five error terms--four are explicitly given. libs(lme4) libs(lucid) m1 <- lmer(yield ~ cultivar * spacing * pop + (1|block) + (1|block:cultivar) + (1|block:cultivar:spacing) + (1|block:cultivar:pop), data=dat) vc(m1) ## Variances match Schabenberger, page 498. ## grp var1 var2 vcov sdcor ## block:cultivar:pop (Intercept) 2.421 1.556 ## block:cultivar:spacing (Intercept) 1.244 1.116 ## block:cultivar (Intercept) 0.4523 0.6725 ## block (Intercept) 3.037 1.743 ## Residual 3.928 1.982 } # }"},{"path":"/reference/holtsmark.timothy.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of timothy — holtsmark.timothy.uniformity","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"Uniformity trial timothy hay circa 1905","code":""},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"","code":"data(\"holtsmark.timothy.uniformity\")"},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"data frame 240 observations following 3 variables. row row col column yield yield per plot, kg","code":""},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"Field width: 40 plots * 5 m = 200 m. Field length: 6 plots * 5 m = 30 m Holtsmark & Larsen used trial compare standard deviations different sized plots (combined smaller plots).","code":""},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"Holtsmark, G Larsen, BR (1905). Om Muligheder indskraenke de Fejl, som ved Markforsog betinges af Jordens Uensartethed. Tidsskrift Landbrugets Planteavl. 12, 330-351. (Danish) Data page 347. https://books.google.com/books?id=MdM0AQAAMAAJ&pg=PA330 https://dca.au.dk/publikationer/historiske/planteavl/ Uber die Fehler, welche bei Feldversuchen, durch die Ungleichartigkeit des Bodens bedingt werden. Die Landwirtschaftlichen Versuchs-Stationen, 65, 1–22. (German) https://books.google.com/books?id=eXA2AQAAMAAJ&pg=PA1","code":""},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"Theodor Roemer (1920). Der Feldversuch. Page 67, table 11.","code":""},{"path":"/reference/holtsmark.timothy.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of timothy — holtsmark.timothy.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(holtsmark.timothy.uniformity) dat <- holtsmark.timothy.uniformity # Define diagonal 'check' plots like Holtsmark does dat <- transform(dat, check = ifelse(floor((row+col)/3)==(row+col)/3, \"C\", \"\")) libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, text=check, show.key=FALSE, aspect=30/200, # true aspect main=\"holtsmark.timothy.uniformity\") # sd(dat$yield) # 2.92 matches Holtsmark p. 348 } # }"},{"path":"/reference/huehn.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"Multi-environment trial illustrate stability statistics","code":""},{"path":"/reference/huehn.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"","code":"data(\"huehn.wheat\")"},{"path":"/reference/huehn.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"data frame 200 observations following 3 variables. gen genotype env environment yield yield dt/ha","code":""},{"path":"/reference/huehn.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"Yields winter-wheat trial 20 genotypes 10 environments. Note: Huehn 1979 use genotype-centered data calculating stability statistics.","code":""},{"path":"/reference/huehn.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"Manfred Huehn (1979). Beitrage zur Erfassung der phanotypischen Stabilitat . Vorschlag einiger auf Ranginformationen beruhenden Stabilitatsparameter. EDV Medizin und Biologie, 10 (4), 112-117. Table 1. https://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-145979","code":""},{"path":"/reference/huehn.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"Nassar, R Huehn, M. (1987). Studies estimation phenotypic stability: Tests significance nonparametric measures phenotypic stability. Biometrics, 43, 45-53.","code":""},{"path":"/reference/huehn.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat to illustrate stability statistics — huehn.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(huehn.wheat) dat <- huehn.wheat # Nassar & Huehn, p. 51 \"there is no evidence for differences in stability # among the 20 varieties\". libs(gge) m1 <- gge(dat, yield ~ gen*env) biplot(m1, main=\"huehn.wheat\") libs(reshape2) datm <- acast(dat, gen~env, value.var='yield') apply(datm,1,mean) # Gen means match Huehn 1979 table 1 apply(datm,2,mean) # Env means apply(datm, 2, rank) # Ranks match Huehn table 1 # Huehn 1979 did not use genotype-centered data, and his definition # of S2 is different from later papers. # I'm not sure where 'huehn' function is found # apply(huehn(datm, corrected=FALSE), 2, round,2) # S1 matches Huehn ## MeanRank S1 ## Jubilar 6.70 3.62 ## Diplomat 8.35 5.61 ## Caribo 11.20 6.07 ## Cbc710 13.65 6.70 # Very close match to Nassar & Huehn 1987 table 4. # apply(huehn(datm, corrected=TRUE), 2, round,2) ## MeanRank S1 Z1 S2 Z2 ## Jubilar 10.2 4.00 5.51 11.29 4.29 ## Diplomat 11.0 6.31 0.09 27.78 0.27 ## Caribo 10.6 6.98 0.08 34.49 0.01 ## Cbc710 10.9 8.16 1.78 47.21 1.73 } # }"},{"path":"/reference/hughes.grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of grape, disease incidence — hughes.grapes","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"Disease incidence grape leaves RCB experiment 6 different treatments.","code":""},{"path":"/reference/hughes.grapes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"data frame 270 observations following 6 variables. block Block factor, 1-3 trt Treatment factor, 1-6 vine Vine factor, 1-3 shoot Shoot factor, 1-5 diseased Number diseased leaves per shoot total Number total leaves per shoot","code":""},{"path":"/reference/hughes.grapes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"data come study downy mildew grapes. experiment conducted Wooster, Ohio, experimental farm Ohio Agricultural Research Development Center, Ohio State University. 3 blocks 6 treatments. Treatment 1 unsprayed control. 30 Sep 1990, disease incidence measured. plot, 5 randomly chosen shoots 3 vines observed. canopy closed shoots intertwined. shoot, total number leaves number infected leaves recorded. Used permission Larry Madden.","code":""},{"path":"/reference/hughes.grapes.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"Hughes, G. Madden, LV. 1995. methods allowing aggregated patterns disease incidence analysis data designed experiments. Plant Pathology, 44, 927–943. https://doi.org/10.1111/j.1365-3059.1995.tb02651.x","code":""},{"path":"/reference/hughes.grapes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"Hans-Pieter Piepho. 1999. Analysing disease incidence data designed experiments generalized linear mixed models. Plant Pathology, 48, 668–684. https://doi.org/10.1046/j.1365-3059.1999.00383.x","code":""},{"path":"/reference/hughes.grapes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of grape, disease incidence — hughes.grapes","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hughes.grapes) dat <- hughes.grapes dat <- transform(dat, rate = diseased/total, plot=trt:block) # Trt 1 has higher rate, more variable, Trt 3 lower rate, less variable libs(lattice) foo <- bwplot(rate ~ vine|block*trt, dat, main=\"hughes.grapes\", xlab=\"vine\") libs(latticeExtra) useOuterStrips(foo) # Table 1 of Piepho 1999 tapply(dat$rate, dat$trt, mean) # trt 1 does not match Piepho tapply(dat$rate, dat$trt, max) # Piepho model 3. Binomial data. May not be exactly the same model # Use the binomial count data with lme4 libs(lme4) m1 <- glmer(cbind(diseased, total-diseased) ~ trt + block + (1|plot/vine), data=dat, family=binomial) m1 # Switch from binomial counts to bernoulli data libs(aod) bdat <- splitbin(cbind(diseased, total-diseased) ~ block+trt+plot+vine+shoot, data=dat)$tab names(bdat)[2] <- 'y' # Using lme4 m2 <- glmer(y ~ trt + block + (1|plot/vine), data=bdat, family=binomial) m2 # Now using MASS:::glmmPQL libs(MASS) m3 <- glmmPQL(y ~ trt + block, data=bdat, random=~1|plot/vine, family=binomial) m3 } # }"},{"path":"/reference/hunter.corn.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"Corn yield response nitrogen","code":""},{"path":"/reference/hunter.corn.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"data frame 54 observations following 4 variables. nitro nitrogen fertilizer, pound/acre year year loc location yield yield, bu/ac","code":""},{"path":"/reference/hunter.corn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"Experiments conducted eastern Oregon years 1950-1952. Planting rates varied 15,000 21,000 planter per acre.","code":""},{"path":"/reference/hunter.corn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"Albert S. Hunter, John . Yungen (1955). Influence Variations Fertility Levels Upon Yield Protein Content Field Corn Eastern Oregon. Soil Science Society America Journal, 19, 214-218. https://doi.org/10.2136/sssaj1955.03615995001900020027x","code":""},{"path":"/reference/hunter.corn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"James Leo Paschal, Burton Leroy French (1956). method economic analysis applied nitrogen fertilizer rate experiments irrigated corn. Tech Bull 1141. United States Dept Agriculture. books.google.com/books?id=gAdZtsEziCcC&pg=PP1","code":""},{"path":"/reference/hunter.corn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn with nitrogen fertilizer — hunter.corn","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hunter.corn) dat <- hunter.corn dat <- transform(dat, env=factor(paste(loc,year))) libs(lattice) xyplot(yield~nitro|env, dat, type='b', main=\"hunter.corn - nitrogen response curves\") } # }"},{"path":"/reference/hutchinson.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton — hutchinson.cotton.uniformity","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"Uniformity trial cotton harvested 1941","code":""},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"","code":"data(\"hutchinson.cotton.uniformity\")"},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"data frame 2000 observations following 3 variables. row row ordinate col column ordinate yield yield per plant, grams","code":""},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"data lint yield single plants cotton uniformity trial St. Vincent 1940-41. experiment planted 50 rows 40 plants row. spacing 1.5 feet within rows 4 feet rows. Field length: 40 plants * 1.5 feet = 60 feet Field width: 50 columns * 4 feet = 200 feet data made available special help staff Rothamsted Research Library. Rothamsted library scanned paper documents pdf. K.Wright used pdf manually type values Excel file immediately checked hand-typed values. Plants marked \"Dead\" PDF left blank. 6 numbers illegible PDF. also left blank.","code":""},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 2.","code":""},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":". C. Brewer R. Mead (1986). Continuous Second Order Models Spatial Variation Application Efficiency Field Crop Experiments. Journal Royal Statistical Society. Series (General), 149(4), 314–348. See page 325. http://doi.org/10.2307/2981720","code":""},{"path":"/reference/hutchinson.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton — hutchinson.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(hutchinson.cotton.uniformity) dat <- hutchinson.cotton.uniformity require(desplot) desplot(dat, yield ~ col*row, tick=TRUE, flip=TRUE, aspect=(40*1.5)/(50*4), # true aspect main=\"hutchinson.cotton.uniformity\") } # }"},{"path":"/reference/igue.sugarcane.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"Uniformity trial sugarcane Brazil, 1982.","code":""},{"path":"/reference/igue.sugarcane.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"","code":"data(\"igue.sugarcane.uniformity\")"},{"path":"/reference/igue.sugarcane.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"data frame 1512 observations following 3 variables. row row col column yield yield, kg/plot","code":""},{"path":"/reference/igue.sugarcane.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"uniformity trial sugarcane state Sao Paulo, Brazil, 1982. field 40 rows, 90 m long, 1.5 m rows. Field width: 36 plots * 1.5 m = 54 m Field length: 42 plots * 2 m = 84 m","code":""},{"path":"/reference/igue.sugarcane.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"Toshio Igue, Ademar Espironelo, Heitor Cantarella, Erseni Joao Nelli. (1991). Tamanho e forma de parcela experimental para cana-de-acucar (Plot size shape sugar cane experiments). Bragantia, 50, 163-180. Appendix, page 169-170. https://dx.doi.org/10.1590/S0006-87051991000100016","code":""},{"path":"/reference/igue.sugarcane.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"None","code":""},{"path":"/reference/igue.sugarcane.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial with sugarcane — igue.sugarcane.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(igue.sugarcane.uniformity) dat <- igue.sugarcane.uniformity # match Igue CV top row of page 171 sd(dat$yield)/mean(dat$yield) # 16.4 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, tick=TRUE, aspect=(42*2)/(36*1.5), main=\"igue.sugarcane.uniformity\") } # }"},{"path":"/reference/ilri.sheep.html","id":null,"dir":"Reference","previous_headings":"","what":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"Birth weight weaning weight 882 lambs partial diallel cross Dorper Red Maasi breeds.","code":""},{"path":"/reference/ilri.sheep.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"data frame 882 observations following 12 variables. year year lamb birth, 1991-1996 lamb lamb id sex sex lamb, M=Male/F=Female gen genotype, DD, DR, RD, RR birthwt weight lamb birth, kg weanwt weight lamb weaning, kg weanage age lamb weaning, days ewe ewe id ewegen ewe genotype: D, R damage ewe (dam) age years ram ram id ramgen ram genotype: D, R","code":""},{"path":"/reference/ilri.sheep.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"Red Maasai sheep East Africa perceived resistant certain parasites. ILRI decided 1990 investigate degree resistance exhibited Red Maasai breed initiated study Kenya. susceptible breed, Dorper, chosen provide direct comparison Red Maasai. Dorper well-adapted area also larger Red Maasai, makes sheep attractive farmers. Throughout six years 1991 1996 Dorper (D), Red Maasai (R) Red Maasai x Dorper crossed ewes mated Red Maasai Dorper rams produce number different lamb genotypes. purposes example, following four offspring genotypes considered (Sire x Dam): D x D, D x R, R x D R x R. Records missing 182 lambs, mostly earlier death.","code":""},{"path":"/reference/ilri.sheep.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"Mixed model analysis estimation components genetic variation lamb weaning weight. International Livestock Research Institute. Permanent link: https://hdl.handle.net/10568/10364 https://biometrics.ilri.org/CS/case Retrieved Dec 2011. Used via license: Creative Commons -NC-SA 3.0.","code":""},{"path":"/reference/ilri.sheep.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"Baker, RL Nagda, S. Rodriguez-Zas, SL Southey, BR Audho, JO Aduda, EO Thorpe, W. (2003). Resistance resilience gastro-intestinal nematode parasites relationships productivity Red Maasai, Dorper Red Maasai x Dorper crossbred lambs sub-humid tropics. Animal Science, 76, 119-136. https://doi.org/10.1017/S1357729800053388 Gota Morota, Hao Cheng, Dianne Cook, Emi Tanaka (2021). ASAS-NANP SYMPOSIUM: prospects interactive dynamic graphics era data-rich animal science. Journal Animal Science, Volume 99, Issue 2, February 2021, skaa402. https://doi.org/10.1093/jas/skaa402","code":""},{"path":"/reference/ilri.sheep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Birth weight and weaning weight of Dorper x Red Maasi lambs — ilri.sheep","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ilri.sheep) dat <- ilri.sheep dat <- transform(dat, lamb=factor(lamb), ewe=factor(ewe), ram=factor(ram), year=factor(year)) # dl is linear covariate, same as damage, but truncated to [2,8] dat <- within(dat, { dl <- damage dl <- ifelse(dl < 3, 2, dl) dl <- ifelse(dl > 7, 8, dl) dq <- dl^2 }) dat <- subset(dat, !is.na(weanage)) # EDA libs(lattice) ## bwplot(weanwt ~ year, dat, main=\"ilri.sheep\", xlab=\"year\", ylab=\"Wean weight\", ## panel=panel.violin) # Year effect bwplot(weanwt ~ factor(dl), dat, main=\"ilri.sheep\", xlab=\"Dam age\", ylab=\"Wean weight\") # Dam age effect # bwplot(weanwt ~ gen, dat, # main=\"ilri.sheep\", xlab=\"Genotype\", ylab=\"Wean weight\") # Genotype differences xyplot(weanwt ~ weanage, dat, type=c('p','smooth'), main=\"ilri.sheep\", xlab=\"Wean age\", ylab=\"Wean weight\") # Age covariate # case study page 4.18 lm1 <- lm(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen, data=dat) summary(lm1) anova(lm1) # ---------- libs(lme4) lme1 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen + (1|ewe) + (1|ram), data=dat) print(lme1, corr=FALSE) lme2 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen + (1|ewe), data=dat) lme3 <- lmer(weanwt ~ year + sex + weanage + dl + dq + ewegen + ramgen + (1|ram), data=dat) anova(lme1, lme2, lme3) # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # case study page 4.20 m1 <- asreml(weanwt ~ year + sex + weanage + dl + dq + ramgen + ewegen, data=dat) # wald(m1) # case study page 4.26 m2 <- asreml(weanwt ~ year + sex + weanage + dl + dq + ramgen + ewegen, random = ~ ram + ewe, data=dat) # wald(m2) # case study page 4.37, year means # predict(m2, data=dat, classify=\"year\") ## year predicted.value standard.error est.status ## 1 91 12.638564 0.2363652 Estimable ## 2 92 11.067659 0.2285252 Estimable ## 3 93 11.561932 0.1809891 Estimable ## 4 94 9.636058 0.2505478 Estimable ## 5 95 9.350247 0.2346849 Estimable ## 6 96 10.188482 0.2755387 Estimable } } # }"},{"path":"/reference/immer.sugarbeet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","title":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","text":"Uniformity trial sugarbeets, Minnesota, 1930, measurements yield, sugar, purity.","code":""},{"path":"/reference/immer.sugarbeet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","text":"data frame 600 observations following 5 variables. year year experiment row row col column yield yield, pounds per plot sugar sugar percentage purity apparent purity","code":""},{"path":"/reference/immer.sugarbeet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","text":"1930 Experiment Beets planted rows 22 inches apart, thinned 1 plant per row. harvest, rows marked segments 33 feet long 2 foot alleys ends plots. harvested area 60 rows 350 feet long. Field width: 10 plots * 33 feet + 9 alleys * 2 feet = 348 feet Field length: 60 plots/rows * 22 /row / 12 /feet = 110 feet Planted 1930. Field conditions uniform. Beets planted rows 22 inches apart. thinning, one beet left 12-inch unit. harvest, field marked plot 33 feet long, 2-foot alley plots minimize carryover harvester. sample 10 beets taken uniformly (approximately every third beet) measured sugar percentage apparent purity. beets counted weighing time yields calculated basis 33 beets per plot. Immer found aggregating data one row two resulted dramatic reduction standard error (yield). ———- 1931 Experiment Planted 13 May 1931. Field layout previous year. Unclear land used. Field width: 10 plots * 33 feet + 9 alleys * 2 feet = 348 feet Field length: 60 plots * 22 inches/row / 12 /feet = 110 feet data experiment published Immer (1933), deposited Rothamsted. data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/immer.sugarbeet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","text":"Immer, F. R. (1932). Size shape plot relation field experiments sugar beets. Journal Agricultural Research, 44, 649-668. https://naldc.nal.usda.gov/download/IND43968078/PDF Immer, F. R. S. M. Raleigh (1933). studies size shape plot relation field experiments sugar beets. Journal Agricultural Research, 47, 591-598. https://naldc.nal.usda.gov/download/IND43968370/PDF Rothamsted Research Library, Box STATS17 WG Cochran, Folder 5.","code":""},{"path":"/reference/immer.sugarbeet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sugarbeets, measurements of yield, sugar, purity — immer.sugarbeet.uniformity","text":"","code":"library(agridat) data(immer.sugarbeet.uniformity) dat <- immer.sugarbeet.uniformity # Immer numbers rows from the top libs(desplot) # Similar to Immer (1932) figure 2 desplot(dat, yield~col*row, subset=year==1930, aspect=110/348, tick=TRUE, flip=TRUE, # true aspect main=\"immer.sugarbeet.uniformity - 1930 yield\") # Similar to Immer (1932) figure 3 desplot(dat, sugar~col*row, subset=year==1930, aspect=110/348, tick=TRUE, flip=TRUE, main=\"immer.sugarbeet.uniformity - 1930 sugar\") # Similar to Immer (1932) figure 4 desplot(dat, purity~col*row, subset=year==1930, aspect=110/348, tick=TRUE, flip=TRUE, main=\"immer.sugarbeet.uniformity - 1930 purity\") pairs(dat[,c('yield','sugar','purity')], main=\"immer.sugarbeet.uniformity\") # Similar to Immer (1933) figure 1 desplot(dat, yield~col*row, subset=year==1931, aspect=110/348, tick=TRUE, flip=TRUE, # true aspect main=\"immer.sugarbeet.uniformity - 1931 yield\")"},{"path":"/reference/ivins.herbs.html","id":null,"dir":"Reference","previous_headings":"","what":"Percent ground cover of herbage species and nettles. — ivins.herbs","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"Percent ground cover herbage species nettles.","code":""},{"path":"/reference/ivins.herbs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"data frame 78 observations following 4 variables. block block, 6 levels gen genotype, 13 levels nettle percent ground cover nettles herb percent ground cover herbage species","code":""},{"path":"/reference/ivins.herbs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"University Nottingham farm, 13 different strains species herbage plants sown 4 acres RCB design. grass species sown together white clover seed. establishment herbage plants, became apparent Urtica dioica (nettle) became established according particular herbage plant plot. particular, nettle became established plots sown leguminous species two grass species. graminaceous plots less nettles. data percentage ground cover nettle herbage plants September 1951. Note, percent ground cover amounts originally reported 'trace'. arbitrarily set 0.1 data.","code":""},{"path":"/reference/ivins.herbs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"Ivins, JD. (1952). Concerning Ecology Urtica Dioica L., Journal Ecology, 40, 380-382. https://doi.org/10.2307/2256806","code":""},{"path":"/reference/ivins.herbs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"Ivins, JD (1950). Weeds relation establishment Ley. Grass Forage Science, 5, 237–242. https://doi.org/10.1111/j.1365-2494.1950.tb01287.x O'Gorman, T.W. (2001). comparison F-test, Friedman's test, several aligned rank tests analysis randomized complete blocks. Journal agricultural, biological, environmental statistics, 6, 367–378. https://doi.org/10.1198/108571101317096578","code":""},{"path":"/reference/ivins.herbs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Percent ground cover of herbage species and nettles. — ivins.herbs","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ivins.herbs) dat <- ivins.herbs # Nettle is primarily established in legumes. libs(lattice) xyplot(herb~nettle|gen, dat, main=\"ivins.herbs - herb yield vs weeds\", xlab=\"Percent groundcover in nettles\", ylab=\"Percent groundcover in herbs\") # O'Brien used first 7 species to test gen differences dat7 <- droplevels(subset(dat, is.element(gen, c('G01','G02','G03','G04','G05','G06','G07')))) m1 <- lm(herb ~ gen + block, data=dat7) anova(m1) # gen p-value is .041 ## Response: herb ## Df Sum Sq Mean Sq F value Pr(>F) ## gen 6 1083.24 180.540 2.5518 0.04072 * ## block 5 590.69 118.138 1.6698 0.17236 ## Residuals 30 2122.48 70.749 friedman.test(herb ~ gen|block, dat7) # gen p-value .056 } # }"},{"path":"/reference/iyer.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of wheat in India — iyer.wheat.uniformity","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"Uniformity trials wheat India.","code":""},{"path":"/reference/iyer.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"","code":"data(\"iyer.wheat.uniformity\")"},{"path":"/reference/iyer.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"data frame 2000 observations following 3 variables. row row col column yield yield, ounces per plot","code":""},{"path":"/reference/iyer.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"Data collected Agricultural Sub-station Karnal, India, April 1978. net area 400 ft x 125 ft harvested dividing 80x25 units 5 ft x 5 ft eliminating minimum border 3.5 ft around net area. Field width: 80 plots * 5 feet = 400 feet Field length: 25 rows * 5 feet = 125 feet second paper, Iyer used data compare random vs. balanced arrangements treatments plots, conclusion \"difficult say [method] better. However, tendency randomized arrangements give accurate results.\"","code":""},{"path":"/reference/iyer.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"P. V. Krishna Iyer (1942). Studies wheat uniformity trial data. . Size shape experimental plots relative efficiency different layouts. Indian Journal Agricultural Science, 12, 240-262. Page 259-262. https://archive.org/stream/.ernet.dli.2015.7638/2015.7638.-Indian-Journal--Agricultural-Science-Vol-xii-1942#page/n267/mode/2up","code":""},{"path":"/reference/iyer.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"None.","code":""},{"path":"/reference/iyer.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of wheat in India — iyer.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(iyer.wheat.uniformity) dat <- iyer.wheat.uniformity libs(desplot) desplot(dat, yield ~ col*row, main=\"iyer.wheat.uniformity\", tick=TRUE, aspect=(25*5)/(80*5)) # true aspect # not exactly the same as Iyer table 1, p. 241 var(subset(dat, col <= 20)$yield) var(subset(dat, col > 20 & col <= 40)$yield) var(subset(dat, col > 40 & col <= 60)$yield) var(subset(dat, col > 60)$yield) # cv for 1x1 whole-field # sd(dat$yield)/mean(dat$yield) # 18.3 } # }"},{"path":"/reference/jansen.apple.html","id":null,"dir":"Reference","previous_headings":"","what":"Infestation of apple shoots by apple canker. — jansen.apple","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"Infestation apple shoots apple canker.","code":""},{"path":"/reference/jansen.apple.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"","code":"data(\"jansen.apple\")"},{"path":"/reference/jansen.apple.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"data frame 36 observations following 5 variables. inoculum inoculum level gen genotype/variety block block y number inoculations developing canker n number inoculations","code":""},{"path":"/reference/jansen.apple.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"Shoots apple trees infected fungus Nectria galligena, may cause apple canker. incoulum density treatment 3 levels, measured macroconidia per ml. 4 blocks. Used permission J. Jansen. Electronic version supplied Miroslav Zoric.","code":""},{"path":"/reference/jansen.apple.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"J. Jansen & J.. Hoekstra (1993). analysis proportions agricultural experiments generalized linear mixed model. Statistica Neerlandica, 47(3), 161-174. https://doi.org/10.1111/j.1467-9574.1993.tb01414.x","code":""},{"path":"/reference/jansen.apple.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"None.","code":""},{"path":"/reference/jansen.apple.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Infestation of apple shoots by apple canker. — jansen.apple","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jansen.apple) dat <- jansen.apple libs(lattice) xyplot(inoculum ~ y/n|gen, data=dat, group=block, layout=c(3,1), main=\"jansen.apple\", xlab=\"Proportion infected per block/inoculum\", ylab=\"Inoculum level\") ## libs(lme4) ## # Tentative model. Needs improvement. ## m1 <- glmer(cbind(y,n-y) ~ gen + factor(inoculum) + (1|block), ## data=dat, family=binomial) ## summary(m1) } # }"},{"path":"/reference/jansen.carrot.html","id":null,"dir":"Reference","previous_headings":"","what":"Infestation of carrots by fly larvae — jansen.carrot","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"Infestation 16 carrot genotypes fly larvae, comparing 2 treatments 16 blocks.","code":""},{"path":"/reference/jansen.carrot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"","code":"data(\"jansen.carrot\")"},{"path":"/reference/jansen.carrot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"data frame 96 observations following 5 variables. trt treatment gen genotype block block n number carrots sampled per plot y number carrots infested per plot","code":""},{"path":"/reference/jansen.carrot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"experiment designed compare different genotypes carrots respect resistance infestation larvae carrotfly. 16 genotypes, 2 levels pest-control treatments, conducted 3 randomized complete blocks. 50 carrots sampled plot evaluated. data show number carrots number infested fly larvae. Used permission J. Jansen. Electronic version supplied Miroslav Zoric.","code":""},{"path":"/reference/jansen.carrot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"J. Jansen & J.. Hoekstra (1993). analysis proportions agricultural experiments generalized linear mixed model. Statistica Neerlandica, 47(3), 161-174. https://doi.org/10.1111/j.1467-9574.1993.tb01414.x","code":""},{"path":"/reference/jansen.carrot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"None.","code":""},{"path":"/reference/jansen.carrot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Infestation of carrots by fly larvae — jansen.carrot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jansen.carrot) dat <- jansen.carrot libs(lattice) dotplot(gen ~ y/n, data=dat, group=trt, auto.key=TRUE, main=\"jansen.carrot\", xlab=\"Proportion of carrots infected per block\", ylab=\"Genotype\") # Not run because CRAN wants < 5 seconds per example. This is close. libs(lme4) # Tentative model. Needs improvement. m1 <- glmer(cbind(y,n-y) ~ gen*trt + (1|block), data=dat, family=binomial) summary(m1) # Todo: Why are these results different from Jansen? # Maybe he used ungrouped bernoulli data? Too slow with 4700 obs } # }"},{"path":"/reference/jansen.strawberry.html","id":null,"dir":"Reference","previous_headings":"","what":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"Ordered disease ratings strawberry crosses.","code":""},{"path":"/reference/jansen.strawberry.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"","code":"data(\"jansen.strawberry\")"},{"path":"/reference/jansen.strawberry.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"data frame 144 observations following 5 variables. male male parent female female parent block block category disease damage, C1 < C2 < C3 count number plants category","code":""},{"path":"/reference/jansen.strawberry.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"strawberries, red core disease caused fungus, Phytophtora fragariae. experiment evaluated different populations damage caused red core disease. 3 male strawberry plants 4 DIFFERENT female strawberry plants crossed create 12 populations. Note: Jansen labeled male parents 1,2,3 female parents 1,2,3,4. reduce confusion, data labels female parents 5,6,7,8. experiment four blocks 12 plots (one population). Plots usually 10 plants, plots 9 plants. plant assessed damage fungus rated belonging category C1, C2, C3 (increasing damage). Used permission Hans Jansen.","code":""},{"path":"/reference/jansen.strawberry.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"J. Jansen, 1990. statistical analysis ordinal data extravariation present. Applied Statistics, 39, 75-84, Table 1. https://doi.org/10.2307/2347813","code":""},{"path":"/reference/jansen.strawberry.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ordered disease ratings of strawberry crosses. — jansen.strawberry","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jansen.strawberry) dat <- jansen.strawberry dat <- transform(dat, category=ordered(category, levels=c('C1','C2','C3'))) dtab <- xtabs(count ~ male + female + category, data=dat) ftable(dtab) mosaicplot(dtab, color=c(\"lemonchiffon1\",\"lightsalmon1\",\"indianred\"), main=\"jansen.strawberry disease ratings\", xlab=\"Male parent\", ylab=\"Female parent\") libs(MASS,vcd) # Friendly suggests a minimal model is [MF][C] # m1 <- loglm( ~ 1*2 + 3, dtab) # Fails, only with devtools # mosaic(m1) } # }"},{"path":"/reference/jayaraman.bamboo.html","id":null,"dir":"Reference","previous_headings":"","what":"Bamboo progeny trial — jayaraman.bamboo","title":"Bamboo progeny trial — jayaraman.bamboo","text":"Bamboo progeny trial 2 locations, 3 blocks","code":""},{"path":"/reference/jayaraman.bamboo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bamboo progeny trial — jayaraman.bamboo","text":"","code":"data(\"jayaraman.bamboo\")"},{"path":"/reference/jayaraman.bamboo.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Bamboo progeny trial — jayaraman.bamboo","text":"data frame 216 observations following 5 variables. loc location factor block block factor tree tree factor family family factor height height, cm","code":""},{"path":"/reference/jayaraman.bamboo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bamboo progeny trial — jayaraman.bamboo","text":"Data replicated trial bamboo two locations Kerala, India. location 3 blocks. block 6 families, 6 trees family.","code":""},{"path":"/reference/jayaraman.bamboo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bamboo progeny trial — jayaraman.bamboo","text":"K. Jayaraman (1999). \"Statistical Manual Forestry Research\". Forestry Research Support Programme Asia Pacific. Page 170.","code":""},{"path":"/reference/jayaraman.bamboo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bamboo progeny trial — jayaraman.bamboo","text":"None","code":""},{"path":"/reference/jayaraman.bamboo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bamboo progeny trial — jayaraman.bamboo","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jayaraman.bamboo) dat <- jayaraman.bamboo # very surprising differences between locations libs(lattice) bwplot(height ~ family|loc, dat, main=\"jayaraman.bamboo\") # match Jayarman's anova table 6.3, page 173 # m1 <- aov(height ~ loc+loc:block + family + family:loc + # family:loc:block, data=dat) # anova(m1) # more modern approach with mixed model, match variance components needed # for equation 6.9, heritability of the half-sib averages as m2 <- lme4::lmer(height ~ 1 + (1|loc/block) + (1|family/loc/block), data=dat) lucid::vc(m2) } # }"},{"path":"/reference/jegorow.oats.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"Uniformity trial oats Russia","code":""},{"path":"/reference/jegorow.oats.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"","code":"data(\"jegorow.oats.uniformity\")"},{"path":"/reference/jegorow.oats.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"data frame 240 observations following 3 variables. row row ordinate col column ordinate yield yield per plot, kg","code":""},{"path":"/reference/jegorow.oats.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"Sumskaya (Ssumy?) agricultural experimental station (Kharkov Governorate), field planted April 1908 harvested summer plots 1 sazhen sqauare. 'sazhen' 7 feet. Field width: 8 plots * 1 sazhen Field length: 30 plots * 1 sazhen Data typed K.Wright Roemer (1920), table 10.","code":""},{"path":"/reference/jegorow.oats.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"Jegorow, M. (1909). Zur Methodik des feldversuches. Russian Journ Expt Agric, 10, 502-520. uniformity trial oats. https://www.google.com/books/edition/Journal_de_l_agriculture_experimentale/510jAQAAIAAJ?hl=en","code":""},{"path":"/reference/jegorow.oats.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"Neyman, J., & Iwaszkiewicz, K. (1935). Statistical problems agricultural experimentation. Supplement Journal Royal Statistical Society, 2(2), 107-180. Roemer, T. (1920). Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ","code":""},{"path":"/reference/jegorow.oats.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of oats in Russia — jegorow.oats.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jegorow.oats.uniformity) dat <- jegorow.oats.uniformity mean(dat$yield) # Jegorow reports 2.03 libs(desplot) desplot(dat, yield~col*row, aspect=10/24, flip=TRUE, tick=TRUE, main=\"jegorow.oats.uniformity\") } # }"},{"path":"/reference/jenkyn.mildew.html","id":null,"dir":"Reference","previous_headings":"","what":"Yields from treatment for mildew control — jenkyn.mildew","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"Yields treatment mildew control","code":""},{"path":"/reference/jenkyn.mildew.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"data frame 38 observations following 4 variables. plot plot number trt treatment factor, 4 levels block block factor, 9 levels yield grain yield, tons/ha","code":""},{"path":"/reference/jenkyn.mildew.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"four spray treatments: 0 (none), 1 (early), 2 (late), R (repeated). treatment occurs 9 ordered pairs treatments. first last plot assigned block.","code":""},{"path":"/reference/jenkyn.mildew.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"Norman Draper Irwin Guttman (1980). Incorporating Overlap Effects Neighboring Units Response Surface Models. Appl Statist, 29, 128–134. https://doi.org/10.2307/2986297","code":""},{"path":"/reference/jenkyn.mildew.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"Maria Durban, Christine Hackett, Iain Currie. Blocks, Trend Interference Field Trials.","code":""},{"path":"/reference/jenkyn.mildew.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yields from treatment for mildew control — jenkyn.mildew","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jenkyn.mildew) dat <- jenkyn.mildew libs(lattice) bwplot(yield ~ trt, dat, main=\"jenkyn.mildew\", xlab=\"Treatment\") # Residuals from treatment model show obvious spatial trends m0 <- lm(yield ~ trt, dat) xyplot(resid(m0)~plot, dat, ylab=\"Residual\", main=\"jenkyn.mildew - treatment model\") # The blocks explain most of the variation m1 <- lm(yield ~ trt + block, dat) xyplot(resid(m1)~plot, dat, ylab=\"Residual\", main=\"jenkyn.mildew - block model\") } # }"},{"path":"/reference/john.alpha.html","id":null,"dir":"Reference","previous_headings":"","what":"Alpha lattice design of spring oats — john.alpha","title":"Alpha lattice design of spring oats — john.alpha","text":"Alpha lattice design spring oats","code":""},{"path":"/reference/john.alpha.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Alpha lattice design of spring oats — john.alpha","text":"data frame 72 observations following 5 variables. plot plot number rep replicate block incomplete block gen genotype (variety) yield dry matter yield (tonnes/ha) row Row ordinate col Column ordinate","code":""},{"path":"/reference/john.alpha.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Alpha lattice design of spring oats — john.alpha","text":"spring oats trial grown Craibstone, near Aberdeen. 24 varieties 3 replicates, consisting 6 incomplete blocks 4 plots. Planted resolvable alpha design. Caution: Note table page 146 John & Williams (1995) physical layout. plots laid single line.","code":""},{"path":"/reference/john.alpha.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Alpha lattice design of spring oats — john.alpha","text":"J. . John & E. R. Williams (1995). Cyclic computer generated designs. Chapman Hall, London. Page 146.","code":""},{"path":"/reference/john.alpha.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Alpha lattice design of spring oats — john.alpha","text":"Piepho, H.P. Mohring, J. (2007), Computing heritability selection response unbalanced plant breeding trials. Genetics, 177, 1881-1888. https://doi.org/10.1534/genetics.107.074229 Paul Schmidt, Jens Hartung, Jörn Bennewitz, Hans-Peter Piepho (2019). Heritability Plant Breeding Genotype-Difference Basis. Genetics, 212, 991-1008. https://doi.org/10.1534/genetics.119.302134","code":""},{"path":"/reference/john.alpha.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Alpha lattice design of spring oats — john.alpha","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(john.alpha) dat <- john.alpha # RCB (no incomplete block) m0 <- lm(yield ~ 0 + gen + rep, data=dat) # Block fixed (intra-block analysis) (bottom of table 7.4 in John) m1 <- lm(yield ~ 0 + gen + rep + rep:block, dat) anova(m1) # Block random (combined inter-intra block analysis) libs(lme4, lucid) m2 <- lmer(yield ~ 0 + gen + rep + (1|rep:block), dat) anova(m2) ## Analysis of Variance Table ## Df Sum Sq Mean Sq F value ## gen 24 380.43 15.8513 185.9942 ## rep 2 1.57 0.7851 9.2123 vc(m2) ## grp var1 var2 vcov sdcor ## rep:block (Intercept) 0.06194 0.2489 ## Residual 0.08523 0.2919 # Variety means. John and Williams table 7.5. Slight, constant # difference for each method as compared to John and Williams. means <- data.frame(rcb=coef(m0)[1:24], ib=coef(m1)[1:24], intra=fixef(m2)[1:24]) head(means) ## rcb ib intra ## genG01 5.201233 5.268742 5.146433 ## genG02 4.552933 4.665389 4.517265 ## genG03 3.381800 3.803790 3.537934 ## genG04 4.439400 4.728175 4.528828 ## genG05 5.103100 5.225708 5.075944 ## genG06 4.749067 4.618234 4.575394 libs(lattice) splom(means, main=\"john.alpha - means for RCB, IB, Intra-block\") # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Heritability calculation of Piepho & Mohring, Example 1 m3 <- asreml(yield ~ 1 + rep, data=dat, random=~ gen + rep:block) sg2 <- summary(m3)$varcomp['gen','component'] # .142902 # Average variance of a difference of two adjusted means (BLUP) p3 <- predict(m3, data=dat, classify=\"gen\", sed=TRUE) # Matrix of pair-wise SED values, squared vdiff <- p3$sed^2 # Average variance of two DIFFERENT means (using lower triangular of vdiff) vblup <- mean(vdiff[lower.tri(vdiff)]) # .05455038 # Note that without sed=TRUE, asreml reports square root of the average variance # of a difference between the variety means, so the following gives the same value # predict(m3, data=dat, classify=\"gen\")$avsed ^ 2 # .05455038 # Average variance of a difference of two adjusted means (BLUE) m4 <- asreml(yield ~ 1 + gen + rep, data=dat, random = ~ rep:block) p4 <- predict(m4, data=dat, classify=\"gen\", sed=TRUE) vdiff <- p4$sed^2 vblue <- mean(vdiff[lower.tri(vdiff)]) # .07010875 # Again, could use predict(m4, data=dat, classify=\"gen\")$avsed ^ 2 # H^2 Ad-hoc measure of heritability sg2 / (sg2 + vblue/2) # .803 # H^2c Similar measure proposed by Cullis. 1-(vblup / (2*sg2)) # .809 } # ---------- # lme4 to calculate Cullis H2 # https://stackoverflow.com/questions/38697477 libs(lme4) cov2sed <- function(x){ # Convert var-cov matrix to SED matrix # sed[i,j] = sqrt( x[i,i] + x[j,j]- 2*x[i,j] ) n <- nrow(x) vars <- diag(x) sed <- sqrt( matrix(vars, n, n, byrow=TRUE) + matrix(vars, n, n, byrow=FALSE) - 2*x ) diag(sed) <- 0 return(sed) } # Same as asreml model m4. Note 'gen' must be first term m5blue <- lmer(yield ~ 0 + gen + rep + (1|rep:block), dat) libs(emmeans) ls5blue <- emmeans(m5blue, \"gen\") con <- ls5blue@linfct[,1:24] # contrast matrix for genotypes # The 'con' matrix is identity diagonal, so we don't need to multiply, # but do so for a generic approach # sed5blue <- cov2sed(con tmp <- tcrossprod( crossprod(t(con), vcov(m5blue)[1:24,1:24]), con) sed5blue <- cov2sed(tmp) # vblue Average variance of difference between genotypes vblue <- mean(sed5blue[upper.tri(sed5blue)]^2) vblue # .07010875 matches 'vblue' from asreml # Now blups m5blup <- lmer(yield ~ 0 + (1|gen) + rep + (1|rep:block), dat) # Need lme4::ranef in case ordinal is loaded re5 <- lme4::ranef(m5blup,condVar=TRUE) vv1 <- attr(re5$gen,\"postVar\") vblup <- 2*mean(vv1) # .0577 not exactly same as 'vblup' above vblup # H^2 Ad-hoc measure of heritability sg2 <- c(lme4::VarCorr(m5blup)[[\"gen\"]]) # 0.142902 sg2 / (sg2 + vblue/2) # .803 matches asreml # H^2c Similar measure proposed by Cullis. 1-(vblup / 2 / sg2) # .809 from asreml, .800 from lme4 # ---------- # Sommer to calculate Cullis H2 libs(sommer) m2.ran <- mmer(fixed = yield ~ rep, random = ~ gen + rep:block, data = dat) vc_g <- m2.ran$sigma$gen # genetic variance component n_g <- n_distinct(dat$gen) # number of genotypes C22_g <- m2.ran$PevU$gen$yield # Prediction error variance matrix for genotypic BLUPs trC22_g <- sum(diag(C22_g)) # trace # Mean variance of a difference between genotypic BLUPs. Smith eqn 26 # I do not see the algebraic reason for this...2 av2 <- 2/n_g * (trC22_g - (sum(C22_g)-trC22_g) / (n_g-1)) ### H2 Cullis 1-(av2 / (2 * vc_g)) #0.8091 } # }"},{"path":"/reference/johnson.blight.html","id":null,"dir":"Reference","previous_headings":"","what":"Potato blight due to weather in Prosser, Washington — johnson.blight","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"Potato blight due weather Prosser, Washington","code":""},{"path":"/reference/johnson.blight.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"data frame 25 observations following 6 variables. year year area area affected, hectares blight blight detected, 0/1 numeric rain.number rainy days April May rain.ja number rainy days July August precip.m precipitation May temp > 5C, milimeters","code":""},{"path":"/reference/johnson.blight.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"variable 'blight detected' 1 'area' > 0.","code":""},{"path":"/reference/johnson.blight.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"Johnson, D.. Alldredge, J.R. Vakoch, D.L. (1996). Potato late blight forecasting models semiarid environment south-central Washington. Phytopathology, 86, 480–484. https://doi.org/10.1094/Phyto-86-480","code":""},{"path":"/reference/johnson.blight.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"Vinayanand Kandala, Logistic Regression","code":""},{"path":"/reference/johnson.blight.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Potato blight due to weather in Prosser, Washington — johnson.blight","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(johnson.blight) dat <- johnson.blight # Define indicator for blight in previous year dat$blight.prev[2:25] <- dat$blight[1:24] dat$blight.prev[1] <- 0 # Need this to match the results of Johnson dat$blight.prev <- factor(dat$blight.prev) dat$blight <- factor(dat$blight) # Johnson et al developed two logistic models to predict outbreak of blight m1 <- glm(blight ~ blight.prev + rain.am + rain.ja, data=dat, family=binomial) summary(m1) ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -11.4699 5.5976 -2.049 0.0405 * ## blight.prev1 3.8796 1.8066 2.148 0.0318 * ## rain.am 0.7162 0.3665 1.954 0.0507 . ## rain.ja 0.2587 0.2468 1.048 0.2945 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## (Dispersion parameter for binomial family taken to be 1) ## Null deviance: 34.617 on 24 degrees of freedom ## Residual deviance: 13.703 on 21 degrees of freedom ## AIC: 21.703 m2 <- glm(blight ~ blight.prev + rain.am + precip.m, data=dat, family=binomial) summary(m2) ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -7.5483 3.8070 -1.983 0.0474 * ## blight.prev1 3.5526 1.6061 2.212 0.0270 * ## rain.am 0.6290 0.2763 2.276 0.0228 * ## precip.m -0.0904 0.1144 -0.790 0.4295 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## (Dispersion parameter for binomial family taken to be 1) ## Null deviance: 34.617 on 24 degrees of freedom ## Residual deviance: 14.078 on 21 degrees of freedom ## AIC: 22.078 libs(lattice) splom(dat[,c('blight','rain.am','rain.ja','precip.m')], main=\"johnson.blight - indicator of blight\") } # }"},{"path":"/reference/johnson.douglasfir.html","id":null,"dir":"Reference","previous_headings":"","what":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"study small-plots old-growth Douglas Fir Oregon.","code":""},{"path":"/reference/johnson.douglasfir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"","code":"data(\"johnson.douglasfir\")"},{"path":"/reference/johnson.douglasfir.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"data frame 1600 observations following 3 variables. row row col column volume volume per plot","code":""},{"path":"/reference/johnson.douglasfir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"study 40 acres old-growth Douglas-Fir near Eugene, Oregon. area divided 40--40 grid plots, 1/40 acre. volume represents total timber volume (Scribner Decimal C) 1/40 acre plot. authors conclude 1-chain 3-chain 3/10 acre rectangle efficient intensive cruise work. convert plot volume total volume per acre, multiply 40 ( plot 1/40 acre) multiply 10 (correction Scribner scale).","code":""},{"path":"/reference/johnson.douglasfir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"Floyd . Johnson, Homer J. Hixon. (1952). efficient size shape plot use cruising old-growth Douglas-fir timber. Jour. Forestry, 50, 17-20. https://doi.org/10.1093/jof/50.1.17","code":""},{"path":"/reference/johnson.douglasfir.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"None","code":""},{"path":"/reference/johnson.douglasfir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A study of small-plots of old-growth Douglas Fir in Oregon. — johnson.douglasfir","text":"","code":"library(agridat) data(johnson.douglasfir) dat <- johnson.douglasfir # Average volume per acre. Johnson & Hixon give 91000. # Transcription may have some errors...the pdf was blurry. mean(dat$volume) * 400 #> [1] 91124.25 # 91124 libs(lattice) levelplot(volume ~ col*row, dat, main=\"johnson.douglasfir\", aspect=1) histogram( ~ volume, data=dat, main=\"johnson.douglasfir\")"},{"path":"/reference/jones.corn.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of corn. — jones.corn.uniformity","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"Uniformity trial corn Iowa 2016.","code":""},{"path":"/reference/jones.corn.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"","code":"data(\"jones.corn.uniformity\")"},{"path":"/reference/jones.corn.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"data frame 144 observations following 3 variables. col column ordinate row row ordinate yield yield, bu/ac","code":""},{"path":"/reference/jones.corn.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"data corresponds field \"ISU.SE\" paper Jones. Field width: 12 columns, 4.6 meters . Field length: 12 rows, 3 meters . Electronic version provided online supplement. \"row\" \"col\" variables supplement swapped presentation data order consistent figures paper. electronic supplemental data bu/ac, paper uses kg/ha. Used permission Marcus Jones.","code":""},{"path":"/reference/jones.corn.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"Jones, M., Harbur, M., & Moore, K. J. (2021). Automating Uniformity Trials Optimize Precision Agronomic Field Trials. Agronomy, 11(6), 1254. https://doi.org/10.3390/agronomy11061254","code":""},{"path":"/reference/jones.corn.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"None","code":""},{"path":"/reference/jones.corn.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of corn. — jones.corn.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jones.corn.uniformity) dat <- jones.corn.uniformity library(desplot) # Compare to figure 5 of Jones et al. desplot(dat, yield ~ col*row, aspect=(12*4.6)/(12*3), main=\"jones.corn.uniformity\") } # }"},{"path":"/reference/jurowski.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"Uniformity trial wheat Russia","code":""},{"path":"/reference/jurowski.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"","code":"data(\"jurowski.wheat.uniformity\")"},{"path":"/reference/jurowski.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"data frame 480 observations following 3 variables. row row ordinate col column ordinate yield yield, Pud per plot","code":""},{"path":"/reference/jurowski.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"experiment conducted Russia Ofrossimowka. word \"Ofrossimowka\" appeared German text Sapehin, otherwise extremely difficult find. may alternate ways actual Russian name translated German/English. Likewise, name \"Jurowski\" difficult find may transliterations. Sapehin gives original source : Arbeiten d. Vers.-St. d. Russ. Ges. f. Zuckerind. 1913. may expand Arbeiten der Versuchsstationen der Russ. Ges. f. Zuckerindustrie. 1913. Sepehin appendix says plot size \"4 x 12 m^2\". clear plot dimension 4 m 12 m. 4m wide 12m tall, field 48m wide x 480m long. 4m tall 12m wide, field 144m wide x 160m long. seems much likely. Sapehin says std dev \"21.8 pud\". \"pud\" Russian unit weight equal 16.38 kilograms. Data converted OCR Sapehin hand-checked K.Wright.","code":""},{"path":"/reference/jurowski.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"Sapehin, . . (1927). Beitrage zur Methodik des Feldversuches. Die Landwirtschaflichen Versuchsstationen, 105, 243-259. https://www.google.com/books/edition/Die_Landwirthschaftlichen_Versuchs_Stati/cLZGAAAAYAAJ?hl=en&pg=PA243","code":""},{"path":"/reference/jurowski.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"None","code":""},{"path":"/reference/jurowski.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat in Russia — jurowski.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(jurowski.wheat.uniformity) dat <- jurowski.wheat.uniformity sd(dat$yield) libs(desplot) desplot(dat, yield~col*row, aspect=(40*4)/(12*12), flip=TRUE, tick=TRUE, main=\"jurowski.wheat.uniformity\") } # }"},{"path":"/reference/kadam.millet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of millet — kadam.millet.uniformity","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"Uniformity trial millet India 2 years","code":""},{"path":"/reference/kadam.millet.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"","code":"data(\"kadam.millet.uniformity\")"},{"path":"/reference/kadam.millet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"data frame 240 observations following 4 variables. year year row row col column yield yield, ounces","code":""},{"path":"/reference/kadam.millet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"Uniformity trials conducted kharip (monsoon) seasons 1933 1934 Kundewadi, Niphad, district Nasik, India. Bajari (pearl millet) strain 54 used. 1933: Field width: 8 plots * 16.5 feet Field length: 10 plots * 33 feet 1934: Field width: 8 plots * 16.5 feet Field length: 20 plots * 16.5 feet","code":""},{"path":"/reference/kadam.millet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"B. S. Kadam S. M. Patel. (1937). Studies Field-Plot Technique P. Typhoideum Rich. Empire Journal Experimental Agriculture, 5, 219-230. https://archive.org/details/.ernet.dli.2015.25282","code":""},{"path":"/reference/kadam.millet.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"None.","code":""},{"path":"/reference/kadam.millet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of millet — kadam.millet.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kadam.millet.uniformity) dat <- kadam.millet.uniformity # similar to Kadam fig 1 libs(desplot) desplot(dat, yield ~ col*row, subset=year==1933, flip=TRUE, aspect=(10*33)/(8*16.5), # true aspect main=\"kadam.millet.uniformity 1933\") desplot(dat, yield ~ col*row, subset=year==1934, flip=TRUE, aspect=(20*16.5)/(8*16.5), # true aspect main=\"kadam.millet.uniformity 1934\") } # }"},{"path":"/reference/kalamkar.potato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of potatoes — kalamkar.potato.uniformity","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"Uniformity trial potatoes Saskatchewan, Canada, 1929.","code":""},{"path":"/reference/kalamkar.potato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"","code":"data(\"kalamkar.potato.uniformity\")"},{"path":"/reference/kalamkar.potato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"data frame 576 observations following 3 variables. row row col column yield yield, pounds per plot","code":""},{"path":"/reference/kalamkar.potato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"data potato yields 96 rows, 132 feet long, 3 feet rows. row harvested six plots, 22 feet long. hill one seed piece. Hills spaced 2 feet apart row. Field width: 6 plots * 22 feet = 132 feet Field length: 96 rows * 3 feet = 288 feet Units yield given. experiment, 22 plants per plot. Today potato plants yield 3-5 pounds. assume experiment yield 2 pound per plant, 22 pounds per plot, similar data values. Also, Kirk 1929 mentions \"200 bushels per acre\", 22 pounds per plot x (43560/66) divided (60 pounds per bushel) = 242, seems reasonable. Also `kirk.potato` data author recorded pounds per plot.","code":""},{"path":"/reference/kalamkar.potato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"Kalamkar, R.J. (1932). Experimental Error Field-Plot Technique Potatoes. Journal Agricultural Science, 22, 373-385. https://doi.org/10.1017/S0021859600053697","code":""},{"path":"/reference/kalamkar.potato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"Kirk, L. E. (1929) Field plot technique potatoes special reference Latin square. Scientific Agriculture, 9, 719. https://cdnsciencepub.com/doi/10.4141/sa-1929-0067 https://doi.org/10.4141/sa-1929-0067 https://www.google.com/books/edition/Revue_Agronomique_Canadien/-gMkAQAAMAAJ","code":""},{"path":"/reference/kalamkar.potato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of potatoes — kalamkar.potato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kalamkar.potato.uniformity) dat <- kalamkar.potato.uniformity # Similar to figure 1 of Kalamkar libs(desplot) desplot(dat, yield~col*row, flip=TRUE, tick=TRUE, aspect=288/132, # true aspect main=\"kalamkar.potato.uniformity\") } # }"},{"path":"/reference/kalamkar.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — kalamkar.wheat.uniformity","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"Uniformity trial wheat Rothamsted, UK 1931.","code":""},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"","code":"data(\"kalamkar.wheat.uniformity\")"},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"data frame 1280 observations following 4 variables. row row col column yield yield, grams/half-meter ears ears per half-meter","code":""},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"Kalamkar's paper published 1932. Estimated crop year 1931. Plot 18 Four Course Rotation Experiment, Great Hoos, Rothamsted, UK used. Sown Yeoman II wheat. Field width = 16 segments * 0.5 meters = 8 meters. Field length: 80 rows * 6 inches apart = 40 feet. grain yield number ears half-meter length recorded. quite small field, 1/40 acre size. Edge rows higher yields. Row-end units higher yields interior units. border effects significant. Variation rows greater variation within rows. Negative correlation rows may indicate competition effects. ears, Kalamkar discarded 4 rows side 3 half-meter lengths end. Kalamkar suggested using four parallel half-meter rows sampling unit. Note, Rothamsted report 1931, page 57, says: year three workers (F. R. Immer, S. H. Justensen R. J. Kalamkar) taken question efficient use land experiments edge row must discarded...","code":""},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"Kalamkar, R. J (1932). Study Sampling Technique Wheat. Journal Agricultural Science, Vol.22(4), pp.783-796. https://doi.org/10.1017/S0021859600054599","code":""},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"None.","code":""},{"path":"/reference/kalamkar.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — kalamkar.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kalamkar.wheat.uniformity) dat <- kalamkar.wheat.uniformity plot(yield ~ ears, dat, main=\"kalamkar.wheat.uniformity\") # totals match Kalamkar # sum(dat$yield) # 24112.5 # sum(dat$ears) # 25850 libs(desplot) desplot(dat, ears ~ col*row, flip=TRUE, aspect=(80*0.5)/(16*1.64042), # true aspect main=\"kalamkar.wheat.uniformity - ears\") desplot(dat, yield ~ col*row, flip=TRUE, aspect=(80*0.5)/(16*1.64042), # true aspect main=\"kalamkar.wheat.uniformity - yield\") # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Show the negative correlation between rows dat <- transform(dat, rowf=factor(row), colf=factor(col)) dat <- dat[order(dat$rowf, dat$colf),] m1 = asreml(yield ~ 1, data=dat, resid= ~ ar1(rowf):ar1(colf)) lucid::vc(m1) ## effect component std.error z.ratio bound pctch ## rowf:colf!R 81.53 3.525 23 P 0 ## rowf:colf!rowf!cor -0.09464 0.0277 -3.4 U 0.1 ## rowf:colf!colf!cor 0.2976 0.02629 11 U 0.1 } } # }"},{"path":"/reference/kang.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"Maize yields 4 locs 3 years Louisianna.","code":""},{"path":"/reference/kang.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"","code":"data(\"kang.maize\")"},{"path":"/reference/kang.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"gen genotype, 17 levels env environment, 12 levels yield yield, tonnes/ha environment environment, 13 levels year year, 85-87 loc location, 4 levels","code":""},{"path":"/reference/kang.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"Yield trials conducted four locations (Alexandria, Baton Rouge, Bossier City, St. Joseph) Louisiana 1985 1987. loc planted RCB design 4 reps. Mean yields given data. Used permission Dan Gorman.","code":""},{"path":"/reference/kang.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"Kang, MS Gorman, DP. (1989). Genotype x environment interaction maize. Agronomy Journal, 81, 662-664. Table 2.","code":""},{"path":"/reference/kang.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize in Louisianna at 4 locs in 3 years — kang.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kang.maize) dat <- kang.maize # Sweep out loc means, then show interaction plot. libs(reshape2) mat <- acast(dat, gen~env, value.var='yield') mat <- sweep(mat, 2, colMeans(mat)) dat2 <- melt(mat) names(dat2) <- c('gen','env','yield') libs(lattice) xyplot(yield~env|gen, data=dat2, type='l', group=gen, panel=function(x,y,...){ panel.abline(h=0,col=\"gray70\") panel.xyplot(x,y,...) }, ylab=\"Environment-centered yield\", main=\"kang.maize - maize hybrid yields\", scales=list(x=list(rot=90))) # Weather covariates for each environment. covs <- data.frame(env=c(\"AL85\",\"AL86\",\"AL87\", \"BR85\",\"BR86\",\"BR87\", \"BC85\",\"BC86\",\"BC87\", \"SJ85\",\"SJ86\",\"SJ87\"), maxt=c(30.7,30.2,29.7,31.5,29.4,28.5, 31.9, 30.4,31.7, 32,29.6,28.9), mint=c(18.7,19.3,18.5, 19.7,18,17.2, 19.1,20.4,20.3, 20.4,19.1,17.5), rain=c(.2,.34,.22, .28,.36,.61, .2,.43,.2, .36,.41,.22), humid=c(82.8,91.1,85.4, 88.1,90.9,88.6, 95.4,90.4,86.7, 95.6,89.5,85)) } # }"},{"path":"/reference/kang.peanut.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"Peanut yields 10 genotypes 15 environments","code":""},{"path":"/reference/kang.peanut.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"","code":"data(\"kang.peanut\")"},{"path":"/reference/kang.peanut.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"data frame 590 observations following 4 variables. gen genotype factor, 10 levels rep replicate factor, 4 levels yield yield env environment factor, 15 levels","code":""},{"path":"/reference/kang.peanut.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"Florman, Tegua, mf484, mf485, mf487, mf489 long crop cycle. others short crop cycle. data also likely used Casanoves et al 2005, \"Evaluation Multienvironment Trials Peanut Cultivars\", appears slightly smaller subset (10 genotypes, perhaps years 96,97,98,99). Based d.f. table 5, appears environment E13 grown 1998. (5 loc * (4-1) = 15, table 14, 98-99 3 reps instead 4 reps.) Data National Institute Agricultural Technology, Argentina.","code":""},{"path":"/reference/kang.peanut.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"M. S. Kang, M. Balzarini, J. L. L. Guerra (2004). Genotype--environment interaction\". : . Saxton (2004). \"Genetic Analysis Complex Traits Using SAS\".","code":""},{"path":"/reference/kang.peanut.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"Johannes Forkman, Julie Josse, Hans-Peter Piepho (2019). Hypothesis Tests Principal Component Analysis Variables Standardized. JABES https://doi.org/10.1007/s13253-019-00355-5","code":""},{"path":"/reference/kang.peanut.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of peanuts for 10 genotypes in 15 environments — kang.peanut","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kang.peanut) dat <- kang.peanut # Table 5.1 of Kang et al. (Chapter 5 of Saxton) libs(reshape2) Y0 <- acast(dat, env~gen, value.var='yield', fun=mean) round(Y0,2) # GGE biplot of Kang, p. 82. libs(gge) m1 <- gge(dat, yield~gen*env, scale=FALSE) biplot(m1, flip=c(1,1), main=\"kang.peanut - GGE biplot\") # Forkman 2019, fig 2 # m2 <- gge(dat, yield~gen*env, scale=TRUE) # biplot(m2, main=\"kang.peanut - GGE biplot\") # biplot(m2, comps=3:4, main=\"kang.peanut - GGE biplot\") } # }"},{"path":"/reference/karcher.turfgrass.html","id":null,"dir":"Reference","previous_headings":"","what":"Turfgrass ratings for different treatments — karcher.turfgrass","title":"Turfgrass ratings for different treatments — karcher.turfgrass","text":"Turfgrass ratings different treatments","code":""},{"path":"/reference/karcher.turfgrass.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Turfgrass ratings for different treatments — karcher.turfgrass","text":"data frame 128 observations following 6 variables. week week number rep blocking factor manage management factor, 4 levels nitro nitrogen factor, 2 levels rating turfgrass rating, 4 ordered levels count number samples given rating","code":""},{"path":"/reference/karcher.turfgrass.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Turfgrass ratings for different treatments — karcher.turfgrass","text":"Turf color assessed scale Poor, Average, Good, Excellent. data number times combination management style nitrogen level received particular rating across four replicates four sampling weeks. eight treatments completely randomized design. Nitrogen level 1 2.5 g/m^2, level 2 5 g/m^2. Management 1 = N applied supplemental water injection. M2 = surface applied supplemental water injection. M3 = nitrogen injected 7.6 cm deep M4 = nitrogen injected 12.7 cm deep.","code":""},{"path":"/reference/karcher.turfgrass.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Turfgrass ratings for different treatments — karcher.turfgrass","text":"Schabenberger, Oliver Francis J. Pierce. 2002. Contemporary Statistical Models Plant Soil Sciences. CRC Press. Page 380.","code":""},{"path":"/reference/karcher.turfgrass.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turfgrass ratings for different treatments — karcher.turfgrass","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(karcher.turfgrass) dat <- karcher.turfgrass dat$rating <- ordered(dat$rating, levels=c('Poor','Average', 'Good','Excellent')) ftable(xtabs(~manage+nitro+rating, dat)) # Table 6.19 of Schabenberger # Probably would choose management M3, nitro N2 mosaicplot(xtabs(count ~ manage + rating + nitro, dat), shade=TRUE, dir=c('h','v','h'), main=\"karcher.turfgrass - turfgrass ratings\") # Multinomial logistic model. Probit Ordered Logistic Regression. libs(MASS) m1 <- polr(rating ~ nitro*manage + week, dat, weights=count, Hess=TRUE, method='logistic') summary(m1) # Try to match the \"predicted marginal probability distribution\" of # Schabenberger table 6.20. He doesn't define \"marginal\". # Are the interaction terms included before aggregation? # Are 'margins' calculated before/after back-transforming? # At what level is the covariate 'week' included? # Here is what Schabenberger presents: ## M1 M2 M3 M4 | N1 N2 ## Poor .668 .827 .001 .004 | .279 .020 ## Avg .330 .172 .297 .525 | .712 .826 ## Good .002 .001 .695 .008 | .008 .153 ## Exc .000 .000 .007 .003 | .001 .001 ## We use week=3.5, include interactions, then average newd <- expand.grid(manage=levels(dat$manage), nitro=levels(dat$nitro), week=3.5) newd <- cbind(newd, predict(m1, newdata=newd, type='probs')) # probs) print(aggregate( . ~ manage, data=newd, mean), digits=2) ## manage nitro week Poor Average Good Excellent ## 1 M1 1.5 3.5 0.67 0.33 0.0011 0.0000023 ## 2 M2 1.5 3.5 0.76 0.24 0.00059 0.0000012 ## 3 M3 1.5 3.5 0.0023 0.48 0.52 0.0042 ## 4 M4 1.5 3.5 0.0086 0.57 0.42 0.0035 } # }"},{"path":"/reference/kayad.alfalfa.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"Yield monitor data 4 cuttings alfalfa Saudi Arabia.","code":""},{"path":"/reference/kayad.alfalfa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"","code":"data(\"kayad.alfalfa\")"},{"path":"/reference/kayad.alfalfa.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"data frame 8628 observations following 4 variables. harvest harvest number lat latitude long longitude yield yield, tons/ha","code":""},{"path":"/reference/kayad.alfalfa.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"Data collected 23.5 ha field alfalfa Saudia Arabia. field harvested four consecutive times (H8 = 5 Dec 2013, H9 = 16 Feb 2014, H10 = 2 Apr 2014, H11 = 6 May 2014). Data collected using geo-referenced yield monitor. Supporting information contains yield monitor data 4 hay harvests center-pivot field. # TODO: Normalize yields harvest, average together # create productivity map. Two ways normalize: # Normalize 0-100: ((mapValue - min) * 100) / (max - min) # Standardize: ((mapValue - mean) / stdev) * 100","code":""},{"path":"/reference/kayad.alfalfa.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"Ahmed G. Kayad, et al. (2016). Assessing Spatial Variability Alfalfa Yield Using Satellite Imagery Ground-Based Data. PLOS One, 11(6). https://doi.org/10.1371/journal.pone.0157166","code":""},{"path":"/reference/kayad.alfalfa.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"None","code":""},{"path":"/reference/kayad.alfalfa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. — kayad.alfalfa","text":"","code":"library(agridat) data(kayad.alfalfa) dat <- kayad.alfalfa # match Kayad table 1 stats libs(dplyr) #> #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:gridExtra’: #> #> combine #> The following object is masked from ‘package:MASS’: #> #> select #> The following object is masked from ‘package:nlme’: #> #> collapse #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union dat <- group_by(dat, harvest) summarize(dat, min=min(yield), max=max(yield), mean=mean(yield), stdev=sd(yield), var=var(yield)) #> # A tibble: 4 × 6 #> harvest min max mean stdev var #> #> 1 H10 0 6.68 2.86 1.24 1.55 #> 2 H11 0 9.96 4.01 1.69 2.85 #> 3 H8 0 5.86 2.32 1.01 1.03 #> 4 H9 0.191 5.97 2.45 1.07 1.14 # Figure 4 of Kayad libs(latticeExtra) catcols <- c(\"#cccccc\",\"#ff0000\",\"#ffff00\",\"#55ff00\",\"#0070ff\",\"#c500ff\",\"#73004c\") levelplot(yield ~ long*lat |harvest, dat, aspect=1, at = c(0,2,3,4,5,6,7,10), col.regions=catcols, main=\"kayad.alfalfa\", prepanel=prepanel.default.xyplot, panel=panel.levelplot.points) # Similar to Kayad fig 5. ## levelplot(yield ~ long*lat |harvest, dat, ## prepanel=prepanel.default.xyplot, ## panel=panel.levelplot.points, ## col.regions=pals::brewer.reds)"},{"path":"/reference/keen.potatodamage.html","id":null,"dir":"Reference","previous_headings":"","what":"Damage to potato tubers from lifting rods. — keen.potatodamage","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"Damage potato tubers lifting rods.","code":""},{"path":"/reference/keen.potatodamage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"","code":"data(\"keen.potatodamage\")"},{"path":"/reference/keen.potatodamage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"data frame 1152 observations following 6 variables. energy energy factor weight weight class gen genotype/variety factor rod rod factor damage damage category count count tubers combination categories","code":""},{"path":"/reference/keen.potatodamage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"Experiments performed Wageningen, Netherlands. Potatoes can damaged lifter. experiment, eight types lifting rod compared. Two energy levels, six genotypes/varieties three weight classes used. combinations treatments involved 20 potato tubers. Tubers rated undamaged (D1) severely damaged (D4). main interest differences rods, interactions. factors (besides rod) introduced create variety experimental conditions interest. Keen Engle estimated following rod effects. # Rod: 1 2 3 4 5 6 7 8 # Effect: 0 -1.26 -0.42 0.55 -1.50 -1.85 -1.76 -2.09 Used permission Bas Engel.","code":""},{"path":"/reference/keen.potatodamage.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":". Keen B. Engel. Analysis mixed model ordinal data iterative re-weighted REML. Statistica Neerlandica, 51, 129–144. Table 2. https://doi.org/10.1111/1467-9574.00044","code":""},{"path":"/reference/keen.potatodamage.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"R. Larsson & Jesper Ryden (2021). Applications discrete factor analysis. Communications Statistics - Simulation Computation. https://doi.org/10.1080/03610918.2021.1964528","code":""},{"path":"/reference/keen.potatodamage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Damage to potato tubers from lifting rods. — keen.potatodamage","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(keen.potatodamage) dat <- keen.potatodamage # Energy E1, Rod R4, Weight W1 have higher proportions of severe damage # Rod 8 has the least damage d2 <- xtabs(count~energy+rod+gen+weight+damage, data=dat) mosaicplot(d2, color=c(\"lemonchiffon1\",\"moccasin\",\"lightsalmon1\",\"indianred\"), xlab=\"Energy / Genotype\", ylab=\"Rod / Weight\", main=\"keen.potatodamage\") # Not run because CRAN prefers examples less than 5 seconds. libs(ordinal) # Note, the clmm2 function can have only 1 random term. Results are # similar to Keen & Engle, but necessarily different (they had multiple # random terms). m1 <- clmm2(damage ~ rod + energy + gen + weight, data=dat, weights=count, random=rod:energy, link='probit') round(coef(m1)[4:10],2) ## rodR2 rodR3 rodR4 rodR5 rodR6 rodR7 rodR8 ## -1.19 -0.41 0.50 -1.46 -1.73 -1.67 -1.99 # Alternative # m2 <- clmm(damage ~ rod + energy + gen + weight + # (1|rod:energy), data=dat, weights=count, link='probit') } # }"},{"path":"/reference/kempton.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of barley — kempton.barley.uniformity","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"Uniformity trial barley Cambridge, England, 1978.","code":""},{"path":"/reference/kempton.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"data frame 196 observations following 3 variables. row row col column yield grain yield, kg","code":""},{"path":"/reference/kempton.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"uniformity trial spring barley planted 1978. Conducted Plant Breeding Institute Cambridge, England. plot 5 feet wide, 14 feet long. Field width: 7 plots * 14 feet = 98 feet Field length: 28 plots * 5 feet = 140 feet","code":""},{"path":"/reference/kempton.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"R. . Kempton C. W. Howes (1981). use neighbouring plot values analysis variety trials. Applied Statistics, 30, 59–70. https://doi.org/10.2307/2346657","code":""},{"path":"/reference/kempton.barley.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science. 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667","code":""},{"path":"/reference/kempton.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of barley — kempton.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kempton.barley.uniformity) dat <- kempton.barley.uniformity libs(desplot) desplot(dat, yield~col*row, aspect=140/98, tick=TRUE, # true aspect main=\"kempton.barley.uniformity\") # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91 dat <- transform(dat, xf = factor(col), yf=factor(row)) # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) dat <- transform(dat, xf = factor(col), yf=factor(row)) m1 <- asreml(yield ~ 1, data=dat, resid = ~ar1(xf):ar1(yf)) # lucid::vc(m1) ## effect component std.error z.ratio bound ## xf:yf!R 0.1044 0.02197 4.7 P 0 ## xf:yf!xf!cor 0.2458 0.07484 3.3 U 0 ## xf:yf!yf!cor 0.8186 0.03821 21 U 0 # asreml estimates auto-regression correlations of 0.25, 0.82 # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91 } # ---------- if(0){ # Kempton defines 4 blocks, randomly assigns variety codes 1-49 in each block, fits # RCB model, computes mean squares for variety and residual. Repeat 40 times. # Kempton's estimate: variety = 1032, residual = 1013 # Our estimate: variety = 825, residual = 1080 fitfun <- function(dat){ dat <- transform(dat, block=factor(ceiling(row/7)), gen=factor(c(sample(1:49),sample(1:49),sample(1:49),sample(1:49)))) m2 <- lm(yield*100 ~ block + gen, dat) anova(m2)[2:3,'Mean Sq'] } set.seed(251) out <- replicate(50, fitfun(dat)) rowMeans(out) # 826 1079 } } # }"},{"path":"/reference/kempton.competition.html","id":null,"dir":"Reference","previous_headings":"","what":"Sugar beet trial with competition effects — kempton.competition","title":"Sugar beet trial with competition effects — kempton.competition","text":"Yield sugar beets 36 varieties 3-rep RCB experiment. Competition effects present.","code":""},{"path":"/reference/kempton.competition.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sugar beet trial with competition effects — kempton.competition","text":"data frame 108 observations following 5 variables. gen genotype, 36 levels rep rep, 3 levels row row col column yield yield, kg/plot","code":""},{"path":"/reference/kempton.competition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sugar beet trial with competition effects — kempton.competition","text":"Entries grown 12m rows, 0.5m apart. Guard rows grown alongside replicate boundaries, yields plots included.","code":""},{"path":"/reference/kempton.competition.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sugar beet trial with competition effects — kempton.competition","text":"R Kempton, 1982. Adjustment competition varieties plant breeding trials, Journal Agricultural Science, 98, 599-611. https://doi.org/10.1017/S0021859600054381","code":""},{"path":"/reference/kempton.competition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sugar beet trial with competition effects — kempton.competition","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kempton.competition) dat <- kempton.competition # Raw means in Kempton table 2 round(tapply(dat$yield, dat$gen, mean),2) # Fixed genotype effects, random rep effects, # Autocorrelation of neighboring plots within the same rep, phi = -0.22 libs(nlme) m1 <- lme(yield ~ -1+gen, random=~1|rep, data=dat, corr=corAR1(form=~col|rep)) # Lag 1 autocorrelation is negative--evidence of competition plot(ACF(m1), alpha=.05, grid=TRUE, main=\"kempton.competition\", ylab=\"Autocorrelation between neighborning plots\") # Genotype effects round(fixef(m1),2) # Variance of yield increases with yield plot(m1, main=\"kempton.competition\") } # }"},{"path":"/reference/kempton.rowcol.html","id":null,"dir":"Reference","previous_headings":"","what":"Row-column experiment of wheat — kempton.rowcol","title":"Row-column experiment of wheat — kempton.rowcol","text":"Row-column experiment wheat, 35 genotypes, 2 reps.","code":""},{"path":"/reference/kempton.rowcol.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Row-column experiment of wheat — kempton.rowcol","text":"data frame 68 observations following 5 variables. rep replicate factor, 2 levels row row col column gen genotype factor, 35 levels yield yield","code":""},{"path":"/reference/kempton.rowcol.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Row-column experiment of wheat — kempton.rowcol","text":"Included illustrate REML analysis row-column design.","code":""},{"path":"/reference/kempton.rowcol.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Row-column experiment of wheat — kempton.rowcol","text":"R Kempton P N Fox, Statistical Methods Plant Variety Evaluation, Chapman Hall, 1997.","code":""},{"path":"/reference/kempton.rowcol.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row-column experiment of wheat — kempton.rowcol","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kempton.rowcol) dat <- kempton.rowcol dat <- transform(dat, rowf=factor(row), colf=factor(col)) libs(desplot) desplot(dat, yield~col*row|rep, num=gen, out1=rep, # unknown aspect main=\"kempton.rowcol\") # Model with rep, row, col as random. Kempton, page 62. # Use \"-1\" so that the vcov matrix doesn't include intercept libs(lme4) m1 <- lmer(yield ~ -1 + gen + rep + (1|rep:rowf) + (1|rep:colf), data=dat) # Variance components match Kempton. print(m1, corr=FALSE) # Standard error of difference for genotypes. Kempton page 62, bottom. covs <- as.matrix(vcov(m1)[1:35, 1:35]) vars <- diag(covs) vdiff <- outer(vars, vars, \"+\") - 2 * covs sed <- sqrt(vdiff[upper.tri(vdiff)]) min(sed) # Minimum SED mean(sed) # Average SED max(sed) # Maximum SED } # }"},{"path":"/reference/kempton.slatehall.html","id":null,"dir":"Reference","previous_headings":"","what":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"Yields Slate Hall Farm 1976 spring wheat trial.","code":""},{"path":"/reference/kempton.slatehall.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"data frame 150 observations following 5 variables. rep rep, 6 levels row row col column gen genotype, 25 levels yield yield (grams/plot)","code":""},{"path":"/reference/kempton.slatehall.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"trial balanced lattice 25 varieties 6 replicates, 10 ranges 15 columns. plot size 1.5 meters 4 meters. row within rep (incomplete) block. Field width: 15 columns * 1.5m = 22.5m Field length: 10 ranges * 4m = 40m","code":""},{"path":"/reference/kempton.slatehall.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"R Kempton P N Fox. (1997). Statistical Methods Plant Variety Evaluation, Chapman Hall. Page 84. Julian Besag David Higdon. 1993. Bayesian Inference Agricultural Field Experiments. Bull. Int. Statist. Table 4.1.","code":""},{"path":"/reference/kempton.slatehall.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"Gilmour, Arthur R Robin Thompson Brian R Cullis. (1994). Average Information REML: Efficient Algorithm Variance Parameter Estimation Linear Mixed Models, Biometrics, 51, 1440-1450.","code":""},{"path":"/reference/kempton.slatehall.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Slate Hall Farm 1976 spring wheat — kempton.slatehall","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kempton.slatehall) dat <- kempton.slatehall # Besag 1993 figure 4.1 (left panel) libs(desplot) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) desplot(dat, yield ~ col * row, aspect=40/22.5, # true aspect num=gen, out1=rep, col.regions=grays, # unknown aspect main=\"kempton.slatehall - spring wheat yields\") # ---------- # Incomplete block model of Gilmour et al 1995 libs(lme4, lucid) dat <- transform(dat, xf=factor(col), yf=factor(row)) m1 <- lmer(yield ~ gen + (1|rep) + (1|rep:yf) + (1|rep:xf), data=dat) vc(m1) ## groups name variance stddev ## rep:xf (Intercept) 14810 121.7 ## rep:yf (Intercept) 15600 124.9 ## rep (Intercept) 4262 65.29 ## Residual 8062 89.79 # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Incomplete block model of Gilmour et al 1995 dat <- transform(dat, xf=factor(col), yf=factor(row)) m2 <- asreml(yield ~ gen, random = ~ rep/(xf+yf), data=dat) lucid::vc(m2) ## effect component std.error z.ratio constr ## rep!rep.var 4262 6890 0.62 pos ## rep:xf!rep.var 14810 4865 3 pos ## rep:yf!rep.var 15600 5091 3.1 pos ## R!variance 8062 1340 6 pos # Table 4 # asreml4 # predict(m2, data=dat, classify=\"gen\")$pvals } } # }"},{"path":"/reference/kenward.cattle.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"Repeated measurements weights calves trial control intestinal parasites.","code":""},{"path":"/reference/kenward.cattle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"","code":"data(\"kenward.cattle\")"},{"path":"/reference/kenward.cattle.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"data frame 660 observations following 4 variables. animal animal factor trt treatment factor, B day day, numberic, 0-133 weight bodyweight, kg","code":""},{"path":"/reference/kenward.cattle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"Grazing cattle can ingest larvae, deprives host animal nutrients weakens immune system, affecting growth animal. Two treatments B applied randomly 60 animals (30 two groups) control disease. animal weighed 11 times two-week intervals (one week final two measurements). difference treatments, difference first become manifest?","code":""},{"path":"/reference/kenward.cattle.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"Kenward, Michael G. (1987). Method Comparing Profiles Repeated Measurements. Applied Statistics, 36, 296-308. Table 1. https://doi.org/10.2307/2347788","code":""},{"path":"/reference/kenward.cattle.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"W. Zhang, C. Leng C. Y. Tang (2015). joint modelling approach longitudinal studies J. R. Statist. Soc. B, 77 (2015), 219–238. https://doi.org/10.1111/rssb.12065","code":""},{"path":"/reference/kenward.cattle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Repeated measurement of weights of calves with two treatments. — kenward.cattle","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kenward.cattle) dat <- kenward.cattle # Profile plots libs(lattice) foo1 <- xyplot(weight~day|trt, data=dat, type='l', group=animal, xlab=\"Day\", ylab=\"Animal weight\", main=\"kenward.cattle\") print(foo1) # ---------- # lme4. Fixed treatment intercepts, treatment polynomial trend. # Random deviation for each animal libs(lme4) m1a <-lmer(weight ~ trt*poly(day, 4) + (1|animal), data=dat, REML = FALSE) # Change separate polynomials into common polynomial m1b <-lmer(weight ~ trt + poly(day, 4) + (1|animal), data=dat, REML = FALSE) # Drop treatment differences m1c <-lmer(weight ~ poly(day, 4) + (1|animal), data=dat, REML = FALSE) anova(m1a, m1b, m1c) # Significant differences between trt polynomials # Overlay polynomial predictions on plot libs(latticeExtra) dat$pred <- predict(m1a, re.form=NA) foo1 + xyplot(pred ~ day|trt, data=dat, lwd=2, col=\"black\", type='l') # A Kenward-Roger Approximation and Parametric Bootstrap # libs(pbkrtest) # KRmodcomp(m1b, m1c) # Non-signif # Model comparison of nested models using parametric bootstrap methods # PBmodcomp(m1b, m1c, nsim=500) ## Parametric bootstrap test; time: 13.20 sec; samples: 500 extremes: 326; ## large : weight ~ trt + poly(day, 4) + (1 | animal) ## small : weight ~ poly(day, 4) + (1 | animal) ## stat df p.value ## LRT 0.2047 1 0.6509 ## PBtest 0.2047 0.6527 # ----------- # ASREML approach to model. Not final by any means. # Maybe a spline curve for each treatment, plus random deviations for each time if(require(\"asreml\", quietly=TRUE)){ libs(asreml) m1 <- asreml(weight ~ 1 + lin(day) + # overall line trt + trt:lin(day), # different line for each treatment data=dat, random = ~ spl(day) + # overall spline trt:spl(day) + # different spline for each treatment dev(day) + trt:dev(day) ) # non-spline deviation at each time*trt p1 <- predict(m1, data=dat, classify=\"trt:day\") p1 <- p1$pvals foo2 <- xyplot(predicted.value ~ day|trt, p1, type='l', lwd=2, lty=1, col=\"black\") libs(latticeExtra) print(foo1 + foo2) # Not much evidence for treatment differences # wald(m1) ## Df Sum of Sq Wald statistic Pr(Chisq) ## (Intercept) 1 37128459 139060 <2e-16 *** ## trt 1 455 2 0.1917 ## lin(day) 1 570798 2138 <2e-16 *** ## trt:lin(day) 1 283 1 0.3031 ## residual (MS) 267 # lucid::vc(m1) ## effect component std.error z.ratio constr ## spl(day) 25.29 24.09 1 pos ## dev(day) 1.902 4.923 0.39 pos ## trt:spl(day)!trt.var 0.00003 0.000002 18 bnd ## trt:dev(day)!trt.var 0.00003 0.000002 18 bnd ## R!variance 267 14.84 18 pos } } # }"},{"path":"/reference/kerr.sugarcane.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"Uniformity trials sugarcane, 4 fields","code":""},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"","code":"data(\"kerr.sugarcane.uniformity\")"},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"data frame 564 observations following 4 variables. row row col column yield yield, pounds per plot trial trial number","code":""},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"Experiment conducted Sugar Experiment Station, Brisbane, Queensland, Australia 1937. Four trials harvested, 12 plots 12 plots, plot 19 feet 19 feet (one field used 18-foot plots). Trial 1 plant cane. Trial 2 ratoon cane. Trial 3 plant cane, irrigated. Trial 4 ratoon cane, irrigated. Field length: 12 plots * 19 feet = 228 feet. Field width: 12 plots * 19 feet = 228 feet.","code":""},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"H. W. Kerr (1939). Notes plot technique. Proc. Internat. Soc. Sugarcane Technol. 6, 764–778.","code":""},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"None","code":""},{"path":"/reference/kerr.sugarcane.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of sugarcane, 4 fields — kerr.sugarcane.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kerr.sugarcane.uniformity) dat <- kerr.sugarcane.uniformity # match Kerr figure 4 libs(desplot) desplot(dat, yield ~ col*row|trial, flip=TRUE, aspect=1, # true aspect main=\"kerr.sugarcane.uniformity\") # CV matches Kerr table 2, page 768 # aggregate(yield ~ trial, dat, FUN= function(x) round(100*sd(x)/mean(x),2)) ## trial yield ## 1 T1 7.95 ## 2 T2 9.30 ## 3 T3 10.37 ## 4 T4 13.76 } # }"},{"path":"/reference/khan.brassica.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of brassica. — khan.brassica.uniformity","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"Uniformity trial brassica India.","code":""},{"path":"/reference/khan.brassica.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"","code":"data(\"khan.brassica.uniformity\")"},{"path":"/reference/khan.brassica.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"data frame 648 observations following 4 variables. field Field, F1 F2 row row ordinate col column ordinate yield yield, 1/8 ounce","code":""},{"path":"/reference/khan.brassica.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"Two different fields used, representing average type soil Lyallpur. area 90 ft 90 ft marked harvested individual plots 5 feet per side. data copied pdf hand-corrected.","code":""},{"path":"/reference/khan.brassica.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"Khan, Abdur Rashid Jage Ram Dalal (1943). Optimum Size Shape Plots Brassica Experiments Punjab. Sankhyā: Indian Journal Statistics ,6, 3. Proceedings Indian Statistical Conference 1942 (1943), pp. 317-320. https://www.jstor.org/stable/25047782","code":""},{"path":"/reference/khan.brassica.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"None.","code":""},{"path":"/reference/khan.brassica.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of brassica. — khan.brassica.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(khan.brassica.uniformity) dat <- khan.brassica.uniformity # Slightly different results than Khan Table 1. ## dat ## mutate(yield=yield/8) ## group_by(field) ## summarize(mn=mean(yield), sd=sd(yield)) libs(desplot) desplot(dat, yield ~ col*row | field, flip=TRUE, aspect=1, main=\"khan.brassica.uniformity\") } # }"},{"path":"/reference/khin.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice — khin.rice.uniformity","title":"Uniformity trial of rice — khin.rice.uniformity","text":"Uniformity trial rice Burma, 1948.","code":""},{"path":"/reference/khin.rice.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of rice — khin.rice.uniformity","text":"","code":"data(\"khin.rice.uniformity\")"},{"path":"/reference/khin.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice — khin.rice.uniformity","text":"data frame 1080 observations following 3 variables. row row col column yield yield, oz/plot","code":""},{"path":"/reference/khin.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice — khin.rice.uniformity","text":"uniformity trial rice. Conducted Mudon Agricultural Station, Burma, 1947-48. Basic plots 3 feet square. Field width: 30 plots * 3 feet. Field length: 36 plots * 3 feet. Data typed K.Wright.","code":""},{"path":"/reference/khin.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice — khin.rice.uniformity","text":"Khin, San. 1950. Investigation relative costs rice experiments based efficiency designs. Dissertation: Imperial College Tropical Agriculture (ICTA). Appendix XV. https://hdl.handle.net/2139/42422","code":""},{"path":"/reference/khin.rice.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of rice — khin.rice.uniformity","text":"None.","code":""},{"path":"/reference/khin.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice — khin.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(khin.rice.uniformity) dat <- khin.rice.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, main=\"khin.rice.uniformity\", aspect=(36*3)/(30*3)) # true aspect } # }"},{"path":"/reference/kiesselbach.oats.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of oats — kiesselbach.oats.uniformity","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"Uniformity trial oats Nebraska 1916.","code":""},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"","code":"data(\"kiesselbach.oats.uniformity\")"},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"data frame 207 observations following 3 variables. row row col column yield yield bu/ac","code":""},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"Experiment conducted 1916. Crop Kerson oats. plot covered 1/30th acre. Oats drilled plats 66 inches wide 16 rods long. drill 66 inches wide. Plats separated space 16 inches outside drill rows. source document includes three photographs field. 1 acre = 43560 sq feet 1/30 acre = 1452 sq feet = 16 rods * 16.5 ft/rod * 5.5 ft Field width: 3 plats * 16 rods/plat * 16.5 ft/rod = 792 feet Field length: 69 plats * 5.5 ft + 68 gaps * 1.33 feet = 469 feet","code":""},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"Kiesselbach, Theodore . (1917). Studies Concerning Elimination Experimental Error Comparative Crop Tests. University Nebraska Agricultural Experiment Station Research Bulletin . 13. Pages 51-72. https://archive.org/details/StudiesConcerningTheEliminationOfExperimentalErrorInComparativeCrop https://digitalcommons.unl.edu/extensionhist/430/","code":""},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"None.","code":""},{"path":"/reference/kiesselbach.oats.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of oats — kiesselbach.oats.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kiesselbach.oats.uniformity) dat <- kiesselbach.oats.uniformity range(dat$yield) # 56.7 92.8 match Kiesselbach p 64. libs(desplot) desplot(dat, yield ~ col*row, tick=TRUE, flip=TRUE, aspect=792/469, # true aspect main=\"kiesselbach.oats.uniformity\") } # }"},{"path":"/reference/kirk.potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Variety trial of potatoes, highly replicated — kirk.potato","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"Variety trial potatoes, highly replicated","code":""},{"path":"/reference/kirk.potato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"","code":"data(\"kirk.potato\")"},{"path":"/reference/kirk.potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"data frame 380 observations following 5 variables. row row ordinate col column ordinate rep replicate (block) gen genotype (variety) yield yield, pounds per plot","code":""},{"path":"/reference/kirk.potato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"highly-replicated variety trial potatoes planted 1924 check plots every 5th row. Entries randomized. rod rows planted series across field, rows spaced five links apart (nearly 3.5 feet) 3.5 foot passes series. replicates sometimes dis-jointed, really blocks.","code":""},{"path":"/reference/kirk.potato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"Kirk, L. E. C. H. Goulden (1925) statistical observations yield test potato varieties. Scientific Agriculture, 6, 89-97. https://doi.org/10.4141/sa-1925-0088 (paywall) https://www.google.com/books/edition/Canadian_Journal_of_Agriculture_Science/TgIkAQAAMAAJ","code":""},{"path":"/reference/kirk.potato.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"None","code":""},{"path":"/reference/kirk.potato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variety trial of potatoes, highly replicated — kirk.potato","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kirk.potato) dat <- kirk.potato libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=1, main=\"kirk.potato\") # Match means in Table I libs(dplyr) dat } # }"},{"path":"/reference/kling.augmented.html","id":null,"dir":"Reference","previous_headings":"","what":"Augmented design of meadowfoam — kling.augmented","title":"Augmented design of meadowfoam — kling.augmented","text":"Augmented design meadowfoam","code":""},{"path":"/reference/kling.augmented.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augmented design of meadowfoam — kling.augmented","text":"","code":"data(\"kling.augmented\")"},{"path":"/reference/kling.augmented.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Augmented design of meadowfoam — kling.augmented","text":"data frame 68 observations following 7 variables. plot Plot number gen Genotype / Entry name Genotype name block Block, text tsw Thousand seed weight row Row ordinate col Column ordinate","code":""},{"path":"/reference/kling.augmented.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Augmented design of meadowfoam — kling.augmented","text":"experiment meadowfoam. Blocks one direction, serpentine layout. 50 new genotypes 3 checks (C1=Ross, C2=OMF183, C3=Starlight). New genotypes 1 rep, checks 6 reps. response variable thousand seed weight.","code":""},{"path":"/reference/kling.augmented.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Augmented design of meadowfoam — kling.augmented","text":"Jennifer Kling, \"Introduction Augmented Experimental Design\" https://plant-breeding-genomics.extension.org/introduction--augmented-experimental-design/ Accessed May 2022.","code":""},{"path":"/reference/kling.augmented.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Augmented design of meadowfoam — kling.augmented","text":"None","code":""},{"path":"/reference/kling.augmented.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Augmented design of meadowfoam — kling.augmented","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kling.augmented) dat <- kling.augmented libs(desplot,lattice,lme4) # Layout and yields desplot(dat, tsw ~ col*row, text=name, cex=1.5) # Mixed model, fixed blocks, random genotypes m1 <- lmer(tsw ~ block + (1|name), data=dat) ran1 <- ranef(m1, condVar=TRUE) ran1 dotplot(ran1) # Caterpillar plot } # }"},{"path":"/reference/kotowski.potato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of potato in Poland. — kotowski.potato.uniformity","title":"Uniformity trial of potato in Poland. — kotowski.potato.uniformity","text":"Uniformity trial potato Poland.","code":""},{"path":"/reference/kotowski.potato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of potato in Poland. — kotowski.potato.uniformity","text":"","code":"data(\"kotowski.potato.uniformity\")"},{"path":"/reference/kotowski.potato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of potato in Poland. — kotowski.potato.uniformity","text":"data frame 152 observations following 5 variables. field field name row row ordinate col column ordinate yield yield per plot, kg starch starch content per plot, percent","code":""},{"path":"/reference/kotowski.potato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of potato in Poland. — kotowski.potato.uniformity","text":"Kotowski (Google translate): examined experimental field Vegetable Cultivation Breeding Plant Skierniewice. material provided \"Wohltmany\" Lochowa potatoes. planted 5 May 1922 two adjacent plots, combined one complex; soil strong, \"even\" surface, supplied manure mineral fertilizers 3 years, previous crop two-year clover ploughed autumn. development potatoes, initially weak, due drought, later (August) much better. dug 12 Oct way 48 plots marked one plot (field 1), 104 plots (field 2); plot 10 m long 5 rows potatoes wide, 50 cm apart, plot area theoretically 25 sq m, reality minor deviations. entire number tufts (tubers?) weighed plot, starch determined twice Reimann's scales immediately digging finished. Field F1 width: 12 plots * 2.5 m = 30 m Field F1 length: 4 plots * 10m = 40 m Field F2 width: 26 plots * 2.5 m = 67 m Field F2 length: 4 plots * 10m = 40 m Data typed K.Wright 2024.12.09. Text translation via Google Translate.","code":""},{"path":"/reference/kotowski.potato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of potato in Poland. — kotowski.potato.uniformity","text":"Kotowski, Feliks. (1924). criterion field homegenity value field trials. (English title). Roczniki Nauk Rolniczych, 11, 26-35. https://www.google.com/books/edition/Roczniki_nauk_rolniczych/mz0iAQAAIAAJ Polish version page 26. English abstract page 35.","code":""},{"path":"/reference/kotowski.potato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of potato in Poland. — kotowski.potato.uniformity","text":"None","code":""},{"path":"/reference/kotowski.potato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of potato in Poland. — kotowski.potato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ libs(agridat) data(kotowski.potato.uniformity) dat <- kotowski.potato.uniformity libs(desplot) desplot(dat, yield~col*row|field, subset=field==\"F1\", tick=TRUE, flip=TRUE, aspect=(4*10)/(12*2.5), main=\"kotowski.potato.uniformity - yield, field F1\") desplot(dat, yield~col*row|field, subset=field==\"F2\", tick=TRUE, flip=TRUE, aspect=(4*10)/(26*2.5), main=\"kotowski.potato.uniformity - yield, field F2\") desplot(dat, starch~col*row|field, subset=field==\"F1\", tick=TRUE, flip=TRUE, aspect=(4*10)/(12*2.5), main=\"kotowski.potato.uniformity - starch, field F1\") desplot(dat, starch~col*row|field, subset=field==\"F2\", tick=TRUE, flip=TRUE, aspect=(4*10)/(26*2.5), main=\"kotowski.potato.uniformity - starch, field F2\") } # }"},{"path":"/reference/kreusler.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"Growth maize plants Germany 1875-1878.","code":""},{"path":"/reference/kreusler.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"","code":"data(\"kreusler.maize\")"},{"path":"/reference/kreusler.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"data frame 165 observations following 17 variables. gen genotype year year date calendar date raindays number days rain per week (zahl der regenstage) rain rain amount (mm) temp temperature mean (deg C) (temperatur mittel) parentseed weight parent seed (g) (alte korner) roots weight roots (g) (wurzel) leaves weight leaves (g) (blatter) stem weight stem (g) (stengel) tassel weight tassel (g) (blutenstande) grain weight grain (korner) plantweight weight entire plant (ganze pflanze) plantheight plant height (cm) (mittlere hohe der pflanzen) leafcount number leaves (anzahl der blatter) leafarea leaf area (cm^2) (flachenmaass der blatter)","code":""},{"path":"/reference/kreusler.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"Experiments performed Poppelsdorf, Germany (near Bonn) years 1875 1878. Observations collected weekly throughout growing season. Five varieties grown 1875. Two 1876, one 1877 1878. plants selected eye representative, number plants chosen decreasing growing season. example, dry-weight data based following number plants: 1875 number sampled began 20 dropped 10. 1876 number sampled began 45 dropped 24. 1877 number sampled began 90 dropped 36. 1878 number sampled began 120 dropped 40. observations included fresh weight dry weight entire plants, along leaf area, date inflorescence, fertilization, kernel development. data Hornberger 71 Kreusler/Hornberger, complete. temperature data originally given degrees Reaumur 1875 1876, degrees Celsius 1877 1878. temperatures data degrees Celsius. Note: deg C = 1.25 deg R. Briggs, Kidd & West (1920) give temperature Celsius.","code":""},{"path":"/reference/kreusler.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"1875-1876 data : . Prehn & G. Becker. (1878) Jahresbericht fur Agrikultur-chemie, Vol 20, p. 216-220. https://books.google.com/books?id=ZfxNAAAAYAAJ&pg=216 1877 data : . Kreusler, . Prehn, Hornberger. (1880) Jahresbericht fur Agrikultur-Chemie, Vol 21, p 248. https://books.google.com/books?id=U3IYAQAAIAAJ&pg=248 1878 data : U. Kreusler, . Prehn, R. Hornberger. (1880). Jahresbericht fur Agrikultur-Chemie, Vol 22, p. 211. https://books.google.com/books?id=9HIYAQAAIAAJ&pg=211 Dry plant weight leaf area genotypes years repeated : G. E. Briggs, Franklin Kidd, Cyril West. (1920). Quantitative Analysis Plant Growth. Part . Annals Applied Biology, 7, 103-123. G. E. Briggs, Franklin Kidd, Cyril West. (1920). Quantitative Analysis Plant Growth. Part II. Annals Applied Biology, 7, 202-223.","code":""},{"path":"/reference/kreusler.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"Roderick Hunt, G. Clifford Evans. 1980. Classical Data Growth Maize: Curve Fitting Statistical Analysis. New Phytol, 86, 155-180.","code":""},{"path":"/reference/kreusler.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Growth of maize plants in Germany during 1875-1878 — kreusler.maize","text":"","code":"if (FALSE) { # \\dontrun{ data(kreusler.maize) dat <- kreusler.maize dat$date2 <- as.Date(dat$date,\"%d %b %Y\") dat$doy <- as.numeric(strftime(dat$date2, format=\"%j\")) # Hunt & Evans Fig 2a libs(lattice) xyplot(log10(plantweight)~doy|factor(year), data=dat, group=gen, type=c('p','smooth'), span=.4, as.table=TRUE, xlab=\"Day of year\", main=\"kreusler.maize - growth of maize\", auto.key=list(columns=5)) # Hunt & Evans Fig 2b xyplot(log10(plantweight)~doy|gen, data=dat, group=factor(year), type=c('p','smooth'), span=.5, as.table=TRUE, xlab=\"Day of year\", auto.key=list(columns=4)) # Hunt & Evans Fig 3a xyplot(log10(leafarea)~doy|factor(year), data=dat, group=gen, type=c('p','smooth'), span=.5, as.table=TRUE, xlab=\"Day of year\", auto.key=list(columns=5)) # Hunt & Evans Fig 3a xyplot(log10(leafarea)~doy|gen, data=dat, group=factor(year), type=c('p','smooth'), span=.5, as.table=TRUE, xlab=\"Day of year\", auto.key=list(columns=4)) # All traits xyplot(raindays~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(rain~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(temp~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(parentseed~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(roots~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(leaves~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(stem~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(grain~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(plantweight~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(plantheight~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(leafcount~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(leafarea~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) xyplot(tassel~doy|factor(year), data=dat, group=gen, type='l', auto.key=list(columns=5), as.table=TRUE, layout=c(1,4)) } # }"},{"path":"/reference/kristensen.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of barley — kristensen.barley.uniformity","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"Uniformity trial barley conducted Denmark, 1905.","code":""},{"path":"/reference/kristensen.barley.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"","code":"data(\"kristensen.barley.uniformity\")"},{"path":"/reference/kristensen.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"data frame 718 observations following 3 variables. row row col column yield yield, hectograms/plot","code":""},{"path":"/reference/kristensen.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"Experiment conducted 1905 Askov, Denmark. Harvested plot size 10 x 14 'alen', 6.24 x 8.79 meters. soil uniform, attack mildew spread adjacent field. Yield measured hectograms/plot straw grain together. (Page 468). Orientation plots dimensions clear text, aspect used example aligns well Kristensen figure 1. Field width: 22 plots * 8.79 m Field length: 11 plots * 6.24 m Notes Kristensen: Fig 5 3x3 moving average, Fig 6 deviation trend, Fig 7 field average added deviation. Fig 13 another uniformity trial barley 1924, Fig 14 uniformity trial oats 1924.","code":""},{"path":"/reference/kristensen.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"R. K. Kristensen (1925). Anlaeg og Opgoerelse af Markforsoeg. Tidsskrift landbrugets planteavl, Vol 31, 464-494. Fig 1, pg. 467. https://dca.au.dk/publikationer/historiske/planteavl/","code":""},{"path":"/reference/kristensen.barley.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"J. Neyman, K. Iwaszkiewicz, St. Kolodziejczyk. (1935). Statistical Problems Agricultural Experimentation. Supplement Journal Royal Statistical Society, Vol. 2, . 2 (1935), pp. 107-180. https://doi.org/10.2307/2983637","code":""},{"path":"/reference/kristensen.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of barley — kristensen.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kristensen.barley.uniformity) dat <- kristensen.barley.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(11*6.24)/(22*8.79), main=\"kristensen.barley.uniformity\") } # }"},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"Uniformity trial sorghum India, 3 years plots 1930-1932.","code":""},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"","code":"data(\"kulkarni.sorghum.uniformity\")"},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"data frame 480 observations following 4 variables. row row col column yield grain yield, tolas per plot year year","code":""},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"experiment conducted Sholapur district India three consecutive years 1930-1932. One acre land (290 ft x 150 ft) chosen midst bigger area (plot 13 Mohol Plot) sowing sorghum. harvested plots 1/160 acre (72 ft 6 x 3 ft 9 ) containing three rows plants 15 . apart. 160 plots arranged forty rows four columns, yields measured tolas. plot division kept intact three years, yields 160 plots available three consecutive harvests. original data given Appendix . Field width: 4 plots * 72.5 feet = 290 feet Field length: 40 plots * 3.75 feet = 150 feet Conclusions: \"Thus, highly narrow strips plots (length much greater breadth) lead greater precision plots area much wider narrow.\" Correlation plots year years low.","code":""},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"Kulkarni, R. K., Bose, S. S., Mahalanobis, P. C. (1936). influence shape size plots effective precision field experiments sorghum. Indian J. Agric. Sci., 6, 460-474. Appendix 1, page 172. https://archive.org/details/.ernet.dli.2015.271737","code":""},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"None.","code":""},{"path":"/reference/kulkarni.sorghum.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sorghum — kulkarni.sorghum.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(kulkarni.sorghum.uniformity) dat <- kulkarni.sorghum.uniformity # match means on page 462 # tapply(dat$yield, dat$year, mean) # 1930 1931 1932 # 116.2875 67.2250 126.3688 libs(reshape2) libs(lattice) dmat <- acast(dat, row+col ~ year, value.var=\"yield\") splom(dmat, main=\"kulkarni.sorghum.uniformity\") cor(dmat) libs(desplot) desplot(dat, yield ~ col*row|year, flip=TRUE, aspect=150/290, main=\"kulkarni.sorghum.uniformity\") } # }"},{"path":"/reference/lambert.soiltemp.html","id":null,"dir":"Reference","previous_headings":"","what":"Average monthly soil temperature near Zurich — lambert.soiltemp","title":"Average monthly soil temperature near Zurich — lambert.soiltemp","text":"Average monthly soil temperature near Zurich, seven depths, averaged four years.","code":""},{"path":"/reference/lambert.soiltemp.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Average monthly soil temperature near Zurich — lambert.soiltemp","text":"data frame 84 observations following 3 variables. month month depth depth soil (feet) temp temperature (units \"du Crest\")","code":""},{"path":"/reference/lambert.soiltemp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Average monthly soil temperature near Zurich — lambert.soiltemp","text":"one earliest time series scientific literature. data show monthly soil temperature near Zurich, averaged four years (beginning 1762), 7 different depths. temperature measurements related 'du Crest' scale. (measurements seem exactly according du Crest scale. can read German, use Google books link see can figure .) Even scale Lambert's graph match data. Greater depths show less variation greater lag temperature responsiveness air temperature. data also appears Pedometrics, issue 23, December 2007. , formula converting temperature make sense data Table 1 directly match corresponding figure.","code":""},{"path":"/reference/lambert.soiltemp.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Average monthly soil temperature near Zurich — lambert.soiltemp","text":"Johann Heinrich Lambert (1779), Pyrometrie. Page 358. https://books.google.com/books?id=G5I_AAAAcAAJ&pg=PA358 Graph: https://www.fisme.science.uu.nl/wiskrant/artikelen/hist_grafieken/begin/images/pyrometrie.gif","code":""},{"path":"/reference/lambert.soiltemp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average monthly soil temperature near Zurich — lambert.soiltemp","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) # Reproduce Lambert figure 39. data(lambert.soiltemp) dat <- lambert.soiltemp # Make 3 cycles of the data so that the loess line bends back up at # month 1 and month 12 dat <- rbind(dat, transform(dat, month=month-12), transform(dat, month=month+12)) libs(lattice) xyplot(temp ~ month, dat, group=depth, type=c('p','smooth'), main=\"lambert.soiltemp\", xlim=c(-3,15), ylab=\"Soil temperature (du Crest) at depth (feet)\", span=.2, auto.key=list(columns=4)) # To do: Find a good model for this data } # }"},{"path":"/reference/lander.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"Uniformity trials wheat chari, 4 years land, India.","code":""},{"path":"/reference/lander.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"","code":"data(\"lander.multi.uniformity\")"},{"path":"/reference/lander.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"data frame 780 observations following 5 variables. row row col column yield yield, maunds per plot year year crop crop","code":""},{"path":"/reference/lander.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"Note, \"chari\" paper Andropogon Sorghum, \"wheat\" Triticum vulgare. Uniformity trials carried Rawalpindi, India. area consisted 5 fields (D4,D5,D6,D7,D8), 5 acres size. 5 fields divided three sub-divisions , B, C, means two strong bunds 5 feet wide. 3 sub-divisions divided 5 blocks, consisting 13 experimental plots 14 non-experiment strips 5 feet wide separating plots . dimensions plot 207 ft 5 19 ft 1 . land used 4 consecutive crops. first crop wheat, followed chari (sorghum), followed wheat 2 times. Field width: 207.42 * 5 plots = 1037.1 feet Field length: (19.08+5)*39 rows = 939.12 feet Conclusions: evident, therefore, soil heterogenity revealed one crop true index subsequent behavior area respect crops. Even crop raised different seasons shown constancy regards soil heterogeneity.","code":""},{"path":"/reference/lander.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"Lander, P. E. et al. (1938). Soil Uniformity Trials Punjab . Ind. J. Agr. Sci. 8:271-307.","code":""},{"path":"/reference/lander.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"None","code":""},{"path":"/reference/lander.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of wheat and chari, 4 years on the same land. — lander.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lander.multi.uniformity) dat <- lander.multi.uniformity # Yearly means, similar to Lander table 7 ## filter(dat) ## 1 1929 18.1 ## 2 1930 58.3 ## 3 1931 22.8 ## 4 1932 14.1 # heatmaps for all years libs(desplot) dat$year <- factor(dat$year) desplot(dat, yield ~ col*row|year, flip=TRUE, aspect=(1037.1/939.12), main=\"lander.multi.uniformity\") } # }"},{"path":"/reference/lasrosas.corn.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"Yield monitor data corn field Argentina variable nitrogen.","code":""},{"path":"/reference/lasrosas.corn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"","code":"data(\"lasrosas.corn\")"},{"path":"/reference/lasrosas.corn.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"data frame 3443 observations following 8 variables. year year, 1999 2001 lat latitude long longitude yield yield, quintals/ha nitro nitrogen fertilizer, kg/ha topo topographic factor bv brightness value (proxy low organic matter content) rep rep factor nf nitrogen factor, N0-N4","code":""},{"path":"/reference/lasrosas.corn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"Corn yield nitrogen fertilizer treatment field characteristics Las Rosas farm, Rio Cuarto, Cordoba, Argentina. Data 6 nitro treatments, 3 reps, strips. Data collected using yield monitor, harvests 1999 2001. points within long strip averaged distance points _within_ strip distance _between_ strips (9.8 meters). topographic factor factor levels W = West slope, HT = Hilltop, E = East slope, LO = Low East. 'rep' factor data added hand appear original data. Slightly different levels nitrogen used two years, nitrogen factor 'nf' created common levels across years. Published descriptions data describe experiment design randomized nitrogen treatments. nitrogen treatments randomized within one rep, randomization used two reps. Anselin et al. used corn grain price $6.85/quintal nitrogen cost $0.4348/kg. corners field 1999 : https://www.google.com/maps/place/-33.0501258,-63.8488636 https://www.google.com/maps/place/-33.05229635,-63.84181819 Anselin et al. found significant response nitrogen slope. However, Bongiovanni Lowenberg-DeBoer (2002) found slope position significant 2001. Used permission ASU GeoDa Center.","code":""},{"path":"/reference/lasrosas.corn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"Las Rosas data files obtained https://geodacenter.asu.edu/sdata converted ESRI shape files flat data.frame.","code":""},{"path":"/reference/lasrosas.corn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"Bongiovanni Lowenberg-DeBoer (2000). Nitrogen management corn spatial regression model. Proceedings Fifth International Conference Precision Agriculture. Anselin, L., R. Bongiovanni, J. Lowenberg-DeBoer (2004). spatial econometric approach economics site-specific nitrogen management corn production. American Journal Agricultural Economics, 86, 675–687. https://doi.org/10.1111/j.0002-9092.2004.00610.x Lambert, Lowenberg-Deboer, Bongiovanni (2004). Comparison Four Spatial Regression Models Yield Monitor Data: Case Study Argentina. Precision Agriculture, 5, 579-600. https://doi.org/10.1007/s11119-004-6344-3 Suman Rakshit, Adrian Baddeley, Katia Stefanova, Karyn Reeves, Kefei Chen, Zhanglong Cao, Fiona Evans, Mark Gibberd (2020). Novel approach analysis spatially-varying treatment effects -farm experiments. Field Crops Research, 255, 15 September 2020, 107783. https://doi.org/10.1016/j.fcr.2020.107783","code":""},{"path":"/reference/lasrosas.corn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield monitor data for a corn field in Argentina with variable nitrogen. — lasrosas.corn","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lasrosas.corn) dat <- lasrosas.corn # yield map libs(lattice,latticeExtra) # for panel.levelplot.points redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(yield ~ long*lat|factor(year), data=dat, main=\"lasrosas.corn grain yield\", xlab=\"Longitude\", ylab=\"Latitude\", scales=list(alternating=FALSE), prepanel = prepanel.default.xyplot, panel = panel.levelplot.points, type = c(\"p\", \"g\"), aspect = \"iso\", col.regions=redblue) d1 <- subset(dat, year==1999) # Experiment design xyplot(lat~long, data=d1, col=as.numeric(as.factor(d1$nitro)), pch=d1$topo, main=\"lasrosas.corn experiment layout 1999\") # A quadratic response to nitrogen is suggested xyplot(yield~nitro|topo, data=d1, type=c('p','smooth'), layout=c(4,1), main=\"lasrosas.corn yield by topographic zone 1999\") # Full-field quadratic response to nitrogen. Similar to Bongiovanni 2000, # table 1. m1 <- lm(yield ~ 1 + nitro + I(nitro^2), data=d1, subset=year==1999) coef(m1) } # }"},{"path":"/reference/lavoranti.eucalyptus.html","id":null,"dir":"Reference","previous_headings":"","what":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"Height Eucalyptus trees southern Brazil","code":""},{"path":"/reference/lavoranti.eucalyptus.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"data frame 490 observations following 4 variables. gen genotype (progeny) factor origin origin progeny loc location height height, meters","code":""},{"path":"/reference/lavoranti.eucalyptus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"genotypes originated three different locations Queensland, Australia, tested southern Brazil. experiment conducted randomized complete block design 6 plants per plot 10 blocks. Mean tree height reported. testing locations described following table: Arciniegas-Alarcon (2010) used 'Ravenshoe' subset data illustrate imputation missing values.","code":""},{"path":"/reference/lavoranti.eucalyptus.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"O J Lavoranti (2003). Estabilidade e adaptabilidade fenotipica atraves da reamostragem bootstrap modelo AMMI, PhD thesis, University Sao Paulo, Brazil.","code":""},{"path":"/reference/lavoranti.eucalyptus.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"Arciniegas-Alarcon, S. Garcia-Pena, M. dos Santos Dias, C.T. Krzanowski, W.J. (2010). alternative methodology imputing missing data trials genotype--environment interaction, Biometrical Letters, 47, 1-14. https://doi.org/10.2478/bile-2014-0006","code":""},{"path":"/reference/lavoranti.eucalyptus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Height of Eucalyptus trees in southern Brazil — lavoranti.eucalyptus","text":"","code":"if (FALSE) { # \\dontrun{ # Arciniegas-Alarcon et al use SVD and regression to estimate missing values. # Partition the matrix X as a missing value xm, row vector xr1, column # vector xc1, and submatrix X11 # X = [ xm xr1 ] # [ xc1 X11 ] and let X11 = UDV'. # Estimate the missing value xm = xr1 V D^{-1} U' xc1 data(lavoranti.eucalyptus) dat <- lavoranti.eucalyptus libs(lattice) levelplot(height~loc*gen, dat, main=\"lavoranti.eucalyptus - GxE heatmap\") dat <- droplevels(subset(dat, origin==\"Ravenshoe\")) libs(reshape2) dat <- acast(dat, gen~loc, value.var='height') dat[1,1] <- NA x11 <- dat[-1,][,-1] X11.svd <- svd(x11) xc1 <- dat[-1,][,1] xr1 <- dat[,-1][1,] xm <- xr1 xm # = 18.29, Original value was 17.4 } # }"},{"path":"/reference/laycock.tea.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of tea — laycock.tea.uniformity","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"Uniformity trials tea","code":""},{"path":"/reference/laycock.tea.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"","code":"data(\"laycock.tea.uniformity\")"},{"path":"/reference/laycock.tea.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"data frame 54 observations following 4 variables. loc location, L1 L2 row row col column yield yield (pounds)","code":""},{"path":"/reference/laycock.tea.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"Actual physical dimensions tea shrubs given, use estimate four feet square shrub (similar eden.tea.uniformity experiment). Location 1 (Laycock, page 108) Research Station, Nyasaland. Plots 10 15 bushes, harvested 23 times 1942. Field length: 8 plots * 10 bushes * 4 feet = 320 feet. Field width: 4 plots * 15 bushes * 4 feet = 240 feet. Location 2 (Laycock page 110) Mianga Estate, Nyasaland. Plots 9 11 bushes, harvested 18 times 1951/52. Field length: 9 plots * 9 bushes * 4 feet = 324 feet. Field width: 6 plots * 11 bushes * 4 feet = 264 feet.","code":""},{"path":"/reference/laycock.tea.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"Laycock, D. H. (1955). effect plot shape reducing errors tea experiments. Tropical Agriculture, 32, 107-114.","code":""},{"path":"/reference/laycock.tea.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"Zimmerman, Dale L., David . Harville. (1991). random field approach analysis field-plot experiments spatial experiments. Biometrics, 47, 223-239.","code":""},{"path":"/reference/laycock.tea.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of tea — laycock.tea.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(laycock.tea.uniformity) dat <- laycock.tea.uniformity libs(desplot) desplot(dat, yield ~ col*row|loc, flip=TRUE, aspect=322/252, # average of 2 locs main=\"laycock.tea.uniformity\") } # }"},{"path":"/reference/lee.potatoblight.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeated measurements of resistance to potato blight — lee.potatoblight","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"Repeated measurements resistance potato blight.","code":""},{"path":"/reference/lee.potatoblight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"","code":"data(\"lee.potatoblight\")"},{"path":"/reference/lee.potatoblight.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"data frame 14570 observations following 7 variables. year planting year gen genotype / cultivar factor col column row row rep replicate block (numeric) date date data collection y score 1-9 blight resistance","code":""},{"path":"/reference/lee.potatoblight.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"data werre collected biennial screening trials conducted New Zealand Institute Crop Food Research Pukekohe Field Station. trials evaluate resistance potato cultivars late blight caused fungus Phytophthora infestans. trial, damage necrotic tissue rated 1-9 scale multiple time points growing season. Lee (2009) used Bayesian model extends ordinal regression McCullagh include spatial variation sigmoid logistic curves model time dependence repeated measurements plot. Data 1989 included due different trial setup used. trials laid latinized row-column designs 4 5 reps. plot consisted four seed tubers planted two Ilam Hardy spread plants single row 2 meters long 76 centimeter spacing rows. 1997, 18 plots lost due flooding. 2001, end season plants nearly dead. Note, plant-breeding, common use \"breeder code\" genotype, several years testing changed registered commercial variety name. R package, Potato Pedigree Database, https://www.plantbreeding.wur.nl/potatopedigree/reverselookup.php, used change breeder codes (early testing) variety names used later testing. example, among changes made following: Used permission Arier Chi-Lun Lee John Anderson. Data retrieved https://researchspace.auckland.ac.nz/handle/2292/5240. Licensed via Open Database License 1.0. (allows sub-licensing). See: https://opendatacommons.org/licenses/dbcl/1.0/","code":""},{"path":"/reference/lee.potatoblight.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"Lee, Arier Chi-Lun (2009). Random effects models ordinal data. Ph.D. thesis, University Auckland. https://researchspace.auckland.ac.nz/handle/2292/4544.","code":""},{"path":"/reference/lee.potatoblight.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Repeated measurements of resistance to potato blight — lee.potatoblight","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lee.potatoblight) dat <- lee.potatoblight # Common cultivars across years. # Based on code from here: https://stackoverflow.com/questions/20709808 gg <- tapply(dat$gen, dat$year, function(x) as.character(unique(x))) tab <- outer(1:11, 1:11, Vectorize(function(a, b) length(Reduce(intersect, gg[c(a, b)])))) head(tab) # Matches Lee page 27. ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] ## [1,] 20 10 7 5 3 2 3 2 3 3 2 ## [2,] 10 30 17 5 4 3 4 4 5 4 2 ## [3,] 7 17 35 9 6 3 4 5 6 4 3 ## [4,] 5 5 9 35 16 8 9 14 15 13 11 ## [5,] 3 4 6 16 40 12 11 18 18 16 14 # Note the progression to lower scores as time passes in each year skp <- c(rep(0,10), rep(0,7),1,1,1, rep(0,8),1,1, rep(0,6),1,1,1,1, rep(0,5),1,1,1,1,1, rep(0,5),1,1,1,1,1, rep(0,6),1,1,1,1, rep(0,5),1,1,1,1,1, rep(0,5),1,1,1,1,1, rep(0,5),1,1,1,1,1) libs(desplot) desplot(dat, y ~ col*row|date, ylab=\"Year of testing\", # unknown aspect layout=c(10,11),skip=as.logical(skp), main=\"lee.potatoblight - maps of blight resistance over time\") # 1983 only. I.Hardy succumbs to blight quickly libs(lattice) xyplot(y ~ date|gen, dat, subset=year==1983, group=rep, xlab=\"Date\", ylab=\"Blight resistance score\", main=\"lee.potatoblight 1983\", as.table=TRUE, auto.key=list(columns=5), scales=list(alternating=FALSE, x=list(rot=90, cex=.7))) } # }"},{"path":"/reference/lehmann.millet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of millet in India — lehmann.millet.uniformity","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"Uniformity trial millet India, 3 years land.","code":""},{"path":"/reference/lehmann.millet.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"","code":"data(\"lehmann.millet.uniformity\")"},{"path":"/reference/lehmann.millet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"data frame 396 observations following 5 variables. year year plot plot (row) range range (column) yield grain yield (pounds) total total crop yield (pounds)","code":""},{"path":"/reference/lehmann.millet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"uniformity experiment ragi (millet) Experimental Farm Hebbal (near Bangalore). plots year--year. 6th report P. 2: plots 1/10 acre, 50 links wide 200 links long. Map (partially scanned pdf). \"part dry lands nearest tank, quite uniform remainder, already excluded experimental ground proper\". 7th report P. 12, pdf p. 233: Table 3 grain/straw yield 1905. 9th report P. 1-10 comments. P. 36-39 data: Table 1 grain yield, table 2 total yield grain straw. Columns , left-right, -F. Rows , top-bottom, 1-22. season 1906 abnormally wet compared 1905 1907. [9th report] Field width: 6 plots * 200 links Field length: 22 plots * 50 links","code":""},{"path":"/reference/lehmann.millet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"Lehmann, . Ninth Annual Report Agricultural Chemist Year 1907-08. Department Agriculture, Mysore State. [2nd-9th] Annual Report Agricultural Chemist. https://books.google.com/books?id=u_dHAAAAYAAJ","code":""},{"path":"/reference/lehmann.millet.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"Theodor Roemer (1920). Der Feldversuch. Page 69, table 13. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ","code":""},{"path":"/reference/lehmann.millet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of millet in India — lehmann.millet.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lehmann.millet.uniformity) dat <- lehmann.millet.uniformity libs(desplot) dat$year = factor(dat$year) desplot(dat, yield ~ range*plot|year, aspect=(22*50)/(6*200), main=\"lehmann.millet.uniformity\", flip=TRUE, tick=TRUE) desplot(dat, total ~ range*plot|year, aspect=(22*50)/(6*200), main=\"lehmann.millet.uniformity\", flip=TRUE, tick=TRUE) # libs(dplyr) # group_by(dat, year) } # }"},{"path":"/reference/lehmann.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice in India — lehmann.rice.uniformity","title":"Uniformity trial of rice in India — lehmann.rice.uniformity","text":"Uniformity trial rice India, 3 years land.","code":""},{"path":"/reference/lehmann.rice.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of rice in India — lehmann.rice.uniformity","text":"","code":"data(\"lehmann.rice.uniformity\")"},{"path":"/reference/lehmann.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice in India — lehmann.rice.uniformity","text":"data frame 153 observations following 5 variables. year year plot plot (row) range range (column) yield grain yield (pounds) total total crop yield (pounds)","code":""},{"path":"/reference/lehmann.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice in India — lehmann.rice.uniformity","text":"uniformity experiment paddy rice Experimental Farm Hebbal (near Bangalore). plots year--year. 6th report P. 2: Plots 1/10 acre, 50 links wide, 200 links long. 7th report P. 6 table 1 yield (pounds) paddy produced wet area farm 1905-196. (total weight weight given). 9th report P. 19 commenets. P. 47 tables 6 & 7 grain/total yield Range B Range C 1906-1908. Field width: 3 plots * 200 links Field length: 17 plots * 50 links","code":""},{"path":"/reference/lehmann.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice in India — lehmann.rice.uniformity","text":"Lehmann, . Ninth Annual Report Agricultural Chemist Year 1907-08. Department Agriculture, Mysore State. [2nd-9th] Annual Report Agricultural Chemist. https://books.google.com/books?id=u_dHAAAAYAAJ","code":""},{"path":"/reference/lehmann.rice.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of rice in India — lehmann.rice.uniformity","text":"Theodor Roemer (1920). Der Feldversuch. Page 68, table 12. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ","code":""},{"path":"/reference/lehmann.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice in India — lehmann.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lehmann.rice.uniformity) dat <- lehmann.rice.uniformity libs(desplot) dat$year = factor(dat$year) desplot(dat, yield ~ range*plot|year, aspect=(17*50)/(2*200), main=\"lehmann.rice.uniformity\", flip=TRUE, tick=TRUE) desplot(dat, total ~ range*plot|year, aspect=(17*50)/(2*200), main=\"lehmann.rice.uniformity\", flip=TRUE, tick=TRUE) # libs(dplyr) # group_by(dat, year) } # }"},{"path":"/reference/lehner.soybeanmold.html","id":null,"dir":"Reference","previous_headings":"","what":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"Yield, white mold, sclerotia soybeans Brazil","code":""},{"path":"/reference/lehner.soybeanmold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"","code":"data(\"lehner.soybeanmold\")"},{"path":"/reference/lehner.soybeanmold.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"data frame 382 observations following 9 variables. study study number year year harvest loc location name elev elevation region region trt treatment number yield crop yield, kg/ha mold white mold incidence, percent sclerotia weight sclerotia g/ha","code":""},{"path":"/reference/lehner.soybeanmold.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"Data mean 4 reps. Original source (Portuguese) https://ainfo.cnptia.embrapa.br/digital/bitstream/item/101371/1/Ensaios-cooperativos-de-controle-quimico-de-mofo-branco-na-cultura-da-soja-safras-2009--2012.pdf Data included via GPL3 license.","code":""},{"path":"/reference/lehner.soybeanmold.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"Lehner, M. S., Pethybridge, S. J., Meyer, M. C., & Del Ponte, E. M. (2016). Meta-analytic modelling incidence-yield incidence-sclerotial production relationships soybean white mould epidemics. Plant Pathology. doi:10.1111/ppa.12590","code":""},{"path":"/reference/lehner.soybeanmold.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"Full commented code analysis https://emdelponte.github.io/paper-white-mold-meta-analysis/","code":""},{"path":"/reference/lehner.soybeanmold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yield, white mold, and sclerotia for soybeans in Brazil — lehner.soybeanmold","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lehner.soybeanmold) dat <- lehner.soybeanmold if(0){ op <- par(mfrow=c(2,2)) hist(dat$mold, main=\"White mold incidence\") hist(dat$yield, main=\"Yield\") hist(dat$sclerotia, main=\"Sclerotia weight\") par(op) } libs(lattice) xyplot(yield ~ mold|study, dat, type=c('p','r'), main=\"lehner.soybeanmold\") # xyplot(sclerotia ~ mold|study, dat, type=c('p','r')) # meta-analysis. Could use metafor package to construct the forest plot, # but latticeExtra is easy; ggplot is slow/clumsy libs(latticeExtra, metafor) # calculate correlation & confidence for each loc cors <- split(dat, dat$study) cors <- sapply(cors, FUN=function(X){ res <- cor.test(X$yield, X$mold) c(res$estimate, res$parameter[1], conf.low=res$conf.int[1], conf.high=res$conf.int[2]) }) cors <- as.data.frame(t(as.matrix(cors))) cors$study <- rownames(cors) # Fisher Z transform cors <- transform(cors, ri = cor) cors <- transform(cors, ni = df + 2) cors <- transform(cors, yi = 1/2 * log((1 + ri)/(1 - ri)), vi = 1/(ni - 3)) # Overall correlation across studies overall <- rma.uni(yi, vi, method=\"ML\", data=cors) # metafor package # back transform overall <- predict(overall, transf=transf.ztor) # weight and size for forest plot wi <- 1/sqrt(cors$vi) size <- 0.5 + 3.0 * (wi - min(wi))/(max(wi) - min(wi)) # now the forest plot # must use latticeExtra::layer in case ggplot2 is also loaded segplot(factor(study) ~ conf.low+conf.high, data=cors, draw.bands=FALSE, level=size, centers=ri, cex=size, col.regions=colorRampPalette(c(\"gray85\", \"dodgerblue4\")), main=\"White mold vs. soybean yield\", xlab=paste(\"Study correlation, confidence, and study weight (blues)\\n\", \"Overall (black)\"), ylab=\"Study ID\") + latticeExtra::layer(panel.abline(v=overall$pred, lwd=2)) + latticeExtra::layer(panel.abline(v=c(overall$cr.lb, overall$cr.ub), lty=2, col=\"gray\")) # Meta-analyses are typically used when the original data is not available. # Since the original data is available, a mixed model is probably better. libs(lme4) m1 <- lmer(yield ~ mold # overall slope + (1+mold |study), # random intercept & slope per study data=dat) summary(m1) } # }"},{"path":"/reference/lessman.sorghum.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sorghum — lessman.sorghum.uniformity","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"Uniformity trial sorghum Ames, Iowa, 1959.","code":""},{"path":"/reference/lessman.sorghum.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"","code":"data(\"lessman.sorghum.uniformity\")"},{"path":"/reference/lessman.sorghum.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"data frame 2640 observations following 3 variables. row row col column yield yield, ounces","code":""},{"path":"/reference/lessman.sorghum.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"uniformity trial conducted Agronomy Farm Ames, Iowa, 1959. field planted grain sorghum rows spaces 40 inches apart, thinned stand three inches plants. entire field 48 rows (40 inches apart), 300 feet long harvested 5-foot lengths. Threshed grain dried 8-10 percent moisture weighing. Weights ounces. Average yield field 95.3 bu/ac. Field width: 48 rows * 40 inches / 12in/ft = 160 feet Field length: 60 plots * 5 feet = 300 feet Plot yields two outer rows side field omitted analysis. CV values data quite match Lessman's value. first page Table 17 manually checked correctness problems optical character recognition ( obvious errors like 0/o).","code":""},{"path":"/reference/lessman.sorghum.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"Lessman, Koert James (1962). Comparisons methods testing grain yield sorghum. Iowa State University. Retrospective Theses Dissertations. Paper 2063. Appendix Table 17. https://lib.dr.iastate.edu/rtd/2063","code":""},{"path":"/reference/lessman.sorghum.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"None.","code":""},{"path":"/reference/lessman.sorghum.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sorghum — lessman.sorghum.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lessman.sorghum.uniformity) dat <- lessman.sorghum.uniformity libs(desplot) desplot(dat, yield ~ col*row, aspect=300/160, tick=TRUE, flip=TRUE, # true aspect main=\"lessman.sorghum.uniformity\") # Omit outer two columns (called 'rows' by Lessman) dat <- subset(dat, col > 2 & col < 47) nrow(dat) var(dat$yield) # 9.09 sd(dat$yield)/mean(dat$yield) # CV 9.2 libs(reshape2) libs(agricolae) dmat <- acast(dat, row~col, value.var='yield') index.smith(dmat, main=\"lessman.sorghum.uniformity\", col=\"red\") # Similar to Lessman Table 1 # Lessman said that varying the width of plots did not have an appreciable # effect on CV, and optimal row length was 3.2 basic plots, about 15-20 } # }"},{"path":"/reference/li.millet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of millet — li.millet.uniformity","title":"Uniformity trial of millet — li.millet.uniformity","text":"Uniformity trial millet China 1934.","code":""},{"path":"/reference/li.millet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of millet — li.millet.uniformity","text":"data frame 600 observations following 3 variables. row row col column yield yield (grams)","code":""},{"path":"/reference/li.millet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of millet — li.millet.uniformity","text":"Crop date estimated 1934. Field 100 ft x 100 ft. Plots 15 feet long 1 foot wide. Field width: 100 plots * 1 foot = 100 feet Field length: 6 plots * 15 feet = 100 feet Li found efficient use land obtained plats 15 feet long two rowss wide. Also satisfactory one row 30 feet long.","code":""},{"path":"/reference/li.millet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of millet — li.millet.uniformity","text":"Li, HW Meng, CJ Liu, TN. 1936. Field Results Millet Breeding Experiment. Agronomy Journal, 28, 1-15. Table 1. https://doi.org/10.2134/agronj1936.00021962002800010001x","code":""},{"path":"/reference/li.millet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of millet — li.millet.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(li.millet.uniformity) dat <- li.millet.uniformity mean(dat$yield) # matches Li et al. libs(desplot) desplot(dat, yield~col*row, aspect=100/100, # true aspect main=\"li.millet.uniformity\") } # }"},{"path":"/reference/libs.html","id":null,"dir":"Reference","previous_headings":"","what":"Load multiple packages and install if needed — libs","title":"Load multiple packages and install if needed — libs","text":"Install load packages \"fly\".","code":""},{"path":"/reference/libs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load multiple packages and install if needed — libs","text":"","code":"libs(...)"},{"path":"/reference/libs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load multiple packages and install if needed — libs","text":"... Comma-separated unquoted package names","code":""},{"path":"/reference/libs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load multiple packages and install if needed — libs","text":"None","code":""},{"path":"/reference/libs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Load multiple packages and install if needed — libs","text":"'agridat' package uses dozens packages examples dataset. 'libs' function provides simple way load multiple packages , can install missing packages --fly. similar `pacman::p_load` function.","code":""},{"path":"/reference/libs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Load multiple packages and install if needed — libs","text":"None","code":""},{"path":"/reference/libs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Load multiple packages and install if needed — libs","text":"Kevin Wright","code":""},{"path":"/reference/libs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load multiple packages and install if needed — libs","text":"","code":"if (FALSE) { # \\dontrun{ libs(dplyr,reshape2) } # }"},{"path":"/reference/lillemo.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"Resistance wheat powdery mildew","code":""},{"path":"/reference/lillemo.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"","code":"data(\"lillemo.wheat\")"},{"path":"/reference/lillemo.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"data frame 408 observations following 4 variables. gen genotype, 24 levels env environrment, 13 levels score score scale scale used score","code":""},{"path":"/reference/lillemo.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"data means across reps original scores. Lower scores indicate better resistance mildew. location used one four different measurement scales scoring resistance powdery mildew: 0-5 scale, 1-9 scale, 0-9 scale, percent. Environment codes consist two letters location name two digits year testing. Location names: CA=Cruz Alta, Brazil. Ba= Bawburgh, UK. Aa=, Norway. Ha=Hamar, Norway. Ch=Choryn, Poland. Ce=Cerekwica, Poland. Ma=Martonvasar, Hungary. Kh=Kharkiv, Ukraine. BT=Bila Tserkva, Ukraine. Gl=Glevakha, Ukraine. Bj=Beijing, China. Note, Lillemo et al. remove genotype effects customary calculating Huehn's non-parametric stability statistics. examples , results quite match results Lillemo. easily result original data table rounded 1 decimal place. example, environment 'Aa03' 3 reps mean genotype 1 probably 16.333, 16.3. Used permission Morten Lillemo. Electronic data supplied Miroslav Zoric.","code":""},{"path":"/reference/lillemo.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"Morten Lillemo, Ravi Sing, Maarten van Ginkel. (2011). Identification Stable Resistance Powdery Mildew Wheat Based Parametric Nonparametric Methods Crop Sci. 50:478-485. https://doi.org/10.2135/cropsci2009.03.0116","code":""},{"path":"/reference/lillemo.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"None.","code":""},{"path":"/reference/lillemo.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat susceptibile to powdery mildew — lillemo.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lillemo.wheat) dat <- lillemo.wheat # Change factor levels to match Lillemo dat$env <- as.character(dat$env) dat$env <- factor(dat$env, levels=c(\"Bj03\",\"Bj05\",\"CA03\",\"Ba04\",\"Ma04\", \"Kh06\",\"Gl05\",\"BT06\",\"Ch04\",\"Ce04\", \"Ha03\",\"Ha04\",\"Ha05\",\"Ha07\",\"Aa03\",\"Aa04\",\"Aa05\")) # Interesting look at different measurement scales by environment libs(lattice) qqmath(~score|env, dat, group=scale, as.table=TRUE, scales=list(y=list(relation=\"free\")), auto.key=list(columns=4), main=\"lillemo.wheat - QQ plots by environment\") # Change data to matrix format libs(reshape2) datm <- acast(dat, gen~env, value.var='score') # Environment means. Matches Lillemo Table 3 apply(datm, 2, mean) # Two different transforms within envts to approximate 0-9 scale datt <- datm datt[,\"CA03\"] <- 1.8 * datt[,\"CA03\"] ix <- c(\"Ba04\",\"Kh06\",\"Gl05\",\"BT06\",\"Ha03\",\"Ha04\",\"Ha05\",\"Ha07\",\"Aa03\",\"Aa04\",\"Aa05\") datt[,ix] <- apply(datt[,ix],2,sqrt) # Genotype means of transformed data. Matches Lillemo table 3. round(rowMeans(datt),2) # Biplot of transformed data like Lillemo Fig 2 libs(gge) biplot(gge(datt, scale=FALSE), main=\"lillemo.wheat\") # Median polish of transformed table m1 <- medpolish(datt) # Half-normal prob plot like Fig 1 # libs(faraway) # halfnorm(abs(as.vector(m1$resid))) # Nonparametric stability statistics. Lillemo Table 4. huehn <- function(mat){ # Gen in rows, Env in cols nenv <- ncol(mat) # Corrected yield. Remove genotype effects # Remove the following line to match Table 4 of Lillemo mat <- sweep(mat, 1, rowMeans(mat)) + mean(mat) # Ranks in each environment rmat <- apply(mat, 2, rank) # Mean genotype rank across envts MeanRank <- apply(rmat, 1, mean) # Huehn S1 gfun <- function(x){ oo <- outer(x,x,\"-\") sum(abs(oo)) # sum of all absolute pairwise differences } S1 <- apply(rmat, 1, gfun)/(nenv*(nenv-1)) # Huehn S2 S2 <- apply((rmat-MeanRank)^2,1,sum)/(nenv-1) out <- data.frame(MeanRank,S1,S2) rownames(out) <- rownames(mat) return(out) } round(huehn(datm),2) # Matches table 4 # I do not think phenability package gives correct values for S1 # libs(phenability) # nahu(datm) } # }"},{"path":"/reference/lin.superiority.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"Multi-environment trial 33 barley genotypes 12 locations","code":""},{"path":"/reference/lin.superiority.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"","code":"data(\"lin.superiority\")"},{"path":"/reference/lin.superiority.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"data frame 396 observations following 4 variables. gen genotype/cultivar region region loc location yield yield (kg/ha)","code":""},{"path":"/reference/lin.superiority.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"Yield six-row barley 1983 annual report Eastern Cooperative Test Canada. named cultivars Bruce, Conquest, Laurier, Leger checks, cultivars tests.","code":""},{"path":"/reference/lin.superiority.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"C. S. Lin, M. R. Binns (1985). Procedural approach assessing cultivar-location data: Pairwise genotype-environment interactions test cultivars checks Canadian Journal Plant Science, 1985, 65(4): 1065-1071. Table 1. https://doi.org/10.4141/cjps85-136","code":""},{"path":"/reference/lin.superiority.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"C. S. Lin, M. R. Binns (1988). Superiority Measure Cultivar Performance Cultivar x Location Data. Canadian Journal Plant Science, 68, 193-198. https://doi.org/10.4141/cjps88-018 Mohammed Ali Hussein, Asmund Bjornstad, . H. Aastveit (2000). SASG x ESTAB: SAS Program Computing Genotype x Environment Stability Statistics. Agronomy Journal, 92; 454-459. https://doi.org/10.2134/agronj2000.923454x","code":""},{"path":"/reference/lin.superiority.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of 33 barley genotypes in 12 locations — lin.superiority","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lin.superiority) dat <- lin.superiority libs(latticeExtra) libs(reshape2) # calculate the superiority measure of Lin & Binns 1988 dat2 <- acast(dat, gen ~ loc, value.var=\"yield\") locmean <- apply(dat2, 2, mean) locmax <- apply(dat2, 2, max) P <- apply(dat2, 1, function(x) { sum((x-locmax)^2)/(2*length(x)) })/1000 P <- sort(P) round(P) # match Lin & Binns 1988 table 2, column Pi # atlantic & quebec regions overlap # libs(gge) # m1 <- gge(dat, yield ~ gen*loc, env.group=region, # main=\"lin.superiority\") # biplot(m1) # create a figure similar to Lin & Binns 1988 # add P, locmean, locmax back into the data dat$locmean <- locmean[match(dat$loc, names(locmean))] dat$locmax <- locmax[match(dat$loc, names(locmax))] dat$P <- P[match(dat$gen, names(P))] dat$gen <- reorder(dat$gen, dat$P) xyplot(locmax ~ locmean|gen, data=dat, type=c('p','r'), as.table=TRUE, col=\"gray\", main=\"lin.superiority - Superiority index\", xlab=\"Location Mean\", ylab=\"Yield of single cultivars (blue) & Maximum (gray)\") + xyplot(yield ~ locmean|gen, data=dat, type=c('p','r'), as.table=TRUE, pch=19) } # }"},{"path":"/reference/lin.unbalanced.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"Multi-environment trial 33 barley genotypes 18 locations","code":""},{"path":"/reference/lin.unbalanced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"","code":"data(\"lin.unbalanced\")"},{"path":"/reference/lin.unbalanced.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"data frame 405 observations following 4 variables. gen genotype/cultivar loc location yield yield (kg/ha) region region","code":""},{"path":"/reference/lin.unbalanced.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"Yield six-row barley 1986 Eastern Cooperative trial named cultivars Bruce, Laurier, Leger checks, cultivars tests. Cultivar names use following codes: \"\" Atlantic-Quebec. \"O\" \"Ontario\". \"S\" second-year. \"T\" third-year.","code":""},{"path":"/reference/lin.unbalanced.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"C. S. Lin, M. R. Binns (1988). Method Assessing Regional Trial Data Test Cultivars Unbalanced Respect Locations. Canadian Journal Plant Science, 68(4): 1103-1110. https://doi.org/10.4141/cjps88-130","code":""},{"path":"/reference/lin.unbalanced.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"None","code":""},{"path":"/reference/lin.unbalanced.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of 33 barley genotypes in 18 locations — lin.unbalanced","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lin.unbalanced) dat <- lin.unbalanced # location maximum, Lin & Binns table 1 # aggregate(yield ~ loc, data=dat, FUN=max) # location mean/index, Lin & Binns, table 1 dat2 <- subset(dat, is.element(dat$gen, c('Bruce','Laurier','Leger','S1','S2', 'S3','S4','S5','S6','S7','T1','T2'))) aggregate(yield ~ loc, data=dat2, FUN=mean) libs(reshape2) dat3 <- acast(dat, gen ~ loc, value.var=\"yield\") libs(lattice) lattice::levelplot(t(scale(dat3)), main=\"lin.unbalanced\", xlab=\"loc\", ylab=\"genotype\") # calculate the superiority measure of Lin & Binns 1988. # lower is better locmax <- apply(dat3, 2, max, na.rm=TRUE) P <- apply(dat3, 1, function(x) { sum((x-locmax)^2, na.rm=TRUE)/(2*length(na.omit(x))) })/1000 P <- sort(P) round(P) # match Lin & Binns 1988 table 2, column P } # }"},{"path":"/reference/linder.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat in Switzerland — linder.wheat","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"Multi-environment trial wheat Switzerland","code":""},{"path":"/reference/linder.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"","code":"data(\"linder.wheat\")"},{"path":"/reference/linder.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"data frame 252 observations following 4 variables. env environment block block gen genotype yield yield, 10 kg/ha","code":""},{"path":"/reference/linder.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"experiment 9 varieties wheat 7 localities Switzerland 1960, RCB design.","code":""},{"path":"/reference/linder.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"Arthur Linder (1960). Design Analysis Experiments, notes lectures held fall semester 1963 Statistics Department, University North Carolina, page 160. https://www.stat.ncsu.edu/information/library/mimeo.archive/ISMS_1964_398-.pdf","code":""},{"path":"/reference/linder.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"None.","code":""},{"path":"/reference/linder.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat in Switzerland — linder.wheat","text":"","code":"library(agridat) data(linder.wheat) dat <- linder.wheat libs(gge) dat <- transform(dat, eb=paste0(env,block)) m1 <- gge(dat, yield~gen*eb, env.group=env) biplot(m1, main=\"linder.wheat\")"},{"path":"/reference/little.splitblock.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-block experiment of sugar beets — little.splitblock","title":"Split-block experiment of sugar beets — little.splitblock","text":"Split-block experiment sugar beets.","code":""},{"path":"/reference/little.splitblock.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split-block experiment of sugar beets — little.splitblock","text":"","code":"data(\"little.splitblock\")"},{"path":"/reference/little.splitblock.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-block experiment of sugar beets — little.splitblock","text":"data frame 80 observations following 6 variables. row row col column yield sugar beet yield, tons/acre harvest harvest date, weeks planting nitro nitrogen, pounds/acre block block","code":""},{"path":"/reference/little.splitblock.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split-block experiment of sugar beets — little.splitblock","text":"Four rates nitrogen, laid 4x4 Latin-square experiment. Within column block, sub-plots strips (across 4 rows) 5 different harvest dates. use sub-plots s strips necessitates care determining error terms ANOVA table. Note, Little yield value 22.3 row 3, column -H3. data uses 23.3 order match marginal totals given Little.","code":""},{"path":"/reference/little.splitblock.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-block experiment of sugar beets — little.splitblock","text":"Thomas M. Little, F. Jackson Hills. (1978) Agricultural Experimentation","code":""},{"path":"/reference/little.splitblock.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-block experiment of sugar beets — little.splitblock","text":"None.","code":""},{"path":"/reference/little.splitblock.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-block experiment of sugar beets — little.splitblock","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(little.splitblock) dat <- little.splitblock # Match marginal totals given by Little. ## sum(dat$yield) ## with(dat, tapply(yield,col,sum)) ## with(dat, tapply(yield,row,sum)) # Layout shown by Little figure 10.2 libs(desplot) desplot(dat, yield ~ col*row, out1=block, out2=col, col=nitro, cex=1, num=harvest, main=\"little.splitblock\") # Convert continuous traits to factors dat <- transform(dat, R=factor(row), C=factor(block), H=factor(harvest), N=factor(nitro)) if(0){ libs(lattice) xyplot(yield ~ nitro|H,dat) xyplot(yield ~ harvest|N,dat) } # Anova table matches Little, table 10.3 m1 <- aov(yield ~ R + C + N + H + N:H + Error(R:C:N + C:H + C:N:H), data=dat) summary(m1) } # }"},{"path":"/reference/loesell.bean.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of white pea beans — loesell.bean.uniformity","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"Uniformity trial white pea beans","code":""},{"path":"/reference/loesell.bean.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"","code":"data(\"loesell.bean.uniformity\")"},{"path":"/reference/loesell.bean.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"data frame 1890 observations following 3 variables. row row ordinate col column ordinate yield yield, grams per plot","code":""},{"path":"/reference/loesell.bean.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"Trial conducted Michigan Agricultural Experiment Station, 1.75 acres. Beans planted rows 28 inches apart 15 Jun 1932. Plants spaced 1 2 inches apart. planting, area 210 ft x 210 feet. area divided 21 columns, 10 foot wide, containing90 rows. Field length: 90 rows * 28 inches = 210 feet. Field width: 21 series * 10 feet = 210 feet. Author's conclusion: Increasing size plot increasing length efficient increasing width. Note, missing values dataset result PDF scan omitting corners table.","code":""},{"path":"/reference/loesell.bean.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"Loesell, Clarence (1936). Size plot & number replications necessary varietal trials white pea beans. PhD Thesis, Michigan State. Table 3, p. 9-10. https://d.lib.msu.edu/etd/5271","code":""},{"path":"/reference/loesell.bean.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"None","code":""},{"path":"/reference/loesell.bean.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of white pea beans — loesell.bean.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ require(agridat) data(loesell.bean.uniformity) dat <- loesell.bean.uniformity require(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=1, tick=TRUE, main=\"loesell.bean.uniformity\") } # }"},{"path":"/reference/lonnquist.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize, half diallel — lonnquist.maize","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"Half diallel maize","code":""},{"path":"/reference/lonnquist.maize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"","code":"data(\"lonnquist.maize\")"},{"path":"/reference/lonnquist.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"data frame 78 observations following 3 variables. p1 parent 1 factor p2 parent 2 factor yield yield","code":""},{"path":"/reference/lonnquist.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"Twelve hybrids selfed/crossed half-diallel design. data means adjusted block effects. Original experiment 3 reps 2 locations 2 years.","code":""},{"path":"/reference/lonnquist.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"J. H. Lonnquist, C. O. Gardner. (1961) Heterosis Intervarietal Crosses Maize Implication Breeding Procedures. Crop Science, 1, 179-183. Table 1.","code":""},{"path":"/reference/lonnquist.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"Mohring, Melchinger, Piepho. (2011). REML-Based Diallel Analysis. Crop Science, 51, 470-478. https://doi.org/10.2135/cropsci2010.05.0272 C. O. Gardner S. . Eberhart. 1966. Analysis Interpretation Variety Cross Diallel Related Populations. Biometrics, 22, 439-452. https://doi.org/10.2307/2528181","code":""},{"path":"/reference/lonnquist.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize, half diallel — lonnquist.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lonnquist.maize) dat <- lonnquist.maize dat <- transform(dat, p1=factor(p1, levels=c(\"C\",\"L\",\"M\",\"H\",\"G\",\"P\",\"B\",\"RM\",\"N\",\"K\",\"R2\",\"K2\")), p2=factor(p2, levels=c(\"C\",\"L\",\"M\",\"H\",\"G\",\"P\",\"B\",\"RM\",\"N\",\"K\",\"R2\",\"K2\"))) libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(yield ~ p1*p2, dat, col.regions=redblue, main=\"lonnquist.maize - yield of diallel cross\") # Calculate the F1 means in Lonnquist, table 1 # libs(reshape2) # mat <- acast(dat, p1~p2) # mat[upper.tri(mat)] <- t(mat)[upper.tri(mat)] # make symmetric # diag(mat) <- NA # round(rowMeans(mat, na.rm=TRUE),1) ## C L M H G P B RM N K R2 K2 ## 94.8 89.2 95.0 96.4 95.3 95.2 97.3 93.7 95.0 94.0 98.9 102.4 # Griffings method # https://www.statforbiology.com/2021/stat_met_diallel_griffing/ # libs(lmDiallel) # dat2 <- lonnquist.maize # dat2 <- subset(dat2, # is.element(p1, c(\"M\",\"H\",\"G\",\"B\",\"K\",\"K2\")) & # is.element(p2, c(\"M\",\"H\",\"G\",\"B\",\"K\",\"K2\"))) # dat2 <- droplevels(dat2) # dmod1 <- lm(yield ~ GCA(p1, p2) + tSCA(p1, p2), # data = dat2) # dmod2 <- lm.diallel(yield ~ p1 + p2, # data = dat2, fct = \"GRIFFING2\") # anova.diallel(dmod1, MSE=7.1, dfr=60) ## Response: yield ## Df Sum Sq Mean Sq F value Pr(>F) ## GCA(p1, p2) 5 234.23 46.846 6.5980 5.923e-05 *** ## tSCA(p1, p2) 15 238.94 15.929 2.2436 0.01411 * ## Residuals 60 7.100 # ---------- if(require(\"asreml\", quietly=TRUE)){ # Mohring 2011 used 6 varieties to calculate GCA & SCA # Matches Table 3, column 2 d2 <- subset(dat, is.element(p1, c(\"M\",\"H\",\"G\",\"B\",\"K\",\"K2\")) & is.element(p2, c(\"M\",\"H\",\"G\",\"B\",\"K\",\"K2\"))) d2 <- droplevels(d2) libs(asreml,lucid) m2 <- asreml(yield~ 1, data=d2, random = ~ p1 + and(p2)) lucid::vc(m2) ## effect component std.error z.ratio con ## p1!p1.var 3.865 3.774 1 Positive ## R!variance 15.93 5.817 2.7 Positive # Calculate GCA effects m3 <- asreml(yield~ p1 + and(p2), data=d2) coef(m3)$fixed-1.462 # Matches Gardner 1966, Table 5, Griffing method } } # }"},{"path":"/reference/lord.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice — lord.rice.uniformity","title":"Uniformity trial of rice — lord.rice.uniformity","text":"Uniformity trial rice Ceylon, 1929.","code":""},{"path":"/reference/lord.rice.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of rice — lord.rice.uniformity","text":"","code":"data(\"lord.rice.uniformity\")"},{"path":"/reference/lord.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice — lord.rice.uniformity","text":"data frame 560 observations following 5 variables. field field row row col column grain grain weight, pounds per plot straw straw weight, pounds per plot","code":""},{"path":"/reference/lord.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice — lord.rice.uniformity","text":"1929, eight fields 1/5 acre size broadcast seeded rice Anuradhapura Experiment Station northern dry zone Ceylon. broadcast, fields marked 10 ft 10 ft squares. harvest, weights grain straw recorded. Fields 10-14 one side drain, fields 26-28 side. field surrounded bund. Plots next bunds higher yields. Field width: 5 plots * 10 feet = 50 feet Field length: 14 plots * 10 feet = 140 feet Conclusions: \"appear plots 1/87 acre effective.\"","code":""},{"path":"/reference/lord.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice — lord.rice.uniformity","text":"Lord, L. (1931). Uniformity Trial Irrigated Broadcast Rice. Journal Agricultural Science, 21(1), 178-188. https://doi.org/10.1017/S0021859600008029","code":""},{"path":"/reference/lord.rice.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of rice — lord.rice.uniformity","text":"None","code":""},{"path":"/reference/lord.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice — lord.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lord.rice.uniformity) dat <- lord.rice.uniformity # match table on page 180 ## libs(dplyr) ## dat ## field grain straw ## ## 1 10 590 732 ## 2 11 502 600 ## 3 12 315 488 ## 4 13 291 538 ## 5 14 489 670 ## 6 26 441 560 ## 7 27 451 629 ## 8 28 530 718 # There are consistently high yields along all edges of the field # libs(lattice) # bwplot(grain ~ factor(col)|field,dat) # bwplot(grain ~ factor(col)|field,dat) # Heatmaps libs(desplot) desplot(dat, grain ~ col*row|field, flip=TRUE, aspect=140/50, main=\"lord.rice.uniformity\") # bivariate scatterplots # xyplot(grain ~ straw|field, dat) } # }"},{"path":"/reference/love.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton — love.cotton.uniformity","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"Uniformity trial cotton","code":""},{"path":"/reference/love.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"","code":"data(\"love.cotton.uniformity\")"},{"path":"/reference/love.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"data frame 170 observations following 3 variables. row row col column yield yield, unknown units","code":""},{"path":"/reference/love.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"Within 100-foot row, first 20 feet harvested single plot, rest row harvested 5-foot lengths. Field width: 17 plots. First plot 20 foot segment, remaining 5 foot segments. Field length: 10 plots. distance rows given. Crop location certain. However, Love & Reisner (2012) mentions cotton \"blank test\" 200 plots Nanking 1929-1930. Neither document mentions weight unit. Possibly information collected papers Harry Love Cornell: https://rmc.library.cornell.edu/EAD/htmldocs/RMA00890.html Cotton - Plot Technic Study 1930-1932. Box 3, Folder 34 However, turned hand-written manuscript Shiao .k.. Siao, contained trial data ","code":""},{"path":"/reference/love.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"Harry Love (1937). Application Statistical Methods Agricultural Research. Commercial Press, Shanghai. Page 411. https://archive.org/details/.ernet.dli.2015.233346/page/n421","code":""},{"path":"/reference/love.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"Harry Houser Love & John Henry Reisner (2012). Cornell-Nanking Story. Internet-First University Press. https://ecommons.cornell.edu/bitstream/1813/29080/2/Cornell-Nanking_15Jun12_PROOF.pdf","code":""},{"path":"/reference/love.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton — love.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(love.cotton.uniformity) # omit first column which has 20-foot plots dat <- subset(love.cotton.uniformity, col > 1) libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=20/80, # just a guess main=\"love.cotton.uniformity\") } # }"},{"path":"/reference/lu.stability.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"Multi-environment trial illustrate stability statistics","code":""},{"path":"/reference/lu.stability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"","code":"data(\"lu.stability\")"},{"path":"/reference/lu.stability.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"data frame 120 observations following 4 variables. yield yield gen genotype factor, 5 levels env environment factor, 6 levels block block factor, 4 levels","code":""},{"path":"/reference/lu.stability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"Data 5 maize genotypes 2 years x 3 sites = 6 environments.","code":""},{"path":"/reference/lu.stability.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"H.Y. Lu C. T. Tien. (1993) Studies nonparametric method phenotypic stability: II. Selection stability agroeconomic concept. J. Agric. Assoc. China 164:1-17.","code":""},{"path":"/reference/lu.stability.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"Hsiu Ying Lu. 1995. PC-SAS Program Estimating Huehn's Nonparametric Stability Statistics. Agron J. 87:888-891. Kae-Kang Hwu Li-yu D Liu. (2013) Stability Analysis Using Multiple Environment Trials Data Linear Regression. (Chinese) Crop, Environment & Bioinformatics 10:131-142.","code":""},{"path":"/reference/lu.stability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize, to illustrate stability statistics — lu.stability","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lu.stability) dat <- lu.stability # GxE means. Match Lu 1995 table 1 libs(reshape2) datm <- acast(dat, gen~env, fun=mean, value.var='yield') round(datm, 2) # Gen/Env means. Match Lu 1995 table 3 apply(datm, 1, mean) apply(datm, 2, mean) # Traditional ANOVA. Match Hwu table 2 # F value for gen,env m1 = aov(yield~env+gen+Error(block:env+env:gen), data=dat) summary(m1) # F value for gen:env, block:env m2 <- aov(yield ~ gen + env + gen:env + block:env, data=dat) summary(m2) # Finlay Wilkinson regression coefficients # First, calculate env mean, merge in libs(dplyr) dat2 <- group_by(dat, env) dat2 <- mutate(dat2, locmn=mean(yield)) m4 <- lm(yield ~ gen -1 + gen:locmn, data=dat2) coef(m4) # Match Hwu table 4 # Table 6: Shukla's heterogeneity test dat2$ge = paste0(dat2$gen, dat2$env) # Create a separate ge interaction term m6 <- lm(yield ~ gen + env + ge + ge:locmn, data=dat2) m6b <- lm( yield ~ gen + env + ge + locmn, data=dat2) anova(m6, m6b) # Non-significant difference # Table 7 - Shukla stability # First, environment means emn <- group_by(dat2, env) emn <- summarize(emn, ymn=mean(yield)) # Regress GxE terms on envt means getab = (model.tables(m2,\"effects\")$tables)$'gen:env' getab for (ll in 1:nrow(getab)){ m7l <- lm(getab[ll, ] ~ emn$ymn) cat(\"\\n\\n*************** Gen \",ll,\" ***************\\n\") cat(\"Regression coefficient: \",round(coefficients(m7l)[2],5),\"\\n\") print(anova(m7l)) } # Match Hwu table 7. } # } # dontrun"},{"path":"/reference/lucas.switchback.html","id":null,"dir":"Reference","previous_headings":"","what":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"Switchback experiment dairy cattle, milk yield 3 treatments","code":""},{"path":"/reference/lucas.switchback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"","code":"data(\"lucas.switchback\")"},{"path":"/reference/lucas.switchback.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"data frame 36 observations following 5 variables. cow cow factor, 12 levels trt treatment factor, 3 levels period period factor, 3 levels yield yield (FCM = fat corrected milk), pounds/day block block factor","code":""},{"path":"/reference/lucas.switchback.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"Lucas says \"data feeding trials employing present designs yet available, uniformity data used\". Six cows started together block 1, three cows block 2 three cows block 3.","code":""},{"path":"/reference/lucas.switchback.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"Lucas, HL. 1956. Switchback trials two treatments. Journal Dairy Science, 39, 146-154. https://doi.org/10.3168/jds.S0022-0302(56)94721-X","code":""},{"path":"/reference/lucas.switchback.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"Sanders, WL Gaynor, PJ. 1987. Analysis Switchback Data Using Statistical Analysis System. Journal Dairy Science, 70, 2186-2191. https://doi.org/10.3168/jds.S0022-0302(87)80273-4","code":""},{"path":"/reference/lucas.switchback.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Switchback experiment on dairy cattle, milk yield for 3 treatments — lucas.switchback","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lucas.switchback) dat <- lucas.switchback # Create a numeric period variable dat$per <- as.numeric(substring(dat$period,2)) libs(lattice) xyplot(yield ~ period|block, data=dat, group=cow, type=c('l','r'), auto.key=list(columns=6), main=\"lucas.switchback - (actually uniformity data)\") # Need to use 'terms' to preserve the order of the model terms # Really, cow(block), per:cow(block), period(block) m1 <- aov(terms(yield ~ block + cow:block + per:cow:block + period:block + trt, keep.order=TRUE), data=dat) anova(m1) # Match Sanders & Gaynor table 3 ## Analysis of Variance Table ## Df Sum Sq Mean Sq F value Pr(>F) ## block 2 30.93 15.464 55.345 5.132e-05 *** ## block:cow 9 1700.97 188.997 676.426 1.907e-09 *** ## block:cow:per 12 120.47 10.040 35.932 4.137e-05 *** ## block:period 3 14.85 4.950 17.717 0.001194 ** ## trt 2 1.58 0.789 2.825 0.126048 ## Residuals 7 1.96 0.279 coef(m1) # trtT2 and trtT3 match Sanders table 3 trt diffs } # }"},{"path":"/reference/lyon.potato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of potatoes — lyon.potato.uniformity","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"Uniformity trial potatoes Nebraska Experiment Station, 1909.","code":""},{"path":"/reference/lyon.potato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"data frame 204 observations following 3 variables. row row col column, section yield yield, pounds","code":""},{"path":"/reference/lyon.potato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"1909, potatoes harvested uniform land Nebraska Experiment Station. 34 rows, 34 inches apart. Lyon, page 97 says \"harvested row six sections, seventy-two feet seven inches long.\" clear SECTION 72 feet long, ROW 72 feet long. Yield potato roughly 0.5 0.8 pounds per square foot, seems plausible entire row 72 feet long (see calculations ). Field width: 6 plots = 72 feet Field length: 34 rows * 34 / 12in/ft = 96 ft","code":""},{"path":"/reference/lyon.potato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"Lyon, T.L. (1911). experiments estimate errors field plat tests. Proc. Amer. Soc. Agron, 3, 89-114. Table III. https://doi.org/10.2134/agronj1911.00021962000300010016x","code":""},{"path":"/reference/lyon.potato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"None.","code":""},{"path":"/reference/lyon.potato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of potatoes — lyon.potato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lyon.potato.uniformity) dat <- lyon.potato.uniformity # Yield per square foot, assuming 72 foot rows sum(dat$yield)/(72*96) # 0.67 # seems about right # Yield per square foot, assuming 72 foot plots sum(dat$yield)/(6*72*96) # 0.11 libs(desplot) desplot(dat, yield ~ col*row, tick=TRUE, flip=TRUE, aspect=96/72, # true aspect main=\"lyon.potato.uniformity\") } # }"},{"path":"/reference/lyons.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"Yield winter wheat 12 sites 4 years.","code":""},{"path":"/reference/lyons.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"data frame 48 observations following 3 variables. loc location, 12 levels year year, numeric yield yield (kg)","code":""},{"path":"/reference/lyons.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"Krzanowski uses briefly multi-dimensional scaling.","code":""},{"path":"/reference/lyons.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"R. Lyons (1980). review multidimensional scaling. Unpublished M.Sc. dissertation, University Reading.","code":""},{"path":"/reference/lyons.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"Krzanowski, W.J. (1988) Principles multivariate analysis. Oxford University Press.","code":""},{"path":"/reference/lyons.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of winter wheat at 12 sites in 4 years. — lyons.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(lyons.wheat) dat <- lyons.wheat libs(lattice) xyplot(yield~factor(year), dat, group=loc, main=\"lyons.wheat\", auto.key=list(columns=4), type=c('p','l')) } # }"},{"path":"/reference/magistad.pineapple.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of pineapple — magistad.pineapple.uniformity","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"Uniformity trial pineapple Hawaii 1932","code":""},{"path":"/reference/magistad.pineapple.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"","code":"data(\"magistad.pineapple.uniformity\")"},{"path":"/reference/magistad.pineapple.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"data frame 137 observations following 6 variables. field field number plat plat number row row col column number number fruits weight weight fruits, grams","code":""},{"path":"/reference/magistad.pineapple.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"Field 19. Kunia. Harvested 1932. \"field, harvested 1932, four rows per bed. 300-foot bed divided four equal parts form plats 1, 2, 3, 4. third [sic, second] bed similarly divided form plats 5 8, inclusive. manner plats 9 24 formed. way 24 plats 75 feet long 1 bed wide formed.\" Page 635: \"smallest plats 75 6.5 feet\". Field length: 4 plats * 75 feet = 300 feet Field width: 6 plats * 6.5 feet = 39 feet Field 82. Pearl City. \"Eight beds, separated two beds, selected harvested. Beds 8 feet center center. bed divided three plats 76 feet long.\" columns data bed 1, 4, 7, 10, 13, 16, 19, 22 Note: Layout plats rows/columns assumes pattern field 19. Field length: 3 plats * 76 feet = 228 feet Field width: 22 plats * 8 feet = 176 feet. Field 21. Kahuku. \"field 21, Kahuku, experimental plan Latin square type, five beds five plats . beds 7.5 feet center center. plat approximately 60 feet long third bed selected harvested.\" Note: Layout plats rows/columns assumes pattern field 19. Field lenght: 5 plats * 60 feet = 300 feet Field width: 13 plats * 7.5 feet = 97.5 feet Field 1. Kunia. \"experiment another Latin square test eight plats column eight plats row. harvested 1930. plat consisted two beds 150 feet long. Beds 6 feet center center consisted three rows . entire experimental area occupied 2.85 acres.\" Field length: 8 plats * 150 feet = 1200 feet Field width: 8 plats * 2 beds * 6 feet = 96 feet Total area: 1200*96/43560=2.64 acres","code":""},{"path":"/reference/magistad.pineapple.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"O. C. Magistad & C. . Farden (1934). Experimental Error Field Experiments Pineapples. Journal American Society Agronomy, 26, 631–643. https://doi.org/10.2134/agronj1934.00021962002600080001x","code":""},{"path":"/reference/magistad.pineapple.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"None","code":""},{"path":"/reference/magistad.pineapple.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of pineapple — magistad.pineapple.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(magistad.pineapple.uniformity) dat <- magistad.pineapple.uniformity # match table page 641 ## dat ## summarize(number=mean(number), ## weight=mean(weight)) ## field number weight ## 1 1 596.4062 2499.922 ## 2 19 171.1667 2100.250 ## 3 21 171.1600 2056.800 ## 4 82 220.7500 1264.500 libs(desplot) desplot(dat, weight ~ col*row, subset=field==19, aspect=300/39, main=\"magistad.pineapple.uniformity - field 19\") desplot(dat, weight ~ col*row, subset=field==82, aspect=228/176, main=\"magistad.pineapple.uniformity - field 82\") desplot(dat, weight ~ col*row, subset=field==21, aspect=300/97.5, main=\"magistad.pineapple.uniformity - field 21\") desplot(dat, weight ~ col*row, subset=field==1, aspect=1200/96, main=\"magistad.pineapple.uniformity - field 1\") } # }"},{"path":"/reference/masood.rice.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of rice — masood.rice.uniformity","title":"Uniformity trial of rice — masood.rice.uniformity","text":"Uniformity trial rice Lahore, Punjab, circa 2011.","code":""},{"path":"/reference/masood.rice.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of rice — masood.rice.uniformity","text":"","code":"data(\"masood.rice.uniformity\")"},{"path":"/reference/masood.rice.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of rice — masood.rice.uniformity","text":"data frame 288 observations following 3 variables. row row col column yield yield, kg/m^2","code":""},{"path":"/reference/masood.rice.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of rice — masood.rice.uniformity","text":"Data collected Rice Research Institute paddy yield trial. single variety rice harvested area 12m x 24 m. Yield kilograms measured square meter. Masood et al report low degree similarity neighboring plots. Note, Smith index calculations match results Pakistan Journal Agricultural Research, match results American-Eurasian Journal, seems paper seems refer data. results may simply differ scaling factor. yield values Masood labeled \"gm^2\" (gram per sq meter), extremely low. Probably \"kgm^2\". Field length: 24 plots x 1m = 24m. Field width: 12 plots x 1m = 12m. Used permission Asif Masood.","code":""},{"path":"/reference/masood.rice.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of rice — masood.rice.uniformity","text":"Masood, M Asif Raza, Irum. 2012. Estimation optimum field plot size shape paddy yield trial. Pakistan J. Agric. Res., Vol. 25 . 4, 2012","code":""},{"path":"/reference/masood.rice.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of rice — masood.rice.uniformity","text":"Masood, M Asif Raza, Irum. 2012. Estimation optimum field plot size shape paddy yield trial. American-Eurasian Journal Scientific Research, 7, 264-269. Table 1. https://doi.org/10.5829/idosi.aejsr.2012.7.6.1926","code":""},{"path":"/reference/masood.rice.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of rice — masood.rice.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(masood.rice.uniformity) dat <- masood.rice.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, tick=TRUE, aspect=24/12, # true aspect main=\"masood.rice.uniformity - yield heatmap\") libs(agricolae) libs(reshape2) dmat <- acast(dat, row~col, value.var='yield') index.smith(dmat, main=\"masood.rice.uniformity\", col=\"red\") # CVs match Table 3 } # }"},{"path":"/reference/mcclelland.corn.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of corn — mcclelland.corn.uniformity","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"Uniformity trial corn Arkansas Experiment Station, 1925.","code":""},{"path":"/reference/mcclelland.corn.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"","code":"data(\"mcclelland.corn.uniformity\")"},{"path":"/reference/mcclelland.corn.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"data frame 438 observations following 3 variables. row row col column yield yield","code":""},{"path":"/reference/mcclelland.corn.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"uniformity trial corn 1925 Arkansas Experimental Station. Unit measure given. Field width = 66ft * 2 = 132 feet. Field length = 219 rows * 44 inches / 12 inches/ft = 803 ft. Note: source document, table 2, first 'west' column second--last row (page 822), value 1.40 assumed typographical error changed 14.0 data. source document give unit measure plot yields. yield bu/ac, value 12 bu/ac low. hand, value 12 pounds per plot * 180 plots per acre / 56 pounds per bushel = 39 bu/ac reasonable yield corn 1925, whereas 12 kg per plot unlikely high. Also, 1925, pound likely kilogram.","code":""},{"path":"/reference/mcclelland.corn.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"McClelland, Chalmer Kirk (1926). determinations plat variability. Agronomy Journal, 18, 819-823. https://doi.org/10.2134/agronj1926.00021962001800090009x","code":""},{"path":"/reference/mcclelland.corn.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"None","code":""},{"path":"/reference/mcclelland.corn.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of corn — mcclelland.corn.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mcclelland.corn.uniformity) dat <- mcclelland.corn.uniformity # McClelland table 3, first row, gives 11.2 # Probable error = 0.67449 * sd(). Relative to mean. # 0.67449 * sd(dat$yield)/mean(dat$yield) # 11.2 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(219*44/12)/132, # true aspect, 219 rows * 44 inches x 132 feet main=\"mcclelland.corn.uniformity\") } # }"},{"path":"/reference/mcconway.turnip.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of turnips — mcconway.turnip","title":"RCB experiment of turnips — mcconway.turnip","text":"RCB experiment turnips, 2 treatments planting date density","code":""},{"path":"/reference/mcconway.turnip.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of turnips — mcconway.turnip","text":"data frame 64 observations following 6 variables. gen genotype date planting date, levels 21Aug1990 28Aug1990 density planting density, 1, 2, 4, 8 kg/ha block block, 4 levels yield yield","code":""},{"path":"/reference/mcconway.turnip.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of turnips — mcconway.turnip","text":"randomized block experiment 16 treatments allocated random four blocks. 16 treatments combinations two varieties, two planting dates, four densities. Lee et al (2008) proposed analysis using mixed models changing treatment variances. Piepho (2009) proposed ordinary ANOVA using transformed data. Used permission Kevin McConway.","code":""},{"path":"/reference/mcconway.turnip.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of turnips — mcconway.turnip","text":"K. J. McConway, M. C. Jones, P. C. Taylor. Statistical Modelling Using Genstat.","code":""},{"path":"/reference/mcconway.turnip.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of turnips — mcconway.turnip","text":"Michael Berthold, D. J. Hand. Intelligent data analysis: introduction, 1998. Pages 75–82. Lee, C.J. O Donnell, M. O Neill, M. (2008). Statistical analysis field trials changing treatment variance. Agronomy Journal, 100, 484–489. Piepho, H.P. (2009), Data transformation statistical analysis field trials changing treatment variance. Agronomy Journal, 101, 865–869. https://doi.org/10.2134/agronj2008.0226x","code":""},{"path":"/reference/mcconway.turnip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of turnips — mcconway.turnip","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mcconway.turnip) dat <- mcconway.turnip dat$densf <- factor(dat$density) # Table 2 of Lee et al. m0 <- aov( yield ~ gen * densf * date + block, dat ) summary(m0) ## Df Sum Sq Mean Sq F value Pr(>F) ## gen 1 84.0 83.95 8.753 0.00491 ** ## densf 3 470.4 156.79 16.347 2.51e-07 *** ## date 1 233.7 233.71 24.367 1.14e-05 *** ## block 3 163.7 54.58 5.690 0.00216 ** ## gen:densf 3 8.6 2.88 0.301 0.82485 ## gen:date 1 36.5 36.45 3.800 0.05749 . ## densf:date 3 154.8 51.60 5.380 0.00299 ** ## gen:densf:date 3 18.0 6.00 0.626 0.60224 ## Residuals 45 431.6 9.59 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # Boxplots suggest heteroskedasticity for date, density libs(\"HH\") interaction2wt(yield ~ gen + date + densf +block, dat, x.between=0, y.between=0, main=\"mcconway.turnip - yield\") libs(nlme) # Random block model m1 <- lme(yield ~ gen * date * densf, random= ~1|block, data=dat) summary(m1) anova(m1) # Multiplicative variance model over densities and dates m2 <- update(m1, weights=varComb(varIdent(form=~1|densf), varIdent(form=~1|date))) summary(m2) anova(m2) # Unstructured variance model over densities and dates m3 <- update(m1, weights=varIdent(form=~1|densf*date)) summary(m3) anova(m3) # Table 3 of Piepho, using transformation m4 <- aov( yield^.235 ~ gen * date * densf + block, dat ) summary(m4) } # }"},{"path":"/reference/mckinstry.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"Uniformity trial cotton South Rhodesia","code":""},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"","code":"data(\"mckinstry.cotton.uniformity\")"},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"data frame 480 observations following 3 variables. row row ordinate col column ordinate yield yield per plot, ounces","code":""},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"uniformity trial cotton experiment Gatooma, South Rhodesia. Conducted Empire Cotton Growing Corporation. Planted Nov 1934. Harvested Jun 1935. Field length: 20 rows x 25 feet. Field width: 24 columns x 3.5 feet. Crop History: season good peak flowering - good growth, heavy flowering - 5 weeks drought critical period crop, aggravated exceptionally heavy aphis attack heavy boll-worm attack accounts. Lay-: harvest, block 24 rows x 500 ft, row marked 20 lengths 25 ft , giving 480 small plots. use made data advisable ignore row 1 row 20, bordering roads. data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 5.","code":""},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"None","code":""},{"path":"/reference/mckinstry.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton in South Rhodesia — mckinstry.cotton.uniformity","text":"","code":"library(agridat) data(mckinstry.cotton.uniformity) dat <- mckinstry.cotton.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, tick=TRUE, aspect=(20*25)/(24*3.5), main=\"mckinstry.cotton.uniformity\")"},{"path":"/reference/mcleod.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"Yield yield components barley different seeding rates.","code":""},{"path":"/reference/mcleod.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"data frame 40 observations following 10 variables. year year, numeric site site factor rate rate, numeric plants plants per sq meter tillers tillers per plant heads heads per plant surviving percent surviving tillers grains grains per head weight weight 1000 grains yield yield tons/hectare","code":""},{"path":"/reference/mcleod.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"Trials conducted 5 sites, 3 years South Canterbury. (sites every year). Values average 6 blocks. 1974 severe drought. years favorable growing conditions.","code":""},{"path":"/reference/mcleod.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"C. C. McLeod (1982). Effects rates seeding barley sown grain. New Zealand Journal Experimental Agriculture, 10, 133-136. https://doi.org/10.1080/03015521.1982.10427857.","code":""},{"path":"/reference/mcleod.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"Maindonald (1992).","code":""},{"path":"/reference/mcleod.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley in South Canterbury with yield and yield components — mcleod.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mcleod.barley) dat <- mcleod.barley # Table 3 of McLeod. Across-environment means by planting rate d1 <- aggregate(cbind(plants, tillers, heads, surviving, grains, weight, yield) ~ rate, dat, FUN=mean) # Calculate income based on seed cost of $280/ton, grain $140/ton. d1 <- transform(d1, income=140*yield-280*rate/1000) signif(d1,3) ## rate plants tillers heads surviving grains weight yield ## 50 112.12 5.22 4.36 83.95 21.25 46.11 3.97 ## 75 162.75 4.04 3.26 80.89 19.95 45.10 4.26 ## 100 202.62 3.69 2.73 74.29 19.16 44.66 4.38 ## 125 239.00 3.28 2.33 71.86 18.45 43.45 4.41 ## 150 293.62 2.90 2.00 69.54 17.94 42.77 4.47 # Even though tillers/plant, heads/plant, surviving tillers, # grains/head, weight/1000 grains are all decreasing as planting # rate increases, the total yield is still increasing. # But, income peaks around seed rate of 100. libs(lattice) xyplot(yield +income +surviving +grains +weight +plants +tillers +heads ~ rate, data=d1, outer=TRUE, type=c('p','l'), scales=list(y=list(relation=\"free\")), xlab=\"Nitrogen rate\", ylab=\"Trait value\", main=\"mcleod.barley - nitrogen response curves\" ) } # }"},{"path":"/reference/mead.cauliflower.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaves for cauliflower plants at different times — mead.cauliflower","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"Leaves cauliflower plants different times two years.","code":""},{"path":"/reference/mead.cauliflower.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"data frame 14 observations following 4 variables. year year factor degdays degree days 32F leaves number leaves","code":""},{"path":"/reference/mead.cauliflower.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"Numbers leaves 10 cauliflower plants two years, temperature degree-days 32F, divided 100. year 1956-57 1957-58. data range shown, number leaves increasing linearly. Extrapolating backwards shows linear model inappropriate, glm used.","code":""},{"path":"/reference/mead.cauliflower.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"Roger Mead, Robert N Curnow, Anne M Hasted. 2002. Statistical Methods Agriculture Experimental Biology, 3rd ed. Chapman Hall. Page 251.","code":""},{"path":"/reference/mead.cauliflower.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"Mick O'Neill. Regression & Generalized Linear (Mixed) Models. Statistical Advisory & Training Service Pty Ltd.","code":""},{"path":"/reference/mead.cauliflower.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leaves for cauliflower plants at different times — mead.cauliflower","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.cauliflower) dat <- mead.cauliflower dat <- transform(dat, year=factor(year)) m1 <- glm(leaves ~ degdays + year, data=dat, family=poisson) coef(m1) ## (Intercept) degdays year1957 ## 3.49492453 0.08512651 0.21688760 dat$pred <- predict(m1, type=\"response\") libs(lattice) libs(latticeExtra) xyplot(leaves~degdays, data=dat, groups=year, type=c('p'), auto.key=list(columns=2), main=\"mead.cauliflower - observed (symbol) & fitted (line)\", xlab=\"degree days\", ylab=\"Number of leaves\", ) + xyplot(pred~degdays, data=dat, groups=year, type=c('l'), col=\"black\") } # }"},{"path":"/reference/mead.cowpea.maize.html","id":null,"dir":"Reference","previous_headings":"","what":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"Intercropping experiment maize/cowpea, multiple nitrogen treatments.","code":""},{"path":"/reference/mead.cowpea.maize.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"data frame 72 observations following 6 variables. block block, 3 levels nitro nitrogen, 4 levels cowpea cowpea variety, 2 levels maize maize variety, 3 levels cyield cowpea yield, kg/ha myield maize yield, kg/ha","code":""},{"path":"/reference/mead.cowpea.maize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"intercropping experiment conducted Nigeria. four nitrogen treatments 0, 40, 80, 120 kg/ha.","code":""},{"path":"/reference/mead.cowpea.maize.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"Roger Mead. 1990. Review Methodology Analysis Intercropping Experiments. Training Working Document . 6. CIMMYT. https://repository.cimmyt.org/xmlui/handle/10883/868","code":""},{"path":"/reference/mead.cowpea.maize.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"Roger Mead, Robert N Curnow, Anne M Hasted. 2002. Statistical Methods Agriculture Experimental Biology, 3rd ed. Chapman Hall. Page 390.","code":""},{"path":"/reference/mead.cowpea.maize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Intercropping experiment of maize/cowpea — mead.cowpea.maize","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.cowpea.maize) dat <- mead.cowpea.maize # Cowpea and maize yields are clearly in competition libs(\"latticeExtra\") useOuterStrips(xyplot(myield ~ cyield|maize*cowpea, dat, group=nitro, main=\"mead.cowpea.maize - intercropping\", xlab=\"cowpea yield\", ylab=\"maize yield\", auto.key=list(columns=4))) # Mead Table 2 Cowpea yield anova...strongly affected by maize variety. anova(aov(cyield ~ block + maize + cowpea + nitro + maize:cowpea + maize:nitro + cowpea:nitro + maize:cowpea:nitro, dat)) # Cowpea mean yields for nitro*cowpea aggregate(cyield ~ nitro+cowpea, dat, FUN=mean) # Cowpea mean yields for each maize variety aggregate(cyield ~ maize, dat, FUN=mean) # Bivariate analysis aov.c <- anova(aov(cyield/1000 ~ block + maize + cowpea + nitro + maize:cowpea + maize:nitro + cowpea:nitro + maize:cowpea:nitro, dat)) aov.m <- anova(aov(myield/1000 ~ block + maize + cowpea + nitro + maize:cowpea + maize:nitro + cowpea:nitro + maize:cowpea:nitro, dat)) aov.cm <- anova(aov(cyield/1000 + myield/1000 ~ block + maize + cowpea + nitro + maize:cowpea + maize:nitro + cowpea:nitro + maize:cowpea:nitro, dat)) biv <- cbind(aov.m[,1:2], aov.c[,2], aov.cm[,2]) names(biv) <- c('df','maize ss','cowpea ss','ss for sum') biv$'sum of prod' <- (biv[,4] - biv[,2] - biv[,3] ) /2 biv$cor <- biv[,5]/(sqrt(biv[,2] * biv[,3])) signif(biv,2) ## df maize ss cowpea ss ss for sum sum of prod cor ## block 2 0.290 0.0730 0.250 -0.058 -0.400 ## maize 2 18.000 0.4100 13.000 -2.600 -0.980 ## cowpea 1 0.027 0.0060 0.058 0.013 1.000 ## nitro 3 29.000 0.1100 25.000 -1.800 -0.980 ## maize:cowpea 2 1.100 0.0099 0.920 -0.099 -0.950 ## maize:nitro 6 1.300 0.0680 0.920 -0.200 -0.680 ## cowpea:nitro 3 0.240 0.1700 0.150 -0.130 -0.640 ## maize:cowpea:nitro 6 1.300 0.1400 1.300 -0.033 -0.079 ## Residuals 46 16.000 0.6000 14.000 -1.400 -0.460 } # }"},{"path":"/reference/mead.germination.html","id":null,"dir":"Reference","previous_headings":"","what":"Seed germination with different temperatures/concentrations — mead.germination","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"Seed germination different temperatures/concentrations","code":""},{"path":"/reference/mead.germination.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"data frame 64 observations following 5 variables. temp temperature regimen rep replication factor (blocking) conc chemical concentration germ number seeds germinating seeds number seeds tested = 50","code":""},{"path":"/reference/mead.germination.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"rep factor blocking factor. Used permission Roger Mead, Robert Curnow, Anne Hasted.","code":""},{"path":"/reference/mead.germination.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"Roger Mead, Robert N Curnow, Anne M Hasted. 2002. Statistical Methods Agriculture Experimental Biology, 3rd ed. Chapman Hall. Page 350-351.","code":""},{"path":"/reference/mead.germination.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"Schabenberger, O. Pierce, F.J., 2002. Contemporary statistical models plant soil sciences. CRC.","code":""},{"path":"/reference/mead.germination.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seed germination with different temperatures/concentrations — mead.germination","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.germination) dat <- mead.germination dat <- transform(dat, concf=factor(conc)) libs(lattice) xyplot(germ~log(conc+.01)|temp, dat, layout=c(4,1), main=\"mead.germination\", ylab=\"number of seeds germinating\") m1 <- glm(cbind(germ, seeds-germ) ~ 1, dat, family=binomial) m2 <- glm(cbind(germ, seeds-germ) ~ temp, dat, family=binomial) m3 <- glm(cbind(germ, seeds-germ) ~ concf, dat, family=binomial) m4 <- glm(cbind(germ, seeds-germ) ~ temp + concf, dat, family=binomial) m5 <- glm(cbind(germ, seeds-germ) ~ temp * concf, dat, family=binomial) anova(m1,m2,m3,m4,m5) ## Resid. Df Resid. Dev Df Deviance ## 1 63 1193.80 ## 2 60 430.11 3 763.69 ## 3 60 980.10 0 -549.98 ## 4 57 148.11 3 831.99 ## 5 48 55.64 9 92.46 # Show logit and fitted values. T2 has highest germination subset(cbind(dat, predict(m5), fitted(m5)), rep==\"R1\") } # }"},{"path":"/reference/mead.lamb.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"Number lambs born 3 breeds 3 farms","code":""},{"path":"/reference/mead.lamb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"","code":"data(\"mead.lamb\")"},{"path":"/reference/mead.lamb.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"data frame 36 observations following 4 variables. farm farm: F1, F2, F3 breed breed: B1, B2, B3 lambclass lambing class: L0, L1, L2, L3 y count ewes class","code":""},{"path":"/reference/mead.lamb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"data 'y' counts ewes different lambing classes. classes number live lambs per birth 0, 1, 2, 3+ lambs.","code":""},{"path":"/reference/mead.lamb.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"Roger Mead, Robert N Curnow, Anne M Hasted. 2002. Statistical Methods Agriculture Experimental Biology, 3rd ed. Chapman Hall. Page 359.","code":""},{"path":"/reference/mead.lamb.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"None","code":""},{"path":"/reference/mead.lamb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Number of lambs born to 3 breeds on 3 farms — mead.lamb","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.lamb) dat <- mead.lamb # farm 1 has more ewes in lambclass 3 d2 <- xtabs(y ~ farm+breed+lambclass, data=dat) mosaicplot(d2, color=c(\"lemonchiffon1\",\"moccasin\",\"lightsalmon1\",\"indianred\"), xlab=\"farm/lambclass\", ylab=\"breed\", main=\"mead.lamb\") names(dat) <- c('F','B','L','y') # for compactness # Match totals in Mead example 14.6 libs(dplyr) dat <- group_by(dat, F,B) summarize(dat, y=sum(y)) ## F B y ## ## 1 F1 A 150 ## 2 F1 B 46 ## 3 F1 C 78 ## 4 F2 A 72 ## 5 F2 B 79 ## 6 F2 C 28 ## 7 F3 A 224 ## 8 F3 B 129 ## 9 F3 C 34 # Models m1 <- glm(y ~ F + B + F:B, data=dat, family=poisson(link=log)) m2 <- update(m1, y ~ F + B + F:B + L) m3 <- update(m1, y ~ F + B + F:B + L + B:L) m4 <- update(m1, y ~ F + B + F:B + L + F:L) m5 <- update(m1, y ~ F + B + F:B + L + B:L + F:L) AIC(m1, m2, m3, m4, m5) # Model 4 has best AIC ## df AIC ## m1 9 852.9800 ## m2 12 306.5457 ## m3 18 303.5781 ## m4 18 206.1520 ## m5 24 213.8873 # Change contrasts for Miroslav m4 <- update(m4, contrasts=list(F=contr.sum,B=contr.sum,L=contr.sum)) summary(m4) # Match deviance table from Mead libs(broom) all <- do.call(rbind, lapply(list(m1, m2, m3, m4, m5), broom::glance)) all$model <- unlist(lapply(list(m1, m2, m3, m4, m5), function(x) as.character(formula(x)[3]))) all[,c('model','deviance','df.residual')] ## model deviance df.residual ## 1 F + B + F:B 683.67257 27 ## 2 F + B + L + F:B 131.23828 24 ## 3 F + B + L + F:B + B:L 116.27069 18 ## 4 F + B + L + F:B + F:L 18.84460 18 ## 5 F + B + L + F:B + B:L + F:L 14.57987 12 if(0){ # Using MASS::loglm libs(MASS) # Note: without 'fitted=TRUE', devtools::run_examples has an error m4b <- MASS::loglm(y ~ F + B + F:B + L + F:L, data = dat, fitted=TRUE) # Table of farm * class interactions. Match Mead p. 360 round(coef(m4b)$F.L,2) fitted(m4b) resid(m4b) # libs(vcd) # mosaic(m4b, shade=TRUE, # formula = ~ F + B + F:B + L + F:L, # residual_type=\"rstandard\", keep_aspect=FALSE) } } # }"},{"path":"/reference/mead.strawberry.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of strawberry — mead.strawberry","title":"RCB experiment of strawberry — mead.strawberry","text":"RCB experiment strawberry","code":""},{"path":"/reference/mead.strawberry.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of strawberry — mead.strawberry","text":"data frame 32 observations following 5 variables. row row col column block block, 4 levels gen genotype, 8 levels yield yield, pounds","code":""},{"path":"/reference/mead.strawberry.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of strawberry — mead.strawberry","text":"hedge along right side (column 8) caused shading lower yields. R. Mead said (discussion Besag & Higdon paper), \"blocks defined (given experimenter) entire horizontal rows...design trial actually (unrecognized also) checker-board eight half-blocks two groups split-plot varieties\". two sub-groups genotypes G, V, R1, F Re, M, E, P.","code":""},{"path":"/reference/mead.strawberry.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of strawberry — mead.strawberry","text":"Unknown, prior 1968 according Besag. Probably via R. Mead.","code":""},{"path":"/reference/mead.strawberry.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of strawberry — mead.strawberry","text":"R. Mead, 1990, Design Experiments. Julian Besag D Higdon, 1999. Bayesian Analysis Agricultural Field Experiments, Journal Royal Statistical Society: Series B (Statistical Methodology),61, 691–746. Table 4.","code":""},{"path":"/reference/mead.strawberry.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of strawberry — mead.strawberry","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.strawberry) dat <- mead.strawberry dat$sub <- ifelse(is.element(dat$gen, c('G', 'V', 'R1', 'F')), \"S1\",\"S2\") libs(desplot) desplot(dat, yield~col*row, text=gen, cex=1, out1=block, out2=sub, # unknown aspect main=\"mead.strawberry\") } # }"},{"path":"/reference/mead.turnip.html","id":null,"dir":"Reference","previous_headings":"","what":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"Density/spacing experiment turnips 3 blocks.","code":""},{"path":"/reference/mead.turnip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"","code":"data(\"mead.turnip\")"},{"path":"/reference/mead.turnip.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"data frame 60 observations following 4 variables. yield log yield (pounds/plot) block block spacing row spacing, inches density density seeds, pounds/acre","code":""},{"path":"/reference/mead.turnip.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"experiment turnips, 3 blocks, 20 treatments factorial arrangement 5 seeding rates (density) 4 widths (spacing).","code":""},{"path":"/reference/mead.turnip.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"Roger Mead. (1988). Design Experiments: Statistical Principles Practical Applications. Example 12.3. Page 323.","code":""},{"path":"/reference/mead.turnip.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"H. P. Piepho, R. N. Edmondson. (2018). tutorial statistical analysis factorial experiments qualitative quantitative treatment factor levels. Jour Agronomy Crop Science, 8, 1-27. https://doi.org/10.1111/jac.12267","code":""},{"path":"/reference/mead.turnip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Density/spacing experiment for turnips in 3 blocks. — mead.turnip","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mead.turnip) dat <- mead.turnip dat$ratef <- factor(dat$density) dat$widthf <- factor(dat$spacing) m1 <- aov(yield ~ block + ratef + widthf + ratef:widthf, data=dat) anova(m1) # table 12.10 in Mead # Similar to Piepho fig 10 libs(lattice) xyplot(yield ~ log(spacing)|ratef, data=dat, auto.key=list(columns=5), main=\"mead.turnip - log(yield) for each density\", group=ratef) } # }"},{"path":"/reference/mercer.mangold.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of mangolds — mercer.mangold.uniformity","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"Uniformity trial mangolds Rothamsted Experiment Station, England, 1910.","code":""},{"path":"/reference/mercer.mangold.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"","code":"data(\"mercer.mangold.uniformity\")"},{"path":"/reference/mercer.mangold.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"data frame 200 observations following 4 variables. row row col column roots root yields, pounds leaves leaf yields, pounds","code":""},{"path":"/reference/mercer.mangold.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"Grown 1910. plot 3 drills, drill 2.4 feet wide. Plots 1/200 acres, 7.2 feet 30.25 feet long \"length plots runs horizontal lines figures [ Table ], also direction drills across field.\" Field width: 10 plots * 30.25ft = 302.5 feet Field length: 20 plots * 7.25 ft = 145 feet","code":""},{"path":"/reference/mercer.mangold.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"Mercer, WB Hall, AD, 1911. experimental error field trials Journal Agricultural Science, 4, 107-132. Table 1. https://doi.org/10.1017/S002185960000160X","code":""},{"path":"/reference/mercer.mangold.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667 Theodor Roemer (1920). Der Feldversuch. Page 64, table 5.","code":""},{"path":"/reference/mercer.mangold.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of mangolds — mercer.mangold.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mercer.mangold.uniformity) dat <- mercer.mangold.uniformity libs(desplot) desplot(dat, leaves~col*row, aspect=145/302, # true aspect main=\"mercer.mangold.uniformity - leaves\") libs(desplot) desplot(dat, roots~col*row, aspect=145/302, # true aspect main=\"mercer.mangold.uniformity - roots\") libs(lattice) xyplot(roots~leaves, data=dat) } # }"},{"path":"/reference/mercer.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — mercer.wheat.uniformity","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"Uniformity trial wheat Rothamsted Experiment Station, England, 1910.","code":""},{"path":"/reference/mercer.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"data frame 500 observations following 4 variables. row row col column grain grain yield, pounds straw straw yield, pounds","code":""},{"path":"/reference/mercer.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"wheat crop grown summer 1910 Rothamsted Experiment Station (Harpenden, Hertfordshire, England). Great Knott, seemingly uniform area 1 acre harvested separate plots, 1/500th acre size. grain straw plot weighed separately. McCullagh gives information plot size. Field width: 25 plots * 8 ft = 200 ft Field length: 20 plots * 10.82 ft = 216 ft D. G. Rossiter (2014) uses data extensive data analysis tutorial.","code":""},{"path":"/reference/mercer.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"Mercer, WB Hall, AD, (1911). experimental error field trials Journal Agricultural Science, 4, 107-132. Table 5. https://doi.org/10.1017/S002185960000160X","code":""},{"path":"/reference/mercer.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667 Theodor Roemer (1920). Der Feldversuch. Page 65, table 6. D. G. Rossiter (2014). Tutorial: Using R Environment Statistical Computing example Mercer & Hall wheat yield dataset. G. . Baker (1941). Fundamental Distribution Errors Agricultural Field Trials. National Mathematics Magazine, 16, 7-19. https://doi.org/10.2307/3028105 'spdep' package includes grain yields () spatial positions plot centres example dataset 'wheat'. Note, checked '4.03' values data match original document.","code":""},{"path":"/reference/mercer.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — mercer.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(mercer.wheat.uniformity) dat <- mercer.wheat.uniformity libs(desplot) desplot(dat, grain ~ col*row, aspect=216/200, # true aspect main=\"mercer.wheat.uniformity - grain yield\") libs(lattice) xyplot(straw ~ grain, data=dat, type=c('p','r'), main=\"mercer.wheat.uniformity - regression\") libs(hexbin) hexbinplot(straw ~ grain, data=dat) libs(sp, gstat) plot.wid <- 2.5 plot.len <- 3.2 nr <- length(unique(dat$row)) nc <- length(unique(dat$col)) xy <- expand.grid(x = seq(plot.wid/2, by=plot.wid, length=nc), y = seq(plot.len/2, by=plot.len, length=nr)) dat.sp <- dat coordinates(dat.sp) <- xy # heatmap spplot(dat.sp, zcol = \"grain\", cuts=8, cex = 1.6, col.regions = bpy.colors(8), main = \"Grain yield\", key.space = \"right\") # variogram # Need gstat::variogram to get the right method vg <- gstat::variogram(grain ~ 1, dat.sp, cutoff = plot.wid * 10, width = plot.wid) plot(vg, plot.numbers = TRUE, main=\"mercer.wheat.uniformity - variogram\") } # }"},{"path":"/reference/miguez.biomass.html","id":null,"dir":"Reference","previous_headings":"","what":"Biomass of 3 crops in Greece — miguez.biomass","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"Biomass 3 crops Greece","code":""},{"path":"/reference/miguez.biomass.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"","code":"data(\"miguez.biomass\")"},{"path":"/reference/miguez.biomass.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"data frame 212 observations following 5 variables. doy day year block block, 1-4 input management input, Lo/Hi crop crop type yield yield tons/ha","code":""},{"path":"/reference/miguez.biomass.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"Experiment conducted Greece 2009. Yield values destructive Measurements -ground biomass fiber sorghum, maize, sweet sorghum. Hi management refers weekly irrigation high nitrogen applications. Lo management refers bi-weekly irrigation low nitrogen. experiment 4 blocks. Crops planted DOY 141 0 yield.","code":""},{"path":"/reference/miguez.biomass.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"Fernando E. Miguez. R package nlraa. https://github.com/femiguez/nlraa","code":""},{"path":"/reference/miguez.biomass.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"Sotirios V. Archontoulis Fernando E. Miguez (2013). Nonlinear Regression Models Applications Agricultural Research. Agron. Journal, 105:1-13. https://doi.org/10.2134/agronj2012.0506 Hamze Dokoohaki. https://www.rpubs.com/Para2x/100378 https://rstudio-pubs-static.s3.amazonaws.com/100440_26eb9108524c4cc99071b0db8e648e7d.html","code":""},{"path":"/reference/miguez.biomass.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Biomass of 3 crops in Greece — miguez.biomass","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(miguez.biomass) dat <- miguez.biomass dat <- subset(dat, doy > 141) libs(lattice) xyplot(yield ~ doy | crop*input, data = dat, main=\"miguez.biomass\", groups = crop, type=c('p','smooth'), auto.key=TRUE) # ---------- # Archontoulis et al fit some nonlinear models. # Here is a simple example which does NOT account for crop/input # Slow, so dont run if(0){ dat2 <- transform(dat, eu = paste(block, input, crop)) dat2 <- groupedData(yield ~ doy | eu, data = dat2) fit.lis <- nlsList(yield ~ SSfpl(doy, A, B, xmid, scal), data = dat2, control=nls.control(maxiter=100)) print(plot(intervals(fit.lis))) libs(nlme) # use all data to get initial values inits <- getInitial(yield ~ SSfpl(doy, A, B, xmid, scal), data = dat2) inits xvals <- 150:325 y1 <- with(as.list(inits), SSfpl(xvals, A, B, xmid, scal)) plot(yield ~ doy, dat2) lines(xvals,y1) # must have groupedData object to use augPred dat2 <- groupedData(yield ~ doy|eu, data=dat2) plot(dat2) # without 'random', all effects are included in 'random' m1 <- nlme(yield ~ SSfpl(doy, A, B, xmid,scale), data= dat2, fixed= A + B + xmid + scale ~ 1, # random = B ~ 1|eu, # to make only B random random = A + B + xmid + scale ~ 1|eu, start=inits) fixef(m1) summary(m1) plot(augPred(m1, level=0:1), main=\"miguez.biomass - observed/predicted data\") # only works with groupedData object } } # }"},{"path":"/reference/minnesota.barley.weather.html","id":null,"dir":"Reference","previous_headings":"","what":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"monthly weather summaries 6 sites barley yield trials conducted.","code":""},{"path":"/reference/minnesota.barley.weather.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"data frame 719 observations following 8 variables. site site, 6 levels year year, 1927-1936 mo month, 1-12, numeric cdd monthly cooling degree days, Fahrenheit hdd monthly heating degree days, Fahrenheit precip monthly precipitation, inches min monthly average daily minimum temp, Fahrenheit max monthly average daily maximum temp, Fahrenheit","code":""},{"path":"/reference/minnesota.barley.weather.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"weather data extracted National Climate Data Center, following weather stations chosen, based availability weather data given time frame (1927-1936) proximity town (site) barley data. 'cdd' cooling degree days, number degree days temperature _above_ 65 Fahrenheit. 'hdd' heating degree days, _below_ 65 Fahrenheit. data available Duluth Dec, 1931.","code":""},{"path":"/reference/minnesota.barley.weather.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"National Climate Data Center, https://www.ncdc.noaa.gov/.","code":""},{"path":"/reference/minnesota.barley.weather.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"Kevin Wright. 2013. Revisiting Immer's Barley Data. American Statistitician, 67, 129-133. https://doi.org/10.1080/00031305.2013.801783","code":""},{"path":"/reference/minnesota.barley.weather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Monthly weather at 6 sites in Minnesota 1927-1936. — minnesota.barley.weather","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(minnesota.barley.yield) dat <- minnesota.barley.yield data( minnesota.barley.weather) datw <- minnesota.barley.weather # Weather trends over time libs(latticeExtra) useOuterStrips(xyplot(cdd~mo|year*site, datw, groups=year, main=\"minnesota.barley\", xlab=\"month\", ylab=\"Cooling degree days\", subset=(mo > 3 & mo < 10), scales=list(alternating=FALSE), type='l', auto.key=list(columns=5))) # Total cooling/heating/precip in Apr-Aug for each site/yr ww <- subset(datw, mo>=4 & mo<=8) ww <- aggregate(cbind(cdd,hdd,precip)~site+year, data=ww, sum) # Average yield per each site/env yy <- aggregate(yield~site+year, dat, mean) minn <- merge(ww, yy) # Higher yields generally associated with cooler temps, more precip libs(reshape2) me <- melt(minn, id.var=c('site','year')) mey <- subset(me, variable==\"yield\") mey <- mey[,c('site','year','value')] names(mey) <- c('site','year','y') mec <- subset(me, variable!=\"yield\") names(mec) <- c('site','year','covar','x') mecy <- merge(mec, mey) mecy$yr <- factor(mecy$year) foo <- xyplot(y~x|covar*site, data=mecy, groups=yr, cex=1, ylim=c(5,65), par.settings=list(superpose.symbol=list(pch=substring(levels(mecy$yr),4))), xlab=\"\", ylab=\"yield\", main=\"minnesota.barley\", panel=function(x,y,...) { panel.lmline(x,y,..., col=\"gray\") panel.superpose(x,y,...) }, scales=list(x=list(relation=\"free\"))) libs(latticeExtra) foo <- useOuterStrips(foo, strip.left = strip.custom(par.strip.text=list(cex=.7))) combineLimits(foo, margin.x=2L) # Use a common x axis for all rows } # }"},{"path":"/reference/minnesota.barley.yield.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"data come barley breeding experiments conducted Minnesota years 1893-1942. early years, experiments conducted StPaul. late 1920s, experiments expanded 6 sites across state.","code":""},{"path":"/reference/minnesota.barley.yield.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"data frame 647 observations following 4 variables. site site factor, 6 levels gen_name genotype name gen genotype (CI cereal introduction ID) year year yield yield bu/ac","code":""},{"path":"/reference/minnesota.barley.yield.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"lattice package contains smaller version data years 1931 1932. expanded version barley data often used illustrate dot plots. following comments reference mentioned source documents. —– Notes Immer (1934) —– University Farm location Saint Paul. source provides yield data three blocks location 1931 1932. following registration numbers names given: —– Notes Harlan et al (1925) —– data early tests accurate stations, may problems stations. (p. 14). Identification many varieties inadequate...chance incorrectly identified small...Officials StPaul station expressed desire conclusions drawn yields limitations earlier experiments taken full consideration. (p. 72) Chevalier Hanna varieties well adapted StPaul (p. 73). —– Notes Harlan et al (1929) —– —– Notes Harlan et al (1935) —– 1931 yields match average values Immer (1934). Minnesota 474 475 cultivars 'Svanhals x Lion' crosses. yields reported Crookston 1928 crop failure. (Page 20) Also, report North Dakota says \"zero yields Williston, ND 1931 caused drought\". (Page 31) —– Notes Wiebe et al (1935) —– —– Notes Wiebe et al (1940) —– 1932 data generally match average values Immer (1934) following notes. data Glabron St Paul 1932 missing, given 36.8 Immer (1934). value treated missing R dataset. data Svansota Morris 1932 missing, given 35.0 Immer (1934). value treated missing R dataset. yield 'Wisconsin 38' St Paul 1932 shown 3.80, 38 Immer (1934). latter value used R dataset. yields No475 1932 reported Wiebe (1940), reported Immer (1934). yields reported Morris 1933 1934, crop failure owing drought. —– Notes Hayes (1942) —– source gives block-level yield data 5 cultivars 4 sites 1932 1935. Cultivar 'Barbless' 'Wisconsin No38'.","code":""},{"path":"/reference/minnesota.barley.yield.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"Harry V. Harlan Mary L. Martini Merrit N. Pope (1925). Tests barley varieties America. United States Department Agriculture, Department Bulletin 1334. https://archive.org/details/testsofbarleyvar1334harl H. V. Harlan L. H. Newman Mary L. Martini (1929). Yields barley United States Canada 1922-1926. United States Department Agriculture, Technical Bulletin 96. https://handle.nal.usda.gov/10113/CAT86200091 Harlan, H. V. Philip Russell Cowan Lucille Reinbach. (1935). Yields barley United States Canada 1927-1931. United States Dept Agriculture, Technical Bulletin 446. https://naldc.nal.usda.gov/download/CAT86200440/PDF Wiebe, Gustav . Philip Russell Cowan, Lucille Reinbach-Welch. (1940). Yields barley varieties United States Canada 1932-36. United States Dept Agriculture, Technical Bulletin 735. https://books.google.com/books?id=OUfxLocnpKkC&pg=PA19 Wiebe, Gustav . Philip Russell Cowan, Lucille Reinbach-Welch. (1944). Yields barley varieties United States Canada, 1937-41. United States Dept Agriculture, Technical Bulletin 881. https://handle.nal.usda.gov/10113/CAT86200873","code":""},{"path":"/reference/minnesota.barley.yield.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"Immer, R. F. H. K. Hayes LeRoy Powers. (1934). Statistical Determination Barley Varietal Adaptation. Journal American Society Agronomy, 26, 403-419. https://doi.org/10.2134/agronj1934.00021962002600050008x Hayes, H.K. Immer, F.R. (1942). Methods plant breeding. McGraw Hill. Kevin Wright. (2013). Revisiting Immer's Barley Data. American Statistitician, 67, 129-133. https://doi.org/10.1080/00031305.2013.801783","code":""},{"path":"/reference/minnesota.barley.yield.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. — minnesota.barley.yield","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(minnesota.barley.yield) dat <- minnesota.barley.yield dat$yr <- factor(dat$year) # Drop Dryland, Jeans, CompCross, MechMixture because they have less than 5 # year-loc values dat <- subset(dat, !is.element(gen_name, c(\"CompCross\",\"Dryland\",\"Jeans\",\"MechMixture\"))) dat <- subset(dat, year >= 1927 & year <= 1936) dat <- droplevels(dat) # 1934 has huge swings from one loc to the next libs(lattice) dotplot(gen_name~yield|site, dat, groups=yr, main=\"minnesota.barley.yield\", auto.key=list(columns=5), scales=list(y=list(cex=.5))) } # }"},{"path":"/reference/montgomery.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"Uniformity trial wheat Nebraska Experiment Station, 1909 & 1911.","code":""},{"path":"/reference/montgomery.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"","code":"data(\"montgomery.wheat.uniformity\")"},{"path":"/reference/montgomery.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"data frame 448 observations following 3 variables. year year col column row row yield yield, grams","code":""},{"path":"/reference/montgomery.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"Experiments conducted Nebraska Experiment Station. field sown Turkey winter wheat fall 1908 harvested 1909. drill, 5.5 feet wide, driven across first series 14 blocks, boundaries blocks later established. series sown way, space allowed blocks. block 5.5 ft square. experiment done 3 times harvests 1909, 1910, 1911. simple heatmap 3 years' yields shown Montgomery (1912), figure 3, p. 178. 1909 data given Montgomery (1913), figure 10, page 37. NOTE: North right side diagram (determined comparing yield values fertility map Montgomery 1912, p. 178). 1910 data available. 1911 data given Montgomery (1912), figure 1, page 165. NOTE: North top diagram. Field width: 14 plots * 5.5 feet Field length: 16 blocks * 5.5 feet Surface & Pearl (1916) give simple method adjusting yield due fertility effects using 1909 data.","code":""},{"path":"/reference/montgomery.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"E. G. Montgomery (1912). Variation Yield Methods Arranging Plats Secure Comparative Results. Twenty-Fifth Annual Report Agricultural Experiment Station Nebraska, 164-180. https://books.google.com/books?id=M-5BAQAAMAAJ&pg=RA4-PA164 E. G. Montgomery (1913). Experiments Wheat Breeding: Experimental Error Nursery Variation Nitrogen Yield. U.S. Dept Agriculture, Bureau Plant Industry, Bulletin 269. Figure 10, page 37. https://doi.org/10.5962/bhl.title.43602","code":""},{"path":"/reference/montgomery.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"Surface & Pearl, (1916). method correcting soil heterogeneity variety tests. Journal Agricultural Research, 5, 22, 1039-1050. Figure 2. https://books.google.com/books?id=BVNyoZXFVSkC&pg=PA1039","code":""},{"path":"/reference/montgomery.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat, 2 years on the same land — montgomery.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(montgomery.wheat.uniformity) dat <- montgomery.wheat.uniformity dat09 <- subset(dat, year==1909) dat11 <- subset(dat, year==1911) # Match the figures of Montgomery 1912 Fig 3, p. 178 libs(desplot) desplot(dat09, yield ~ col*row, aspect=1, # true aspect main=\"montgomery.wheat.uniformity - 1909 yield\") desplot(dat, yield ~ col*row, subset= year==1911, aspect=1, # true aspect main=\"montgomery.wheat.uniformity - 1911 yield\") # Surface & Pearl adjust 1909 yield for fertility effects. # They calculate smoothed yield as (row sum)*(column sum)/(total) # and subtract this from the overall mean to get 'deviation'. # We can do something similar with a linear model with rows and columns # as factors, then predict yield to get the smooth trend. # Corrected yield = observed - deviation = observed - (smooth-mean) m1 <- lm(yield ~ factor(col) + factor(row), data=dat09) dev1 <- predict(m1) - mean(dat09$yield) # Corrected. Similar (but not exact) to Surface, fig 2. dat09$correct <- round(dat09$yield - dev1,0) libs(desplot) desplot(dat09, yield ~ col*row, shorten=\"none\", text=yield, main=\"montgomery.wheat.uniformity 1909 observed\") desplot(dat09, correct ~ col*row, text=correct, cex=0.8, shorten=\"none\", main=\"montgomery.wheat.uniformity 1909 corrected\") # Corrected yields are slightly shrunk toward overall mean plot(correct~yield,dat09, xlim=c(350,1000), ylim=c(350,1000)) abline(0,1) } # }"},{"path":"/reference/moore.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"Uniformity trials pole beans, bush beans, sweet corn, carrots, spring fall cauliflower Washington, 1952-1955.","code":""},{"path":"/reference/moore.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"data frame following columns minimum. datasets additional trait column. row row col column yield yield (pounds)","code":""},{"path":"/reference/moore.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"trials grown sandy loam soil Puyallup valley Washington. experiments gradient soil fertility evident. Moore & Darroch appear assigned 4 treatments plots used residual variation calculate CV. examples 'raw' CV calculated always higher CV given Moore & Darroch. Blue Lake Pole Beans. Conducted 1952. Seven pickings made 5-day intervals. Table 26. Field width: 12 rows x 5 feet = 60 feet. Field length: 12 ranges x 10 feet = 120 feet. Bush Beans. Conducted 1955. Two harvests. Table 27. Field width: 24 rows x 3 feet = 72 feet. Field length: 24 ranges x 5 feet = 120 feet. Sweet Corn. Conducted 1952. Table 28-29. Field width: 24 rows x 3 feet = 72 feet. Field length: 12 ranges x 10 feet = 120 feet. Carrot. Conducted 1952. Table 30. Field width: 24 rows * 1.5 feet = 36 feet. Field length: 12 ranges * 5 feet = 60 feet. Spring Cauliflower. Conducted spring 1951. Five harvests. Table 31-32. Field width: 12 rows x 3 feet = 36 feet. Field length: 10 plants * 1.5 feet * 20 ranges = 300 feet. Fall Cauliflower. Conducted fall 1951. Five harvests. Table 33-34. Field width: 12 rows x 3 feet = 36 feet. Field length: 10 plants * 1.5 feet * 20 ranges = 300 feet.","code":""},{"path":"/reference/moore.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"Moore, John F Darroch, JG. (1956). Field plot technique Blue Lake pole beans, bush beans, carrots, sweet corn, spring fall cauliflower, page 25-30. Washington Agricultural Experiment Stations, Institute Agricultural Sciences, State College Washington. https://babel.hathitrust.org/cgi/pt?id=uiug.30112019919072&view=1up&seq=33&skin=2021","code":""},{"path":"/reference/moore.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"None.","code":""},{"path":"/reference/moore.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower — moore.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) cv <- function(x) sd(x)/mean(x) libs(desplot) # Pole Bean data(moore.polebean.uniformity) cv(moore.polebean.uniformity$yield) # 8.00. Moore says 6.73. desplot(moore.polebean.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=120/60, # true aspect main=\"moore.polebean.uniformity - yield\") # Bush bean data(moore.bushbean.uniformity) cv(moore.bushbean.uniformity$yield) # 12.1. Moore says 10.8 desplot(moore.bushbean.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=120/72, # true aspect main=\"moore.bushbean.uniformity - yield\") # Sweet corn data(moore.sweetcorn.uniformity) cv(moore.sweetcorn.uniformity$yield) # 17.5. Moore says 13.6 desplot(moore.sweetcorn.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=120/72, # true aspect main=\"moore.sweetcorn.uniformity - yield\") ## desplot(moore.sweetcorn.uniformity, ears~col*row, ## flip=TRUE, tick=TRUE, aspect=120/72, # true aspect ## main=\"moore.sweetcorn.uniformity - ears\") ## libs(lattice) ## xyplot(yield ~ ears, moore.sweetcorn.uniformity) libs(desplot) # Carrot data(moore.carrot.uniformity) cv(moore.carrot.uniformity$yield) # 33.4. Moore says 27.6 desplot(moore.carrot.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=60/36, # true aspect main=\"moore.carrot.uniformity - yield\") libs(desplot) # Spring cauliflower data(moore.springcauliflower.uniformity) cv(moore.springcauliflower.uniformity$yield) # 21. Moore says 19.5 desplot(moore.springcauliflower.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=300/36, # true aspect main=\"moore.springcauliflower.uniformity - yield\") ## desplot(moore.springcauliflower.uniformity, heads~col*row, ## flip=TRUE, tick=TRUE, aspect=300/36, # true aspect ## main=\"moore.springcauliflower.uniformity - heads\") ## libs(lattice) ## xyplot(yield ~ heads, moore.springcauliflower.uniformity) libs(desplot) # Fall cauliflower data(moore.fallcauliflower.uniformity) cv(moore.fallcauliflower.uniformity$yield) # 17.7. Moore says 17.0 desplot(moore.fallcauliflower.uniformity, yield~col*row, flip=TRUE, tick=TRUE, aspect=300/36, # true aspect main=\"moore.fallcauliflower.uniformity - yield\") ## desplot(moore.fallcauliflower.uniformity, heads~col*row, ## flip=TRUE, tick=TRUE, aspect=300/36, # true aspect ## main=\"moore.fallcauliflower.uniformity - heads\") ## libs(lattice) ## xyplot(yield ~ heads, moore.fallcauliflower.uniformity) } # }"},{"path":"/reference/nagai.strawberry.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of strawberry — nagai.strawberry.uniformity","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"Uniformity trial strawberry Brazil.","code":""},{"path":"/reference/nagai.strawberry.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"","code":"data(\"nagai.strawberry.uniformity\")"},{"path":"/reference/nagai.strawberry.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"data frame 432 observations following 3 variables. row row col column yield yield, grams/plot","code":""},{"path":"/reference/nagai.strawberry.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"uniformity trial strawberry, Jundiai, Brazil, April 1976. spacing plants rows 0.3 m. Test area 233.34 m^2. 18 rows 144 plants. plat consisted 6 consecutive plants. 432 plats, 0.54 m^2. Field length: 18 rows * 0.3 m = 5.4 m. Field width: 24 columns * 6 plants * 0.3 m = 43.2 m.","code":""},{"path":"/reference/nagai.strawberry.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"Violeta Nagai (1978). Tamanho da parcela e numero de repeticoes em experimentos com morangueiro (Plot size number repetitions experiments strawberry). Bragantia, 37, 71-81. Table 2, page 75. https://dx.doi.org/10.1590/S0006-87051978000100009","code":""},{"path":"/reference/nagai.strawberry.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"None","code":""},{"path":"/reference/nagai.strawberry.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of strawberry — nagai.strawberry.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(nagai.strawberry.uniformity) dat <- nagai.strawberry.uniformity # CV matches Nagai # with(dat, sd(yield)/mean(yield)) # 23.42 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(5.4)/(43.2), # true aspect main=\"nagai.strawberry.uniformity\") } # }"},{"path":"/reference/nair.turmeric.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of turmeric. — nair.turmeric.uniformity","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"Uniformity trial turmeric India, 1984.","code":""},{"path":"/reference/nair.turmeric.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"","code":"data(\"nair.turmeric.uniformity\")"},{"path":"/reference/nair.turmeric.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"data frame 864 observations following 3 variables. row row ordinate col column ordinate yield yield, grams per plot","code":""},{"path":"/reference/nair.turmeric.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"experiment conducted College Horticulture, Vellanikkara, India, 1984. crop grown raised beds. gross experimental area 74.2 m long x 15.2 m wide. Small elevated beds 0.6 m x 1.5 m raised providing channels 0.4 m around bed. One row beds around experiment discarded eliminate border effects. discarding borders, 432 beds experiment. time harvest, bed divided equal plots size .6 m x .75 m, yield plot recorded. Field map page 64 Nair. Nair focused mostly statistical methods discuss actual experimental results much detail. excess number plots 0 yield. Field length: 14 plots * .6 m + 13 alleys * .4 m = 13.6 m Field width: 72 plots * .75 m + 35 alleys * .4 m = 68 m Data found appendix.","code":""},{"path":"/reference/nair.turmeric.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"Nair, B. Gopakumaran (1984). Optimum plot size field experiments turmeric. Thesis, Kerala Agriculture University. http://hdl.handle.net/123456789/7829","code":""},{"path":"/reference/nair.turmeric.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"None.","code":""},{"path":"/reference/nair.turmeric.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of turmeric. — nair.turmeric.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(nair.turmeric.uniformity) dat <- nair.turmeric.uniformity libs(lattice) qqmath( ~ yield, dat) libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=13.6/68, main=\"nair.turmeric.uniformity\") } # }"},{"path":"/reference/narain.sorghum.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sorghum — narain.sorghum.uniformity","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"Uniformity trial sorghum Pakistan, 1936.","code":""},{"path":"/reference/narain.sorghum.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"","code":"data(\"narain.sorghum.uniformity\")"},{"path":"/reference/narain.sorghum.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"data frame 160 observations following 3 variables. row row col column yield yield, maunds per 1/40 acre","code":""},{"path":"/reference/narain.sorghum.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"uniformity trial chari (sorghum) Rawalpindi Agricultural Station (Pakistan) kharif (monsoon season) 1936. plot 36 feet 30.25 feet. source document describe orientation plots, fertility map shown Narain figure 1 shows plots taller wide. Field width: 10 plots * 30.25 feet Field length: 16 plots * 36 feet","code":""},{"path":"/reference/narain.sorghum.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"R. Narain . Singh, (1940). Note Shape Blocks Field Experiments. Ind. J. Agr. Sci., 10, 844-853. Page 845. https://archive.org/stream/.ernet.dli.2015.271745","code":""},{"path":"/reference/narain.sorghum.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"None","code":""},{"path":"/reference/narain.sorghum.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sorghum — narain.sorghum.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(narain.sorghum.uniformity) dat <- narain.sorghum.uniformity # Narain figure 1 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=(16*36)/(10*30.25), main=\"narain.sorghum.uniformity\") } # }"},{"path":"/reference/nass.corn.html","id":null,"dir":"Reference","previous_headings":"","what":"U.S. historical crop yields by state — nass.corn","title":"U.S. historical crop yields by state — nass.corn","text":"Yields acres harvested state major agricultural crops United States, approximately 1900 2011. Crops include: barley, corn, cotton, hay, rice, sorghum, soybeans, wheat.","code":""},{"path":"/reference/nass.corn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"U.S. historical crop yields by state — nass.corn","text":"","code":"nass.barley nass.corn nass.cotton nass.hay nass.sorghum nass.wheat nass.rice nass.soybean"},{"path":"/reference/nass.corn.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"U.S. historical crop yields by state — nass.corn","text":"year year state state factor acres acres harvested yield average yield","code":""},{"path":"/reference/nass.corn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"U.S. historical crop yields by state — nass.corn","text":"cautious yield values states small acres harvested. Yields bushels/acre, except: cotton pounds/acre, hay tons/acre, rice pounds/acre. crop separate dataset: nass.barley, nass.corn, nass.cotton, nass.hay, nass.sorghum, nass.wheat, nass.rice, nass.soybean.","code":""},{"path":"/reference/nass.corn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"U.S. historical crop yields by state — nass.corn","text":"United States Department Agriculture, National Agricultural Statistics Service. https://quickstats.nass.usda.gov/","code":""},{"path":"/reference/nass.corn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"U.S. historical crop yields by state — nass.corn","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(nass.corn) dat <- nass.corn # Use only states that grew at least 100K acres of corn in 2011 keep <- droplevels(subset(dat, year == 2011 & acres > 100000))$state dat <- droplevels(subset(dat, is.element(state, keep))) # Acres of corn grown each year libs(lattice) xyplot(acres ~ year|state, dat, type='l', as.table=TRUE, main=\"nass.corn: state trends in corn acreage\") ## Plain levelplot, using only states ## libs(reshape2) ## datm <- acast(dat, year~state, value.var='yield') ## redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) ## levelplot(datm, aspect=.7, col.regions=redblue, ## main=\"nass.corn\", ## scales=list(x=list(rot=90, cex=.7))) # Model the rate of genetic gain in Illinois as a piecewise regression # Breakpoints define periods of open-pollinated varieties, double-cross, # single-cross, and transgenic hybrids. dil <- subset(nass.corn, state==\"Illinois\" & year >= 1900) m1 <- lm(yield ~ pmin(year,1932) + pmax(1932, pmin(year, 1959)) + pmax(1959, pmin(year, 1995)) + pmax(1995, year), dil) signif(coef(m1)[-1],3) # Rate of gain for each segment plot(yield ~ year, dil, main=\"nass.corn: piecewise linear model of Illinois corn yields\") lines(dil$year, fitted(m1)) abline(v=c(1932,1959,1995), col=\"wheat\") } # }"},{"path":"/reference/nebraska.farmincome.html","id":null,"dir":"Reference","previous_headings":"","what":"Nebraska farm income in 2007 by county — nebraska.farmincome","title":"Nebraska farm income in 2007 by county — nebraska.farmincome","text":"Nebraska farm income 2007 county","code":""},{"path":"/reference/nebraska.farmincome.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Nebraska farm income in 2007 by county — nebraska.farmincome","text":"data frame 93 observations following 4 variables. county county crop crop income, thousand dollars animal livestock poultry income, thousand dollars area area county, square miles","code":""},{"path":"/reference/nebraska.farmincome.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Nebraska farm income in 2007 by county — nebraska.farmincome","text":"variables county : Value farm products sold - crops (NAICS) 2007 (adjusted) Value farm products sold - livestock, 2007 (adjusted). Area square miles. Note: Cuming county important beef-producing county. counties reported protect privacy. Western Nebraska dryer lower income. South-central Nebraska irrigated higher crop income per square mile.","code":""},{"path":"/reference/nebraska.farmincome.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Nebraska farm income in 2007 by county — nebraska.farmincome","text":"U.S. Department Agriculture-National Agriculture Statistics Service. https://censtats.census.gov/usa/usa.shtml","code":""},{"path":"/reference/nebraska.farmincome.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nebraska farm income in 2007 by county — nebraska.farmincome","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(nebraska.farmincome) dat <- nebraska.farmincome libs(maps, mapproj, latticeExtra) # latticeExtra for mapplot dat$stco <- paste0('nebraska,', dat$county) # Scale to million dollars per county dat <- transform(dat, crop=crop/1000, animal=animal/1000) # Raw, county-wide incomes. Note the outlier Cuming county redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) mapplot(stco ~ crop + animal, data = dat, colramp=redblue, main=\"nebraska.farmincome\", xlab=\"Farm income from animals and crops (million $ per county)\", scales = list(draw = FALSE), map = map('county', 'nebraska', plot = FALSE, fill = TRUE, projection = \"mercator\") ) # Now scale to income/mile^2 dat <- within(dat, { crop.rate <- crop/area animal.rate <- animal/area }) # And use manual breakpoints. mapplot(stco ~ crop.rate + animal.rate, data = dat, colramp=redblue, main=\"nebraska.farmincome: income per square mile (percentile breaks)\", xlab=\"Farm income (million $ / mi^2) from animals and crops\", scales = list(draw = FALSE), map = map('county', 'nebraska', plot = FALSE, fill = TRUE, projection = \"mercator\"), # Percentile break points # breaks=quantile(c(dat$crop.rate, dat$animal.rate), # c(0,.1,.2,.4,.6,.8,.9,1), na.rm=TRUE) # Fisher-Jenks breakpoints via classInt package # breaks=classIntervals(na.omit(c(dat$crop.rate, dat$animal.rate)), # n=7, style='fisher')$brks breaks=c(0,.049, .108, .178, .230, .519, .958, 1.31)) } # }"},{"path":"/reference/nonnecke.peas.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of canning peas — nonnecke.peas.uniformity","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"Uniformity trial canning peas southern Alberta, 1957.","code":""},{"path":"/reference/nonnecke.peas.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"","code":"data(\"nonnecke.peas.uniformity\")"},{"path":"/reference/nonnecke.peas.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"data frame 540 observations following 5 variables. block block factor row row col column vines vines weight, pounds peas shelled peas weight, pounds","code":""},{"path":"/reference/nonnecke.peas.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"Width basic plot 10 feet, length 5 feet, limited viner. two blocks/locations, planting consisted 18 rows (15 rows harvested) 10 feet wide 90 feet long. Rows separated 7 foot bare ground facilitate harvesting. Nonnecke 1960 shows map one block. Plots harvested five foot mower. Vines plot weighed, shelled. two blocks/locations side side combined Nonnecke. optimum plot size found 5 feet long 10 feet wide. Field width: 15 rows * 10 ft/row + 14 gaps * 7 ft/gap = 248 feet Field length: 18 plots * 5 ft/plot = 90 feet","code":""},{"path":"/reference/nonnecke.peas.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"Ib Libner Nonnecke. 1958. Yield variability sweet corn canning peas affected plot size shape. Thesis Oregon State College. https://hdl.handle.net/1957/23367","code":""},{"path":"/reference/nonnecke.peas.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":". L. Nonnecke, 1960. precision field experiments vegetable crops influenced plot block size shape: II. Canning peas. Canadian Journal Plant Science, 40(2): 396-404. https://doi.org/10.4141/cjps60-053","code":""},{"path":"/reference/nonnecke.peas.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of canning peas — nonnecke.peas.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(nonnecke.peas.uniformity) dat <- nonnecke.peas.uniformity libs(desplot) desplot(dat, vines~col*row|block, tick=TRUE, flip=TRUE, aspect=248/90, # true aspect main=\"nonnecke.peas.uniformity - vines\") desplot(dat, peas~col*row|block, tick=TRUE, flip=TRUE, aspect=248/90, # true aspect main=\"nonnecke.peas.uniformity - peas\") libs(lattice) xyplot(peas~vines|block,dat, xlab=\"vine weight\", ylab=\"shelled pea weight\", main=\"nonnecke.peas.uniformity\") } # }"},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"Uniformity trials sweet corn Alberta, 1956.","code":""},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"","code":"data(\"nonnecke.sweetcorn.uniformity\")"},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"data frame: loc location row row col column yield yield marketable ears, pounds","code":""},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"Experiments conducted three locations Southern Alberta Lethbridge, Vauxhall, Cranford 1956. Plot layout 32 rows, 179 feet long, allowing 18 ten-foot plots per row. Rows 3 feet apart, thinned one foot plants. double guard row surrounded entire plot. two persons assigned harvest corn locations. 576 plots harvested one day. Optimal plot sizes found 10ft x 6ft 20ft 3ft. R data uses row/column plot/row. Field width: 18 plots * 10 ft = 180 feet Field length: 32 rows * 3 ft = 96 feet","code":""},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"Ib Libner Nonnecke. 1958. Yield variability sweet corn canning peas affected plot size shape. Thesis Oregon State College. https://hdl.handle.net/1957/23367","code":""},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":". L. Nonnecke, 1959. precision field experiments vegetable crops influenced plot block size shape: . Sweet corn. Canadian Journal Plant Science, 39(4): 443-457. Tables 1-7. https://doi.org/10.4141/cjps59-061","code":""},{"path":"/reference/nonnecke.sweetcorn.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sweet corn — nonnecke.sweetcorn.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) # Corn 1 data(nonnecke.sweetcorn.uniformity) dat <- nonnecke.sweetcorn.uniformity libs(desplot) desplot(dat, yield~col*row|loc, flip=TRUE, tick=TRUE, aspect=96/180, # true aspect main=\"nonnecke.sweetcorn.uniformity\") } # }"},{"path":"/reference/obsi.potato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"Uniformity trial potato Africa 2001","code":""},{"path":"/reference/obsi.potato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"","code":"data(\"obsi.potato.uniformity\")"},{"path":"/reference/obsi.potato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"data frame 2569 observations following 4 variables. loc location, 2 levels row row col column yield yield, kg/m^2","code":""},{"path":"/reference/obsi.potato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"Data collected potato uniformity trials Hollota (L1) Kulumsa (L2). field 0.15 hectares. field, 75cm rows 60cm plants. basic units harvested 1.2m x 1.5m. clear way plots oriented field respect rows columns. location L1, plot (10,7) 22.5 source document, changed 2.25 electronic data. Hollota: Field width: 26 * 1.2 m Field length: 63 rows * 1.5 m Note horizontal banding 8 9 rows location L1. Kulumsa Field width: 19 * 1.2 m Field length: 49 * 1.5 m","code":""},{"path":"/reference/obsi.potato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"Dechassa Obsi. 2008. Application Spatial Modeling Study Soil Fertility Pattern. MS Thesis, Addis Ababa University. Page 122-125. https://etd.aau.edu.et/handle/123456789/3221","code":""},{"path":"/reference/obsi.potato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"None.","code":""},{"path":"/reference/obsi.potato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of potato in Africa 2001 — obsi.potato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(obsi.potato.uniformity) dat <- obsi.potato.uniformity # Mean plot yield according to Obsi p. 54 # libs(dplyr) # dat <- group_by(dat, loc) # summarize(dat, yield=mean(yield)) ## loc yield ## ## 1 L1 2.54 # Obsi says 2.55 ## 2 L2 5.31 # Obsi says 5.36 libs(desplot) desplot(dat, yield ~ col*row, subset=loc==\"L1\", main=\"obsi.potato.uniformity - loc L1\", flip=TRUE, tick=TRUE) desplot(dat, yield ~ col*row, subset=loc==\"L2\", main=\"obsi.potato.uniformity - loc L2\", flip=TRUE, tick=TRUE) } # }"},{"path":"/reference/odland.soybean.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","title":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","text":"Uniformity trials soy hay soybeans Virginia Experiment Station, 1925-1926.","code":""},{"path":"/reference/odland.soybean.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","text":"Data frames 3 variables. row row col column yield yield: hay tons/acre, beans bushels/acre","code":""},{"path":"/reference/odland.soybean.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","text":"Grown West Virginia Experiment Station 1925 & 1926. Soy forage hay: 1925 crop harvested forage, 42 rows, 200 feet long. Yields 8-foot plats recorded nearest 0.1 tons. Field width: 42 plots * 30 / 12in/ft = 105 ft Field length: 24 plots * 8 feet = 192 feet + border = total 200 feet. Note, hay data Odland & Garber measured 0.1 tons, converted tons . Soy beans: Soybeans planted rows 30 inches apart. 1926 crop harvested seed, 55 rows, 232 feet long. Yields 8-foot plats recorded. 1926, data last row page 96 seems missing. Field width: 55 plots * 30 / 12in/ft = 137.5 feet Field length: 28 plots * 8 feet = 224 feet + border = total 232 feet. Odland Garber provide agronomic context yield variation.","code":""},{"path":"/reference/odland.soybean.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","text":"Odland, T.E. Garber, R.J. (1928). Size Plat Number Replications Field Experiments Soybeans. Agronomy Journal, 20, 93–108. https://doi.org/10.2134/agronj1928.00021962002000020002x","code":""},{"path":"/reference/odland.soybean.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of soy hay and soybeans — odland.soybean.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(desplot) data(odland.soyhay.uniformity) dat1 <- odland.soyhay.uniformity desplot(dat1, yield ~ col*row, flip=TRUE, aspect=200/105, # true aspect main=\"odland.soyhay.uniformity\") data(odland.soybean.uniformity) dat2 <- odland.soybean.uniformity desplot(dat2, yield ~ col*row, flip=TRUE, aspect = 232/137, main=\"odland.soybean.uniformity\") } # }"},{"path":"/reference/omer.sorghum.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"Multi-environment trial sorghum, 6 environments","code":""},{"path":"/reference/omer.sorghum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"","code":"data(\"omer.sorghum\")"},{"path":"/reference/omer.sorghum.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"data frame 432 observations following 4 variables. env environment rep replication gen genotype factor yield yield, kg/ha","code":""},{"path":"/reference/omer.sorghum.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"Trials conducted Sudan, 3 years 2 locations, 4 reps RCBD location. year location combined form 6 environments. environments given data, individual year location.","code":""},{"path":"/reference/omer.sorghum.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"Siraj Osman Omer, Abdel Wahab Hassan Abdalla, Mohammed Hamza Mohammed, Murari Singh (2015). Bayesian estimation genotype--environment interaction sorghum variety trials Communications Biometry Crop Science, 10 (2), 82-95. Electronic data provided Siraj Osman Omer.","code":""},{"path":"/reference/omer.sorghum.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"None.","code":""},{"path":"/reference/omer.sorghum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of sorghum, 6 environments — omer.sorghum","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(omer.sorghum) dat <- omer.sorghum # REML approach libs(lme4) libs(lucid) # 1 loc, 2 years. Match Omer table 1. m1 <- lmer(yield ~ 1 + env + (1|env:rep) + (1|gen) + (1|gen:env), data=subset(dat, is.element(env, c('E2','E4')))) vc(m1) ## grp var1 var2 vcov sdcor ## gen:env (Intercept) 17050 130.6 ## gen (Intercept) 2760 52.54 ## env:rep (Intercept) 959.1 30.97 ## Residual 43090 207.6 # 1 loc, 3 years. Match Omer table 1. m2 <- lmer(yield ~ 1 + env + (1|env:rep) + (1|gen) + (1|gen:env), data=subset(dat, is.element(env, c('E2','E4','E6')))) vc(m2) ## grp var1 var2 vcov sdcor ## gen:env (Intercept) 22210 149 ## gen (Intercept) 9288 96.37 ## env:rep (Intercept) 1332 36.5 ## Residual 40270 200.7 # all 6 locs. Match Omer table 3, frequentist approach m3 <- lmer(yield ~ 1 + env + (1|env:rep) + (1|gen) + (1|gen:env), data=dat) vc(m3) ## grp var1 var2 vcov sdcor ## gen:env (Intercept) 21340 146.1 ## env:rep (Intercept) 1152 33.95 ## gen (Intercept) 1169 34.2 ## Residual 24660 157 } # }"},{"path":"/reference/onofri.winterwheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"Multi-environment trial winter wheat, 7 years, 8 gen","code":""},{"path":"/reference/onofri.winterwheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"","code":"data(\"onofri.winterwheat\")"},{"path":"/reference/onofri.winterwheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"data frame 168 observations following 5 variables. year year, numeric block block, 3 levels plot plot, numeric gen genotype, 7 levels yield yield plot","code":""},{"path":"/reference/onofri.winterwheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"Yield 8 durum winter wheat varieties across 7 years 3 reps. Downloaded electronic version Nov 2015: https://www.casaonofri./Biometry/index.html Used permission Andrea Onofri.","code":""},{"path":"/reference/onofri.winterwheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"Andrea Onofri, Egidio Ciriciofolo (2007). Using R Perform AMMI Analysis Agriculture Variety Trials. R News, Vol. 7, . 1, pp. 14-19.","code":""},{"path":"/reference/onofri.winterwheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"F. Mendiburu. AMMI. https://tarwi.lamolina.edu.pe/~fmendiburu/AMMI.htm . Onofri. https://accounts.unipg./~onofri/RTutorial/CaseStudies/WinterWheat.htm","code":""},{"path":"/reference/onofri.winterwheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of winter wheat, 7 years — onofri.winterwheat","text":"","code":"library(agridat) data(onofri.winterwheat) dat <- onofri.winterwheat dat <- transform(dat, year=factor(dat$year)) m1 <- aov(yield ~ year + block:year + gen + gen:year, dat) anova(m1) # Matches Onofri figure 1 #> Analysis of Variance Table #> #> Response: yield #> Df Sum Sq Mean Sq F value Pr(>F) #> year 6 159.279 26.5466 178.3996 < 2.2e-16 *** #> gen 7 11.544 1.6491 11.0824 2.978e-10 *** #> year:block 14 3.922 0.2801 1.8826 0.03738 * #> year:gen 42 27.713 0.6598 4.4342 6.779e-10 *** #> Residuals 98 14.583 0.1488 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 libs(agricolae) m2 <- AMMI(dat$year, dat$gen, dat$block, dat$yield) plot(m2) title(\"onofri.winterwheat - AMMI biplot\")"},{"path":"/reference/ortiz.tomato.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"Multi-environment trial tomato Latin America, weight/yield environmental covariates","code":""},{"path":"/reference/ortiz.tomato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"","code":"data(\"ortiz.tomato.covs\") data(\"ortiz.tomato.yield\")"},{"path":"/reference/ortiz.tomato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"ortiz.tomato.covs data frame 18 observations following 18 variables. env environment Day degree days (base 10) Dha days harvest Driv drivings (0/1) ExK extra potassium (kg / ha) ExN extra nitrogen (kg / ha) ExP extra phosphorous (kg / ha) Irr irrigation (0/1) K potassium (/100 g) Lat latitude Long longitude MeT mean temperature (C) MnT min temperature (C) MxT max temperature (C) OM organic matter (percent) P phosphorous (ppm) pH soil pH Prec precipitation (mm) Tri trimming (0/1) ortiz.tomato.yield data frame 270 observations following 4 variables. env environment gen genotype yield marketable fruit yield t/ha weight fruit weight, g","code":""},{"path":"/reference/ortiz.tomato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"environment locations : Used permission Rodomiro Ortiz.","code":""},{"path":"/reference/ortiz.tomato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"Rodomiro Ortiz Jose Crossa Mateo Vargas Juan Izquierdo, 2007. Studying Effect Environmental Variables Genotype x Environment Interaction Tomato. Euphytica, 153, 119–134. https://doi.org/10.1007/s10681-006-9248-7","code":""},{"path":"/reference/ortiz.tomato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates — ortiz.tomato","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ortiz.tomato.covs) data(ortiz.tomato.yield) libs(pls, reshape2) # Double-centered yield matrix Y <- acast(ortiz.tomato.yield, env ~ gen, value.var='yield') Y <- sweep(Y, 1, rowMeans(Y, na.rm=TRUE)) Y <- sweep(Y, 2, colMeans(Y, na.rm=TRUE)) # Standardized covariates X <- ortiz.tomato.covs rownames(X) <- X$env X <- X[,c(\"MxT\", \"MnT\", \"MeT\", \"Prec\", \"Day\", \"pH\", \"OM\", \"P\", \"K\", \"ExN\", \"ExP\", \"ExK\", \"Trim\", \"Driv\", \"Irr\", \"Dha\")] X <- scale(X) # Now, PLS relating the two matrices. # Note: plsr deletes observations with missing values m1 <- plsr(Y~X) # Inner-product relationships similar to Ortiz figure 1. biplot(m1, which=\"x\", var.axes=TRUE, main=\"ortiz.tomato - env*cov biplot\") #biplot(m1, which=\"y\", var.axes=TRUE) } # }"},{"path":"/reference/pacheco.soybean.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","title":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","text":"Yields 18 soybean genotypes 11 environments Brazil.","code":""},{"path":"/reference/pacheco.soybean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","text":"gen genotype, 18 levels env environment, 11 levels yield yield, kg/ha","code":""},{"path":"/reference/pacheco.soybean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","text":"environment used RCB design 3 reps. means reps shown . Used permission Robert Pacheco.","code":""},{"path":"/reference/pacheco.soybean.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","text":"R M Pacheco, J B Duarte, R Vencovsky, J B Pinheiro, B Oliveira, (2005). Use supplementary genotypes AMMI analysis. Theor Appl Genet, 110, 812-818. https://doi.org/10.1007/s00122-004-1822-6","code":""},{"path":"/reference/pacheco.soybean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of soybean in Brazil. — pacheco.soybean","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(pacheco.soybean) dat <- pacheco.soybean # AMMI biplot similar to Fig 2 of Pacheco et al. libs(agricolae) m1 <- with(dat, AMMI(env, gen, REP=1, yield)) bip <- m1$biplot[,1:3] # Fig 1 of Pacheco et al. with(bip, plot(yield, PC1, cex=0.0, text(yield,PC1,labels=row.names(bip), col=\"blue\"), xlim=c(1000,3000),main=\"pacheco.soybean - AMMI biplot\",frame=TRUE)) with(bip[19:29,], points(yield, PC1, cex=0.0, text(yield,PC1,labels=row.names(bip[19:29,]), col=\"darkgreen\"))) } # }"},{"path":"/reference/paez.coffee.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of coffee — paez.coffee.uniformity","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"Uniformity trial coffee Caldas Columbia","code":""},{"path":"/reference/paez.coffee.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"","code":"data(\"paez.coffee.uniformity\")"},{"path":"/reference/paez.coffee.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"data frame 4190 observations following 5 variables. plot plot number row row col column year year yield yield per tree, kilograms","code":""},{"path":"/reference/paez.coffee.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"field map Paez page 56, plots 1 838. data tables page 79-97 data plots 1 900. Note: 'row' ordinate data imply rows columns perpendicular. field map page 56 Paez shows rows 90-degree angle compared columns, 60-degree angle compared columns. words, columns vertical, rows sloping right 30 degrees. Paez looks blocks 1,2,...36 trees size. Page 30 shows annual CV.","code":""},{"path":"/reference/paez.coffee.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"Gilberto Paez Bogarin (1962). Estudios sobre tamano y forma de parcela para ensayos en cafe. Instituto Interamericano de Ciencias Agricolas de la O.E.. Centro Tropical de Investigacion y Ensenanza para Graduados. Costa Rica. https://hdl.handle.net/11554/1892","code":""},{"path":"/reference/paez.coffee.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"None","code":""},{"path":"/reference/paez.coffee.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of coffee — paez.coffee.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(paez.coffee.uniformity) dat <- paez.coffee.uniformity libs(reshape2, corrgram) datt <- acast(dat, plot ~ year) corrgram(datt, lower.panel=panel.pts, main=\"paez.coffee.uniformity\") # Not quite right. The rows are not actually horizontal. See notes above. libs(desplot) desplot(dat, yield ~ col*row,subset=year==\"Y1\", tick=TRUE, aspect=1, main=\"paez.coffee.uniformity - Y1\") desplot(dat, yield ~ col*row,subset=year==\"Y2\", tick=TRUE, aspect=1, main=\"paez.coffee.uniformity - Y2\") desplot(dat, yield ~ col*row,subset=year==\"Y3\", tick=TRUE, aspect=1, main=\"paez.coffee.uniformity - Y3\") desplot(dat, yield ~ col*row,subset=year==\"Y4\", tick=TRUE, aspect=1, main=\"paez.coffee.uniformity - Y4\") desplot(dat, yield ~ col*row,subset=year==\"Y5\", tick=TRUE, aspect=1, main=\"paez.coffee.uniformity - Y5\") } # }"},{"path":"/reference/panse.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of cotton — panse.cotton.uniformity","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"Uniformity trial cotton India 1934.","code":""},{"path":"/reference/panse.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"","code":"data(\"panse.cotton.uniformity\")"},{"path":"/reference/panse.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"data frame 1280 observations following 3 variables. row row col column yield total yield per plot, grams","code":""},{"path":"/reference/panse.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"uniformity trial cotton Institute Plant Industry, Indore, India. trial consisted 128 rows cotton spacing 14 inches rows length 186 feet 8 inches. harvested plot 4 rows wide 4 ft 8 long, measuring 1/2000 acre. Four pickings made Nov 1933 Jan 1934. data total yields. fertility map shows appreciable variation, following systematic pattern. Field length: 40 plots * 4 feet 8 inches = 206 feet 8 inches Field width: 32 plots * 4 rows/plot * 14 inches/row = 150 feet Conclusions: Lower error obtained plots long rows instead across rows. data typed K.Wright Panse (1941) p. 864-865.","code":""},{"path":"/reference/panse.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"V. G. Panse (1941). Studies technique field experiments. V. Size shape blocks arrangements plots cotton trials. Indian Journal Agricultural Science, 11, 850-867 https://archive.org/details/.ernet.dli.2015.271747/page/n955","code":""},{"path":"/reference/panse.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"Hutchinson, J. B. V. G. Panse (1936). Studies technique field experiments. . Size, shape arrangement plots cotton trials. Indian J. Agric. Sci., 5, 523-538. https://archive.org/details/.ernet.dli.2015.271739/page/n599 V.G. Panse P.V. Sukhatme. (1954). Statistical Methods Agricultural Workers. First edition page 137. Fourth edition, page 131.","code":""},{"path":"/reference/panse.cotton.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of cotton — panse.cotton.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(panse.cotton.uniformity) dat <- panse.cotton.uniformity # match the CV of Panse 1954 # sd(dat$yield)/mean(dat$yield) * 100 # 32.1 # match the fertility map of Hutchinson, fig 1 libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=207/150, # true aspect main=\"panse.cotton.uniformity\") } # }"},{"path":"/reference/parker.orange.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of oranges — parker.orange.uniformity","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"Uniformity trial oranges Riverside, CA, 1921-1927.","code":""},{"path":"/reference/parker.orange.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"","code":"data(\"parker.orange.uniformity\")"},{"path":"/reference/parker.orange.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"data frame 1364 observations following 4 variables. year year row row col column yield yield, pounds/tree plot","code":""},{"path":"/reference/parker.orange.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"orchard naval oranges planted 1917 University California Citrus Experiment Station Riverside. orchard maintained uniform conditions 10 years. Eight Washington Navel orange trees single row constituted plot. planting distance 20 feet trees within row 24 feet rows. Every row guard row, row 2 row 4 observational units, row 3 guard row. example, row 2 row 4 2*24 = 48 feet. Another way think plot 48 feet wide, middle 24 feet harvested. end plot one guard tree. Including guard trees row ends, row plot 10 trees * 20 feet = 200 feet long. Field width (west-east) 10 plots * 200 feet = 2000 feet. Field length (north-south) 27 plots * 48 feet = 1296 feet. investigation variability plots included systematic soil surveys, soil moisture, soil nitrates, inspection differences infestation citrus nematode. None factors considered primary cause variations yield. 7 years uniformity trials, different treatments applied plots. Parker et al. state soil heterogeneity considerable first-year yields predictive future yields. Table 25 mean top volume per tree plot 1926. Table 26 mean area trunk cross section.","code":""},{"path":"/reference/parker.orange.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"E. R. Parker & L. D. Batchelor. (1932). Variation Yields Fruit Trees Relation Planning Future Experiments. Hilgardia, 7(2), 81-161. Tables 3-9. https://doi.org/10.3733/hilg.v07n02p081","code":""},{"path":"/reference/parker.orange.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"Batchelor, L. D. (Leon Dexter), b. 1884; Parker, E. R. (Edwin Robert), 1896-1952; McBride, Robert, d. 1927. (1928) Studies preliminary establishment series fertilizer trials bearing citrus grove. Vol B451. Berkeley, Cal. : Agricultural Experiment Station https://archive.org/details/studiesprelimina451batc","code":""},{"path":"/reference/parker.orange.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of oranges — parker.orange.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(parker.orange.uniformity) dat <- parker.orange.uniformity # Parker fig 2, field plan libs(desplot) dat$year <- factor(dat$year) # 27 rows * 48 ft x 10 cols * 200 feet desplot(dat, yield ~ col*row|year, flip = TRUE, aspect = 27*48/(10*200), # true aspect main = \"parker.orange.uniformity\") # CV across plots in each year. Similar to Parker table 11 cv <- function(x) { x <- na.omit(x) sd(x)/mean(x) } round(100*tapply(dat$yield, dat$year, cv),2) # Correlation of plot yields across years. Similar to Parker table 15. # Paker et al may have calculated correlation differently. libs(reshape2) libs(corrgram) dat2 <- acast(dat, row+col ~ year, value.var = 'yield') round(cor(dat2, use = \"pair\"),3) corrgram(dat2, lower = panel.pts, upper = panel.conf, main=\"parker.orange.uniformity\") # Fertility index. Mean across years (ignoring 1921). Parker table 16 dat3 <- aggregate(yield ~ row+col, data = subset(dat, year !=1921 ), FUN = mean, na.rm = TRUE) round(acast(dat3, row ~ col, value.var = 'yield'),0) libs(desplot) desplot(dat3, yield ~ col*row, flip = TRUE, aspect = 27*48/(10*200), # true aspect main = \"parker.orange.uniformity - mean across years\") } # }"},{"path":"/reference/patterson.switchback.html","id":null,"dir":"Reference","previous_headings":"","what":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"Switchback experiment dairy cattle, milk yield 4 treatments","code":""},{"path":"/reference/patterson.switchback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"","code":"data(\"patterson.switchback\")"},{"path":"/reference/patterson.switchback.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"data frame 36 observations following 4 variables. y response, milk FCM trt treatment factor, 4 levels period period factor, 3 levls cow cow factor, 12 levels","code":""},{"path":"/reference/patterson.switchback.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"three periods. cow assigned one treatment cycle like T1-T2-T1, T1 treatment period P1 P3, T2 treatment period P2. four treatments. 4*3 = 12 treatment cycles represented. Data extracted Lowry, page 70.","code":""},{"path":"/reference/patterson.switchback.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"Patterson, H.D. Lucas, H.L. 1962. Change-designs. Technical Bulletin 147, North Carolina Agricultural Experimental Station.","code":""},{"path":"/reference/patterson.switchback.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"Lowry, S.R. 1989. Statistical design analysis dairy nutrition experiments improve detection milk response differences. Proceedings Conference Applied Statistics Agriculture, 1989. https://newprairiepress.org/agstatconference/1989/proceedings/7/","code":""},{"path":"/reference/patterson.switchback.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Switchback experiment on dairy cattle, milk yield for 4 treatments — patterson.switchback","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(patterson.switchback) dat <- patterson.switchback # Create groupings for first treatment, second treatment datp1 <- subset(dat, period==\"P1\") datp2 <- subset(dat, period==\"P2\") dat$p1trt <- datp1$trt[match(dat$cow, datp1$cow)] dat$p2trt <- datp2$trt[match(dat$cow, datp2$cow)] libs(latticeExtra) useOuterStrips(xyplot(y ~ period|p1trt*p2trt, data=dat, group=cow, type=c('l','r'), auto.key=list(columns=5), main=\"patterson.switchback\", xlab=\"First/Third period treatment\", ylab=\"Second period treatment\")) # Create a numeric period variable dat$per <- as.numeric(substring(dat$period,2)) # Need to use 'terms' to preserve the order of the model terms m1 <- aov(terms(y ~ cow + per:cow + period + trt, keep.order=TRUE), data=dat) anova(m1) # Match table 2 of Lowry ## Analysis of Variance Table ## Df Sum Sq Mean Sq F value Pr(>F) ## cow 11 3466.0 315.091 57.1773 2.258e-06 *** ## cow:per 12 953.5 79.455 14.4182 0.0004017 *** ## period 1 19.7 19.740 3.5821 0.0950382 . ## trt 3 58.3 19.418 3.5237 0.0685092 . ## Residuals 8 44.1 5.511 } # }"},{"path":"/reference/payne.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Long term rotation experiment at Rothamsted — payne.wheat","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"Long term rotation experiment Rothamsted","code":""},{"path":"/reference/payne.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"","code":"data(\"payne.wheat\")"},{"path":"/reference/payne.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"data frame 480 observations following 4 variables. rotation rotation treatment nitro nitrogen rate kg/ha year year yield metric tons per hectare","code":""},{"path":"/reference/payne.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"rotation treatments : AB = arable rotation spring barley. AF = arable rotation bare fallow. Ln3 = 3-year grass lay crops. Ln8 = 8-year grass lay crops. Lc3 = 3-year grass-clover lay crops. Lc8 = 8-year grass-clover lay crops. full data available via CC-4.0 license : Margaret Glendining, Paul Poulton, Andrew Macdonald, Chloe MacLaren, Suzanne Clark (2022). Dataset: Woburn Ley-arable experiment: yields wheat first test crop, 1976-2018 Electronic Rothamsted Archive, Rothamsted Research. https://doi.org/10.23637/wrn3-wheat7618-01 data used subset appearing paper Payne.","code":""},{"path":"/reference/payne.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"Payne, R. (2013) Design analysis long-term rotation experiments. Agronomy Journal, 107, 772-785. https://doi.org/10.2134/agronj2012.0411","code":""},{"path":"/reference/payne.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"None","code":""},{"path":"/reference/payne.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Long term rotation experiment at Rothamsted — payne.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(payne.wheat) dat <- payne.wheat # make factors dat <- transform(dat, rotf = factor(rotation), yrf = factor(year), nitrof = factor(nitro)) # visualize the response to nitrogen libs(lattice) # Why does Payne use nitrogen factor, when it is an obvious polynomial term? # Probably doesn't matter too much. xyplot(yield ~ nitro|yrf, dat, groups=rotf, type='b', auto.key=list(columns=6), main=\"payne.wheat\") # What are the long-term trends? Yields are decreasing xyplot(yield ~ year | rotf, data=dat, groups=nitrof, type='l', auto.key=list(columns=4)) if(require(\"asreml\", quietly=TRUE)){ libs(asreml) # Model 5: drop 3-way interaction and return to pol function (easier prediction) m5 <- asreml(yield ~ rotf * nitrof * pol(year,2) - (rotf:nitrof:pol(year,2)), data=dat, random = ~yrf, residual = ~ dsum( ~ units|yrf)) summary(m5)$varcomp # Table 7 of Payne # lucid::vc(m5) # Table 8 of Payne wald(m5, denDF=\"default\") # Predictions of three-way interactions from final model p5 <- predict(m5, classify=\"rotf:nitrof:year\") p5 <- p5$pvals # Matches Payne table 8 head(p5) # Plot the predictions. Matches Payne figure 1 xyplot(predicted.value ~ year | rotf, data=p5, groups=nitrof, ylab=\"yield t/ha\", type='l', auto.key=list(columns=5)) } } # }"},{"path":"/reference/pearce.apple.html","id":null,"dir":"Reference","previous_headings":"","what":"Apple tree yields for 6 treatments with covariate — pearce.apple","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"Apple tree yields 6 treatments covariate previous yield.","code":""},{"path":"/reference/pearce.apple.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"data frame 24 observations following 4 variables. block block factor, 4 levels trt treatment factor, 6 levels prev previous yield boxes yield yield per plot","code":""},{"path":"/reference/pearce.apple.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"Treatment 'S' standard practice English apple orchards keeping land clean summer. previous yield number boxes fruit, four seasons previous application treatments.","code":""},{"path":"/reference/pearce.apple.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"S. C. Pearce (1953). Field Experiments Fruit Trees Perennial Plants. Commonwealth Bureau Horticulture Plantation Crops, Farnham Royal, Slough, England, App. IV.","code":""},{"path":"/reference/pearce.apple.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"James G. Booth, Walter T. Federer, Martin T. Wells Russell D. Wolfinger (2009). Multivariate Variance Components Model Analysis Covariance Designed Experiments. Statistical Science, 24, 223-237.","code":""},{"path":"/reference/pearce.apple.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apple tree yields for 6 treatments with covariate — pearce.apple","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(pearce.apple) dat <- pearce.apple libs(lattice) xyplot(yield~prev|block, dat, main=\"pearce.apple\", xlab=\"previous yield\") # Univariate fixed-effects model of Booth et al, using previous # yield as a covariate. m1 <- lm(yield ~ trt + block + prev, data=dat) # Predict values, holding the covariate at its overall mean of 8.3 newdat <- expand.grid(trt=c('A','B','C','D','E','S'), block=c('B1','B2','B3','B4'), prev=8.308333) newdat$pred <- predict(m1, newdata=newdat) # Average across blocks to get the adjusted mean, Booth et al. Table 1 tapply(newdat$pred, newdat$trt, mean) # A B C D E S # 280.4765 266.5666 274.0666 281.1370 300.9175 251.3357 # Same thing, but with blocks random libs(lme4) m2 <- lmer(yield ~ trt + (1|block) + prev, data=dat) newdat$pred2 <- predict(m2, newdata=newdat) tapply(newdat$pred2, newdat$trt, mean) # A B C D E S # 280.4041 266.5453 274.0453 281.3329 301.3432 250.8291 } # }"},{"path":"/reference/pearl.kernels.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","title":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","text":"Counts yellow/white sweet/starchy kernels 4 maize ears 15 observers.","code":""},{"path":"/reference/pearl.kernels.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","text":"data frame 59 observations following 6 variables. ear ear, 8-11 obs observer, 1-15 ys number yellow starchy kernels yt yellow sweet ws white starchy wt white sweet","code":""},{"path":"/reference/pearl.kernels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","text":"ear white sweet corn crossed ear yellow starchy corn. F1 kernels cross grown sample four ears harvested. F2 kernels ears classified 15 observers white/yellow sweet/starchy. Mendelian genetics, kernels occur ratio 9 yellow starch, 3 white starch, 3 yellow sweet, 1 white sweet. observers following positions:","code":""},{"path":"/reference/pearl.kernels.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","text":"Raymond Pearl, 1911. Personal Equation Breeding Experiments Involving Certain Characters Maize, Biol. Bull., 21, 339-366. https://www.biolbull.org/cgi/reprint/21/6/339.pdf","code":""},{"path":"/reference/pearl.kernels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts of yellow/white and sweet/starchy maize kernels by 15 observers — pearl.kernels","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(pearl.kernels) dat <- pearl.kernels libs(lattice) xyplot(ys+yt+ws+wt~obs|ear, dat, type='l', as.table=TRUE, auto.key=list(columns=4), main=\"pearl.kernels\", xlab=\"observer\",ylab=\"kernels\", layout=c(4,1), scales=list(x=list(rot=90))) # Test hypothesis that distribution is 'Mendelian' 9:3:3:1 dat$pval <- apply(dat[, 3:6], 1, function(x) chisq.test(x, p=c(9,3,3,1)/16)$p.val) dotplot(pval~obs|ear, dat, layout=c(1,4), main=\"pearl.kernels\", ylab=\"P-value for test of 9:3:3:1 distribution\") } # }"},{"path":"/reference/pederson.lettuce.repeated.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"Repeated measurements lettuce growth 3 treatments.","code":""},{"path":"/reference/pederson.lettuce.repeated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"","code":"data(\"pederson.lettuce.repeated\")"},{"path":"/reference/pederson.lettuce.repeated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"data frame 594 observations following 4 variables. plant plant number day day observation trt treatment weight weight","code":""},{"path":"/reference/pederson.lettuce.repeated.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"Experiment conducted greenhouse Silver Bay, Minnesota. Plants grown hydroponically. Treatment 1 9 plants per raft. Treatment 2 18 plants, treatment 3 36 plants. response variable weight plant, roots, soil, cup, water. plants measured repeatedly beginning Dec 1, ending Jan 9, plants harvested.","code":""},{"path":"/reference/pederson.lettuce.repeated.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"Levi Dawson Pederson (2015). Mixed Model Analysis Repeated Measures Lettuce Growth Thesis University Minnesota. Appendix C. https://scse.d.umn.edu/sites/scse.d.umn.edu/files/pedersonprojectthesis.pdf","code":""},{"path":"/reference/pederson.lettuce.repeated.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"None","code":""},{"path":"/reference/pederson.lettuce.repeated.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Repeated measurements of lettuce growth — pederson.lettuce.repeated","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(pederson.lettuce.repeated) dat <- pederson.lettuce.repeated libs(lattice) dat <- dat[order(dat$day),] xyplot(weight ~ day|trt, dat, type='l', group=plant, layout=c(3,1), main=\"pederson.lettuce.repeated\") # Pederson used this SAS MIXED model for unstructured covariance # proc mixed data=Project.Spacingdata; # class trt plant day; # model weight=trt day trt*day; # repeated day / subject=plant type=un r rcorr; # This should give the same results as SAS, but does not. libs(nlme) dat <- transform(dat, plant=factor(plant), day=factor(day)) datg <- groupedData(weight ~ day|plant, data=dat) un1 <- gls(weight ~ trt * day, data=datg, correlation=corSymm(value=rep(.6,55), form = ~ 1 | plant), control=lmeControl(opt=\"optim\", msVerbose=TRUE, maxIter=500, msMaxIter=500)) logLik(un1)*2 # nlme has 1955, SAS had 1898.6 # Comparing the SAS results in Pederson (page 16) and the nlme results, we notice # the SAS correlations in table 5.2 are unusually low for the first # column. The nlme results have a higher correlation in the first column # and just \"look\" better un1 } # }"},{"path":"/reference/perry.springwheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"Yields wheat cultivars introduced 1860-1982. Grown 20 environments.","code":""},{"path":"/reference/perry.springwheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"","code":"data(\"perry.springwheat\")"},{"path":"/reference/perry.springwheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"data frame 560 observations following 6 variables. yield yield, kg/ha gen genotype/cultivar factor, 28 levels env environment factor, 20 levels site site factor year year, 1979-1982 yor year release, 1860-1982","code":""},{"path":"/reference/perry.springwheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"Twenty-eight significant wheat cultivars past century Western Australia, grown 20 field trials 4 years Central Eastern wheat-belt Australia. Wongan Hills site separate early late sown trials 1979 1980. Later sowing dates generally lower yields. Note: Although indicated original paper, may Merredin site 1979 also early/late sowing dates. Used permission Mario D'Antuono CSIRO Publishing.","code":""},{"path":"/reference/perry.springwheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"MW Perry MF D'Antuono. (1989). Yield improvement associated characteristics Australian spring wheat cultivars introduced 1860 1982. Australian Journal Agricultural Research, 40(3), 457–472. https://www.publish.csiro.au/nid/43/issue/1237.htm","code":""},{"path":"/reference/perry.springwheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of wheat cultivars introduced 1860-1982. — perry.springwheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(perry.springwheat) dat <- perry.springwheat libs(lattice) xyplot(yield~yor|env, dat, type=c('p','r'), xlab=\"year of release\", main=\"perry.springwheat\") # Show the genetic trend for each testing location * year. # libs(latticeExtra) # useOuterStrips(xyplot(yield~yor|site*factor(year), dat, # type=c('p','r'))) # Perry reports a rate of gain of 5.8 kg/ha/year. No model is given. # We fit a model with separate intercept/slope for each env m1 <- lm(yield ~ env + yor + env:yor, data=dat) # Average slope across environments mean(c(coef(m1)[21], coef(m1)[21]+coef(m1)[22:40])) ## [1] 5.496781 # ---------- # Now a mixed-effects model. Fixed overall int/slope. Random env int/slope. # First, re-scale response so we don't have huge variances dat$y <- dat$yield / 100 libs(lme4) # Use || for uncorrelated int/slope. Bad model. See below. # m2 <- lmer(y ~ 1 + yor + (1+yor||env), data=dat) ## Warning messages: ## 1: In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : ## Model failed to converge with max|grad| = 0.55842 (tol = 0.002, component 1) ## 2: In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : ## Model is nearly unidentifiable: very large eigenvalue ## - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio ## - Rescale variables? # Looks like lme4 is having trouble with variance of intercepts # There is nothing special about 1800 years, so change the # intercept -- 'correct' yor by subtracting 1800 and try again. dat$yorc <- dat$yor - 1800 m3 <- lmer(y ~ 1 + yorc + (1+yorc||env), data=dat) # Now lme4 succeeds. Rate of gain is 100*0.0549 = 5.49 fixef(m3) ## (Intercept) yorc ## 5.87492444 0.05494464 if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) m3a <- asreml(y ~ 1 + yorc, data=dat, random = ~ env + env:yorc) lucid::vc(m3) ## grp var1 var2 vcov sdcor ## env (Intercept) 11.61 3.407 ## env.1 yorc 0.00063 0.02511 ## Residual 3.551 1.884 lucid::vc(m3a) ## effect component std.error z.ratio con ## env!env.var 11.61 4.385 2.6 Positive ## env:yorc!env.var 0.00063 0.000236 2.7 Positive ## R!variance 3.551 0.2231 16 Positive } } # }"},{"path":"/reference/petersen.sorghum.cowpea.html","id":null,"dir":"Reference","previous_headings":"","what":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"Intercropping experiment sorghum/cowpea.","code":""},{"path":"/reference/petersen.sorghum.cowpea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"","code":"data(\"petersen.sorghum.cowpea\")"},{"path":"/reference/petersen.sorghum.cowpea.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"data frame 18 observations following 5 variables. block block srows sorghum rows crows cowpea rows syield sorghum yield, kg/ha cyield cowpea yield, kg/ha","code":""},{"path":"/reference/petersen.sorghum.cowpea.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"intercropping experiment Tanzania. treatments consisted four ratios sorghum rows cowpea rows 1:4, 2:3, 3:2, 4:1. sole-crop yields 5 rows per crop also given (part blocks).","code":""},{"path":"/reference/petersen.sorghum.cowpea.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"Roger G Petersen (1994). Agricultural Field Experiments. Marcel Dekker Inc, New York. Page 372.","code":""},{"path":"/reference/petersen.sorghum.cowpea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"None","code":""},{"path":"/reference/petersen.sorghum.cowpea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Intercropping experiment of sorghum/cowpea — petersen.sorghum.cowpea","text":"","code":"if (FALSE) { # \\dontrun{ libs(agridat) data(petersen.sorghum.cowpea) dat <- petersen.sorghum.cowpea # Petersen figure 10.4a tmp <- dat with(tmp, plot(srows, syield + cyield, col=\"blue\", type='l', xlim=c(0,5), ylim=c(0,4000)) ) with(tmp, lines(srows, syield) ) with(tmp, lines(srows, cyield, col=\"red\") ) title(\"Cow Pea (red), Sorghum (black), Total (blue)\") title(\"petersen.sorghum.cowpea\", line=0.5) } # }"},{"path":"/reference/piepho.barley.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of barley — piepho.barley.uniformity","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"Uniformity trial barley Germany","code":""},{"path":"/reference/piepho.barley.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"","code":"data(\"piepho.barley.uniformity\")"},{"path":"/reference/piepho.barley.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"data frame 1080 observations following 5 variables. row row ordinate col column ordinate yield yield per plot","code":""},{"path":"/reference/piepho.barley.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"Uniformity trial barley Ihinger Hof farm, conducted University Hohenheim, Germany, 2007. Note: paper Piepho says \"trial 30 rows 36 columns. Plot widths 1.90 m along rows 3.73 m along columns.\" confirmed variograms Figure 1. clear \"along rows\" \"along columns\" means English. However, SAS code supplement paper, called \"PBR_1654_sm_example1.sas\", row=1-36, col=1-30.","code":""},{"path":"/reference/piepho.barley.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"H. P. Piepho & E. R. Williams (2010). Linear variance models plant breeding trials. Plant Breeding, 129, 1-8. https://doi.org/10.1111/j.1439-0523.2009.01654.x","code":""},{"path":"/reference/piepho.barley.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"None","code":""},{"path":"/reference/piepho.barley.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of barley — piepho.barley.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ data(piepho.barley.uniformity) dat <- piepho.barley.uniformity libs(desplot) desplot(dat, yield ~ col*row, tick=TRUE, aspect=(36*3.73)/(30*1.90), main=\"piepho.barley.uniformity.csv\") if(require(\"asreml\", quietly=TRUE)){ libs(asreml,dplyr,lucid) dat <- mutate(dat, x=factor(col), y=factor(row)) dat <- arrange(dat, x, y) # Piepho AR1xAR1 model (in random term, NOT residual) m1 <- asreml(data=dat, yield ~ 1, random = ~ x + y + ar1(x):ar1(y), residual = ~ units, na.action=na.method(x=\"keep\") ) m1 <- update(m1) # Match Piepho table 3, footnote 4: .9671, .9705 for col,row correlation # Note these parameters are basically at the boundary of the parameter # space. Questionable fit. lucid::vc(m1) } } # }"},{"path":"/reference/piepho.cocksfoot.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"Multi-environment trial cock's foot, heading dates 25 varieties 7 yearsyears","code":""},{"path":"/reference/piepho.cocksfoot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"","code":"data(\"piepho.cocksfoot\")"},{"path":"/reference/piepho.cocksfoot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"data frame 111 observations following 3 variables. gen genotype factor, 25 levels year year, numeric date heading date (days April 1)","code":""},{"path":"/reference/piepho.cocksfoot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"data heading dates (days April 1 heading) 25 cock's foot Dactylis glomerata varieties trials Hannover, Germany, repeated seven years. Values means replications. Piepho fits model similar Finlay-Wilkinson regression, genotype environment swapped.","code":""},{"path":"/reference/piepho.cocksfoot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"Hans-Pieter Piepho. (1999). Fitting Regression Model Genotype--Environment Data Heading Dates Grasses Methods Nonlinear Mixed Models. Biometrics, 55, 1120-1128. https://doi.org/10.1111/j.0006-341X.1999.01120.x","code":""},{"path":"/reference/piepho.cocksfoot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years — piepho.cocksfoot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(piepho.cocksfoot) dat <- piepho.cocksfoot dat$year <- factor(dat$year) libs(lattice) # Gaussian, not gamma distn densityplot(~date|year, data=dat, main=\"piepho.cocksfoot - heading date\") if(require(\"mumm\", quietly=TRUE)){ libs(mumm) # The mumm package can reproduce Piepho's results levelplot(date ~ year*gen, dat) # note mp(random,fixed) mod3 <- mumm(date ~ -1 + gen + (1|year) + mp(year, gen), dat) # Compare to Piepho table 3, \"full maximum likelihood\" mod3$sigmas^2 # variances for year:gen, residual match # year mp year:gen Residual # 17.70287377 0.02944158 0.49024737 # mod3$par_fix # fixed genotypes match # mod3$sdreport # estim/stderr # Estimate Std. Error # nu 49.0393183 1.55038652 # nu 42.0889493 1.67597832 # nu 45.3411252 1.59818620 # etc # mod3$par_rand # random year:gen match # $`mp year:gen` # 1990 1991 1992 1993 1994 1995 # 0.10595661 -0.05298523 0.08228274 -0.09629696 -0.11045540 0.29637268 } } # }"},{"path":"/reference/polson.safflower.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of safflower — polson.safflower.uniformity","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"Uniformity trial safflower Farmington, Utah, 1962.","code":""},{"path":"/reference/polson.safflower.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"","code":"data(\"polson.safflower.uniformity\")"},{"path":"/reference/polson.safflower.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"data frame 1716 observations following 3 variables. row row col column yield yield (grams)","code":""},{"path":"/reference/polson.safflower.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"uniformity trial safflower Utah State University field station Farmington, Utah, 1962. field approximately 0.5 acres size, 110 x 189 feet. four-row planter used, 22 inches rows. Four rows either side 12 feet ends removed harvesting. Yield threshed grain recorded grams. Field width: (52 rows + 8 border rows) * 22 = 110 ft Field length: 33 sections * 5ft + 2 borders * 12 ft = 189 ft","code":""},{"path":"/reference/polson.safflower.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"David Polson. 1964. Estimation Optimum Size, Shape, Replicate Number Safflower Plots Yield Trials. Utah State University, Graduate Theses Dissertations, 2979. Table 6, p. 52. https://digitalcommons.usu.edu/etd/2979","code":""},{"path":"/reference/polson.safflower.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"None.","code":""},{"path":"/reference/polson.safflower.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of safflower — polson.safflower.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(polson.safflower.uniformity) dat <- polson.safflower.uniformity libs(desplot) desplot(dat, yield ~ col*row, flip=TRUE, aspect=189/110, # true aspect main=\"polson.safflower.uniformity\") libs(agricolae) libs(reshape2) dmat <- acast(dat, row~col, value.var=\"yield\") # Similar to Polson fig 4. tab <- index.smith(dmat, col=\"red\", main=\"polson.safflower.uniformity - Smith Index\", xlab=\"Plot size in number of basic plots\") # Polson p. 25 said CV decreased from 14.3 to 4.5 # for increase from 1 unit to 90 units. Close match. tab <- data.frame(tab$uniformity) # Polson only uses log(Size) < 2 in his Fig 5, obtained slope -0.63 coef(lm(log(Vx) ~ log(Size), subset(tab, Size <= 6))) # -0.70 # Polson table 2 reported labor for # K1, number of plots, 133 hours 75 # K2, size of plot, 43.5 hours 24 # Optimum plot size # X = b K1 / ((1-b) K2) # Polson suggests optimum plot size 2.75 to 11 basic plots } # }"},{"path":"/reference/ratkowsky.onions.html","id":null,"dir":"Reference","previous_headings":"","what":"Onion yields for different densities at two locations — ratkowsky.onions","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"Onion yields different densities two locations","code":""},{"path":"/reference/ratkowsky.onions.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"data frame contains following columns: density planting density (plants per square meter) yield yield (g / plant) loc location, Purnong Landing Virginia","code":""},{"path":"/reference/ratkowsky.onions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"Spanish white onions.","code":""},{"path":"/reference/ratkowsky.onions.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"Ratkowsky, D. . (1983). Nonlinear Regression Modeling: Unified Practical Approach. New York: Marcel Dekker.","code":""},{"path":"/reference/ratkowsky.onions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"Ruppert, D., Wand, M.P. Carroll, R.J. (2003). Semiparametric Regression. Cambridge University Press. https://stat.tamu.edu/~carroll/semiregbook/","code":""},{"path":"/reference/ratkowsky.onions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Onion yields for different densities at two locations — ratkowsky.onions","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ratkowsky.onions) dat <- ratkowsky.onions # Model inverse yield as a quadratic. Could be better... libs(lattice) dat <- transform(dat, iyield = 1/yield) m1 <- lm(iyield ~ I(density^2)*loc, dat) dat$pred <- predict(m1) libs(latticeExtra) foo <- xyplot(iyield ~ density, data=dat, group=loc, auto.key=TRUE, main=\"ratkowski.onions\",ylab=\"Inverse yield\") foo + xyplot(pred ~ density, data=dat, group=loc, type='l') } # }"},{"path":"/reference/reid.grasses.html","id":null,"dir":"Reference","previous_headings":"","what":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"Yields four grasses wide range nitrogen fertilizer, conducted 3 years.","code":""},{"path":"/reference/reid.grasses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"","code":"data(\"reid.grasses\")"},{"path":"/reference/reid.grasses.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"data frame 210 observations following 5 variables. nitro nitrogen, 21 numeric levels year Y1, Y2, Y3 gen genotype drymatter dry matter content protein protein content","code":""},{"path":"/reference/reid.grasses.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"Experiment Hannah Research Institute, Ayr. Single plots planted 4 different kinds grasses. Within plot, 21 nitrogen treatments randomized. Reid modeled dry matter yield four-parameter logistic curves form y = - b exp(-cx^d).","code":""},{"path":"/reference/reid.grasses.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"D. Reid (1985). comparison yield responses four grasses wide range nitrogen application rates. J. Agric. Sci., 105, 381-387. Table 1 & 3. https://doi.org/10.1017/S0021859600056434","code":""},{"path":"/reference/reid.grasses.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"None","code":""},{"path":"/reference/reid.grasses.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Yields of four grasses for a wide range of nitrogen fertilizer — reid.grasses","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(reid.grasses) dat <- reid.grasses libs(latticeExtra) foo <- xyplot(drymatter + protein ~ nitro|year, dat, group=gen, auto.key=list(columns=4), as.table=TRUE, type=c('p','l'), main=\"reid.grasses\",ylab=\"drymatter/protein trait value\", scales=list(y=list(relation=\"free\"))) combineLimits(foo) # devtools::run_examples does NOT like groupedData if (0){ libs(nlme) dat2 <- dat dat2$indiv <- paste(dat$year, dat$gen) # individual year+genotype curves # use all data to get initial values inits <- getInitial(drymatter ~ SSfpl(nitro, A, B, xmid, scal), data = dat2) inits ## A B xmid scal ## -4.167902 12.139796 68.764796 128.313106 xvals <- 0:800 y1 <- with(as.list(inits), SSfpl(xvals, A, B, xmid, scal)) plot(drymatter ~ nitro, dat2) lines(xvals,y1) # must have groupedData object to use augPred dat2 <- groupedData(drymatter ~ nitro|indiv, data=dat2) plot(dat2) # without 'random', all effects are included in 'random' m1 <- nlme(drymatter ~ SSfpl(nitro, A, B, xmid,scale), data= dat2, fixed= A + B + xmid + scale ~ 1, random = A + B + xmid + scale ~ 1|indiv, start=inits) fixef(m1) summary(m1) plot(augPred(m1, level=0:1), main=\"reid.grasses - observed/predicted data\") # only works with groupedData object } # if(0) } # }"},{"path":"/reference/riddle.wheat.html","id":null,"dir":"Reference","previous_headings":"","what":"Modified Latin Square experiments of wheat — riddle.wheat","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"Modified Latin Square experiments wheat two varieties 2 years","code":""},{"path":"/reference/riddle.wheat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"","code":"data(\"riddle.wheat\")"},{"path":"/reference/riddle.wheat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"data frame 650 observations following 7 variables. expt experiment strain strain rep replicate row row (nested column) year year yield yield, grams col column (group rows)","code":""},{"path":"/reference/riddle.wheat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"experiment \"Baart\" varieties 1939 another experiment \"White Federation\" varieties 1939. experiments repeated 1940. experimental design Modified Latin Square. 5 reps, horizontal. 5 \"columns\". rep*column contains multiple plots strain planted 16-foot row. Field length: 5 reps * 16 feet Field width: 25 30 rows, perhaps 0.5 feet rows Riddle & Baker note: Two strains, 5129 (Baart) 1617 (White Federation) reversed position significantly LOWER 1939 significantly HIGHER general mean 1940.","code":""},{"path":"/reference/riddle.wheat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"Riddle, O. C. G. . Baker. (1944). Biases encountered large-scale yield tests. Hilgardia, 16, 1-14. https://doi.org/10.3733/hilg.v16n01p001","code":""},{"path":"/reference/riddle.wheat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"None","code":""},{"path":"/reference/riddle.wheat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Modified Latin Square experiments of wheat — riddle.wheat","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(riddle.wheat) dat <- riddle.wheat datb39 <- subset(dat, expt==\"Baart\" & year==1939) datb40 <- subset(dat, expt==\"Baart\" & year==1940) datw39 <- subset(dat, expt==\"WhiteFed\" & year==1939) datw40 <- subset(dat, expt==\"WhiteFed\" & year==1940) # Match table 4, sections a, b, d, e anova(aov(yield ~ factor(rep) + factor(col) + strain, datb39)) anova(aov(yield ~ factor(rep) + factor(col) + strain, datb40)) anova(aov(yield ~ factor(rep) + factor(col) + strain, datw39)) anova(aov(yield ~ factor(rep) + factor(col) + strain, datw40)) libs(desplot) # Show the huge variaion between reps dat$yrexpt <- paste0(dat$year, dat$expt) desplot(dat, yield ~ row*rep|yrexpt, tick=TRUE, out1=col, main=\"riddle.wheat\", aspect=(5*16)/(30*.5)) # Show the randomization was the same in each year (but not each expt). desplot(dat, strain ~ row*rep|yrexpt, tick=TRUE, out1=col, main=\"riddle.wheat\") } # }"},{"path":"/reference/ridout.appleshoots.html","id":null,"dir":"Reference","previous_headings":"","what":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"Root counts propagated columnar apple shoots.","code":""},{"path":"/reference/ridout.appleshoots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"","code":"data(\"ridout.appleshoots\")"},{"path":"/reference/ridout.appleshoots.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"data frame 270 observations following 4 variables. roots number roots per shoot trtn number shoots per treatment combination photo photoperiod, 8 16 bap BAP concentration, numeric","code":""},{"path":"/reference/ridout.appleshoots.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"270 micropropagated shoots columnar apple cultivar Trajan. rooting period, shoot tips length 1.0-1.5 cm cultured media different concentrations cytokinin BAP two growth chambers 8 16 hour photoperiod. response variable number roots 4 weeks 22 degrees C. Almost shoots 8 hour photoperiod rooted. 16 hour photoperiod half rooted. High BAP concentrations often inhibit root formation apples, perhaps columnar varieties. Used permission Martin Ridout.","code":""},{"path":"/reference/ridout.appleshoots.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"Ridout, M. S., Hinde, J. P., Demetrio, C. G. B. (1998). Models Count Data Many Zeros. Proceedings 19th International Biometric Conference, 179-192.","code":""},{"path":"/reference/ridout.appleshoots.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"SAS. Fitting Zero-Inflated Count Data Models Using PROC GENMOD. support.sas.com/rnd/app/examples/stat/GENMODZIP/roots.pdf","code":""},{"path":"/reference/ridout.appleshoots.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Root counts for propagated columnar apple shoots. — ridout.appleshoots","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ridout.appleshoots) dat <- ridout.appleshoots # Change photo and bap to factors dat <- transform(dat, photo=factor(photo), bap=factor(bap)) libs(lattice) # histogram(~roots, dat, breaks=0:18-0.5) # For photo=8, Poisson distribution looks reasonable. # For photo=16, half of the shoots had no roots # Also, photo=8 has very roughly 1/45 as many zeros as photo=8, # so we anticipate prob(zero) is about 1/45=0.22 for photo=8. histogram(~roots|photo, dat, breaks=0:18-0.5, main=\"ridout.appleshoots\") libs(latticeExtra) foo.obs <- histogram(~roots|photo*bap, dat, breaks=0:18-0.5, type=\"density\", xlab=\"Number of roots for photoperiod 8, 16\", ylab=\"Density for BAP levels\", main=\"ridout.appleshoots\") useOuterStrips(foo.obs) # Ordinary (non-ZIP) Poisson GLM m1 <- glm(roots ~ bap + photo + bap:photo, data=dat, family=\"poisson\") summary(m1) # Appears to have overdispersion # ----- Fit a Zero-Inflated Poisson model ----- libs(pscl) # Use SAS contrasts to match SAS output oo <- options(contrasts=c('contr.SAS','contr.poly')) # There are unequal counts for each trt combination, which obviously affects # the distribution of counts, so use log(trtn) as an offset. dat$ltrtn <- log(dat$trtn) # Ordinary Poisson GLM: 1 + bap*photo. # Zero inflated probability depends only on photoperiod: 1 + photo m2 <- zeroinfl(roots ~ 1 + bap*photo | 1 + photo, data=dat, dist=\"poisson\", offset=ltrtn) logLik(m2) # -622.2283 matches SAS Output 1 -2 * logLik(m2) # 1244.457 Matches Ridout Table 2, ZIP, H*P, P summary(m2) # Coefficients match SAS Output 3. exp(coef(m2, \"zero\")) # Photo=8 has .015 times as many zeros as photo=16 # Get predicted _probabilities_ # Prediction data newdat <- expand.grid(photo=c(8,16), bap=c(2.2, 4.4, 8.8, 17.6)) newdat <- aggregate(trtn~bap+photo, dat, FUN=mean) newdat$ltrtn <- log(newdat$trtn) # The predicted (Poisson + Zero) probabilities d2 <- cbind(newdat[,c('bap','photo')], predict(m2, newdata=newdat, type=\"prob\")) libs(reshape2) d2 <- melt(d2, id.var = c('bap','photo')) # wide to tall d2$xpos <- as.numeric(as.character(d2$variable)) foo.poi <- xyplot(value~xpos|photo*bap, d2, col=\"black\", pch=20, cex=1.5) # Plot data and model foo.obs <- update(foo.obs, main=\"ridout.appleshoots: observed (bars) & predicted (dots)\") useOuterStrips(foo.obs + foo.poi) # Restore contrasts options(oo) } # }"},{"path":"/reference/robinson.peanut.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of peanuts — robinson.peanut.uniformity","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"Uniformity trial peanuts North Carolina 1939, 1940.","code":""},{"path":"/reference/robinson.peanut.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"","code":"data(\"robinson.peanut.uniformity\")"},{"path":"/reference/robinson.peanut.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"data frame 1152 observations following 4 variables. row row col column yield yield grams/plot year year","code":""},{"path":"/reference/robinson.peanut.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"Two crops peanuts grown North Carolina 1939 1940. different field used year. block 36 rows 3 feet wide 200 feet long harvested 12.5 foot lengths. Field length: 36 plots * 12.5 feet = 200 feet Field width: 16 plots * 3 feet = 48 feet Widening plot effective increasing plot length order reduce error. agrees results uniformity studies. Assuming 30 percent total cost experiment proportional size plots used, optimum plot size approximately 3.2 units.","code":""},{"path":"/reference/robinson.peanut.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"H.F. Robinson J..Rigney P.H.Harvey (1948). Investigations Peanut Plot Technique Peanuts. Univ California Tech. Bul. 86.","code":""},{"path":"/reference/robinson.peanut.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"None","code":""},{"path":"/reference/robinson.peanut.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of peanuts — robinson.peanut.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(robinson.peanut.uniformity) dat <- robinson.peanut.uniformity # Mean yield per year. Robinson has 703.9, 787.3 # tapply(dat$yield, dat$year, mean) # 1939 1940 # 703.7847 787.8125 libs(desplot) desplot(dat, yield ~ col*row|year, flip=TRUE, tick=TRUE, aspect=200/48, main=\"robinson.peanut.uniformity\") } # }"},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"Uniformity trial sugar beets","code":""},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"","code":"data(\"roemer.sugarbeet.uniformity\")"},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"data frame 192 observations following 4 variables. row row ordinate col column ordinate yield yield per plot, kg year year experiment","code":""},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"Roemer p 27: Eigene Versuche mit Zuckerrüben, ausgeführt auf dem Neßthaler Zuchtfeld des Kaiser-Wilhelm-Institutes, Bromberg, den Jahren 1916, 1917 und 1918. 1916 und 1918 war die Versuchsfläche ein und dieselbe, 6,80 groß und den beiden Jahren mit Original Klein-Wanzlebener Zuckerrüben auf 30 X 40 cm bebaut. Vorfrucht für 1916 war Hafer, für 1918 Roggen; 1917 war eine andere Fläche, ebenfalls 6,80 groß, für den Versuch benußt; gesät wurden zwei verschiedene Zuchten von Strube, Schlanstedt. Beide Flächen sind von sehr gleichmäßiger Bodenbeschaffenheit. Bei der Fläche 1916 und 1918 machte sich im ersten Jahre bei den Reihen 31-33 eine geringe Stelle bemerkbar, die 1918 weit weniger Erscheinung trat. Die Bodenunterschiede sind allen drei Jahren geringer als die durch die Versuchstechnik bedingten Fehler. Translated: (Roemer) experiments sugar beets, carried Neßthal breeding field Kaiser Wilhelm Institute, Bromberg, years 1916, 1917 1918. 1916 1918 test area one , 6.80 large original years Klein-Wanzleben sugar beets cultivated 30 x 40 cm. previous crop 1916 oats, 1918 rye; 1917 another area, also 6.80 large, used experiment; Two different varieties Strube, Schlanstedt sown. areas uniform soil conditions. 1916 1918 area, small spot noticeable rows 31-33 first year, much less noticeable 1918. three years soil differences smaller errors caused experimental technology. Field width: 2 plots * 17 m = 34 m Field length: 48 plots * 4.17 m = 200 m Total area = 34 m * 200 m = 6800 sq m = 6.8 . Cochran says: 96 plots, 1 row x 55.8 ft (17m). Two sets (years) 1916 1918. Data typed K.Wright Roemer (1920).","code":""},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"Roemer, T. (1920). Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. Table 1, page 62. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ","code":""},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"Neyman, J., & Iwaszkiewicz, K. (1935). Statistical problems agricultural experimentation. Supplement Journal Royal Statistical Society, 2(2), 107-180.","code":""},{"path":"/reference/roemer.sugarbeet.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sugar beets — roemer.sugarbeet.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(roemer.sugarbeet.uniformity) dat <- roemer.sugarbeet.uniformity libs(desplot) desplot(dat, yield~col*row|year, aspect=(48*4.16)/(2*17), flip=TRUE, tick=TRUE, main=\"roemer.sugarbeet.uniformity\") } # }"},{"path":"/reference/rothamsted.brussels.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"RCB experiment brussels sprouts, 9 fertilizer treatments","code":""},{"path":"/reference/rothamsted.brussels.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"data frame 48 observations following 5 variables. row row col column yield yield saleable sprouts, pounds trt treatment, 9 levels block block, 4 levels","code":""},{"path":"/reference/rothamsted.brussels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"block numbers arbitrary, may match orignal source. Plots 10 yards x 14 yards. Plot orientation clear.","code":""},{"path":"/reference/rothamsted.brussels.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"Rothamsted Experimental Station Report 1934-36. Brussels sprouts: effect sulphate ammonia, poultry manure, soot rape dust, pp. 191-192. Harpenden: Lawes Agricultural Trust.","code":""},{"path":"/reference/rothamsted.brussels.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667","code":""},{"path":"/reference/rothamsted.brussels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of brussels sprouts, 9 fertilizer treatments — rothamsted.brussels","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(rothamsted.brussels) dat <- rothamsted.brussels libs(lattice) bwplot(yield~trt, dat, main=\"rothamsted.brussels\") libs(desplot) desplot(dat, yield~col*row, num=trt, out1=block, cex=1, # aspect unknown main=\"rothamsted.brussels\") } # }"},{"path":"/reference/rothamsted.oats.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"RCB experiment oats, straw grain, 9 fertilizer treatments","code":""},{"path":"/reference/rothamsted.oats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"","code":"data(\"rothamsted.oats\")"},{"path":"/reference/rothamsted.oats.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"data frame 96 observations following 6 variables. block block trt fertilizer treatment 9 levels grain grain, pounds per plot straw straw, pounds per plot row row col column","code":""},{"path":"/reference/rothamsted.oats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"Oats (Grey Winter) grown Rothamsted, Long Hoos field 1926. Values grain straw actual weights pounds. plot 1/40 acre. plot dimensions given, Rothamsted report shows field square. treatment codes : OA,OB,OC,OD = top dressing. E/L = Early/late application. S/M = Sulphate muriate ammonia. 1/2 = Single double dressing.","code":""},{"path":"/reference/rothamsted.oats.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"Rothamsted Report 1925-26, p. 146. https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1925-26-138-155 Electronic version data supplied David Clifford.","code":""},{"path":"/reference/rothamsted.oats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667","code":""},{"path":"/reference/rothamsted.oats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of oats, straw and grain, 9 fertilizer treatments — rothamsted.oats","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(rothamsted.oats) dat <- rothamsted.oats libs(desplot) desplot(dat, grain~col*row, out1=block, text=trt, cex=1, shorten=FALSE, aspect=1, main=\"rothamsted.oats\") desplot(dat, straw~col*row, out1=block, text=trt, cex=1, shorten=FALSE, aspect=1, main=\"rothamsted.oats\") libs(lattice) xyplot(grain~straw, dat, main=\"rothamsted.oats\") # traits are correlated if(0){ # compare to summary at bottom of page 146, first 3 columns libs(dplyr) dat = mutate(dat, nfert=trt, # number of fertilizer applications nfert=dplyr::recode(nfert, \"oa\"=\"None\", \"ob\"=\"None\", \"oc\"=\"None\", \"od\"=\"None\", \"1se\"=\"Single\", \"1sl\"=\"Single\", \"1me\"=\"Single\", \"1ml\"=\"Single\", \"2se\"=\"Double\", \"2sl\"=\"Double\", \"2me\"=\"Double\", \"2ml\"=\"Double\")) # English ton = 2240 pounds, cwt = 112 pounds # multiply by 40 to get pounds/acre # divide by: 112 to get hundredweight/acre, 42 to get bushels/acre # Avoid pipe operator in Rd examples! dat <- group_by(dat, nfert) dat <- summarize(dat, straw=mean(straw), grain=mean(grain)) dat <- mutate(dat, straw= straw * 40/112, grain = grain * 40/42) ## # A tibble: 3 x 3 ## nfert straw grain ## ## 1 Single 50.3 78.9 ## 2 Double 53.7 77.7 ## 3 None 44.1 75.4 } } # }"},{"path":"/reference/ryder.groundnut.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","title":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","text":"RCB experiment groundut, wet dry yields","code":""},{"path":"/reference/ryder.groundnut.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","text":"data frame 24 observations following 6 variables. block block row row col column gen genotype factor wet wet yield, kg/plot dry dry yield, kg/plot","code":""},{"path":"/reference/ryder.groundnut.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","text":"Ryder (1981) uses data discuss importance looking field plan experiment. Based analysis residuals, suggests varieties B block 3 may data swapped.","code":""},{"path":"/reference/ryder.groundnut.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","text":"K. Ryder (1981). Field plans: biometrician finds useful, Experimental Agriculture, 17, 243–256. https://doi.org/10.1017/S0014479700011601","code":""},{"path":"/reference/ryder.groundnut.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of groundut, wet and dry yields — ryder.groundnut","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(ryder.groundnut) dat <- ryder.groundnut # RCB model m1 <- lm(dry~block+gen,dat) dat$res1 <- resid(m1) # Table 3 of Ryder. Scale up from kg/plot to kg/ha round(dat$res1 * 596.6,0) # Visually. Note largest positive/negative residuals are adjacent libs(desplot) desplot(dat, res1 ~ col + row, text=gen, # aspect unknown main=\"ryder.groundnut - residuals\") libs(desplot) # Swap the dry yields for two plots and re-analyze dat[dat$block==\"B3\" & dat$gen==\"A\", \"dry\"] <- 2.8 dat[dat$block==\"B3\" & dat$gen==\"B\", \"dry\"] <- 1.4 m2 <- lm(dry~block+gen, dat) dat$res2 <- resid(m2) desplot(dat, res2 ~ col+row, # aspect unknown text=gen, main=\"ryder.groundnut\") } # }"},{"path":"/reference/salmon.bunt.html","id":null,"dir":"Reference","previous_headings":"","what":"Fungus infection in varieties of wheat — salmon.bunt","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"Fungus infection varieties wheat","code":""},{"path":"/reference/salmon.bunt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"data frame 400 observations following 4 variables. bunt bunt factor, 20 levels pct percent infected rep rep factor, 2 levels gen genotype factor, 10 levels","code":""},{"path":"/reference/salmon.bunt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"Note: Salmon (1938) gives results 69 types bunt, just 20 shown paper. H. . Rodenhiser C. S. Holton (1937) say races two different species bunt used, Tilletia tritici T. levis. data gives results 20 types bunt (fungus) winter wheat varieties Kearneysville, W. Va., 1935. Altogether 69 types bunt included experiment, 20 data representative. type wheat grown short row (5 8 feet), seed innoculated spores bunt. entire seeding repeated order. Infection recorded percentage total number heads counted near harvest. number counted seldom less 200 sometimes 400 per row.","code":""},{"path":"/reference/salmon.bunt.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"S.C. Salmon, 1938. Generalized standard errors evaluating bunt experiments wheat. Agronomy Journal, 30, 647–663. Table 1. https://doi.org/10.2134/agronj1938.00021962003000080003x","code":""},{"path":"/reference/salmon.bunt.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"Salmon says data came : H. . Rodenhiser C. S. Holton (1937). Physiologic races Tilletia tritici T. levis. Journal Agricultural Research, 55, 483-496. naldc.nal.usda.gov/download/IND43969050/PDF","code":""},{"path":"/reference/salmon.bunt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fungus infection in varieties of wheat — salmon.bunt","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(salmon.bunt) dat <- salmon.bunt d2 <- aggregate(pct~bunt+gen, dat, FUN=mean) # average reps d2$gen <- reorder(d2$gen, d2$pct) d2$bunt <- reorder(d2$bunt, d2$pct) # Some wheat varieties (Hohenheimer) are resistant to all bunts, and some (Hybrid128) # are susceptible to all bunts. Note the groups of bunt races that are similar, # such as the first 4 rows of this plot. Also note the strong wheat*bunt interaction. libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(pct~gen+bunt,d2, col.regions=redblue, main=\"salmon.bunt percent of heads infected\", xlab=\"Wheat variety\", ylab=\"bunt line\") # We don't have individual counts, so use beta regression libs(betareg) dat$y <- dat$pct/100 + .001 # Beta regression does not allow 0 dat$gen <- reorder(dat$gen, dat$pct) # For a prettier dot plot m1 <- betareg(y ~ gen + bunt + gen:bunt, data=dat) # Construct 95 percent confidence intervals p1 <- cbind(dat, lo = predict(m1, type='quantile', at=.025), est = predict(m1, type='quantile', at=.5), up = predict(m1, type='quantile', at=.975)) p1 <- subset(p1, rep==\"R1\") # Plot the model intervals over the original data libs(latticeExtra) dotplot(bunt~y|gen, data=dat, pch='x', col='red', main=\"Observed data and 95 pct intervals for bunt infection\") + segplot(bunt~lo+up|gen, data=p1, centers=est, draw.bands=FALSE) # To evaluate wheat, we probably want to include bunt as a random effect... } # }"},{"path":"/reference/saunders.maize.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"Uniformity trial maize South Africa","code":""},{"path":"/reference/saunders.maize.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"","code":"data(\"saunders.maize.uniformity\")"},{"path":"/reference/saunders.maize.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"data frame 2500 observations following 4 variables. row row ordinate col column ordinate yield yield per plot, pounds year year","code":""},{"path":"/reference/saunders.maize.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"two maize uniformity trials conducted Potchefstroom Experiment Station, South Africa. harvested unit plot 10 plants, planted 3 feet 3 feet individual hills. Dataset 1928-1929 experiment Rows 41-43 missing. Field width: 4 plots * 10 yards = 40 yards Field length : 250 plots * 1 yard = 250 yards Dataset 1929-30 experiment Row 255 missing obvious edge effect first column. Field width: 5 plots * 20 yards = 100 yards Field length: 300 plots * 1 yard = 300 yards Two possible outliers 1929-30 data verified correctly transcribed source document. data made available special help staff Rothamsted Research Library. Rothamsted library scanned paper documents pdf. Screen captures pdf saved jpg files, uploaded OCR conversion site. resulting text 95 percent accurate carefully hand-checked formatted csv files.","code":""},{"path":"/reference/saunders.maize.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 5.","code":""},{"path":"/reference/saunders.maize.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"Rayner & . R. Saunders. Statistical Methods, Special Reference Field Experiments.","code":""},{"path":"/reference/saunders.maize.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of maize in South Africa — saunders.maize.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(saunders.maize.uniformity) dat <- saunders.maize.uniformity libs(desplot) desplot(dat, yield ~ col*row, subset=year==1929, flip=TRUE, aspect=250/40, main=\"saunders.maize.uniformity 1928-29\") desplot(dat, yield ~ col*row, subset=year==1930, flip=TRUE, aspect=300/100, main=\"saunders.maize.uniformity 1929-30\") } # }"},{"path":"/reference/sawyer.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"Uniformity trials wheat, swedes, oats Rothamsted, England, 1925-1927.","code":""},{"path":"/reference/sawyer.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"","code":"data(\"sawyer.multi.uniformity\")"},{"path":"/reference/sawyer.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"data frame 48 observations following 7 variables. year year crop crop row row col column grain wheat/oats grain weight, pounds straw wheat/oats straw weight, pounds leafwt swedes leaf weight, pounds rootwt swedes root weight, pounds rootct swedes root count","code":""},{"path":"/reference/sawyer.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"experiment conducted Rothamsted, England, 1925-1927, Sawyers Field. Row 6, column 1 planted year. 1925: Wheat harvested Row 1, column 1 partially missing data wheat values 1925 used Rothamsted summary statistics page 155. 1926: Swedes harvested 1927: Oats harvested Note summaries statistics bottom page report calibrated ACRES. Field width: 8 plots * 22 feet = 528 feet Field length: 6 plots * 22 feet = 396 feet field 8 plots wide, 6 plots long. plots drawn source documents squares .098 acres (1 chain = 66 feet side). Eden & Maskell (page 165) say field clover, ploughed autumn 1924. field laid uniformly lands one chain width plot width made coincide land width ridge ridge. length plot also one chain point view yield data trial comprised 47 plots 8x6 except run hedge allowed rank five plots one ends.","code":""},{"path":"/reference/sawyer.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"Rothamsted Experimental Station, Report 1925-26. Lawes Agricultural Trust, p. 154-155. https://www.era.rothamsted.ac.uk/eradoc/book/84 Rothamsted Experimental Station, Report 1927-1928. Lawes Agricultural Trust, p. 153. https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1927-28-131-175","code":""},{"path":"/reference/sawyer.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"Eden, T. E. J. Maskell. (1928). influence soil heterogeneity growth yield successive crops. Jour Agricultural Science, 18, 163-185. https://archive.org/stream/.ernet.dli.2015.25895/2015.25895.Journal--Agricultural-Science-Vol-xviii-1928#page/n175 McCullagh, P. Clifford, D., (2006). Evidence conformal invariance crop yields, Proceedings Royal Society : Mathematical, Physical Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667 Winifred . Mackenzie. (1926) Note remarkable correlation grain straw, obtained Rothamsted. Journal Agricultural Science, 16, 275-279. https://doi.org/10.1017/S0021859600018256","code":""},{"path":"/reference/sawyer.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of wheat, swedes, oats, 3 years on the same land — sawyer.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(\"sawyer.multi.uniformity\") dat <- sawyer.multi.uniformity libs(desplot) # The field plan shows square plots desplot(dat, grain~col*row, subset= year==1925, main=\"sawyer.multi.uniformity - 1925 wheat grain yield\", aspect=(6)/(8)) # true aspect desplot(dat, rootwt~col*row, subset= year==1926, main=\"sawyer.multi.uniformity - 1926 root weight of swedes\", aspect=(6)/(8)) desplot(dat, grain~col*row, subset= year==1927, main=\"sawyer.multi.uniformity - 1927 oats grain yield\", aspect=(6)/(8)) # This plot shows the \"outlier\" in the wheat data reported by Mackenzie. libs(lattice) xyplot(grain ~ straw, data=subset(dat, year==1925)) round(cor(dat[,7:9], use=\"pair\"),2) # Matches McCullagh p 2121 ## leafwt rootwt rootct ## leafwt 1.00 0.66 0.47 ## rootwt 0.66 1.00 0.43 ## rootct 0.47 0.43 1.00 ## pairs(dat[,7:9], ## main=\"sawyer.multi.uniformity\") } # }"},{"path":"/reference/sayer.sugarcane.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"Uniformity trial sugarcane India, 1932, 1933 & 1934.","code":""},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"","code":"data(\"sayer.sugarcane.uniformity\")"},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"data frame following 4 variables. row row col column yield yield, pounds/plot year year","code":""},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"1932 Experiment, 20 col x 48 row = 960 plots Sayer (1936a, page 685): tonnage Experiment sugarcane, Co. 205, un-irrigated, conducted Harpur Jhilli 1932; 42 rows cane space 3 ft rows selected cut sections, section 30 feet 3 inches long. Thus yield figures plot sizes 30 feet 3 inches 3 feet (.e. 1/480 acre ), numbering 840 plots , available statistical analysis ; convenience data yields first forty rows also considered separately. Field width: 20 sections x 30 ft 3 = 605 feet Field length: 48 rows x 3 feet = 144 feet Note data Rothamsted library contains 48 rows, missing values rows 43-48. may Sayer (1963b) used 42 rows. ———- 1933 Experiment, 8 col x 136 row = 1088 plots Sayer (1936a, page 688). experiment conducted 1933 Meghaul (Monghyr). road cut field, creating blocks 480 ft x 315 ft 480 ft x 93 ft. (See Plate XLI). 136 rows, 3 feet apart, 480 feet long . required 16 days harvest 1088 plots. plot 1/242 acre. authors conclude long narrow plots 12/242 16/242 acre best. Field width: 8 plots * 60 feet = 480 feet Field length: 136 rows * 3 feet = 408 feet ———- 1934 Experiment, 8 col x 121 row = 968 plots experiment conducted New Area, Pusa. experiment laid 6 blocks, separated 3-foot bund. cutting canes began Jan 1934, taking 24 days. (earthquake 15 January delayed harvesting). Conclusion: Variation reduced increasing plot size 9/242 acre. Field width: 8 plots * 60 feet = 480 feet Field length: 121 rows * 3 feet = 363 feet 1932 data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"1932 Data Rothamsted Research Library, Box STATS17 WG Cochran, Folder 5. 1933 Data Wynne Sayer, M. Vaidyanathan S. Subrammonia Iyer (1936a). Ideal size shape sugar-cane experimental plots based upon tonnage experiments Co 205 Co 213 conducted Pusa. Indian J. Agric. Sci., 1936, 6, 684-714. Appendix, page 712. https://archive.org/details/.ernet.dli.2015.271737 1934 data Wynne Sayer Krishna Iyer. (1936b). factors influence error field experiments special reference sugar cane. Indian J. Agric. Sci., 1936, 6, 917-929. Appendix, page 927. https://archive.org/details/.ernet.dli.2015.271737","code":""},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"None","code":""},{"path":"/reference/sayer.sugarcane.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sugarcane in India, 1932, 1933 & 1934. — sayer.sugarcane.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(sayer.sugarcane.uniformity) dat32 <- subset(sayer.sugarcane.uniformity, year==1932) dat33 <- subset(sayer.sugarcane.uniformity, year==1933) dat34 <- subset(sayer.sugarcane.uniformity, year==1934) # The 1933 data have a 15-foot road between row 105 & row 106. # Add 5 to row number of row 106 and above. dat33$row <- ifelse(dat33$row >= 106, dat33$row + 5, dat33$row) b1 <- subset(dat33, row<31) b2 <- subset(dat33, row > 30 & row < 61) b3 <- subset(dat33, row > 60 & row < 91) b4 <- subset(dat33, row > 105 & row < 136) mean(b1$yield) # 340.7 vs Sayer 340.8 mean(b2$yield) # 338.2 vs Sayer 338.6 mean(b3$yield) # 331.3 vs Sayer 330.2 mean(b4$yield) # 295.4 vs Sayer 295.0 mean(dat34$yield) # 270.83 vs Sayer 270.83 libs(desplot) desplot(dat33, yield ~ col*row, flip=TRUE, aspect=408/480, # true aspect main=\"sayer.sugarcane.uniformity 1933\") desplot(dat34, yield ~ col*row, flip=TRUE, aspect=363/480, # true aspect main=\"sayer.sugarcane.uniformity 1934\") } # }"},{"path":"/reference/senshu.rice.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"Response rice solar radiation temperature","code":""},{"path":"/reference/senshu.rice.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"data frame 40 observations following 7 variables. country country loc location year year planting, last two digits month month planting rad solar radiation mint minimum temperature yield yield t/ha","code":""},{"path":"/reference/senshu.rice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"Minimum temperature average across 30 days post flowering. Opinion: Fitting quadratic model data makes sense.","code":""},{"path":"/reference/senshu.rice.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"Seshu, D. V. Cady, F. B. 1984. Response rice solar radiation temperature estimated international yield trials. Crop Science, 24, 649-654. https://doi.org/10.2135/cropsci1984.0011183X002400040006x","code":""},{"path":"/reference/senshu.rice.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"Walter W. Piegorsch, . John Bailer. (2005) Analyzing Environmental Data, Wiley.","code":""},{"path":"/reference/senshu.rice.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of rice, with solar radiation and temperature — senshu.rice","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(senshu.rice) dat <- senshu.rice # Model 1 of Senshu & Cady m1 <- lm(yield ~ 1 + rad + mint + I(mint^2), dat) coef(m1) # Use Fieller to calculate conf int around optimum minimum temp # See: Piegorsch & Bailer, p. 31. # Calculation derived from vegan:::fieller.MOStest m2 <- lm(yield ~ 1 + mint + I(mint^2), dat) b1 <- coef(m2)[2] b2 <- coef(m2)[3] vc <- vcov(m2) sig11 <- vc[2,2] sig12 <- vc[2,3] sig22 <- vc[3,3] u <- -b1/2/b2 tval <- qt(1-.05/2, nrow(dat)-3) gam <- tval^2 * sig22 / b2^2 x <- u + gam * sig12 / (2 * sig22) f <- tval / (-2*b2) sq <- sqrt(sig11 + 4*u*sig12 + 4*u^2*sig22 - gam * (sig11 - sig12^2 / sig22) ) ci <- (x + c(1,-1)*f*sq) / (1-gam) plot(yield ~ mint, dat, xlim=c(17, 32), main=\"senshu.rice: Quadratic fit and Fieller confidence interval\", xlab=\"Minimum temperature\", ylab=\"Yield\") lines(17:32, predict(m2, new=data.frame(mint=17:32))) abline(v=ci, col=\"blue\") } # }"},{"path":"/reference/shafi.tomato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of tomato — shafi.tomato.uniformity","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"Uniformity trial tomato India","code":""},{"path":"/reference/shafi.tomato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"","code":"data(\"shafi.tomato.uniformity\")"},{"path":"/reference/shafi.tomato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"data frame 200 observations following 3 variables. row row ordinate col column ordinate yield yield, kg/plot","code":""},{"path":"/reference/shafi.tomato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"experiment conducted Regional Research Station Faculty Agriculture, SKUAST-K Wadura Campus 2006. original data collected 1m x 1m plots. data aggregated 2m x 2m plots. Field length: 20 row * 2 m = 40 m Field width: 10 col * 2 m = 20 m","code":""},{"path":"/reference/shafi.tomato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"Shafi, Sameera (2007). Aspects Plot Techniques Field Experiments Tomato (Lycopersicon esculentum mill.) Soils Kashmir. Thesis. Univ. Ag. Sciences & Technology Kashmir. Table 2.2.1. https://krishikosh.egranth.ac./assets/pdfjs/web/viewer.html?file=https","code":""},{"path":"/reference/shafi.tomato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"Shafi, Sameera; S..Mir, Nageena Nazir, Anjum Rashid. (2010). Optimum plot size tomato using S-PLUS R-software's soils Kashmir. Asian J. Soil Sci., 4, 311-314. http://researchjournal.co./upload/assignments/4_311-314.pdf","code":""},{"path":"/reference/shafi.tomato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of tomato — shafi.tomato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(shafi.tomato.uniformity) dat <- shafi.tomato.uniformity libs(desplot) desplot(dat, yield ~ col*row, aspect=40/20, # true aspect main=\"shafi.tomato.uniformity\") } # }"},{"path":"/reference/shafii.rapeseed.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"Rapeseed yield multi-environment trial, 6 genotypes, 3 years, 14 loc, 3 rep","code":""},{"path":"/reference/shafii.rapeseed.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"data frame 648 observations following 5 variables. year year, numeric: 87, 88, 89 loc location, 14 levels rep rep, 3 levels gen genotype, 6 levels yield yield, kg/ha","code":""},{"path":"/reference/shafii.rapeseed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"data U.S. National Winter Rapeseed trials conducted 1986, 1987, 1988. Trial locations included Georgia (GGA, TGA), Idaho (ID), Kansas (KS), Mississippi (MS), Montana (MT), New York (NY), North Carolina (NC), Oregon (), South Carolina (SC), Tennessee (TN), Texas (TX), Virginia (VA), Washington (WA). SAS codes analysis can found https://webpages.uidaho.edu/cals-statprog/ammi/index.html Electronic version : https://www.uiweb.uidaho.edu/ag/statprog/ammi/yld.data Used permission Bill Price.","code":""},{"path":"/reference/shafii.rapeseed.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"Bahman Shafii William J Price, 1998. Analysis Genotype--Environment Interaction Using Additive Main Effects Multiplicative Interaction Model Stability Estimates. JABES, 3, 335–345. https://doi.org/10.2307/1400587","code":""},{"path":"/reference/shafii.rapeseed.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"Matthew Kramer (2018). Using Posterior Predictive Distribution Diagnostic Tool Mixed Models. Joint Statistical Meetings 2018, Biometrics Section. https://www.ars.usda.gov/ARSUserFiles/3122/KramerProceedingsJSM2018.pdf Reyhaneh Bijari Sigurdur Olafsson (2022). Accounting G×E interactions plant breeding: probabilistic approach https://doi.org/10.21203/rs.3.rs-2052233/v1","code":""},{"path":"/reference/shafii.rapeseed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of rapeseed in U.S. — shafii.rapeseed","text":"","code":"library(agridat) data(shafii.rapeseed) dat <- shafii.rapeseed dat$gen <- with(dat, reorder(gen, yield, mean)) dat$loc <- with(dat, reorder(loc, yield, mean)) dat$yield <- dat$yield/1000 dat <- transform(dat, rep=factor(rep), year=as.factor(as.character(year))) dat$locyr = paste(dat$loc, dat$year, sep=\"\") # The 'means' of reps datm <- aggregate(yield~gen+year+loc+locyr, data=dat, FUN=mean) datm <- datm[order(datm$gen),] datm$gen <- as.character(datm$gen) datm$gen <- factor(datm$gen, levels=c(\"Bienvenu\",\"Bridger\",\"Cascade\", \"Dwarf\",\"Glacier\",\"Jet\")) dat$locyr <- reorder(dat$locyr, dat$yield, mean) libs(lattice) # This picture tells most of the story dotplot(loc~yield|gen,group=year,data=dat, auto.key=list(columns=3), par.settings=list(superpose.symbol=list(pch = c('7','8','9'))), main=\"shafii.rapeseed\",ylab=\"Location\") # AMMI biplot. Remove gen and locyr effects. m1.lm <- lm(yield ~ gen + locyr, data=datm) datm$res <- resid(m1.lm) # Convert to a matrix libs(reshape2) dm <- melt(datm, measure.var='res', id.var=c('gen', 'locyr')) dmat <- acast(dm, gen~locyr) # AMMI biplot. Figure 1 of Shafii (1998) biplot(prcomp(dmat), main=\"shafii.rapeseed - AMMI biplot\")"},{"path":"/reference/sharma.met.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial — sharma.met","title":"Multi-environment trial — sharma.met","text":"Multi-environment trial","code":""},{"path":"/reference/sharma.met.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial — sharma.met","text":"","code":"data(\"sharma.met\")"},{"path":"/reference/sharma.met.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial — sharma.met","text":"data frame 126 observations following 5 variables. gen genotype loc location year year rep replicate yield yield","code":""},{"path":"/reference/sharma.met.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial — sharma.met","text":"Yield 7 genotypes, 3 years, 2 locations per year, 3 replicates. Might simulated data.","code":""},{"path":"/reference/sharma.met.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial — sharma.met","text":"Jawahar R. Sharma. 1988. Statistical Biometrical Techniques Plant Breeding. New Age International Publishers.","code":""},{"path":"/reference/sharma.met.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial — sharma.met","text":"Andrea Onofri, 2020. Fitting complex mixed models nlme: Example #5. https://www.statforbiology.com/2020/stat_met_jointreg/","code":""},{"path":"/reference/sharma.met.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial — sharma.met","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(sharma.met) dat <- sharma.met dat$env = paste0(dat$year, dat$loc) # Define environment # Calculate environment index as loc mean - overall mean --- libs(dplyr) dat <- group_by(dat, env) dat <- mutate(dat, eix = mean(yield)-mean(dat$yield)) libs(nlme) ## Finlay-Wilkinson model plot-level model --- m1fw <- lme(yield ~ gen/eix - 1, random = list(env = pdIdent(~ gen - 1), env = pdIdent(~ rep - 1)), data=dat) summary(m1fw)$tTable # Match Sharma table 9.6 VarCorr(m1fw) ## Eberhart-Russell plot-level model --- # Use pdDiag to get variance for each genotype m1er <- lme(yield ~ gen/eix - 1, random = list(env = pdDiag(~ gen - 1), env = pdIdent(~ rep - 1)), data=dat) summary(m1er)$tTable # same as FW VarCorr(m1er) # genotype variances differ # Calculate GxE cell means and environment index --- dat2 <- group_by(dat, gen, env) dat2 <- summarize(dat2, yield=mean(yield)) dat2 <- group_by(dat2, env) dat2 <- mutate(dat2, eix=mean(yield)-mean(dat2$yield)) ## Finlay-Wilkinson cell-means model --- m2fw <- lm(yield ~ gen/eix - 1, data=dat2) summary(m2fw) ## Eberhart-Russell cell-means model --- # Note, using varIdent(form=~1) is same as FW model m2er <- gls(yield ~ gen/eix - 1, weights=varIdent(form=~1|gen), data=dat) summary(m2er)$tTable sigma <- summary(m2er)$sigma sigma2i <- (c(1, coef(m2er$modelStruct$varStruct, uncons = FALSE)) * sigma)^2 names(sigma2i)[1] <- \"A\" sigma2i # shifted from m1er because variation from reps was swept out } # }"},{"path":"/reference/shaw.oats.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of oats in India — shaw.oats","title":"Multi-environment trial of oats in India — shaw.oats","text":"Multi-environment trial oats India, 13 genotypes, 3 year, 2 loc, 5 reps","code":""},{"path":"/reference/shaw.oats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of oats in India — shaw.oats","text":"","code":"data(\"shaw.oats\")"},{"path":"/reference/shaw.oats.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of oats in India — shaw.oats","text":"data frame 390 observations following 5 variables. env environment, 2 levels year year, 3 levels block block, 5 levels gen genotype variety, 13 levels yield yield oats, pounds per plot","code":""},{"path":"/reference/shaw.oats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of oats in India — shaw.oats","text":"oat trial India 11 hybrid oats compared 2 established high-yielding varieties, labeled L M. trail conducted 2 locations. size exact locations plots varied year year. Pusa, crop grown without irrigation. Karnal crop given 2-3 irrigations. Five blocks used, plot 1000 square feet. 1932, variety L high-yielding Pusa, low-yielding Karnal. Shaw used data illustrate ANOVA multi-environment trial.","code":""},{"path":"/reference/shaw.oats.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of oats in India — shaw.oats","text":"F.J.F. Shaw (1936). Handbook Statistics Use Plant Breeding Agricultural Problems. Imperial Council Agricultural Research, India. https://archive.org/details/HandbookStatistics1936/page/n12 P. 126","code":""},{"path":"/reference/shaw.oats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of oats in India — shaw.oats","text":"None","code":""},{"path":"/reference/shaw.oats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of oats in India — shaw.oats","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(shaw.oats) dat <- shaw.oats # sum(dat$yield) # 16309 matches Shaw p. 125 # sum( (dat$yield-mean(dat$yield)) ^2) # total SS matches Shaw p. 141 dat$year <- factor(dat$year) libs(lattice) dotplot(yield ~ gen|env, data=dat, groups=year, main=\"shaw.oats\", par.settings=list(superpose.symbol=list(pch=c('2','3','4'))), panel=function(x,y,...){ panel.dotplot(x,y,...) panel.superpose(x,y,..., panel.groups=function(x,y,col.line,...) { dd<-aggregate(y~x,data.frame(x,y),mean) panel.xyplot(x=dd$x, y=dd$y, col=col.line, type=\"l\") })}, auto.key=TRUE) # Shaw & Bose meticulously calculate the ANOVA table, p. 141 m1 <- aov(yield ~ year*env*block*gen - year:env:block:gen, dat) anova(m1) } # }"},{"path":"/reference/siao.cotton.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of cotton in China — siao.cotton.uniformity","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"Uniformity trials cotton China","code":""},{"path":"/reference/siao.cotton.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"","code":"data(\"siao.cotton.uniformity\")"},{"path":"/reference/siao.cotton.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"data frame 858 observations following 4 variables. row row ordinate col column ordinate yield yield, catties per mou crop crop trial number","code":""},{"path":"/reference/siao.cotton.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"1930 test blank test carried Provincial Cotton Station Yuyao, Chekiang, China. 200 rows, 24 feet long, 1 foot apart, planted single series. Seed sown drills, thinned 8 inches plant--plant, 30 plants one row. Appendix Table , Actual yield 200 rows 1930 test. 1931 test piece land, culture, fertilization previous year. Yields much lower due weather. Appendix Table II, Actual yield 200 rows 1931 test. 1931 test B 24 long ridges cotton. ridge 3 rows 1.2 feet apart (rows 3.6 feet wide). ridge cut 12 sections 16.66 feet long plants one foot apart. Siao notes yield border plots lower inner plots. correlation yield number plants plot .09. Appendix Table III, Actual yield 264 rows 1931 test (12 col, 22 row). 1932 test Another 200 rows 24 feet long planted cultural practice 1930 test. Weather unfavorable. Appendix Table IV, Actual yield 194 rows 1932 test. \"catty\" 1.33 pounds (Love & Reisner). \"mou\" 1/6 acre (Siao page 12). See also \"Cornell-Nanking Story\" Love & Reisner tangential information.","code":""},{"path":"/reference/siao.cotton.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"Siao, Fu. field plot technic study cotton. Found : Harry H. Love papers, 1907-1964. Box 3, folder 34, Cotton - Plot Technic Study 1930-1932. https://rmc.library.cornell.edu/EAD/htmldocs/RMA00890.html","code":""},{"path":"/reference/siao.cotton.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of cotton in China — siao.cotton.uniformity","text":"Siao, Fu (1935). Uniformity trials cotton, J. Amer. Soc. Agron., 27, 974-979 https://doi.org/10.2134/agronj1935.00021962002700120004x","code":""},{"path":[]},{"path":"/reference/silva.cotton.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of cotton bolls for different levels of defoliation. — silva.cotton","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"Number cotton bolls, nodes, plant height, plant weight different levels defoliation.","code":""},{"path":"/reference/silva.cotton.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"","code":"data(\"silva.cotton\")"},{"path":"/reference/silva.cotton.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"data frame 125 observations following 4 variables. stage growth stage defoliation level defoliation, 0, 25, 50, 75, 100 plant plant number rep replicate reproductive number reproductive structures bolls number bolls height plant height nodes number nodes weight weight bolls","code":""},{"path":"/reference/silva.cotton.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"Data come greenhouse experiment cotton plants. Completely randomized design 5 replicates, 2 plants per pot. Artificial defoliation used levels 0, 25, 50, 75, 100 percent. Data collected per plant five growth stages: vegetative, flower-bud, blossom, fig cotton boll. primary response variable number bolls. data counts, underdispersed, correlated. Zeviana et al. used data compared Poisson, Gamma-count, quasi-Poisson GLMs. Bonat & Zeviani used data fit multivariate correlated generalized linear model. Used permission Walmes Zeviani. Electronic version : https://www.leg.ufpr.br/~walmes/data/desfolha_algodao.txt","code":""},{"path":"/reference/silva.cotton.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"Silva, Anderson Miguel da; Degrande, Paulo Eduardo; Suekane, Renato; Fernandes, Marcos Gino; & Zeviani, Walmes Marques. (2012). Impacto de diferentes niveis de desfolha artificial nos estadios fenologicos algodoeiro. Revista de Ciencias Agrarias, 35(1), 163-172. https://www.scielo.mec.pt/scielo.php?script=sci_arttext&pid=S0871-018X2012000100016&lng=pt&tlng=pt.","code":""},{"path":"/reference/silva.cotton.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"Zeviani, W. M., Ribeiro, P. J., Bonat, W. H., Shimakura, S. E., Muniz, J. . (2014). Gamma-count distribution analysis experimental underdispersed data. Journal Applied Statistics, 41(12), 1-11. https://doi.org/10.1080/02664763.2014.922168 Online supplement: https://leg.ufpr.br/doku.php/publications:papercompanions:zeviani-jas2014 Regression Models Count Data. https://cursos.leg.ufpr.br/rmcd/applications.html#cotton-bolls Wagner Hugo Bonat & Walmes Marques Zeviani (2017). Multivariate Covariance Generalized Linear Models Analysis Experimental Data. Short-cource : 62nd RBras 17th SEAGRO meeting/ https://github.com/leg-ufpr/mcglm4aed","code":""},{"path":"/reference/silva.cotton.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Number of cotton bolls for different levels of defoliation. — silva.cotton","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(silva.cotton) dat <- silva.cotton dat$stage <- ordered(dat$stage, levels=c(\"vegetative\",\"flowerbud\",\"blossom\",\"boll\",\"bollopen\")) # make stage a numeric factors dat <- transform(dat, stage = factor(stage, levels = unique(stage), labels = 1:nlevels(stage))) # sum data across plants, 1 pot = 2 plants dat <- aggregate(cbind(weight,height,bolls,nodes) ~ stage+defoliation+rep, data=dat, FUN=sum) # all traits, plant-level data libs(latticeExtra) foo <- xyplot(weight + height + bolls + nodes ~ defoliation | stage, data = dat, outer=TRUE, xlab=\"Defoliation percent\", ylab=\"\", main=\"silva.cotton\", as.table = TRUE, jitter.x = TRUE, type = c(\"p\", \"smooth\"), scales = list(y = \"free\")) combineLimits(useOuterStrips(foo)) if(0){ # poisson glm with quadratic effect for defoliation m0 <- glm(bolls ~ 1, data=dat, family=poisson) m1 <- glm(bolls ~ defoliation+I(defoliation^2), data=dat, family=poisson) m2 <- glm(bolls ~ stage:defoliation+I(defoliation^2), data=dat, family=poisson) m3 <- glm(bolls ~ stage:(defoliation+I(defoliation^2)), data=dat, family=poisson) par(mfrow=c(2,2)); plot(m3); layout(1) anova(m0, m1, m2, m3, test=\"Chisq\") # predicted values preddat <- expand.grid(stage=levels(dat$stage), defoliation=seq(0,100,length=20)) preddat$pred <- predict(m3, newdata=preddat, type=\"response\") # Zeviani figure 3 libs(latticeExtra) xyplot(bolls ~ jitter(defoliation)|stage, dat, as.table=TRUE, main=\"silva.cotton - observed and model predictions\", xlab=\"Defoliation percent\", ylab=\"Number of bolls\") + xyplot(pred ~ defoliation|stage, data=preddat, as.table=TRUE, type='smooth', col=\"black\", lwd=2) } if(0){ # ----- mcglm ----- dat <- transform(dat, deffac=factor(defoliation)) libs(car) vars <- c(\"weight\",\"height\",\"bolls\",\"nodes\") splom(~dat[vars], data=dat, groups = stage, auto.key = list(title = \"Growth stage\", cex.title = 1, columns = 3), par.settings = list(superpose.symbol = list(pch = 4)), as.matrix = TRUE) splom(~dat[vars], data=dat, groups = defoliation, auto.key = list(title = \"Artificial defoliation\", cex.title = 1, columns = 3), as.matrix = TRUE) # multivariate linear model. m1 <- lm(cbind(weight, height, bolls, nodes) ~ stage * deffac, data = dat) anova(m1) summary.aov(m1) r0 <- residuals(m1) # Checking the models assumptions on the residuals. car::scatterplotMatrix(r0, gap = 0, smooth = FALSE, reg.line = FALSE, ellipse = TRUE, diagonal = \"qqplot\") } } # }"},{"path":"/reference/sinclair.clover.html","id":null,"dir":"Reference","previous_headings":"","what":"Clover yields in a factorial fertilizer experiment — sinclair.clover","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"Clover yields factorial fertilizer experiment","code":""},{"path":"/reference/sinclair.clover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"","code":"data(\"sinclair.clover\")"},{"path":"/reference/sinclair.clover.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"data frame 25 observations following 3 variables. yield yield t/ha P phosphorous fertilizer kg/ha S sulfur fertilizer kg/ha","code":""},{"path":"/reference/sinclair.clover.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"phosphorous sulfur factorial experiment Dipton Southland, New Zealand. 3 reps. Plots harvested repeatedly Dec 1992 Mar 1994. Yields reported total dry matter across cuttings.","code":""},{"path":"/reference/sinclair.clover.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"Sinclair AG, Risk WH, Smith LC, Morrison JD & Dodds KG (1994) Sulphur phosphorus balanced pasture nutrition. Proc N Z Grass Assoc, 56, 13-16.","code":""},{"path":"/reference/sinclair.clover.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"Dodds, KG Sinclair, AG Morrison, JD. (1995). bivariate response surface growth data. Fertilizer research, 45, 117-122. https://doi.org/10.1007/BF00790661","code":""},{"path":"/reference/sinclair.clover.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clover yields in a factorial fertilizer experiment — sinclair.clover","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(sinclair.clover) dat <- sinclair.clover libs(lattice) xyplot(yield~P|factor(S), dat, layout=c(5,1), main=\"sinclair.clover - Yield by sulfur levels\", xlab=\"Phosphorous\") # Dodds fits a two-dimensional Mitscherlich-like model: # z = a*(1+b*{(s+t*x)/(x+1)}^y) * (1+d*{(th+r*y)/(y+1)}^x) # First, re-scale the problem to a more stable part of the parameter space dat <- transform(dat, x=P/10, y=S/10) # Response value for (x=0, y=maximal), (x=maximal, y=0), (x=max, y=max) z0m <- 5 zm0 <- 5 zmm <- 10.5 # The parameters are somewhat sensitive to starting values. # I had to try a couple different initial values to match the paper by Dodds m1 <- nls(yield ~ alpha*(1 + beta*{(sig+tau*x)/(x+1)}^y) * (1 + del*{(th+rho*y)/(y+1)}^x), data=dat, # trace=TRUE, start=list(alpha=zmm, beta=(zm0/zmm)-1, del=(z0m/zmm)-1, sig=.51, tau=.6, th=.5, rho=.7)) summary(m1) # Match Dodds Table 2 ## Parameters: ## Estimate Std. Error t value Pr(>|t|) ## alpha 11.15148 0.66484 16.773 1.96e-12 *** ## beta -0.61223 0.03759 -16.286 3.23e-12 *** ## del -0.48781 0.04046 -12.057 4.68e-10 *** ## sig 0.26783 0.16985 1.577 0.13224 ## tau 0.68030 0.06333 10.741 2.94e-09 *** ## th 0.59656 0.16716 3.569 0.00219 ** ## rho 0.83273 0.06204 13.421 8.16e-11 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Residual standard error: 0.5298 on 18 degrees of freedom pred <- expand.grid(x=0:17, y=0:9) pred$z <- predict(m1, pred) # 3D plot of data with fitted surface. Matches Dodds figure 2. libs(rgl) bg3d(color = \"white\") clear3d() spheres3d(dat$x, dat$y, dat$yield, radius=.2, col = rep(\"navy\", nrow(dat))) surface3d(seq(0, 17, by = 1), seq(0, 9, by = 1), pred$z, alpha=0.9, col=\"wheat\", front=\"fill\", back=\"fill\") axes3d() title3d(\"sinclair.clover - yield\",\"\", xlab=\"Phosphorous/10\", ylab=\"Sulfur/10\", zlab=\"\", line=3, cex=1.5) view3d(userMatrix=matrix(c(.7,.2,-.7,0, -.7,.2,-.6,0, 0,.9,.3,0, 0,0,0,1),ncol=4)) # snapshot3d(file, \"png\") close3d() } # }"},{"path":"/reference/smith.beans.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"Uniformity trials beans California, 1954-1955, 2 species 2 years","code":""},{"path":"/reference/smith.beans.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"","code":"data(\"smith.beans.uniformity\")"},{"path":"/reference/smith.beans.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"data frame 912 observations following 4 variables. expt experiment row row col column yield yield, kg","code":""},{"path":"/reference/smith.beans.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"Trials conducted California. 1955 plots twice wide twice long 1954. Red Kidney bush variety bean, Standard Pink viny variety. Smith randomly assigned ,B,C,D plots used 'varieties' calculating ANOVA tables. Plots combined side--side end--end make larger plots. Decreasing LSDs observed increases plot sizes. LSDs seldom 200, considered noticeable difference farmers. four datasets: —– 1954 Experiment 1: Red Kidney. 1954 Experiment 2: Standard Pink Field width: 18 plots * 30 inches = 45 ft Field length: 12 plots * 15 ft = 180 ft —– 1955 Experiment 3: Red Kidney. 1955 Experiment 4: Standard Pink Field width: 16 plots * 2 rows * 30 = 80 ft Field length: 15 plots * 30 ft = 450 ft","code":""},{"path":"/reference/smith.beans.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"Francis L. Smith, 1958. Effects plot size, plot shape, number replications efficacy bean yield trials. Hilgardia, 28, 43-63. https://doi.org/10.3733/hilg.v28n02p043","code":""},{"path":"/reference/smith.beans.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"None.","code":""},{"path":"/reference/smith.beans.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trials of beans, 2 species in 2 years — smith.beans.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(smith.beans.uniformity) dat1 <- subset(smith.beans.uniformity, expt==\"E1\") dat2 <- subset(smith.beans.uniformity, expt==\"E2\") dat3 <- subset(smith.beans.uniformity, expt==\"E3\") dat4 <- subset(smith.beans.uniformity, expt==\"E4\") cv <- function(x) { sd(x)/mean(x) } cv(dat1$yield) cv(dat2$yield) # Does not match Smith. Checked all values by hand. cv(dat3$yield) cv(dat4$yield) libs(\"desplot\") desplot(dat1, yield ~ col*row, aspect=180/45, flip=TRUE, # true aspect main=\"smith.beans.uniformity, expt 1 (true aspect)\") desplot(dat2, yield ~ col*row, aspect=180/45, flip=TRUE, # true aspect main=\"smith.beans.uniformity, expt 2 (true aspect)\") desplot(dat3, yield ~ col*row, aspect=450/80, flip=TRUE, # true aspect main=\"smith.beans.uniformity, expt 3 (true aspect)\") desplot(dat4, yield ~ col*row, aspect=450/80, flip=TRUE, # true aspect main=\"smith.beans.uniformity expt 4, (true aspect)\") } # }"},{"path":"/reference/smith.corn.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","title":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","text":"Uniformity trial corn, 3 years ground, 1895-1897, Illinois.","code":""},{"path":"/reference/smith.corn.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","text":"data frame 360 observations following 5 variables. row row col column plot plot number, consistent across years year year. Last two digits 1895, 1896, 1897 yield yield, bushels / acre","code":""},{"path":"/reference/smith.corn.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","text":"Data come Illinois Experiment Station. data values Smith (1910) field map Harris (1920). plot 1/10 acre, dimensions given. Note 1/10 acre also area square 1 chain (66 feet) side. following text abridged Smith (1910). much variability may reasonably expect land apparently uniform? data among records soil plots Illinois Experiment station furnish interesting material study connection. field lain sixteen years pasture broken 1895 laid plots subsequently used soil experiments. land slightly rolling otherwise quite uniform appearance. series considered connection 120 one-tenth acre plots. plots planted corn three consecutive years without soil treatment, records offer rather exceptional opportunity study kind. study data reveals striking variations. noticed first place tremendous difference production different years. first year, 1895, extremely unfavorable one corn yields exceptionally low. weather records show season unusually dry, also cool early part. following year exceptionally favorable corn season, yields run unusually high. third year also good one, yields perhaps somewhat normal locality. observed certain plots appear abnormal. Thus plots 117, 118, 119, 120 give abnormally high yield first season abnormally low one two following years. accounted topography land. plots lie low spot favorable dry year 1895, unfavorable 1896 1897. reason four plots rejected consideration study, also plots 616, 617, 618, 619, 620. leaves 111 plots whose variations apparently unaccounted furnish data following results taken. noticeable variability measured standard deviation becomes less succeeding year. suggests question whether continued cropping might tend induce uniformity. records plots continued corn three years longer, however, support conclusion. seems reasonable expect greater variability seasons unfavorable production, 1895, much may depend upon certain critical factors production coming play suggestion may explanation high standard deviation first year. Results extending longer series years extremely interesting connection. consider total range variation single year, find differences follows: Plots lying adjoining shown following maximum variations: 18 bushels 1895; 11 bushels 1896; 8 bushels 1897. results give us conception unaccountable plot variations deal field tests. possibility remains still closer study might detect abnormal factors play account variations certain cases, study certainly suggests importance conservatism arriving conclusions based upon plot tests. particular value writer derived study strengthening conviction dependence placed upon variety tests field experiments records involving average liberal numbers extending long periods time.","code":""},{"path":"/reference/smith.corn.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","text":"Smith, L.H. 1910. Plot arrangement variety experiments corn. Agronomy Journal, 1, 84–89. Table 1. https://books.google.com/books?id=mQT0AAAAMAAJ&pg=PA84 Harris, J.. 1920. Practical universality field heterogeneity factor influencing plot yields. Journal Agricultural Research, 19, 279–314. Page 296-297. https://books.google.com/books?id=jyEXAAAAYAAJ&pg=PA279","code":""},{"path":"/reference/smith.corn.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of corn, 3 years on same ground — smith.corn.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(smith.corn.uniformity) dat <- smith.corn.uniformity dat = transform(dat, year=factor(year)) libs(desplot) desplot(dat, yield~col*row|year, layout=c(2,2), aspect=1, main=\"smith.corn.uniformity: yield across years 1895-1987\") ## # Outliers are obvious ## libs(lattice) ## xyplot(yield~row|factor(col), dat, groups=year, ## auto.key=list(columns=3), main=\"smith.corn.uniformity\") libs(rgl) # A few odd pairs of outliers in column 6 # black/gray dots very close to each other plot3d(dat$col, dat$row, dat$yield, col=dat$year, xlab=\"col\",ylab=\"row\",zlab=\"yield\") close3d() } # }"},{"path":"/reference/smith.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of wheat — smith.wheat.uniformity","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"Uniformity trial wheat Australia.","code":""},{"path":"/reference/smith.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"","code":"data(\"smith.wheat.uniformity\")"},{"path":"/reference/smith.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"data frame 1080 observations following 4 variables. row row ordinate col column ordinate yield grain yield per plot, grams ears number ears per plot","code":""},{"path":"/reference/smith.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"Experiment grown Canberra, Australia, 1934. data yield grain per plot number \"ears\". plot 1 foot long 0.5 foot. Field width: 36 columns x 1 foot = 36 feet. Field length: 30 rows x 0.5 foot = 15 feet. Notes: 2 copies yield data Rothamsted library. Let Copy one dark, hand-drawn grid lines, Copy B one without hand-drawn grid lines. copies hand-written, likely copied original data. row 4 (top) column 34: Copy yield 164 Copy B yield 154. value 154 appears correct, since leads row column totals shown Copy Copy B. row 20, column 28, Copy Copy B show yield 283. appears copy error. replaced value 283 203, row column totals match values Copy Copy B, also variance data matches value Smith (1938), 2201 page 7. documents Rothamsted claim grain yield shown \"Yields grain decigrams per foot length\". However, believe actual unit weight grams. Note yield values high-yielding parts field close 200 g per plot, plot 0.5 sq feet. Multiply 8 get 1600 g per 4 sq feet. Smith's paper, fertility contour map figure 1 shows high-yielding part field yield close \"16 d.kg per 4 sq ft\", 16 d.kg = 16 kg = 1600 g. data made available special help staff Rothamsted Research Library.","code":""},{"path":"/reference/smith.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"Rothamsted Research Library, Box STATS17 WG Cochran, Folder 7.","code":""},{"path":"/reference/smith.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"H. Fairfield Smith (1938). empirical law describing heterogeneity yields agricultural crops. Journal Agricultural Science, volume 28, Issue 1, January 1938, pp. 1 - 23. https://doi.org/10.1017/S0021859600050516 Peter McCullagh & David Clifford. (2006). Evidence conformal invariance crop yields. Proc. R. Soc. (2006) 462, 2119–2143 http://www.stat.uchicago.edu/~pmcc/reml/ https://doi.org/:10.1098/rspa.2006.1667","code":""},{"path":"/reference/smith.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of wheat — smith.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(smith.wheat.uniformity) dat <- smith.wheat.uniformity libs(desplot) desplot(dat, yield ~ col*row, main=\"smith.wheat.uniformity\", flip=TRUE, aspect=15/30) xyplot(yield ~ ears, data=dat) libs(agricolae,reshape2) # Compare to Smith Fig. 2 m1 <- index.smith(acast(dat, row~col, value.var='yield'), main=\"smith.wheat.uniformity\", col=\"red\")$uni m1 # Compare to Smith table I } # }"},{"path":"/reference/snedecor.asparagus.html","id":null,"dir":"Reference","previous_headings":"","what":"Asparagus yields for different cutting treatments — snedecor.asparagus","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"Asparagus yields different cutting treatments, 4 years.","code":""},{"path":"/reference/snedecor.asparagus.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"data frame 64 observations following 4 variables. block block factor, 4 levels year year, numeric trt treatment factor final cutting date yield yield, ounces","code":""},{"path":"/reference/snedecor.asparagus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"Planted 1927. Cutting began 1929. Yield weight asparagus cuttings Jun 1 plot. plots received continued cuttings Jun 15, Jul 1, Jul 15. past, repeated-measurement experiments like sometimes analyzed split-plot experiment. violates indpendence assumptions.","code":""},{"path":"/reference/snedecor.asparagus.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"Snedecor Cochran, 1989. Statistical Methods.","code":""},{"path":"/reference/snedecor.asparagus.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"Mick O'Neill, 2010. Guide Linear Mixed Models Experimental Design Context. Statistical Advisory & Training Service Pty Ltd.","code":""},{"path":"/reference/snedecor.asparagus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asparagus yields for different cutting treatments — snedecor.asparagus","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(snedecor.asparagus) dat <- snedecor.asparagus dat <- transform(dat, year=factor(year)) dat$trt <- factor(dat$trt, levels=c(\"Jun-01\", \"Jun-15\", \"Jul-01\", \"Jul-15\")) # Continued cutting reduces plant vigor and yield libs(lattice) dotplot(yield ~ trt|year, data=dat, xlab=\"Cutting treatment\", main=\"snedecor.asparagus\") # Split-plot if(0){ libs(lme4) m1 <- lmer(yield ~ trt + year + trt:year + (1|block) + (1|block:trt), data=dat) } # ---------- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # Split-plot with asreml m2 <- asreml(yield ~ trt + year + trt:year, data=dat, random = ~ block + block:trt) lucid::vc(m2) ## effect component std.error z.ratio bound ## block 354.3 405 0.87 P 0.1 ## block:trt 462.8 256.9 1.8 P 0 ## units!R 404.7 82.6 4.9 P 0 ## # Antedependence with asreml. See O'Neill (2010). dat <- dat[order(dat$block, dat$trt), ] m3 <- asreml(yield ~ year * trt, data=dat, random = ~ block, residual = ~ block:trt:ante(year,1), max=50) m3 <- update(m3) m3 <- update(m3) ## # Extract the covariance matrix for years and convert to correlation ## covmat <- diag(4) ## covmat[upper.tri(covmat,diag=TRUE)] <- m3$R.param$`block:trt:year`$year$initial ## covmat[lower.tri(covmat)] <- t(covmat)[lower.tri(covmat)] ## round(cov2cor(covmat),2) # correlation among the 4 years ## # [,1] [,2] [,3] [,4] ## # [1,] 1.00 0.45 0.39 0.31 ## # [2,] 0.45 1.00 0.86 0.69 ## # [3,] 0.39 0.86 1.00 0.80 ## # [4,] 0.31 0.69 0.80 1.00 ## # We can also build the covariance Sigma by hand from the estimated ## # variance components via: Sigma^-1 = U D^-1 U' ## vv <- vc(m3) ## print(vv) ## ## effect component std.error z.ratio constr ## ## block!block.var 86.56 156.9 0.55 pos ## ## R!variance 1 NA NA fix ## ## R!year.1930:1930 0.00233 0.00106 2.2 uncon ## ## R!year.1931:1930 -0.7169 0.4528 -1.6 uncon ## ## R!year.1931:1931 0.00116 0.00048 2.4 uncon ## ## R!year.1932:1931 -1.139 0.1962 -5.8 uncon ## ## R!year.1932:1932 0.00208 0.00085 2.4 uncon ## ## R!year.1933:1932 -0.6782 0.1555 -4.4 uncon ## ## R!year.1933:1933 0.00201 0.00083 2.4 uncon ## U <- diag(4) ## U[1,2] <- vv[4,2] ; U[2,3] <- vv[6,2] ; U[3,4] <- vv[8,2] ## Dinv <- diag(c(vv[3,2], vv[5,2], vv[7,2], vv[9,2])) ## # solve(U ## solve(crossprod(t(U), tcrossprod(Dinv, U)) ) ## ## [,1] [,2] [,3] [,4] ## ## [1,] 428.4310 307.1478 349.8152 237.2453 ## ## [2,] 307.1478 1083.9717 1234.5516 837.2751 ## ## [3,] 349.8152 1234.5516 1886.5150 1279.4378 ## ## [4,] 237.2453 837.2751 1279.4378 1364.8446 } } # }"},{"path":"/reference/snijders.fusarium.html","id":null,"dir":"Reference","previous_headings":"","what":"Fusarium infection in wheat varieties — snijders.fusarium","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"Infection wheat different strains Fusarium.","code":""},{"path":"/reference/snijders.fusarium.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"data frame 204 observations following 4 variables. gen wheat genotype strain fusarium strain year year y percent infected","code":""},{"path":"/reference/snijders.fusarium.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"data percent leaf area affected Fusarium head blight, averaged 4-5 reps, 17 winter wheat genotypes. Van Eeuwijk fit generalized ammi-2 model data. generalized model sense link function used, non-linear AMMI model main effects variety year-strain, additional multiplicative effects interactions. Note, value strain F348 1988, gen SVP75059-32 28.3 (shown VanEeuwijk 1995) 38.3 (shown Snijders 1991). Used permission Fred van Eeuwijk.","code":""},{"path":"/reference/snijders.fusarium.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"Snijders, CHA Van Eeuwijk, FA. 1991. Genotype x strain interactions resistance Fusarium head blight caused Fusarium culmorum winter wheat. Theoretical Applied Genetics, 81, 239–244. Table 1. https://doi.org/10.1007/BF00215729","code":""},{"path":"/reference/snijders.fusarium.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"Fred van Eeuwijk. 1995. Multiplicative interaction generalized linear models. Biometrics, 51, 1017-1032. https://doi.org/10.2307/2533001","code":""},{"path":"/reference/snijders.fusarium.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fusarium infection in wheat varieties — snijders.fusarium","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(snijders.fusarium) dat <- snijders.fusarium aggregate(y ~ strain + year, dat, FUN=mean) # Match means in Snijders table 1 dat <- transform(dat, y=y/100, year=factor(year), yrstr=factor(paste0(year,\"-\",strain))) # Strain F329 shows little variation across years. F39 shows a lot. libs(lattice) dotplot(gen~y|strain, data=dat, group=year, main=\"snijders.fusarium : infection by strain\", xlab=\"Fraction infected\", ylab=\"variety\", auto.key=list(columns=3)) # Logit transform dat <- transform(dat, logit=log(y/(1-y))) m1 <- aov(logit ~ yrstr + gen, data=dat) # Match SS in VanEeuwijk table 4 anova(m1) # Match SS in VanEeuwijk table 4 m2 <- aov(logit ~ year*strain + gen + gen:year + gen:strain, data=dat) anova(m2) # Match to VanEeuwijk table 5 # GLM on untransformed data using logit link, variance mu^2(1-mu)^2 libs(gnm) # for 'wedderburn' family m2 <- glm(y ~ yrstr + gen, data=dat, family=\"wedderburn\") anova(m2) # Main effects match VanEeuwijk table 6 # Generalized AMMI-2 model. Matches VanEeuwijk table 6 bilin2 <- gnm(y ~ yrstr + gen + instances(Mult(yrstr, gen), 2), data=dat, family = wedderburn) # plot(bilin2,1) # Resid vs fitted plot matches VanEeuwijk figure 3c ## anova(bilin2) ## Df Deviance Resid. Df Resid. Dev ## NULL 203 369.44 ## yrstr 11 150.847 192 218.60 ## gen 16 145.266 176 73.33 ## Mult(yrstr, gen, inst = 1) 26 26.128 150 47.20 ## Mult(yrstr, gen, inst = 2) 24 19.485 126 27.72 # Manually extract coordinates for biplot cof <- coef(bilin2) y1 <- cof[29:40] g1 <- cof[41:57] y2 <- cof[58:69] g2 <- cof[70:86] g12 <- cbind(g1,g2) rownames(g12) <- substring(rownames(g12), 29) y12 <- cbind(y1,y2) rownames(y12) <- substring(rownames(y12), 31) g12[,1] <- -1 * g12[,1] y12[,1] <- -1 * y12[,1] # GAMMI biplot. Inner-products of points projected onto # arrows match VanEeuwijk figure 4. Slight rotation of graph is ignorable. biplot(y12, g12, cex=.75, main=\"snijders.fusarium\") # Arrows to genotypes. } # }"},{"path":"/reference/stephens.sorghum.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","title":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","text":"Uniformity trial sorghum silage Chillicothe, Texas, 1915.","code":""},{"path":"/reference/stephens.sorghum.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","text":"data frame 2000 observations following 3 variables. row row col column / rod yield yield, ounces","code":""},{"path":"/reference/stephens.sorghum.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","text":"Grown near Chillicothe, TX 1915. Rows 40 inches apart. row harvested 1-rod (16.5 ft) lengths. East side higher yielding west side. Yields weight (ounces) green forage rod-row. Total area harvested: 100*40/12 = 333.33 feet 20*16.5=330 feet. Field width: 20 plots * 16.5 ft (1 rod) = 330 feet. Field length: 100 plots * 40 = 333 feet","code":""},{"path":"/reference/stephens.sorghum.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","text":"Stephens, Joseph C. 1928. Experimental methods probable error field experiments sorghum. Journal Agricultural Research, 37, 629–646. https://naldc.nal.usda.gov/catalog/IND43967516","code":""},{"path":"/reference/stephens.sorghum.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sorghum silage — stephens.sorghum.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(stephens.sorghum.uniformity) dat <- stephens.sorghum.uniformity dat <- subset(dat, row>2 & row<99) # omit outer two rows # mean(dat$yield) # 180.27 # range(dat$yield) # 75,302 matches Stephens # densityplot(~dat$yield) # Stephens figure 3 # Aggregate 4 side-by-side rows. d4 <- dat d4$row2 <- ceiling((d4$row-2)/4) d4 <- aggregate(yield ~ row2+col, data=d4, FUN=sum) d4$row2 <- 25-d4$row2 # flip horizontally libs(desplot) grays <- colorRampPalette(c(\"#d9d9d9\",\"#252525\")) desplot(d4, yield ~ row2*col, aspect=333/330, flip=TRUE, # true aspect main=\"stephens.sorghum.uniformity\", col.regions=grays(3), at=c(500,680,780,1000)) # Similar to Stephens Figure 7. North at top. East at right. } # }"},{"path":"/reference/steptoe.morex.pheno.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"Phenotypic genotypic data barley population Steptoe x Morex. 150 doubled haploid crosses, evaluated 223 markers. Phenotypic data wascollected 8 traits 16 environments.","code":""},{"path":"/reference/steptoe.morex.pheno.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"","code":"data(\"steptoe.morex.pheno\")"},{"path":"/reference/steptoe.morex.pheno.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"steptoe.morex.pheno data.frame phenotypic data 2432 observations 10 variables: gen genotype factor parents Steptoe Morex, 150 crosses SM1, SM2, ..., SM200. 200 numbers used. env environment, 16 levels amylase alpha amylase (20 Deg Units) diapow diastatic power (degree units) hddate heading date (julian days) lodging lodging (percent) malt malt extract (percent) height plant height (centimeters) protein grain protein (percent) yield grain yield (Mt/Ha) steptoe.morex.geno cross object qtl package genotypic data 223 markers 150 crosses Steptoe x Morex.","code":""},{"path":"/reference/steptoe.morex.pheno.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"described Hayes et al (1993), population 150 barley doubled haploid (DH) lines developed Oregon State University Barley Breeding Program North American Barley Genome Mapping Project. parentage population Steptoe / Morex. Steptoe dominant feed barley northwestern U.S. Morex spring U.S. malting quality standard. Seed single head parent used create F1, set 150 lines developed. Phenotypic values parents Steptoe Morex : https://wheat.pw.usda.gov/ggpages/SxM/parental_values.html 16 locations, average across locations column 17. traits collected every location. location, 150 lines included block 1, random subset 50 lines used block 2. traits : Alpha Amylase (20 Deg Units), Diastatic Power (Deg Units), Heading Date (Julian Days), Lodging (percent), Malt Extract (percent), Grain Protein (percent), Grain Yield (Mt/Ha). Phenotypic values 150 lines F1 population : https://wheat.pw.usda.gov/ggpages/SxM/phenotypes.html trait different file, block numbers represents one location. 223-markers Steptoe/Morex base map : https://wheat.pw.usda.gov/ggpages/SxM/smbasev2.map data markers 150 lines https://wheat.pw.usda.gov/ggpages/SxM/smbasev2.mrk hand-assembled (e.g. marker distances cumulated marker positions) .csv file imported R using qtl::read.cross. class manually changed c('bc','cross') c('dh','cross'). marker data coded = Steptoe, B = Morex, - = missing. pedigrees 150 lines found : https://wheat.pw.usda.gov/ggpages/SxM/pedigrees.html Data provided United States Department Agriculture.","code":""},{"path":"/reference/steptoe.morex.pheno.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"Steptoe x Morex Barley Mapping Population. Map: Version 2, August 1, 1995 https://wheat.pw.usda.gov/ggpages/SxM. Accessed Jan 2015.","code":""},{"path":"/reference/steptoe.morex.pheno.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"P.M. Hayes, B.H. Liu, S.J. Knapp, F. Chen, B. Jones, T. Blake, J. Franckowiak, D. Rasmusson, M. Sorrells, S.E. Ullrich, others. 1993. Quantitative trait locus effects environmental interaction sample North American barley germplasm. Theoretical Applied Genetics, 87, 392–401. https://doi.org/10.1007/BF01184929 Ignacio Romagosa, Steven E. Ullrich, Feng Han, Patrick M. Hayes. 1996. Use additive main effects multiplicative interaction model QTL mapping adaptation barley. Theor Appl Genet, 93, 30-37. https://doi.org/10.1007/BF00225723 Piepho, Hans-Peter. 2000. mixed-model approach mapping quantitative trait loci barley basis multiple environment data. Genetics, 156, 2043-2050. M. Malosetti, J. Voltas, . Romagosa, S.E. Ullrich, F.. van Eeuwijk. (2004). Mixed models including environmental covariables studying QTL environment interaction. Euphytica, 137, 139-145. https://doi.org/10.1023/B:EUPH.0000040511.4638","code":""},{"path":"/reference/steptoe.morex.pheno.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex — steptoe.morex.pheno","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(steptoe.morex.pheno) dat <- steptoe.morex.pheno # Visualize GxE of traits libs(lattice) redblue <- colorRampPalette(c(\"firebrick\", \"lightgray\", \"#375997\")) levelplot(amylase~env*gen, data=dat, col.regions=redblue, scales=list(x=list(rot=90)), main=\"amylase\") ## levelplot(diapow~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"diapow\") ## levelplot(hddate~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"hddate\") ## levelplot(lodging~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"lodging\") ## levelplot(malt~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"malt\") ## levelplot(height~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"height\") ## levelplot(protein~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"protein\") ## levelplot(yield~env*gen, data=dat, col.regions=redblue, ## scales=list(x=list(rot=90)), main=\"yield\") # Calculate avg yield for each loc as in Romagosa 1996, table 3 # t(t(round(tapply(dat$yield, dat$env, FUN=mean),2))) # SKo92,SKg92 means in table 3 are switched. Who is right, him or me? # Draw marker map libs(qtl) data(steptoe.morex.geno) datg <- steptoe.morex.geno qtl::plot.map(datg, main=\"steptoe.morex.geno\") qtl::plotMissing(datg) # This is a very rudimentary example. # The 'wgaim' function works interactively, but fails during # devtools::check(). if(0 & require(\"asreml\", quietly=TRUE)){ libs(asreml) # Fit a simple multi-environment mixed model m1 <- asreml(yield ~ env, data=dat, random=~gen) libs(wgaim) wgaim::linkMap(datg) # Create an interval object for wgaim dati <- wgaim::cross2int(datg, id=\"gen\") # Whole genome qtl q1 <- wgaim::wgaim(m1, intervalObj=dati, merge.by=\"gen\", na.action=na.method(x=\"include\")) #wgaim::linkMap(q1, dati) # Visualize wgaim::outStat(q1, dati) # outlier statistic summary(q1, dati) # Table of important intervals # Chrom Left Marker dist(cM) Right Marker dist(cM) Size Pvalue # 3 ABG399 52.6 BCD828 56.1 0.254 0.000 45.0 # 5 MWG912 148 ABG387A 151.2 0.092 0.001 5.9 # 6 ABC169B 64.8 CDO497 67.5 -0.089 0.001 5.6 } } # }"},{"path":"/reference/stickler.sorghum.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of sorghum — stickler.sorghum.uniformity","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"Uniformity trial sorghum Manhattan, Kansas, 1958-1959.","code":""},{"path":"/reference/stickler.sorghum.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"","code":"data(\"stickler.sorghum.uniformity\")"},{"path":"/reference/stickler.sorghum.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"data frame 1600 observations following 4 variables. expt experiment row row col col yield yield, pounds","code":""},{"path":"/reference/stickler.sorghum.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"Four sorghum experiments Agronomy Farm Manhattan, Kansas. Experiments E1,E2 grown 1958. Expts E3,E5 grown 1959. Experiment E1. Field width = 20 units * 14 inches = 23.3 ft. Field length = 20 units * 10 feet = 200 feet. Experiment E2-E3. Field width = 20 units * 40 inches = 73 feet Field length = 20 units * 5 ft = 100 feet.","code":""},{"path":"/reference/stickler.sorghum.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"F. C. Stickler (1960). Estimates Optimum Plot Size Grain Sorghum Uniformity Trial Data. Technical bulletin, Kansas Agricultural Experiment Station, page 17-20. https://babel.hathitrust.org/cgi/pt?id=uiug.30112019584322&view=1up&seq=21","code":""},{"path":"/reference/stickler.sorghum.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"None.","code":""},{"path":"/reference/stickler.sorghum.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of sorghum — stickler.sorghum.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(stickler.sorghum.uniformity) dat <- stickler.sorghum.uniformity dat1 <- subset(dat, expt==\"E1\") dat2 <- subset(dat, expt!=\"E1\") libs(desplot) desplot(dat, yield ~ col*row|expt, subset=expt==\"E1\", #cex=1,text=yield, shorten=\"none\", xlab=\"row\",ylab=\"range\", flip=TRUE, tick=TRUE, aspect=(20*10)/(20*14/12), # true aspect main=\"stickler.sorghum.uniformity: expt E1\") desplot(dat, yield ~ col*row|expt, subset=expt!=\"E1\", xlab=\"row\",ylab=\"range\", flip=TRUE, tick=TRUE, aspect=(20*5)/(20*44/12), # true aspect main=\"stickler.sorghum.uniformity: expt E2,E3,E4\") # Stickler, p. 10-11 has # E1 E2 E3 E4 # 34.81 11.53 11.97 14.10 cv <- function(x) 100*sd(x)/mean(x) tapply(dat$yield, dat$expt, cv) # 35.74653 11.55062 11.97011 14.11389 } # }"},{"path":"/reference/stirret.borers.html","id":null,"dir":"Reference","previous_headings":"","what":"Corn borer control by application of fungal spores. — stirret.borers","title":"Corn borer control by application of fungal spores. — stirret.borers","text":"Corn borer control application fungal spores.","code":""},{"path":"/reference/stirret.borers.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Corn borer control by application of fungal spores. — stirret.borers","text":"data frame 60 observations following 4 variables. block block, 15 levels trt treatment, 4 levels count1 count borers August 18 count2 count borers October 19","code":""},{"path":"/reference/stirret.borers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Corn borer control by application of fungal spores. — stirret.borers","text":"Experiment conducted 1935, Ottawa. European corn borer infestation established application egg masses plants. Treatments applied July 8 July 19 two levels, 0 40 grams per acre. number borers per plot counted Aug 18 Oct 19.","code":""},{"path":"/reference/stirret.borers.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Corn borer control by application of fungal spores. — stirret.borers","text":"Stirrett, George M Beall, Geoffrey Timonin, M. (1937). field experiment control European corn borer, Pyrausta nubilalis Hubn, Beauveria bassiana Vuill. Sci. Agric., 17, 587–591. Table 2.","code":""},{"path":"/reference/stirret.borers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Corn borer control by application of fungal spores. — stirret.borers","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(stirret.borers) dat <- stirret.borers libs(lattice) xyplot(count2~count1|trt,dat, main=\"stirret.borers - by treatment\", xlab=\"Early count of borers\", ylab=\"Late count\") # Even though the data are counts, Normal distribution seems okay # qqmath(~count1|trt, dat, main=\"stirret.borers\") m1 <- lm(count1 ~ -1 + trt + block, dat) anova(m1) # predicted means = main effect + average of 15 block effects # note block 1 effect is 0 # coef(m1)[1:4] + sum(coef(m1)[-c(1:4)])/15 ## trtBoth trtEarly trtLate trtNone ## 47.86667 62.93333 40.93333 61.13333 } # }"},{"path":"/reference/streibig.competition.html","id":null,"dir":"Reference","previous_headings":"","what":"Competition experiment between barley and sinapis. — streibig.competition","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"Competition experiment barley sinapis, different planting rates.","code":""},{"path":"/reference/streibig.competition.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"data frame 135 observations following 8 variables. pot pot number bseeds barley seeds sown sseeds sinapis seeds sown block block bfwt barley fresh weight sfwt sinapis fresh weight bdwt barley dry weight sdwt sinapis dry weight","code":""},{"path":"/reference/streibig.competition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"source data (McCullagh) also contains count plants harvested (included ) sometimes greater number seeds planted. Used permission Jens Streibig.","code":""},{"path":"/reference/streibig.competition.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"Peter McCullagh, John . Nelder. Generalized Linear Models, page 318-320.","code":""},{"path":"/reference/streibig.competition.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"Oliver Schabenberger Francis J Pierce. 2002. Contemporary Statistical Models Plant Soil Sciences. CRC Press. Page 370-375.","code":""},{"path":"/reference/streibig.competition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Competition experiment between barley and sinapis. — streibig.competition","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(streibig.competition) dat <- streibig.competition # See Schaberger and Pierce, pages 370+ # Consider only the mono-species barley data (no competition from sinapis) d1 <- subset(dat, sseeds<1) d1 <- transform(d1, x=bseeds, y=bdwt, block=factor(block)) # Inverse yield looks like it will be a good fit for Gamma's inverse link libs(lattice) xyplot(1/y~x, data=d1, group=block, auto.key=list(columns=3), xlab=\"Seeding rate\", ylab=\"Inverse yield of barley dry weight\", main=\"streibig.competition\") # linear predictor is quadratic, with separate intercept and slope per block m1 <- glm(y ~ block + block:x + x+I(x^2), data=d1, family=Gamma(link=\"inverse\")) # Predict and plot newdf <- expand.grid(x=seq(0,120,length=50), block=factor(c('B1','B2','B3')) ) newdf$pred <- predict(m1, new=newdf, type='response') plot(y~x, data=d1, col=block, main=\"streibig.competition - by block\", xlab=\"Barley seeds\", ylab=\"Barley dry weight\") for(bb in 1:3){ newbb <- subset(newdf, block==c('B1','B2','B3')[bb]) lines(pred~x, data=newbb, col=bb) } } # }"},{"path":"/reference/strickland.apple.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial in apple — strickland.apple.uniformity","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"Uniformity trial apple Australia","code":""},{"path":"/reference/strickland.apple.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"","code":"data(\"strickland.apple.uniformity\")"},{"path":"/reference/strickland.apple.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"data frame 198 observations following 3 variables. row row col column yield yield per tree, pounds","code":""},{"path":"/reference/strickland.apple.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"recently re-worked trees removed data. distance trees uncertain, likely range 20-30 feet.","code":""},{"path":"/reference/strickland.apple.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial in apple — strickland.apple.uniformity","text":". G. Strickland (1935). Error horticultural experiments. Journal Agriculture, Victoria, 33, 408-416. https://handle.slv.vic.gov.au/10381/386642","code":""},{"path":"/reference/strickland.apple.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"None","code":""},{"path":"/reference/strickland.apple.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial in apple — strickland.apple.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(strickland.apple.uniformity) dat <- strickland.apple.uniformity libs(desplot) desplot(dat, yield ~ col*row, main=\"strickland.apple.uniformity\", flip=TRUE, aspect=(18/11)) } # }"},{"path":"/reference/strickland.grape.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of grape — strickland.grape.uniformity","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"Uniformity trial grape Australia","code":""},{"path":"/reference/strickland.grape.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"","code":"data(\"strickland.grape.uniformity\")"},{"path":"/reference/strickland.grape.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"data frame 155 observations following 3 variables. row row col column yield yield per vine, pounds","code":""},{"path":"/reference/strickland.grape.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"Yields individual grape vines, planted 8 feet apart rows 10 feet apart. Grown Rutherglen, North-East Victoria, Australia, 1930. Certain sections omitted missing vines.","code":""},{"path":"/reference/strickland.grape.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of grape — strickland.grape.uniformity","text":". G. Strickland (1932). vine uniformity trial. Journal Agriculture, Victoria, 30, 584-593. https://handle.slv.vic.gov.au/10381/386462","code":""},{"path":"/reference/strickland.grape.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"None","code":""},{"path":"/reference/strickland.grape.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of grape — strickland.grape.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(strickland.grape.uniformity) dat <- strickland.grape.uniformity libs(desplot) desplot(dat, yield ~ col*row, main=\"strickland.grape.uniformity\", flip=TRUE, aspect=(31*8)/(5*10) ) # CV 43.4 sd(dat$yield, na.rm=TRUE)/mean(dat$yield, na.rm=TRUE) # anova like Strickland, appendix 1 anova(aov(yield ~ factor(row) + factor(col), data=dat)) # numbers ending in .5 much more common than .0 # table(substring(format(na.omit(dat$yield)),4,4)) # 0 5 # 25 100 } # }"},{"path":"/reference/strickland.peach.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of peach — strickland.peach.uniformity","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"Uniformity trial peach trees Australia.","code":""},{"path":"/reference/strickland.peach.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"","code":"data(\"strickland.peach.uniformity\")"},{"path":"/reference/strickland.peach.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"data frame 144 observations following 3 variables. row row col column yield yield, pounds per tree","code":""},{"path":"/reference/strickland.peach.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"Yields weight peaches per individual tree pounds.","code":""},{"path":"/reference/strickland.peach.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of peach — strickland.peach.uniformity","text":". G. Strickland (1935). Error horticultural experiments. Journal Agriculture, Victoria, 33, 408-416. https://handle.slv.vic.gov.au/10381/386642","code":""},{"path":"/reference/strickland.peach.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"None","code":""},{"path":"/reference/strickland.peach.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of peach — strickland.peach.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(strickland.peach.uniformity) dat <- strickland.peach.uniformity mean(dat$yield) # 131.3, Strickland has 131.3 sd(dat$yield)/mean(dat$yield) # 31.1, Strickland has 34.4 libs(desplot) desplot(dat, yield ~ col*row, main=\"strickland.peach.uniformity\", flip=TRUE, aspect=1) } # }"},{"path":"/reference/strickland.tomato.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of tomato — strickland.tomato.uniformity","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"Uniformity trial tomato Australia","code":""},{"path":"/reference/strickland.tomato.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"","code":"data(\"strickland.tomato.uniformity\")"},{"path":"/reference/strickland.tomato.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"data frame 180 observations following 3 variables. row row col column yield yield per plot, pounds","code":""},{"path":"/reference/strickland.tomato.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"Tomato plants placed 2 feet apart rows 4 feet apart. plot contained 6 plants. Field dimensions given, likely design : Field length: 6 plots * 6 plants * 2 feet = 72 feet Field width: 30 plots * 4 feet = 120 feet","code":""},{"path":"/reference/strickland.tomato.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":". G. Strickland (1935). Error horticultural experiments. Journal Agriculture, Victoria, 33, 408-416. https://handle.slv.vic.gov.au/10381/386642","code":""},{"path":"/reference/strickland.tomato.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"None","code":""},{"path":"/reference/strickland.tomato.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of tomato — strickland.tomato.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(strickland.tomato.uniformity) dat <- strickland.tomato.uniformity mean(dat$yield) sd(dat$yield) libs(desplot) desplot(dat, yield ~ col*row, main=\"strickland.tomato.uniformity\", flip=TRUE, aspect=(6*12)/(30*4)) } # }"},{"path":"/reference/stroup.nin.html","id":null,"dir":"Reference","previous_headings":"","what":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"yield data advanced Nebraska Intrastate Nursery (NIN) breeding trial conducted Alliance, Nebraska, 1988/89.","code":""},{"path":"/reference/stroup.nin.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"gen genotype, 56 levels rep replicate, 4 levels yield yield, bu/ac col column row row","code":""},{"path":"/reference/stroup.nin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"Four replicates 19 released cultivars, 35 experimental wheat lines 2 additional triticale lines laid 22 row 11 column rectangular array plots. varieties allocated plots using randomised complete block (RCB) design. plot sown four rows 4.3 m long 0.3 m apart. Plots trimmed 2.4 m length harvest. orientation plots clear paper, data Littel et al given meters make orientation clear. Field length: 11 plots * 4.3 m = 47.3 m Field width: 22 plots * 1.2 m = 26.4 m plots missing data coded gen = \"Lancer\". (ASREML, missing plots need included spatial analysis level 'gen' needs one already data.) data first analyzed Stroup et al (1994) subsequently Littell et al (1996, page 321), Pinheiro Bates (2000, page 260), Butler et al (2004). version data give yield bushels per acre. yield values published Stroup et al (1994) expressed kg/ha. wheat, 1 bu/ac = 67.25 kg/ha. gen names different Stroup et al (1994). (Sometimes experimental genotype given new name released commercial use.) minimum, following differences gen names noted: published versions data use long/lat instead col/row. obtain correct value 'long', multiply 'col' 1.2. obtain correct value 'lat', multiply 'row' 4.3. Relatively low yields clustered northwest corner, explained low rise part field, causing increased exposure winter kill wind damage thus depressed yield. genotype 'Buckskin' known superior variety, disadvantaged assignment unfavorable locations within blocks. Note figures Stroup 2002 claim based data, number rows columns 1 positions Buckskin shown Stroup 2002 appear quite right.","code":""},{"path":"/reference/stroup.nin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"Stroup, Walter W., P Stephen Baenziger, Dieter K Mulitze (1994) Removing Spatial Variation Wheat Yield Trials: Comparison Methods. Crop Science, 86:62–66. https://doi.org/10.2135/cropsci1994.0011183X003400010011x","code":""},{"path":"/reference/stroup.nin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"Littell, R.C. Milliken, G.. Stroup, W.W. Wolfinger, R.D. 1996. SAS system mixed models, SAS Institute, Cary, NC. Jose Pinheiro Douglas Bates, 2000, Mixed Effects Models S S-Plus, Springer. Butler, D., B R Cullis, R Gilmour, B J Goegel. (2004) Spatial Analysis Mixed Models S language environments W. W. Stroup (2002). Power Analysis Based Spatial Effects Mixed Models: Tool Comparing Design Analysis Strategies Presence Spatial Variability. Journal Agricultural, Biological, Environmental Statistics, 7(4), 491-511. https://doi.org/10.1198/108571102780","code":""},{"path":[]},{"path":"/reference/stroup.nin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RCB experiment of wheat at the Nebraska Intrastate Nursery — stroup.nin","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(stroup.nin) dat <- stroup.nin # Experiment layout. All \"Buckskin\" plots are near left side and suffer # from poor fertility in two of the reps. libs(desplot) desplot(dat, yield~col*row, aspect=47.3/26.4, out1=\"rep\", num=gen, cex=0.6, # true aspect main=\"stroup.nin - yield heatmap (true shape)\") # Dataframe to hold model predictions preds <- data.frame(gen=levels(dat$gen)) # ----- # nlme libs(nlme) # Random RCB model lme1 <- lme(yield ~ 0 + gen, random=~1|rep, data=dat, na.action=na.omit) preds$lme1 <- fixef(lme1) # Linear (Manhattan distance) correlation model lme2 <- gls(yield ~ 0 + gen, data=dat, correlation = corLin(form = ~ col + row, nugget=TRUE), na.action=na.omit) preds$lme2 <- coef(lme2) # Random block and spatial correlation. # Note: corExp and corSpher give nearly identical results lme3 <- lme(yield ~ 0 + gen, data=dat, random = ~ 1 | rep, correlation = corExp(form = ~ col + row), na.action=na.omit) preds$lme3 <- fixef(lme3) # AIC(lme1,lme2,lme3) # lme2 is lowest ## df AIC ## lme1 58 1333.702 ## lme2 59 1189.135 ## lme3 59 1216.704 # ----- # SpATS libs(SpATS) dat <- transform(dat, yf = as.factor(row), xf = as.factor(col)) # what are colcode and rowcode??? sp1 <- SpATS(response = \"yield\", spatial = ~ SAP(col, row, nseg = c(10,20), degree = 3, pord = 2), genotype = \"gen\", #fixed = ~ colcode + rowcode, random = ~ yf + xf, data = dat, control = list(tolerance = 1e-03)) #plot(sp1) preds$spats <- predict(sp1, which=\"gen\")$predicted.value # ----- # Template Model Builder # See the ar1xar1 example: # https://github.com/kaskr/adcomp/tree/master/TMB/inst/examples # This example uses dpois() in the cpp file to model a Poisson response # with separable AR1xAR1. I think this example could be used for the # stroup.nin data, changing dpois() to something Normal. # ----- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) # RCB analysis as1 <- asreml(yield ~ gen, random = ~ rep, data=dat, na.action=na.method(x=\"omit\")) preds$asreml1 <- predict(as1, data=dat, classify=\"gen\")$pvals$predicted.value # Two-dimensional AR1xAR1 spatial model dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf, dat$yf),] as2 <- asreml(yield~gen, data=dat, residual = ~ar1(xf):ar1(yf), na.action=na.method(x=\"omit\")) preds$asreml2 <- predict(as2, data=dat, classify=\"gen\")$pvals$predicted.value lucid::vc(as2) ## effect component std.error z.ratio constr ## R!variance 48.7 7.155 6.8 pos ## R!xf.cor 0.6555 0.05638 12 unc ## R!yf.cor 0.4375 0.0806 5.4 unc # Compare the estimates from the two asreml models. # We see that Buckskin has correctly been shifted upward by the spatial model plot(preds$as1, preds$as2, xlim=c(13,37), ylim=c(13,37), xlab=\"RCB\", ylab=\"AR1xAR1\", type='n') title(\"stroup.nin: Comparison of predicted values\") text(preds$asreml1, preds$asreml2, preds$gen, cex=0.5) abline(0,1) } # ----- # sommer # Fixed gen, random row, col, 2D spline libs(sommer) dat <- stroup.nin dat <- transform(dat, yf = as.factor(row), xf = as.factor(col)) so1 <- mmer(yield ~ 0+gen, random = ~ vs(xf) + vs(yf) + spl2Db(row,col), data=dat) preds$so1 <- coef(so1)[,\"Estimate\"] # spatPlot # ----- # compare variety effects from different packages lattice::splom(preds[,-1], main=\"stroup.nin\") } # }"},{"path":"/reference/stroup.splitplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Split-plot experiment of simulated data — stroup.splitplot","title":"Split-plot experiment of simulated data — stroup.splitplot","text":"simulated dataset simple split-plot experiment, used illustrate details calculating predictable functions (broad space, narrow space, etc.). example, density narrow, intermediate broad-space predictable function factor level A1 shown (html help )","code":""},{"path":"/reference/stroup.splitplot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Split-plot experiment of simulated data — stroup.splitplot","text":"y simulated response rep replicate, 4 levels b sub-plot, 2 levels whole-plot, 3 levels Used permission Walt Stroup.","code":""},{"path":"/reference/stroup.splitplot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Split-plot experiment of simulated data — stroup.splitplot","text":"Walter W. Stroup, 1989. Predictable functions prediction space mixed model procedure. Applications Mixed Models Agriculture Related Disciplines.","code":""},{"path":"/reference/stroup.splitplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Split-plot experiment of simulated data — stroup.splitplot","text":"Wolfinger, R.D. Kass, R.E., 2000. Nonconjugate Bayesian analysis variance component models, Biometrics, 56, 768–774. https://doi.org/10.1111/j.0006-341X.2000.00768.x","code":""},{"path":"/reference/stroup.splitplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split-plot experiment of simulated data — stroup.splitplot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(stroup.splitplot) dat <- stroup.splitplot # ---- lme4 --- # libs(lme4) # m0 <- lmer(y~ -1 + a + b + a:b + (1|rep) + (1|a:rep), data=dat) # No predict function # ----- nlme --- # libs(nlme) # m0 <- lme(y ~ -1 + a + b + a:b, data=dat, random = ~ 1|rep/a) # ----- ASREML model --- if(require(\"asreml\", quietly=TRUE)){ libs(asreml,lucid) m1 <- asreml(y~ -1 + a + b + a:b, random=~ rep + a:rep, data=dat) # vc(m1) # Variance components match Stroup p. 41 ## effect component std.error z.ratio bound ## rep 62.42 56.41 1.1 P ## a:rep 15.39 11.8 1.3 P ## units(R) 9.364 4.415 2.1 P # Narrow space predictions predict(m1, data=dat, classify=\"a\", average=list(rep=NULL)) # a Predicted Std Err Status # a1 32.88 1.082 Estimable # a2 34.12 1.082 Estimable # a3 25.75 1.082 Estimable # Intermediate space predictions predict(m1, data=dat, classify=\"a\", ignore=\"a:rep\", average=list(rep=NULL)) # a Predicted Std Err Status # a1 32.88 2.24 Estimable # a2 34.12 2.24 Estimable # a3 25.75 2.24 Estimable # Broad space predictions predict(m1, data=dat, classify=\"a\") # a Predicted Std Err Status # a1 32.88 4.54 Estimable # a2 34.12 4.54 Estimable # a3 25.75 4.54 Estimable } # ----- MCMCglmm model ----- # Use the point estimates from REML with a prior distribution libs(lattice,MCMCglmm) prior2 = list( G = list(G1=list(V=62.40, nu=1), G2=list(V=15.38, nu=1)), R = list(V = 9.4, nu=1) ) m2 <- MCMCglmm(y~ -1 + a + b + a:b, random=~ rep + a:rep, data=dat, pr=TRUE, # save random effects as columns of 'Sol' nitt=23000, # double the default 13000 prior=prior2, verbose=FALSE) # posterior.mode(m2$VCV) # rep a:rep units # 39.766020 9.617522 7.409334 # plot(m2$VCV) # Now create a matrix of coefficients for the prediction. # Each column is for a different prediction. For example, # the values in the column called 'a1a2n' are multiplied times # the model coefficients (identified at the right side) to create # the linear contrast for the the narrow-space predictions # (also called adjusted mean) for the a1:a2 interaction. # a1n a1i a1b a1a2n a1a2ib cm <- matrix(c(1, 1, 1, 1, 1, # a1 0, 0, 0, -1, -1, # a2 0, 0, 0, 0, 0, # a3 1/2, 1/2, 1/2, 0, 0, # b2 0, 0, 0, -1/2, -1/2, # a2:b2 0, 0, 0, 0, 0, # a3:b2 1/4, 1/4, 0, 0, 0, # r1 1/4, 1/4, 0, 0, 0, # r2 1/4, 1/4, 0, 0, 0, # r3 1/4, 1/4, 0, 0, 0, # r4 1/4, 0, 0, 1/4, 0, # a1r1 0, 0, 0, -1/4, 0, # a2r1 0, 0, 0, 0, 0, # a3r1 1/4, 0, 0, 1/4, 0, # a1r2 0, 0, 0, -1/4, 0, # a2r2 0, 0, 0, 0, 0, # a3r2 1/4, 0, 0, 1/4, 0, # a1r3 0, 0, 0, -1/4, 0, # a2r3 0, 0, 0, 0, 0, # a3r3 1/4, 0, 0, 1/4, 0, # a1r4 0, 0, 0, -1/4, 0, # a2r4 0, 0, 0, 0, 0), # a3r4 ncol=5, byrow=TRUE) rownames(cm) <- c(\"a1\", \"a2\", \"a3\", \"b2\", \"a2:b2\", \"a3:b2\", \"r1\", \"r2\", \"r3\", \"r4\", \"a1r1\", \"a1r2\", \"a1r3\", \"a1r4\", \"a2r1\", \"a2r2\", \"a2r3\", \"a2r4\", \"a3r1\", \"a3r2\", \"a3r3\", \"a3r4\") colnames(cm) <- c(\"A1n\",\"A1i\",\"A1b\", \"A1-A2n\", \"A1-A2ib\") print(cm) # post2 <- as.mcmc(m2$Sol post2 <- as.mcmc(crossprod(t(m2$Sol), cm)) # Following table has columns for A1 estimate (narrow, intermediate, broad) # A1-A2 estimate (narrow and intermediat/broad). # The REML estimates are from Stroup 1989. est <- rbind(\"REML est\"=c(32.88, 32.88, 32.88, -1.25, -1.25), \"REML stderr\"=c(1.08, 2.24, 4.54, 1.53, 3.17), \"MCMC mode\"=posterior.mode(post2), \"MCMC stderr\"=apply(post2, 2, sd)) round(est,2) # A1n A1i A1b A1-A2n A1-A2ib # REML est 32.88 32.88 32.88 -1.25 -1.25 # REML stderr 1.08 2.24 4.54 1.53 3.17 # MCMC mode 32.95 32.38 31.96 -1.07 -1.17 # MCMC stderr 1.23 2.64 5.93 1.72 3.73 # plot(post2) post22 <- lattice::make.groups( Narrow=post2[,1], Intermediate=post2[,2], Broad=post2[,3]) print(densityplot(~data|which, data=post22, groups=which, cex=.25, lty=1, layout=c(1,3), main=\"stroup.splitplot\", xlab=\"MCMC model value of predictable function for A1\")) } # }"},{"path":"/reference/student.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley — student.barley","title":"Multi-environment trial of barley — student.barley","text":"Yield two varieties barley grown 51 locations years 1901 1906.","code":""},{"path":"/reference/student.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley — student.barley","text":"data frame 102 observations following 7 variables. year year, 1901-1906 farmer farmer name place place (nearest town) district district, geographical area gen genotype, Archer Goldthorpe yield yield, 'stones' per acre (1 stone = 14 pounds) income income per acre shillings, based yield quality","code":""},{"path":"/reference/student.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley — student.barley","text":"Experiments conducted six years Department Agriculture Ireland. total seven varieties tested, Archer Goldthorpe tested six years (others dropped found inferior, added later). Plots two acres size. value grain depended yield quality. Quality varied much farm farm, much within farm. phrase \"analysis variance\" first appears abstract () 1918 paper Fisher. 1923 paper Student contained first analysis variance table (data). One stone 14 pounds. convert lb/ac tonnes/ha, multiply 0.00112085116 Note: analysis Student reproduced exactly. example, Student states maximum income Goldthorpe 230 shillings. quick glance Table Student shows maximum income Goldthorpe 220 shillings (11 pounds, 0 shillings) 1901 Thurles. Also, results Kempton reproduced exactly, perhaps due rounding conversion factor used.","code":""},{"path":"/reference/student.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley — student.barley","text":"Student. 1923. Testing Varieties Cereals. Biometrika, 15, 271–293. https://doi.org/10.1093/biomet/15.3-4.271","code":""},{"path":"/reference/student.barley.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of barley — student.barley","text":"R Kempton P N Fox, 1997. Statistical Methods Plant Variety Evaluation.","code":""},{"path":"/reference/student.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley — student.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(student.barley) dat <- student.barley libs(lattice) bwplot(yield ~ gen|district, dat, main=\"student.barley - yield\") dat$year <- factor(dat$year) dat$income <- NULL # convert to tons/ha dat <- transform(dat, yield=yield*14 * 0.00112085116) # Define 'loc' the way that Kempton does dat$loc <- rep(\"\",nrow(dat)) dat[is.element(dat$farmer, c(\"Allardyce\",\"Roche\",\"Quinn\")),\"loc\"] <- \"1\" dat[is.element(dat$farmer, c(\"Luttrell\",\"Dooley\")), \"loc\"] <- \"2\" dat[is.element(dat$year, c(\"1904\",\"1905\",\"1906\")) & dat$farmer==\"Kearney\",\"loc\"] <- \"2\" dat[dat$farmer==\"Mulhall\",\"loc\"] <- \"3\" dat <- transform(dat, loc=factor(paste(place,loc,sep=\"\"))) libs(reshape2) datm <- melt(dat, measure.var='yield') # Kempton Table 9.5 round(acast(datm, loc+gen~year),2) # Kempton Table 9.6 d2 <- dcast(datm, year+loc~gen) mean(d2$Archer) mean(d2$Goldthorpe) mean(d2$Archer-d2$Goldthorpe) sqrt(var(d2$Archer-d2$Goldthorpe)/51) cor(d2$Archer,d2$Goldthorpe) if(0){ # Kempton Table 9.6b libs(lme4) m2 <- lmer(yield~1 + (1|loc) + (1|year) + (1|loc:year) + (1|gen:loc) + (1|gen:year), data=dat, control=lmerControl(check.nobs.vs.rankZ=\"ignore\")) } } # }"},{"path":"/reference/summerby.multi.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"Uniformity trial maize, oat, alfalfa, mangolds","code":""},{"path":"/reference/summerby.multi.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"","code":"data(\"summerby.multi.uniformity\")"},{"path":"/reference/summerby.multi.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"data frame 2600 observations following 6 variables. col column ordinate row row ordinate yield yield range range (block field) year year crop crop","code":""},{"path":"/reference/summerby.multi.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"Note plots range across years. example plots range R2 1922, 1923, 1924, 1925. Grown Macdonald College, Quebec. Four ranges land 760 x 100 links used. years 1922-1926, crops harvested 20 link 20 links plots. oats, yields cleaned grain. mangolds alfalfa, yields dry matter calculated. maize, green weights fodder obtained. 1925, range R3 oats damaged birds. 1927, range R4 oats lodges harvested. 1924 range R5 flooding considered 'inadvisable' use. 1914 range R3 oat yield variable, perhaps poor germination. Data included completeness, perhaps included. row numbers data based figure page 13 Summerby. Row 1 bottom. appears approximately blank row ranges. paper Summerby year/range combinations, plots 20 links 100 links single plot wide. data converted PDF png images, OCR converted text, hand-checked K.Wright.","code":""},{"path":"/reference/summerby.multi.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"Summerby, R. (1934). value preliminary uniformity trials increasing precision field experiments. Macdonald College. https://books.google.com/books?id=6zlMAAAAYAAJ&pg=RA14-PA47","code":""},{"path":"/reference/summerby.multi.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"None","code":""},{"path":"/reference/summerby.multi.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of maize, oat, alfalfa, mangolds — summerby.multi.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(summerby.multi.uniformity) dat <- summerby.multi.uniformity libs(desplot) dat <- mutate(dat, env=paste(range, year, crop)) desplot(dat, yield ~ col*row|env, aspect=(5*20)/(35*20), main=\"summerby.multi.uniformity\") # Show all ranges for a single year. # dat # Compare the variance for each dataset in Summerby, page 18, column (a) # with what we calculate. Very slight differences. # libs(dplyr) # dat ## range year var summerby ## 1 R2 1922 82404 82404 ## 2 R2 1923 254780. 254780 ## 3 R2 1924 111978. 111978 ## 4 R2 1925 84515. 84515 ## 5 R2 1926 101008. 100960 ## 6 R3 1922 185031. 185031 ## 7 R3 1923 154777. 154784 ## 8 R3 1924 252451. 252451 ## 9 R3 1926 472087. 472088 ## 10 R4 1924 19.3 19.341 ## 11 R4 1925 14.2 14.234 ## 12 R4 1926 14.2 14.236 ## 13 R5 1924 134472. 134472 ## 14 R5 1925 289001. 289026 ## 15 R5 1926 131714. 131714 ## 16 R5 1927 8.62 8.622 } # }"},{"path":"/reference/tai.potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of potato — tai.potato","title":"Multi-environment trial of potato — tai.potato","text":"Multi-environment trial potato tuber yields","code":""},{"path":"/reference/tai.potato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of potato — tai.potato","text":"","code":"data(\"tai.potato\")"},{"path":"/reference/tai.potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of potato — tai.potato","text":"data frame 48 observations following 6 variables. yield yield, kg/plot gen genotype code variety variety name env environment code loc location year year","code":""},{"path":"/reference/tai.potato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of potato — tai.potato","text":"Mean tuber yield 8 genotypes 3 locations two years. Katahdin Sebago check varieties. location planted 4-rep RCB design. Tai's plot stability parameters, F5751 Sebago average stability area. highest yielding genotype F6032 unstable performance.","code":""},{"path":"/reference/tai.potato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of potato — tai.potato","text":"G.C.C. Tai, 1971. Genotypic stability analysis application potato regional trials. Crop Sci 11, 184-190. Table 2, p. 187. https://doi.org/10.2135/cropsci1971.0011183X001100020006x","code":""},{"path":"/reference/tai.potato.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of potato — tai.potato","text":"George Fernandez (1991). Analysis Genotype x Environment Interaction Stability Estimates. Hort Science, 26, 947-950.","code":""},{"path":"/reference/tai.potato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of potato — tai.potato","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(tai.potato) dat <- tai.potato libs(lattice) dotplot(variety ~ yield|env, dat, main=\"tai.potato\") # fixme - need to add tai() example # note, st4gi::tai assumes there are replications in the data # https://github.com/reyzaguirre/st4gi/blob/master/R/tai.R } # }"},{"path":"/reference/talbot.potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"Yield 14 trait scores 9 potato varieties 12 locations UK.","code":""},{"path":"/reference/talbot.potato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"","code":"data(\"talbot.potato.traits\") data(\"talbot.potato.yield\")"},{"path":"/reference/talbot.potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"talbot.potato.yield dataframe 126 observations following 3 variables. gen genotype/variety trait trait score trait score, 1-9 talbot.potato.yield dataframe 108 observations following 3 variables. gen genotype/variety loc location/center yield yield, t/ha","code":""},{"path":"/reference/talbot.potato.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"talbot.potato.yield dataframe contains mean tuber yields (t/ha) 9 varieties potato 12 centers United Kingdom five years 1983-1987. following abbreviations used centers. Used permission Mike Talbot.","code":""},{"path":"/reference/talbot.potato.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"Mike Talbot V Wheelwright, 1989, analysis genotype x analysis interactions partial least squares regression. Biuletyn Oceny Odmian, 21/22, 19–25.","code":""},{"path":"/reference/talbot.potato.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of potato in UK, yields and trait scores at 12 locations — talbot.potato","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) libs(pls, reshape2) data(talbot.potato.traits) datt <- talbot.potato.traits data(talbot.potato.yield) daty <- talbot.potato.yield datt <- acast(datt, gen ~ trait, value.var='score') daty <- acast(daty, gen ~ loc, value.var='yield') # Transform columns to zero mean and unit variance datt <- scale(datt) daty <- scale(daty) m1 <- plsr(daty ~ datt, ncomp=3) summary(m1) # Loadings factor 1 lo <- loadings(m1)[,1,drop=FALSE] round(-1*lo[order(-1*lo),1,drop=FALSE],2) biplot(m1, main=\"talbot.potato - biplot\") } # }"},{"path":"/reference/tesfaye.millet.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of millet — tesfaye.millet","title":"Multi-environment trial of millet — tesfaye.millet","text":"Multi-environment trial millet","code":""},{"path":"/reference/tesfaye.millet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of millet — tesfaye.millet","text":"","code":"data(\"tesfaye.millet\")"},{"path":"/reference/tesfaye.millet.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of millet — tesfaye.millet","text":"data frame 415 observations following 9 variables. year year site site (location) rep replicate col column ordinate row row ordinate plot plot number gen genotype entry_number entry yield yield, kg/ha","code":""},{"path":"/reference/tesfaye.millet.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of millet — tesfaye.millet","text":"Experiments conducted Bako Assosa research centers Ethiopia. data : 4 years, 2 sites = 7 environments, 2-3 reps per trial, 47 genotypes. Tesfaye et al used asreml fit GxE model Factor Analytic covariance structure GxE part AR1xAR1 spatial residuals site. Data PloS ONE published Creative Commons Attribution License.","code":""},{"path":"/reference/tesfaye.millet.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of millet — tesfaye.millet","text":"Tesfaye K, Alemu T, Argaw T, de Villiers S, Assefa E (2023) Evaluation finger millet (Eleusine coracana (L.) Gaertn.) multi-environment trials using enhanced statistical models. PLoS ONE 18(2): e0277499. https://doi.org/10.1371/journal.pone.0277499","code":""},{"path":"/reference/tesfaye.millet.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of millet — tesfaye.millet","text":"None","code":""},{"path":"/reference/tesfaye.millet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of millet — tesfaye.millet","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(tesfaye.millet) dat <- tesfaye.millet dat <- transform(dat, year=factor(year), site=factor(site)) libs(dplyr,asreml,lucid) dat <- mutate(dat, env=factor(paste0(site,year)), gen=factor(gen), rep=factor(rep), xfac=factor(col), yfac=factor(row)) libs(desplot) desplot(dat, yield~col*row|env, main=\"tesfaye.millet\") dat <- arrange(dat, env, xfac, yfac) # Fixed environment # Random row/col within environment, Factor Analytic GxE # AR1xAR1 spatial residuals within each environment if(require(\"asreml\", quietly=TRUE)){ libs(asreml) m1 <- asreml(yield ~ 1 + env, data=dat, random = ~ at(env):xfac + at(env):yfac + gen:fa(env), residual = ~ dsum( ~ ar1(xfac):ar1(yfac)|env) ) m1 <- update(m1) lucid::vc(m1) } } # }"},{"path":"/reference/theobald.barley.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"Barley yields multiple locs, years, fertilizer levels","code":""},{"path":"/reference/theobald.barley.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"","code":"data(\"theobald.barley\")"},{"path":"/reference/theobald.barley.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"data frame 105 observations following 5 variables. yield yield, tonnes/ha gen genotype loc location, 5 levels nitro nitrogen kg/ha year year, 2 levels","code":""},{"path":"/reference/theobald.barley.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"Theobald Talbot used BUGS fit fully Bayesian model yield response curves. Locations experiment north-east Scotland. Assumed nitrogen cost 400 pounds per tonne. Grain prices used 100, 110, 107.50 pounds per tonne Georgie, Midas Sundance.","code":""},{"path":"/reference/theobald.barley.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"Chris M. Theobald Mike Talbot, (2002). Bayesian choice crop variety fertilizer dose. Appl Statistics, 51, 23-36. https://doi.org/10.1111/1467-9876.04863 Data provided Chris Theobald Mike Talbot.","code":""},{"path":"/reference/theobald.barley.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of barley, multiple years & fertilizer levels — theobald.barley","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(theobald.barley) dat <- theobald.barley dat <- transform(dat, env=paste(loc,year,sep=\"-\")) dat <- transform(dat, income=100*yield - 400*nitro/1000) libs(lattice) xyplot(income~nitro|env, dat, groups=gen, type='b', auto.key=list(columns=3), main=\"theobald.barley\") } # }"},{"path":"/reference/theobald.covariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","title":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","text":"Corn silage yields maize 5 years 7 districts 10 hybrids.","code":""},{"path":"/reference/theobald.covariate.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","text":"data frame 256 observations following 5 variables. year year, 1990-1994 env environment/district, 1-7 gen genotype, 1-10 yield dry-matter silage yield corn chu corn heat units, thousand degrees Celsius Used permission Chris Theobald.","code":""},{"path":"/reference/theobald.covariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","text":"trials carried seven districts maritime provinces Eastern Canada. Different fields used successive years. covariate CHU (Corn Heat Units) accumulated average daily temperatures (thousands degrees Celsius) growing season location.","code":""},{"path":"/reference/theobald.covariate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","text":"Chris M. Theobald Mike Talbot Fabian Nabugoomu, 2002. Bayesian Approach Regional Local-Area Prediction Crop Variety Trials. Journ Agric Biol Env Sciences, 7, 403–419. https://doi.org/10.1198/108571102230","code":""},{"path":"/reference/theobald.covariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn silage, Year * Loc * Variety with covariate — theobald.covariate","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(theobald.covariate) dat <- theobald.covariate libs(lattice) xyplot(yield ~ chu|gen, dat, type=c('p','smooth'), xlab = \"chu = corn heat units\", main=\"theobald.covariate - yield vs heat\") # REML estimates (Means) in table 3 of Theobald 2002 libs(lme4) dat <- transform(dat, year=factor(year)) m0 <- lmer(yield ~ -1 + gen + (1|year/env) + (1|gen:year), data=dat) round(fixef(m0),2) # Use JAGS to fit Theobald (2002) model 3.2 with 'Expert' prior # Requires JAGS to be installed if(0) { libs(reshape2) ymat <- acast(dat, year+env~gen, value.var='yield') chu <- acast(dat, year+env~., mean, value.var='chu', na.rm=TRUE) chu <- as.vector(chu - mean(chu)) # Center the covariate dat$yr <- as.numeric(dat$year) yridx <- as.vector(acast(dat, year+env~., mean, value.var='yr', na.rm=TRUE)) dat$loc <- as.numeric(dat$env) locidx <- acast(dat, year+env~., mean, value.var='loc', na.rm=TRUE) locidx <- as.vector(locidx) jdat <- list(nVar = 10, nYear = 5, nLoc = 7, nYL = 29, yield = ymat, chu = chu, year = yridx, loc = locidx) libs(rjags) m1 <- jags.model(file=system.file(package=\"agridat\", \"files/theobald.covariate.jag\"), data=jdat, n.chains=2) # Table 3, Variety deviations from means (Expert prior) c1 <- coda.samples(m1, variable.names=(c('alpha')), n.iter=10000, thin=10) s1 <- summary(c1) effs <- s1$statistics[,'Mean'] # Perfect match (different order?) rev(sort(round(effs - mean(effs), 2))) } } # }"},{"path":"/reference/thompson.cornsoy.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"Average yield corn soybeans five U.S. states (IA, IL, , MO, OH) years 1930-1962. Pre-season precipitation average temperature precipitation month growing season included.","code":""},{"path":"/reference/thompson.cornsoy.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"state state year year, 1930-1962 rain0 pre-season precipitation inches temp5 may temperature, Fahrenheit rain6 june rain, inches temp6 june temp rain7 july rain temp7 july temp rain8 august rain temp8 august temp corn corn yield, bu/acre soy soybean yield, bu/acre","code":""},{"path":"/reference/thompson.cornsoy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"Note: Iowa corn data sometimes identified ( sources) \"Iowa wheat\" data, incorrect. 'year' variable affects yield (1) improvements plant genetics (2) changes management techniques fertilizer, chemicals, tillage, planting date, (3) climate, pest infestations, etc. Double-cross corn hybrids introduced 1920s. Single-cross hybrids became common around 1960. World War II, nitrogen used production TNT bombs. war, factories switched producing ammonia fertilizer. Nitrogen fertilizer use greatly increased WWII major reason yield gains corn. Soybeans gain little benefit nitrogen fertilizer. major reason increasing yields crops due improved plant genetics. Crops often planted May, harvest begins September. Yields 1936 low due July one hottest driest record. relevant maps yield, heat, precipitation can found Atlas crop yield summer weather patterns, 1931-1975, https://www.isws.illinois.edu/pubdoc/C/ISWSC-150.pdf following notes pertain Iowa data. 1947 June precipitation 10.33 inches wettest June record (new Iowa June record 10.34 inches set 2010). quoted Monthly Weather Review (Dec 1957, p. 396) \"dependence Iowa agriculture upon vagaries weather closely demonstrated 1947 season. cool wet spring delayed crop planting activity plant growth; , addition, hard freeze May 29th ... set back corn. heavy rains subsequent floods June caused appreciable crop acreage abandoned ... followed hot dry weather regime persisted mid-July first week September.\" 1949 soybean yields average corn yields low. source , \"year 1949 saw greatest infestation corn borer history corn Iowa\". 1955 yields reduced due dry weather late July August.","code":""},{"path":"/reference/thompson.cornsoy.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"Thompson, L.M., 1963. Weather technology production corn soybeans. CAED Report 17. Center Agriculture Economic Development, Iowa State University, Ames, Iowa.","code":""},{"path":"/reference/thompson.cornsoy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"Draper, N. R. Smith, H. (1981). Applied Regression Analysis, second ed., Wiley, New York.","code":""},{"path":"/reference/thompson.cornsoy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation — thompson.cornsoy","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(thompson.cornsoy) dat <- thompson.cornsoy # The droughts of 1934/36 were severe in IA/MO. Less so in OH. libs(lattice) xyplot(corn+soy~year|state, dat, type=c('p','l','r'), auto.key=list(columns=2), main=\"thompson.cornsoy\", layout=c(5,1),ylab='yield') # In 1954, only Missouri suffered very hot, dry weather ## xyplot(corn~year, dat, ## groups=state, type=c('p','l'), ## main=\"thompson.cornsoy\", ## auto.key=list(columns=5), ylab='corn yield') # Rain and temperature have negative correlation in each month. # July is a critical month: temp and yield are negatively correlated, # while rain and yield are positively correlated. # splom(~dat[-1,-1], col=dat$state, cex=.5, main=\"thompson.cornsoy\") # Plots similar to those in Venables' Exegeses paper. dat.ia <- subset(dat, state==\"Iowa\") libs(splines) m2 <- aov(corn ~ ns(rain0, 3) + ns(rain7, 3) + ns(temp8, 3) + ns(year,3), dat.ia) op <- par(mfrow=c(2,2)) termplot(m2, se=TRUE, rug=TRUE, partial=TRUE, main=\"thompson.cornsoy\") par(op) # do NOT use gam package libs(mgcv) m1 <- gam(corn ~ s(year, k=5) + s(rain0, k=5) + s(rain7, k=5) + s(temp8, k=5), data=dat.ia) op <- par(mfrow=c(2,2)) plot.gam(m1, residuals=TRUE, se=TRUE, cex=2, main=\"thompson.cornsoy\") par(op) } # }"},{"path":"/reference/tulaikow.wheat.uniformity.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"Uniformity trial winter/spring wheat Russia","code":""},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"","code":"data(\"tulaikow.wheat.uniformity\")"},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"data frame 480 observations following 4 variables. row row ordinate col column ordinate yield yield grams per plot season winter summer","code":""},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"Land fallow 1911, harvested 1912 Bezenchuk Experimental Station Russia. winter wheat field 240 square sazhen (24 x 10 sazhen) divided separate plots 1 square sazhen, cut, threshed weighed separately. way, plot Poltavka spring wheat harvested plot 240 square sazhen dimensions 15 16 sazhen divided plots 1 square sazhen. Winter wheat: Field length: 10 rows * 1 sazhen. Field width: 24 columns * 1 sazhen. Summer wheat: Field length: 16 rows * 1 sazhen. Field width: 15 columns * 1 sazhen. Note: Russian word (looks like \"cax\" vertical line \"x\") refers unit measurement. Specifically, represents sazhen, used traditional Russian systems measurement. sazhen approximately 3 meters (7 feet) long. Google Translate sometimes converts \"sazhen\" \"soot\", \"meter\" \"fathom\". data typed K.Wright Roemer (1920), table 4, p. 63.","code":""},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"N. Tulaikow (1913) Resultate einer mathematischen Bearbeitung von Ernteergebnissen. Russian Journal fur Exp Landw., 14, 88-113. https://www.google.com/books/edition/Journal_de_l_agriculture_experimentale/i2EjAQAAIAAJ?hl=en&gbpv=1&dq=tulaikow","code":""},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"Neyman, J., & Iwaszkiewicz, K. (1935). Statistical problems agricultural experimentation. Supplement Journal Royal Statistical Society, 2(2), 107-180. Roemer, T. (1920). Der Feldversuch. Arbeiten der Deutschen Landwirtschafts-Gesellschaft, 302. https://www.google.com/books/edition/Arbeiten_der_Deutschen_Landwirtschafts_G/7zBSAQAAMAAJ","code":""},{"path":"/reference/tulaikow.wheat.uniformity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniformity trial of winter/spring wheat — tulaikow.wheat.uniformity","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(tulaikow.wheat.uniformity) dat <- tulaikow.wheat.uniformity libs(desplot) desplot(dat, yield~col*row, subset=season==\"winter\", aspect=10/24, flip=TRUE, tick=TRUE, main=\"tulaikow.wheat.uniformity (winter)\") desplot(dat, yield~col*row, subset=season==\"summer\", aspect=16/15, flip=TRUE, tick=TRUE, main=\"tulaikow.wheat.uniformity (summer)\") } # }"},{"path":"/reference/turner.herbicide.html","id":null,"dir":"Reference","previous_headings":"","what":"Herbicide control of larkspur — turner.herbicide","title":"Herbicide control of larkspur — turner.herbicide","text":"Herbicide control larkspur","code":""},{"path":"/reference/turner.herbicide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Herbicide control of larkspur — turner.herbicide","text":"","code":"data(\"turner.herbicide\")"},{"path":"/reference/turner.herbicide.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Herbicide control of larkspur — turner.herbicide","text":"data frame 12 observations following 4 variables. rep rep factor rate rate herbicide live number live plants application dead number plants killed herbicide","code":""},{"path":"/reference/turner.herbicide.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Herbicide control of larkspur — turner.herbicide","text":"Effectiveness herbicide Picloram larkspur plants 4 doses (0, 1.1, 2.2, 4.5) 3 reps. Experiment done 1986 Manti, Utah.","code":""},{"path":"/reference/turner.herbicide.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Herbicide control of larkspur — turner.herbicide","text":"David L. Turner Michael H. Ralphs John O. Evans (1992). Logistic Analysis Monitoring Assessing Herbicide Efficacy. Weed Technology, 6, 424-430. https://www.jstor.org/stable/3987312","code":""},{"path":"/reference/turner.herbicide.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Herbicide control of larkspur — turner.herbicide","text":"Christopher Bilder, Thomas Loughin. Analysis Categorical Data R.","code":""},{"path":"/reference/turner.herbicide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Herbicide control of larkspur — turner.herbicide","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(turner.herbicide) dat <- turner.herbicide dat <- transform(dat, prop=dead/live) # xyplot(prop~rate,dat, pch=20, main=\"turner.herbicide\", ylab=\"Proportion killed\") m1 <- glm(prop~rate, data=dat, weights=live, family=binomial) coef(m1) # -3.46, 2.6567 Same as Turner eqn 3 # Make conf int on link scale and back-transform p1 <- expand.grid(rate=seq(0,to=5,length=50)) p1 <- cbind(p1, predict(m1, newdata=p1, type='link', se.fit=TRUE)) p1 <- transform(p1, lo = plogis(fit - 2*se.fit), fit = plogis(fit), up = plogis(fit + 2*se.fit)) # Figure 2 of Turner libs(latticeExtra) foo1 <- xyplot(prop~rate,dat, cex=1.5, main=\"turner.herbicide (model with 2*S.E.)\", xlab=\"Herbicide rate\", ylab=\"Proportion killed\") foo2 <- xyplot(fit~rate, p1, type='l') foo3 <- xyplot(lo+up~rate, p1, type='l', lty=1, col='gray') print(foo1 + foo2 + foo3) # What dose gives a LD90 percent kill rate? # libs(MASS) # dose.p(m1, p=.9) ## Dose SE ## p = 0.9: 2.12939 0.128418 # Alternative method # libs(car) # logit(.9) = 2.197225 # deltaMethod(m1, g=\"(log(.9/(1-.9))-b0)/(b1)\", parameterNames=c('b0','b1')) ## Estimate SE ## (2.197225 - b0)/(b1) 2.12939 0.128418 # What is a 95 percent conf interval for LD90? Bilder & Loughin page 138 root <- function(x, prob=.9, alpha=0.05){ co <- coef(m1) # b0,b1 covs <- vcov(m1) # b00,b11,b01 # .95 = b0 + b1*x # (b0+b1*x) + Z(alpha/2) * sqrt(b00 + x^2*b11 + 2*x*b01) > .95 # (b0+b1*x) - Z(alpha/2) * sqrt(b00 + x^2*b11 + 2*x*b01) < .95 f <- abs(co[1] + co[2]*x - log(prob/(1-prob))) / sqrt(covs[1,1] + x^2 * covs[2,2] + 2*x*covs[1,2]) return( f - qnorm(1-alpha/2)) } lower <- uniroot(f=root, c(0,2.13)) upper <- uniroot(f=root, c(2.12, 5)) c(lower$root, upper$root) # 1.92 2.45 } # }"},{"path":"/reference/urquhart.feedlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Weight gain calves in a feedlot — urquhart.feedlot","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"Weight gain calves feedlot, given three different diets.","code":""},{"path":"/reference/urquhart.feedlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"","code":"data(\"urquhart.feedlot\")"},{"path":"/reference/urquhart.feedlot.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"data frame 67 observations following 5 variables. animal animal ID herd herd ID diet diet: Low, Medium, High weight1 initial weight weight2 slaughter weight","code":""},{"path":"/reference/urquhart.feedlot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"Calves born 1975 11 different herds entered feedlot yearlings. animal fed one three diets low, medium, high energy. original sources explored use contrasts comparing breeds.","code":""},{"path":"/reference/urquhart.feedlot.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"N. Scott Urquhart (1982). Adjustment Covariance One Factor Affects Covariate Biometrics, 38, 651-660. Table 4, p. 659. https://doi.org/10.2307/2530046","code":""},{"path":"/reference/urquhart.feedlot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"N. Scott Urquhart David L. Weeks (1978). Linear Models Messy Data: Problems Alternatives Biometrics, 34, 696-705. https://doi.org/10.2307/2530391 Also available 'emmeans' package 'feedlot' data.","code":""},{"path":"/reference/urquhart.feedlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Weight gain calves in a feedlot — urquhart.feedlot","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(urquhart.feedlot) dat <- urquhart.feedlot libs(reshape2) d2 <- melt(dat, id.vars=c('animal','herd','diet')) libs(latticeExtra) useOuterStrips(xyplot(value ~ variable|diet*herd, data=d2, group=animal, type='l', xlab=\"Initial & slaughter timepoint for each diet\", ylab=\"Weight for each herd\", main=\"urquhart.feedlot - weight gain by animal\")) # simple fixed-effects model dat <- transform(dat, animal = factor(animal), herd=factor(herd)) m1 <- lm(weight2 ~ weight1 + herd*diet, data = dat) coef(m1) # weight1 = 1.1373 match Urquhart table 5 common slope # random-effects model might be better, for example # libs(lme4) # m1 <- lmer(weight2 ~ -1 + diet + weight1 + (1|herd), data=dat) # summary(m1) # weight1 = 1.2269 } # }"},{"path":"/reference/usgs.herbicides.html","id":null,"dir":"Reference","previous_headings":"","what":"Concentrations of herbicides in streams in the United States — usgs.herbicides","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"Concentrations selected herbicides degradation products determined laboratory method analysis code GCS water samples collected 51 streams nine Midwestern States,2002","code":""},{"path":"/reference/usgs.herbicides.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"","code":"data(\"usgs.herbicides\")"},{"path":"/reference/usgs.herbicides.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"data frame 184 observations following 19 variables. mapnum map number usgsid USGS ID long longitude lat latitude site site name city city sampletype sample type code date date sample collected hour hour sample collected acetochlor concentration character alachlor concentration character ametryn concentration character atrazine concentration character CIAT concentration character CEAT concentration character cyanazine concentration character CAM concentration character dimethenamid concentration character flufenacet concentration character","code":""},{"path":"/reference/usgs.herbicides.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"Concentrations selected herbicides degradation products determined laboratory method analysis code GCS water samples collected 51 streams nine Midwestern States, 2002. concentrations micrograms/liter, \"<\" means \"less \". data character format allow \"<\". original report contains data herbicides. data illustrative purposes. Sample types: CR = concurrent replicate sample, FB = field blank, LD = laboratory duplicate, S1 = sample pre-emergence runoff, S2 = sample post-emergence runoff, S3 = sample harvest-season runoff.","code":""},{"path":"/reference/usgs.herbicides.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"Scribner, E.., Battaglin, W.., Dietze, J.E., Thurman, E.M., \"Reconnaissance Data Glyphosate, Selected Herbicides, Degradation Products, Antibiotics 51 streams Nine Midwestern States, 2002\". U.S. Geological Survey Open File Report 03-217. Herbicide data table 5, page 30-37. Site coordinates page 7-8. https://ks.water.usgs.gov/pubs/reports/ofr.03-217.html","code":""},{"path":"/reference/usgs.herbicides.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"None.","code":""},{"path":"/reference/usgs.herbicides.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concentrations of herbicides in streams in the United States — usgs.herbicides","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(usgs.herbicides) dat <- usgs.herbicides libs(NADA) # create censored data for one trait dat$y <- as.numeric(dat$atrazine) dat$ycen <- is.na(dat$y) dat$y[is.na(dat$y)] <- .05 # percent censored with(dat, censummary(y, censored=ycen)) # median/mean with(dat, cenmle(y, ycen, dist=\"lognormal\")) # boxplot with(dat, cenboxplot(obs=y, cen=ycen, log=FALSE, main=\"usgs.herbicides\")) # with(dat, boxplot(y)) pp <- with(dat, ros(obs=y, censored=ycen, forwardT=\"log\")) # default lognormal plot(pp) plotfun <- function(vv){ dat$y <- as.numeric(dat[[vv]]) dat$ycen <- is.na(dat$y) dat$y[is.na(dat$y)] <- .01 # qqnorm(log(dat$y), main=vv) # ordinary qq plot shows censored values pp <- with(dat, ros(obs=y, censored=ycen, forwardT=\"log\")) plot(pp, main=vv) # omits censored values } op <- par(mfrow=c(3,3)) vnames <- c(\"acetochlor\", \"alachlor\", \"ametryn\", \"atrazine\",\"CIAT\", \"CEAT\", \"cyanazine\", #\"CAM\", \"dimethenamid\", \"flufenacet\") for(vv in vnames) plotfun(vv) par(op) } # }"},{"path":"/reference/vaneeuwijk.drymatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"Multi-environment trial maize, dry matter content","code":""},{"path":"/reference/vaneeuwijk.drymatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"","code":"data(\"vaneeuwijk.drymatter\")"},{"path":"/reference/vaneeuwijk.drymatter.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"data frame 168 observations following 5 variables. year year site site, 4 levels variety variety, 6 levels y dry matter percent","code":""},{"path":"/reference/vaneeuwijk.drymatter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"Percent dry matter given. Site codes soil type classifications: SS=Southern Sand, CS=Central Sand, NS=Northern Sand, RC=River Clay. data balanced subset data analyzed van Eeuwijk, Keizer, Bakker (1995b) Kroonenberg, Basford, Ebskamp (1995). Used permission Fred van Eeuwijk.","code":""},{"path":"/reference/vaneeuwijk.drymatter.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"van Eeuwijk, Fred . Pieter M. Kroonenberg (1998). Multiplicative Models Interaction Three-Way ANOVA, Applications Plant Breeding Biometrics, 54, 1315-1333. https://doi.org/10.2307/2533660","code":""},{"path":"/reference/vaneeuwijk.drymatter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"Kroonenberg, P.M., Basford, K.E. & Ebskamp, .G.M. (1995). Three-way cluster component analysis maize variety trials. Euphytica, 84(1):31-42. https://doi.org/10.1007/BF01677554 van Eeuwijk, F.., Keizer, L.C.P. & Bakker, J.J. Van Eeuwijk. (1995b). Linear bilinear models analysis multi-environment trials: II. application data Dutch Maize Variety Trials Euphytica, 84(1):9-22. https://doi.org/10.1007/BF01677552 Hardeo Sahai, Mario M. Ojeda. Analysis Variance Random Models, Volume 1. Page 261.","code":""},{"path":"/reference/vaneeuwijk.drymatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-environment trial of maize, dry matter content — vaneeuwijk.drymatter","text":"","code":"if (FALSE) { # \\dontrun{ library(agridat) data(vaneeuwijk.drymatter) dat <- vaneeuwijk.drymatter dat <- transform(dat, year=factor(year)) dat <- transform(dat, env=factor(paste(year,site))) libs(HH) HH::interaction2wt(y ~ year+site+variety,dat,rot=c(90,0), x.between=0, y.between=0, main=\"vaneeuwijk.drymatter\") # anova model m1 <- aov(y ~ variety+env+variety:env, data=dat) anova(m1) # Similar to VanEeuwijk table 2 m2 <- aov(y ~ year*site*variety, data=dat) anova(m2) # matches Sahai table 5.5 # variance components model libs(lme4) libs(lucid) m3 <- lmer(y ~ (1|year) + (1|site) + (1|variety) + (1|year:site) + (1|year:variety) + (1|site:variety), data=dat) vc(m3) # matches Sahai page 266 ## grp var1 var2 vcov sdcor ## year:variety (Intercept) 0.3187 0.5645 ## year:site (Intercept) 7.735 2.781 ## site:variety (Intercept) 