- Updated fix for print.BayesianMCP() error

- Harmonized attributes naming convention
- Fixed typo

NEWS.md

@@ -2,7 +2,7 @@
 
 - Re-submission of the 'BayesianMCPMod' package
 - Removed a test that occasionally failed on the fedora CRAN test system
-- Fixed a bug that would return wrong bootstrapped quantiles in getBootstrappedQuantiles()
+- Fixed a bug that would return wrong bootstrapped quantiles in getBootstrapQuantiles()
 - Added getBootstrapSamples(), a separate function for bootstrapping samples
 
 ## BayesianMCPMod 1.0.0 (31-Dec-2023)

R/BMCPMod.R

@@ -409,7 +409,7 @@ performBayesianMCPMod <- function (
 
     if (b_mcp[i, 1]) {
 
-      sign_models <- b_mcp[i, -c(1, 2)] > attr(b_mcp, "crit_prob_adj")
+      sign_models <- b_mcp[i, -c(1, 2)] > attr(b_mcp, "critProbAdj")
 
       model_fits  <- getModelFits(
         models      = model_shapes,
@@ -530,10 +530,10 @@ performBayesianMCP <- function(
 
   }
 
-  class(b_mcp)                 <- "BayesianMCP"
-  attr(b_mcp, "crit_prob_adj") <- crit_prob_adj
-  attr(b_mcp, "successRate")   <- mean(b_mcp[, 1])
-  attr(b_mcp, "ess_avg")       <- ifelse(
+  class(b_mcp)               <- "BayesianMCP"
+  attr(b_mcp, "critProbAdj") <- crit_prob_adj
+  attr(b_mcp, "successRate") <- mean(b_mcp[, 1])
+  attr(b_mcp, "essAvg")      <- ifelse(
     test = is.na(attr(posterior_list[[1]], "ess")),
     yes  = numeric(0),
     no   = rowMeans(sapply(posterior_list, function (posteriors) {

R/s3methods.R

@@ -24,33 +24,33 @@ print.BayesianMCP <- function (
 
   n_sim <- nrow(x)
 
-  model_successes <- getModelSuccesses(x)
-
   cat("Bayesian Multiple Comparison Procedure\n")
 
   if (n_sim == 1L) {
 
-    attr(x, "crit_prob_adj") <- NULL
-    attr(x, "success_rate")  <- NULL
-    class(x) <- NULL
+    attr(x, "critProbAdj") <- NULL
+    attr(x, "successRate") <- NULL
+    class(x)               <- NULL
 
     print.default(x, ...)
 
+    # if (any(!is.na(attr(x, "essAvg")))) {
+    #   
+    #   cat("Average Posterior ESS\n")
+    #   print(attr(x, "essAvg"), ...)
+    #   
+    # }
+
   } else {
 
+    model_successes <- getModelSuccesses(x)
+
     cat("  Estimated Success Rate: ", attr(x, "successRate"), "\n")
     cat("  N Simulations:          ", n_sim)
 
-  }
-
-  cat("\n")
-  cat("Model Significance Frequencies\n")
-  print(model_successes, ...)
-
-  if (any(!is.na(attr(x, "ess_avg")))) {
-
-    cat("Average Posterior ESS\n")
-    print(attr(x, "ess_avg"), ...)
+    cat("\n")
+    cat("Model Significance Frequencies\n")
+    print(model_successes, ...)
 
   }
 

tests/testthat/test-BMCPMod.R

@@ -29,7 +29,7 @@ test_that("base case input throws no error and has correct properties", {
 
   # assessDesign result (in this base case) should have crit_prob = 1 - alpha_crit_val
   expect_equal(
-    attr(eval_design$linear$BayesianMCP, "crit_prob"),
+    attr(eval_design$linear$BayesianMCP, "critProb"),
     1 - alpha_crit_val
   )
 
@@ -180,11 +180,11 @@ test_that("performBayesianMCP returns the right type of object under normal case
   )
 
   expect_true(
-    attr(b_mcp, "crit_prob_adj") == crit_pval
+    attr(b_mcp, "critProbAdj") == crit_pval
   )
 
   expect_type(
-    attr(b_mcp, "ess_avg"), "logical"
+    attr(b_mcp, "essAvg"), "logical"
   )
 
   expect_type(