Updated demo Chap 3 and 8. Cleaned some plot code and corrected some …

…bugs

NAMESPACE

@@ -56,4 +56,5 @@ export(report_paths)
 export(rho_A)
 export(simplePLS)
 export(single_item)
+export(slope_analysis)
 export(two_stage)

R/data.R

@@ -89,10 +89,69 @@
 #'   \item{mga_2}{Multi Group Analysis 2.}
 #'   \item{mga_3}{Multi Group Analysis 3.}
 #'   \item{mga_4}{Multi Group Analysis 4.}
+#'   \item{switch_1}{It takes me a great deal of time to switch to another company.}
+#'   \item{switch_2}{It costs me too much to switch to another company.}
+#'   \item{switch_3}{It takes a lot of effort to get used to a new company with its specific "rules" and practices.}
+#'   \item{switch_4}{In general, it would be a hassle switching to another company.}
 #' }
 #'
 #' @details The data frame mobi contains the observed data for the model specified by Corporate Reputation.
 #' @references Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (2nd ed.). Thousand Oakes, CA: Sage.
 #' @examples data("corp_rep_data")
 #'
 "corp_rep_data"
+
+#' A Second Measurement Instrument for the Corporate Reputation Model
+#'
+#' The data set is used as measurement instrument for corporate reputation.
+#'
+#' @format A data frame with 347 rows and 49 variables:
+#' \describe{
+#'   \item{servicetype}{A categorical variable for the service type: 1=Postpaid plan; 2=Prepaid plan.}
+#'   \item{serviceprovider}{A categorical variable for the service provider: 1, 2, 3, or 4.}
+#'   \item{cusa}{If you consider your experiences with "company", how satisfied are you with "company"?}
+#'   \item{cusl_1}{I would recommend "the company" to friends and relatives.}
+#'   \item{cusl_2}{If I had to choose again, I would choose "the company" as my mobile phone services provider.}
+#'   \item{cusl_3}{I will remain a customer of "the company" in the future.}
+#'   \item{qual_1}{The products/services offered by "the company" are of high quality.}
+#'   \item{qual_2}{"The company" is an innovator, rather than an imitator with respect to the mobile phone service industry.}
+#'   \item{qual_3}{"The company's" services/products offer good quality for money.}
+#'   \item{qual_4}{The services "the company" offers are good.}
+#'   \item{qual_5}{Customer concerns are held in high regard at "the company".}
+#'   \item{qual_6}{"The company" is a reliable partner for customers.}
+#'   \item{qual_7}{"The company" is a trustworthy company.}
+#'   \item{qual_8}{I have a lot of respect for "the company".}
+#'   \item{perf_1}{"The company" is a very well managed company.}
+#'   \item{perf_2}{"The company" is an economically stable company.}
+#'   \item{perf_3}{The business risk of "the company" is reasonable compared to its competitors.}
+#'   \item{perf_4}{The growth of "the company" is promising.}
+#'   \item{perf_5}{"The company" has a clear vision about the future of the company.}
+#'   \item{csor_1}{"The company" behaves in a socially conscious way.}
+#'   \item{csor_2}{"The company" is honest in giving information to the public.}
+#'   \item{csor_3}{"The company" competes fairly in the indsutry.}
+#'   \item{csor_4}{"The company" cares for the preservation of the environment.}
+#'   \item{csor_5}{"The company" is doing more than just making profits.}
+#'   \item{attr_1}{"The company" is succesful in attracting high-quality employees.}
+#'   \item{attr_2}{I could see myself working at "the company".}
+#'   \item{attr_3}{I like the physical appearance of "the company" (company/buildings/shops, etc.).}
+#'   \item{comp_1}{"The company" is a top competitor in its market.}
+#'   \item{comp_2}{As far as I know, "the company" is recognized worldwide.}
+#'   \item{comp_3}{I believe that "the company" performs at a premium level.}
+#'   \item{like_1}{"The company" is a company that I can better identify with than other companies.}
+#'   \item{like_2}{When comparing with other companies, "The company" is the company I would regret more if it no longer existed.}
+#'   \item{like_3}{I regard "the company" as a likeable company.}
+#'   \item{qual_global}{Please assess the general quality of "the company".}
+#'   \item{perf_global}{Please assess the general performance of "the company".}
+#'   \item{csor_global}{Please assess the extent to which "the company" acts in socially conscious ways.}
+#'   \item{attr_global}{Please assess the attractiveness of "the company".}
+#'   \item{switch_1}{It takes me a great deal of time to switch to another mobile phone services provider.}
+#'   \item{switch_2}{It costs me too much to switch to another mobile phone services provider.}
+#'   \item{switch_3}{It takes a lot of effort to get used to a new mobile phone services provider with its specific "rules" and practices.}
+#'   \item{switch_4}{In general, it would be a hassle switching to another mobile phone services provider.}
+#' }
+#'
+#' @details The data frame mobi contains the observed data for the model specified by Corporate Reputation.
+#' @references Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197-211.
+#' @examples data("corp_rep_data2")
+#'
+"corp_rep_data2"

R/evaluate_measurement_model.R

@@ -15,7 +15,8 @@ reliability <- function(seminr_model) {
   mat2 <- rho_A(seminr_model)
   table <- cbind(alpha, mat1, mat2)
   comment(table) <- "Alpha, rhoC, and rhoA should exceed 0.7 while AVE should exceed 0.5"
-  convert_to_table_output(table)
+  class(table) <- append(class(table), c("table_output","reliability_table"))
+  return(table)
 }
 
 # Validity ----

R/plot_results.R

@@ -12,7 +12,7 @@ plot.reliability_table <- function(x, ...) {
   stopifnot(inherits(x, "reliability_table"))
 
   metrics <- cbind(1:nrow(x), x)
-  plot(metrics[,1:2], xlim=c(1, nrow(metrics[,-1])), ylim=c(min(metrics[,-1] - 0.1), max(metrics[,-1])),
+  plot(metrics[,1:2], xlim=c(0.7, nrow(metrics[,-1])+0.3), ylim=c(min(metrics[,-1] - 0.1), max(metrics[,-1])),
        frame.plot = FALSE, xaxt='n', ylab='', xlab = '', pch='')
 
   # Grid
@@ -52,3 +52,31 @@ plot.reliability_table <- function(x, ...) {
 
   invisible(x)
 }
+
+#' @export
+slope_analysis <- function(moderated_model, dv, moderator, iv, leg_place = "bottomright") {
+  mat <- matrix(c(-1, 1, -1,
+                  -1, 0,  0,
+                  -1,-1,  1,
+                  0, 0, -1,
+                  0, 0,  0,
+                  0, 0,  1,
+                  1,-1, -1,
+                  1, 0,  0,
+                  1, 1,  1), nrow = 9, ncol = 3, byrow = TRUE)
+  res <- mat %*% moderated_model$path_coef[c(iv,paste(iv,"*",moderator, sep = ""),moderator),dv]
+
+  plot(c(-1,0,1), res[c(1,4,7)], type="n", xlab = iv, ylab = dv,
+       xlim = c(-1,1), ylim = c(min(res),max(res)))
+  lines(c(-1,0,1), res[c(1,4,7)], lty = 2)
+  lines(c(-1,0,1), res[c(2,5,8)], lty = 1)
+  lines(c(-1,0,1), res[c(3,6,9)], lty = 3)
+  grid()
+  legend(leg_place, c("Mod at -1SD", "Mod at Mean", "Mod at 1SD"), lty=c(2,1,3),
+         horiz=FALSE, bty="n", cex = 0.8
+  )
+}
+
+
+
+

demo/00Index

@@ -5,7 +5,9 @@ seminr-pls-interaction   A demonstration of modeling an interaction using SEMinR
 seminr-alternative-models   A demonstration of alternate model specification, quick and easy
 seminr-pls-higher_order   A demonstration of modeling a higher order construct in SEMinR syntax
 seminr-cbsem-cfa-ecsi   A demonstration of the CBSEM estimation of the ECSI model using SEMinR syntax
+seminr-primer-chap3   A demonstration of the code used in Chapter 3 of the PLS Primer for R Workbook
 seminr-primer-chap4   A demonstration of the code used in Chapter 4 of the PLS Primer for R Workbook
 seminr-primer-chap5   A demonstration of the code used in Chapter 5 of the PLS Primer for R Workbook
 seminr-primer-chap6   A demonstration of the code used in Chapter 6 of the PLS Primer for R Workbook
 seminr-primer-chap7   A demonstration of the code used in Chapter 7 of the PLS Primer for R Workbook
+seminr-primer-chap8   A demonstration of the code used in Chapter 8 of the PLS Primer for R Workbook

demo/seminr-primer-chap3.R

@@ -0,0 +1,63 @@
+### Accompanying Code for the PLS Primer in R Workbook
+## Chapter 3: Introduction to SEMinR
+
+# Load the SEMinR library
+library(seminr)
+
+# Inspect the head of corp_rep_data dataset ----
+head(corp_rep_data)
+
+# Create measurement model ----
+simple_mm <- constructs(
+  composite("COMP", multi_items("comp_", 1:3), weights = mode_A),
+  composite("CSOR", multi_items("csor_", 1:5), weights = mode_B),
+  composite("ATTR", multi_items("attr_", 1:3), weights = mode_B)
+)
+
+# Create structural model ----
+simple_sm <- relationships(
+  paths(from = c("CSOR", "ATTR"), to = "COMP")
+)
+
+# Estimate the model
+corp_rep_simple_model <- estimate_pls(data = corp_rep_data,
+  measurement_model = simple_mm,
+  structural_model  = simple_sm,
+  inner_weights = path_weighting,
+  missing = mean_replacement,
+  missing_value = "-99")
+
+# Estimate the model with default settings
+corp_rep_simple_model <- estimate_pls(data = corp_rep_data,
+  measurement_model = simple_mm,
+  structural_model  = simple_sm)
+
+# Summarize the model results
+summary_simple_corp_rep <- summary(corp_rep_simple_model)
+
+# Inspect the structural paths
+summary_simple_corp_rep$paths
+
+# Inspect the construct reliability metrics
+summary_simple_corp_rep$reliability
+
+# Bootstrap the model
+boot_simple_corp_rep <- bootstrap_model(seminr_model = corp_rep_simple_model,
+  nboot = 1000,
+  cores = NULL,
+  seed = 123)
+
+# Store the summary of the bootstrapped model
+sum_boot_simple_corp_rep <- summary(boot_simple_corp_rep)
+
+# Inspect the bootstrapped structural paths
+sum_boot_simple_corp_rep$bootstrapped_paths
+
+# Inspect the bootstrapped outer weights
+sum_boot_simple_corp_rep$bootstrapped_weights
+
+# Write the bootstrapped paths object to csv file
+write.csv(x = sum_boot_simple_corp_rep$bootstrapped_weights, file = "boot_paths.csv")
+
+# Generate the plot for exporting
+plot(summary_simple_corp_rep$reliability)

demo/seminr-primer-chap7.R

@@ -42,104 +42,20 @@ boot_corp_rep_ext <- bootstrap_model(seminr_model = corp_rep_pls_model_ext,
 summary_boot_corp_rep_ext <- summary(boot_corp_rep_ext,
                                      alpha = 0.05)
 
-# Inspect the structural model collinearity VIF
-summary_corp_rep_ext$vif_antecedents
+# Inspect indirect effects
+summary_corp_rep_ext$total_indirect_effects
 
-# Inspect the structural paths
-summary_boot_corp_rep_ext$bootstrapped_paths
-
-# Inspect the total effects
-summary_boot_corp_rep_ext$bootstrapped_total_paths
+# Obtain confidence intervals for indirect effects
+total_indirect_ci(boot_corp_rep_ext, from = "COMP", to = "CUSL", alpha = 0.05)
+total_indirect_ci(boot_corp_rep_ext, from = "LIKE", to = "CUSL", alpha = 0.05)
 
-# Inspect the model RSquares
+# Inspect the direct effects
 summary_corp_rep_ext$paths
 
-# Inspect the effect sizes
-summary_corp_rep_ext$fSquare
-
-# Generate the model predictions
-predict_corp_rep_ext <- predict_pls(
-  model = corp_rep_pls_model_ext,
-  technique = predict_DA,
-  noFolds = 10,
-  reps = 10)
-
-# Summarize the prediction results
-sum_predict_corp_rep_ext <- summary(predict_corp_rep_ext)
-
-# Analyze the distribution of prediction error
-par(mfrow=c(1,3))
-plot(sum_predict_corp_rep_ext, indicator = "cusl_1")
-plot(sum_predict_corp_rep_ext, indicator = "cusl_2")
-plot(sum_predict_corp_rep_ext, indicator = "cusl_3")
-
-# Inspect the results of PLSpredict
-sum_predict_corp_rep_ext
-
-# Estimate alternative models
-# Create measurement model ----
-measurement_model <- constructs(
-  composite("QUAL", multi_items("qual_", 1:8), weights = mode_B),
-  composite("PERF", multi_items("perf_", 1:5), weights = mode_B),
-  composite("CSOR", multi_items("csor_", 1:5), weights = mode_B),
-  composite("ATTR", multi_items("attr_", 1:3), weights = mode_B),
-  composite("COMP", multi_items("comp_", 1:3)),
-  composite("LIKE", multi_items("like_", 1:3)),
-  composite("CUSA", single_item("cusa")),
-  composite("CUSL", multi_items("cusl_", 1:3))
-)
-
-# Create structural models
-# Model 1
-structural_model1 <- relationships(
-  paths(from = c("QUAL","PERF","CSOR","ATTR"), to = c("COMP", "LIKE")),
-  paths(from = c("COMP","LIKE"),  to = c("CUSA", "CUSL")),
-  paths(from = "CUSA", to = c("CUSL"))
-)
-# Model 2
-structural_model2 <- relationships(
-  paths(from = c("QUAL","PERF","CSOR","ATTR"), to = c("COMP", "LIKE")),
-  paths(from = c("COMP","LIKE"),  to = c("CUSA")),
-  paths(from = c("LIKE"),  to = c("CUSL")),
-  paths(from = "CUSA", to = c("CUSL"))
-)
-# Model 3
-structural_model3 <- relationships(
-  paths(from = c("QUAL","PERF","CSOR","ATTR"),        to = c("COMP", "LIKE")),
-  paths(from = c("COMP","LIKE"),  to = c("CUSA")),
-  paths(from = "CUSA",      to = c("CUSL"))
-)
-
-# Estimate and summarize the models ----
-pls_model1 <- estimate_pls(data  = corp_rep_data,
-                           measurement_model = measurement_model,
-                           structural_model  = structural_model1)
-sum_model1 <- summary(pls_model1)
-pls_model2 <- estimate_pls(data  = corp_rep_data,
-                           measurement_model = measurement_model,
-                           structural_model  = structural_model2)
-sum_model2 <- summary(pls_model2)
-pls_model3 <- estimate_pls(data  = corp_rep_data,
-                           measurement_model = measurement_model,
-                           structural_model  = structural_model3)
-sum_model3 <- summary(pls_model3)
-
-# Inspect the IT Criteria matrix of Model1
-sum_model1$it_criteria
-
-# Subset the matrix to only return the BIC row and CUSL column
-sum_model1$it_criteria["BIC", "CUSL"]
-
-# Collect the vector of BIC values for CUSL
-itcriteria_vector <- c(sum_model1$it_criteria["BIC","CUSL"],
-                       sum_model2$it_criteria["BIC","CUSL"],
-                       sum_model3$it_criteria["BIC","CUSL"])
-
-# Assign the model names to IT Criteria vector
-names(itcriteria_vector) <- c("Model1", "Model2", "Model3")
-
-# Inspect the IT Criteria vector for competing models.
-itcriteria_vector
+# Inspect the confidence intervals for direct effects
+summary_boot_corp_rep_ext$bootstrapped_paths
 
-# Calculate the model BIC Akaike Weights
-compute_itcriteria_weights(itcriteria_vector)
+# Calculate the sign of p1*p2*p3
+summary_corp_rep_ext$paths["LIKE", "CUSL"] *
+  summary_corp_rep_ext$paths["LIKE","CUSA"] *
+  summary_corp_rep_ext$paths["CUSA","CUSL"]

demo/seminr-primer-chap8.R

@@ -0,0 +1,53 @@
+library(seminr)
+
+# Load the data
+corp_rep_data <- corp_rep_data
+
+# Create measurement model ----
+corp_rep_mm_mod <- constructs(
+  composite("QUAL", multi_items("qual_", 1:8), weights = mode_B),
+  composite("PERF", multi_items("perf_", 1:5), weights = mode_B),
+  composite("CSOR", multi_items("csor_", 1:5), weights = mode_B),
+  composite("ATTR", multi_items("attr_", 1:3), weights = mode_B),
+  composite("COMP", multi_items("comp_", 1:3)),
+  composite("LIKE", multi_items("like_", 1:3)),
+  composite("CUSA", single_item("cusa")),
+  composite("SC", multi_items("switch_", 1:4)),
+  composite("CUSL", multi_items("cusl_", 1:3)),
+  interaction_term(iv = "CUSA", moderator = "SC", method = two_stage, weights = mode_A)
+)
+
+# Create structural model ----
+corp_rep_sm_mod <- relationships(
+  paths(from = c("QUAL", "PERF", "CSOR", "ATTR"), to = c("COMP", "LIKE")),
+  paths(from = c("COMP", "LIKE"), to = c("CUSA", "CUSL")),
+  paths(from = c("CUSA", "SC", "CUSA*SC"), to = c("CUSL"))
+)
+
+# Estimate the model ----
+corp_rep_pls_model_mod <- estimate_pls(data = corp_rep_data,
+  measurement_model = corp_rep_mm_mod,
+  structural_model  = corp_rep_sm_mod,
+  missing = mean_replacement,
+  missing_value = "-99")
+
+# Extract the summary
+sum_corp_rep_mod <- summary(corp_rep_pls_model_mod)
+
+# Bootstrap the model ----
+boot_corp_rep_mod <- bootstrap_model(seminr_model = corp_rep_pls_model_mod,
+                                     nboot = 10000)
+
+# Summarize the results of the bootstrap
+sum_boot_corp_rep_mod <- summary(boot_corp_rep_mod,
+                                 alpha = 0.05)
+
+# Inspect the bootstrapped structural paths
+sum_boot_corp_rep_mod$bootstrapped_paths
+
+# Simple slope analysis plot
+slope_analysis(moderated_model = corp_rep_pls_model_mod,
+               dv = "CUSL",
+               moderator = "SC",
+               iv = "CUSA",
+               leg_place = "bottomright")