From ef0f3bffb8f16af0181db188951a8572f6ca0050 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Paul-Christian=20B=C3=BCrkner?= Date: Wed, 5 Jan 2022 16:58:51 +0100 Subject: [PATCH] minor fixes to references --- README.Rmd | 7 ++--- README.md | 73 ++++++++++++++++++++++++++------------------------- inst/CITATION | 4 +-- 3 files changed, 43 insertions(+), 41 deletions(-) diff --git a/README.Rmd b/README.Rmd index 71f68aaf..17b65a2f 100644 --- a/README.Rmd +++ b/README.Rmd @@ -245,9 +245,10 @@ https://mc-stan.org/posterior/>. When using the MCMC convergence diagnostics `rhat`, `ess_bulk`, or `ess_tail`, please also cite -* Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. (2020). +* Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. (2021). Rank-normalization, folding, and localization: An improved Rhat for assessing -convergence of MCMC. *Bayesian Analysis*. +convergence of MCMC (with discussion). *Bayesian Analysis*. 16(2), 667–718. +doi.org/10.1214/20-BA1221 The same information can be obtained by running `citation("posterior")`. @@ -259,7 +260,7 @@ Hall/CRC. Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. (2021). Rank-normalization, folding, and localization: An improved Rhat for assessing -convergence of MCMC (with discussion). *Bayesian Analysis*. 16(2), 667-–718. +convergence of MCMC (with discussion). *Bayesian Analysis*. 16(2), 667–718. doi.org/10.1214/20-BA1221 ### Licensing diff --git a/README.md b/README.md index 81367919..dea72bd8 100644 --- a/README.md +++ b/README.md @@ -294,11 +294,11 @@ print(x4) #> # A draws_matrix: 5 iterations, 1 chains, and 3 variables #> variable #> draw alpha beta theta -#> 1 1.11 1 0.565 -#> 2 -0.41 1 0.015 -#> 3 -0.28 1 0.591 -#> 4 -0.40 1 1.773 -#> 5 -0.30 1 1.357 +#> 1 0.39 1 2.27 +#> 2 -0.80 1 0.86 +#> 3 0.95 1 1.93 +#> 4 0.38 1 0.67 +#> 5 0.18 1 2.04 ``` Or, we can bind `x1` and `x2` together along the `'draw'` dimension: @@ -309,16 +309,16 @@ print(x5) #> # A draws_matrix: 10 iterations, 1 chains, and 2 variables #> variable #> draw alpha beta -#> 1 1.11 1 -#> 2 -0.41 1 -#> 3 -0.28 1 -#> 4 -0.40 1 -#> 5 -0.30 1 -#> 6 0.46 2 -#> 7 0.15 2 -#> 8 -0.60 2 -#> 9 1.84 2 -#> 10 0.62 2 +#> 1 0.39 1 +#> 2 -0.80 1 +#> 3 0.95 1 +#> 4 0.38 1 +#> 5 0.18 1 +#> 6 0.13 2 +#> 7 0.10 2 +#> 8 -0.61 2 +#> 9 0.12 2 +#> 10 1.48 2 ``` As with all **posterior** methods, `bind_draws` can be used with all @@ -337,27 +337,27 @@ x <- as_draws_matrix(x) print(x) #> # A draws_matrix: 10 iterations, 1 chains, and 5 variables #> variable -#> draw V1 V2 V3 V4 V5 -#> 1 0.22 0.621 -0.580 0.813 -0.91 -#> 2 -0.40 -0.372 -0.112 -0.710 0.46 -#> 3 0.41 0.242 0.496 0.175 0.19 -#> 4 1.01 -0.104 0.582 0.071 -0.45 -#> 5 0.56 -1.592 -0.272 -0.110 -0.26 -#> 6 -0.70 -1.492 0.040 0.531 -0.26 -#> 7 0.70 0.191 -0.655 -0.516 -2.75 -#> 8 -1.06 0.033 -0.141 0.359 1.40 -#> 9 0.75 0.109 0.015 -0.449 1.25 -#> 10 0.31 2.171 1.093 -0.749 0.18 +#> draw V1 V2 V3 V4 V5 +#> 1 -0.89 0.37 -0.25 -0.57 -2.85 +#> 2 1.84 0.19 0.39 -0.52 1.26 +#> 3 0.79 -0.74 -1.61 0.99 -0.11 +#> 4 -2.25 0.28 -0.19 -0.33 0.92 +#> 5 0.58 0.35 -0.92 0.56 0.82 +#> 6 -1.38 -0.12 -0.40 -1.23 -0.60 +#> 7 -0.18 1.18 -1.27 0.51 0.78 +#> 8 0.17 1.50 -2.12 -0.45 -0.73 +#> 9 -0.60 0.69 -0.43 -1.40 1.14 +#> 10 0.18 0.96 -1.37 -0.58 -0.63 summarise_draws(x, "mean", "sd", "median", "mad") #> # A tibble: 5 × 5 -#> variable mean sd median mad -#> -#> 1 V1 0.178 0.679 0.356 0.545 -#> 2 V2 -0.0193 1.06 0.0712 0.459 -#> 3 V3 0.0466 0.541 -0.0489 0.559 -#> 4 V4 -0.0587 0.539 -0.0196 0.686 -#> 5 V5 -0.114 1.18 -0.0353 0.670 +#> variable mean sd median mad +#> +#> 1 V1 -0.174 1.16 -0.00490 1.03 +#> 2 V2 0.467 0.651 0.358 0.596 +#> 3 V3 -0.817 0.770 -0.671 0.798 +#> 4 V4 -0.301 0.773 -0.486 0.672 +#> 5 V5 -0.000826 1.27 0.338 1.27 ``` Instead of `as_draws_matrix()` we also could have just used @@ -390,8 +390,9 @@ When using the MCMC convergence diagnostics `rhat`, `ess_bulk`, or `ess_tail`, please also cite - Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. - (2020). Rank-normalization, folding, and localization: An improved - Rhat for assessing convergence of MCMC. *Bayesian Analysis*. + (2021). Rank-normalization, folding, and localization: An improved + Rhat for assessing convergence of MCMC (with discussion). *Bayesian + Analysis*. 16(2), 667–718. doi.org/10.1214/20-BA1221 The same information can be obtained by running `citation("posterior")`. @@ -404,7 +405,7 @@ Chapman and Hall/CRC. Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. (2021). Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of MCMC (with discussion). *Bayesian Analysis*. -16(2), 667-–718. doi.org/10.1214/20-BA1221 +16(2), 667–718. doi.org/10.1214/20-BA1221 ### Licensing diff --git a/inst/CITATION b/inst/CITATION index 147c5f3e..8f9f75dc 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -15,13 +15,13 @@ bibentry(bibtype = "Misc", bibentry(bibtype = "Article", title = "Rank-normalization, folding, and localization: An improved Rhat -for assessing convergence of MCMC", +for assessing convergence of MCMC (with discussion)", author = c(person("Aki", "Vehtari"), person("Andrew", "Gelman"), person("Daniel", "Simpson"), person("Bob", "Carpenter"), person("Paul-Christian", "Bürkner")), - year = "2020", + year = "2021", journal = "Bayesian Analysis", header = "To cite the MCMC convergence diagnostics:" )