Merge pull request #22 from Boehringer-Ingelheim/update-analysis-vign…

…ette

Update analysis vignette

DESCRIPTION

@@ -8,7 +8,8 @@ Authors@R: c(
     person("Lars", "Andersen", , "[email protected]", role = "aut"),
     person("Sebastian", "Bossert", , "[email protected]", role = "aut"),
     person("Steven", "Brooks", , "[email protected]", role = "ctb"),
-    person("Jonas", "Schick", , "[email protected]", role = "ctb")
+    person("Jonas", "Schick", , "[email protected]", role = "ctb"),
+    person("Gina", "Kleibrink", , "[email protected]", role = "ctb")
   )
 Description: Bayesian MCPMod (Fleischer et al. (2022)
     <doi:10.1002/pst.2193>) is an innovative method that improves the
@@ -46,6 +47,11 @@ Suggests:
     dplyr,
     knitr,
     rmarkdown,
+    MCPModPack,
+    data.table,
+    doFuture,
+    doRNG,
+    kableExtra,
     spelling,
     testthat (>= 3.0.0)
 VignetteBuilder: quarto

R/s3methods.R

@@ -16,39 +16,37 @@ print.BayesianMCPMod <- function (
 
 #' @export
 print.BayesianMCP <- function(x, ...) {
-  cat("Bayesian Multiple Comparison Procedure\n\n")
-
-  cat("Summary:\n")
-  cat("  Sign:", x[1, "sign"], "\n")
-  cat("  Critical Probability:", x[1, "crit_prob_adj"], "\n")
-  cat("  Maximum Posterior Probability:", x[1, "max_post_prob"], "\n\n")
+  #cat("Bayesian Multiple Comparison Procedure\n\n")
   n_sim <- nrow(x)
-  cat("Effective Sample Size (ESS) per Dose Group:\n")
-  print(attr(x, "ess_avg"), row.names = FALSE)
-  cat("\n")
-
-  cat("Posterior Probabilities for Model Shapes:\n")
-  model_probs <- x[1, grep("^post_probs\\.", colnames(x))]
-  model_names <- gsub("post_probs\\.", "", names(model_probs))
-  model_df <- data.frame(Model = model_names, Probability = unlist(model_probs))
-  print(model_df, row.names = FALSE)
-
+  #cat("Effective Sample Size (ESS) per Dose Group:\n")
+  #print(attr(x, "ess_avg"), row.names = FALSE)
+  #cat("\n")
   cat("Bayesian Multiple Comparison Procedure\n")
 
   if (n_sim == 1L) {
 
+    cat("Summary:\n")
+    cat("  Sign:", x[1, "sign"], "\n")
+    cat("  Critical Probability:", x[1, "crit_prob_adj"], "\n")
+    cat("  Maximum Posterior Probability:", x[1, "max_post_prob"], "\n\n")
+
     attr(x, "critProbAdj") <- NULL
     attr(x, "successRate") <- NULL
     class(x)               <- NULL
+    cat("Posterior Probabilities for Model Shapes:\n")
+    model_probs <- x[1, grep("^post_probs\\.", colnames(x))]
+    model_names <- gsub("post_probs\\.", "", names(model_probs))
+    model_df <- data.frame(Model = model_names, Probability = unlist(model_probs))
+    print(model_df, row.names = FALSE)
+
+   # print.default(x, ...)
 
-    print.default(x, ...)
+    if (any(!is.na(attr(x, "essAvg")))) {
 
-    # if (any(!is.na(attr(x, "essAvg")))) {
-    #
-    #   cat("Average Posterior ESS\n")
-    #   print(attr(x, "essAvg"), ...)
-    #
-    # }
+      cat("Average Posterior ESS\n")
+      print(attr(x, "essAvg"), ...)
+
+    }
 
   } else {
 

_pkgdown.yml

@@ -12,5 +12,5 @@ articles:
   navbar: ~
   contents:
   - analysis_normal
-  - analysis_normal_noninformative
   - Simulation_Example
+  - Comparison_vignette