Display k per comparison

Author: NightlordTW

R/helper.r

@@ -27,11 +27,10 @@ print.simss <- function(x, ...) {
   cat("Endpoints Tested:\n")
   for (i in 1:length(x$param$list_comparator)) {
     cat(paste0("  - ", paste(x$param$list_comparator[[i]], collapse = " vs. "), ": ",
-               paste(x$param$list_y_comparator[[i]], collapse = ", "), "\n")) #AUCinf and Cmax (multiple co-primary endpoints, m = 2)\n"))
+                  paste(x$param$list_y_comparator[[i]], collapse = ", "), " (k = ",
+               x$param.d$k[i], ")\n"))
   }
 
-
-
   cat("-------------------------------------------------------------\n")
   print(output, row.names = FALSE)  # Suppress row numbers
   #cat("-------------------------------------------------------------\n")

vignettes/sampleSize_parallel_2A3E.Rmd

@@ -217,7 +217,7 @@ Consider now we are interested in demonstrating equivalence for at least $k=1$ o
   ctype = "ROM",             # Test type (Ratio of Means)
   vareq = TRUE,              # Assume equal variances
   lognorm = TRUE,            # Log-normal distribution assumption
-  k = 1,                     # Reject at least one null hypothesis
+  k = c("Comparison" = 1),   # Reject at least one null hypothesis
   adjust = "bon",            # Bonferroni adjustment method
   ncores = 1,                # Number of cores for computation
   nsim = 1000,               # Number of stochastic simulations
@@ -240,7 +240,7 @@ As mentioned in [the Introduction](../articles/intopkg.html), the Bonferroni cor
   ctype = "ROM",             # Test type (Ratio of Means)
   vareq = TRUE,              # Assume equal variances
   lognorm = TRUE,            # Log-normal distribution assumption
-  k = 1,                     # Reject at least one null hypothesis
+  k = c("Comparison" = 1),   # Reject at least one null hypothesis
   adjust = "k",              # Adjustment method
   ncores = 1,                # Number of cores for computation
   nsim = 1000,               # Number of stochastic simulations
@@ -267,7 +267,7 @@ In this example, the sequential adjustment can be implemented by setting the fun
   ctype           = "ROM",                            # Test type (Ratio of Means)
   vareq           = TRUE,                             # Assume equal variances
   lognorm         = TRUE,                             # Log-normal distribution assumption
-  k               = 1,                                # Reject at least one null hypothesis
+  k               = c("Comparison" = 1),              # Reject at least one null hypothesis
   adjust          = "seq",                            # Sequential adjustment method
   type_y          = c("AUCinf" = 1, "AUClast" = 2, "Cmax" = 2), # Endpoint types
   ncores          = 1,                                # Number of cores for computation