Improve documentation

Author: NightlordTW

R/SampleSize.R

@@ -490,7 +490,7 @@ sampleSize <- function(mu_list, varcov_list = NA, sigma_list = NA, cor_mat = NA,
   # Parameters related to design ----
 
   if (lognorm == TRUE & ctype == "DOM"){
-    stop("No test available for DOM when variables are log normal distributed")
+    stop("Testing is not supported for DOM when variables follow a log-normal distribution.")
   }
 
   if( lognorm == FALSE & ctype == "ROM" & dtype =="2x2"){

R/helper.r

@@ -19,7 +19,7 @@ print.simss <- function(x, ...) {
   sst <- x$response[["n_total"]]
 
   message(cat("### Sample Size Calculation Results ###"))
-  message(cat(paste0("For a study targeting ",100*tpower,"% power with a ",100*alpha, "% type-I error.")))
+  message(cat(paste0("For a trial targeting ",100*tpower,"% power with a ",100*alpha, "% type-I error.")))
   message(cat("-------------------------------------------------------------"))
   message(cat(paste0("The total required sample size to achieve ",100*power,"% power is ",sst," sample units.\n")))
   print(x$response[,-1], row.names = FALSE)

R/plot.sims.R

@@ -1,10 +1,23 @@
-#' @title plot.simss
-#' @description Plot of power vs sample size for all comparators and total of comparators
+#' @title Plot Power vs Sample Size for Simulation Results
+#' @description Generates a detailed plot showing the relationship between power and total sample size for each comparator and the overall combined comparators.
+#' The plot also includes confidence intervals for power estimates and highlights the target power with a dashed line for easy visual comparison.
 #'
-#' @param x Object type simss
-#' @param \ldots Additional arguments which are passed to simss object.
+#' @param x An object of class `simss` containing simulation results.
+#' @param \ldots Additional arguments to be passed to the `plot.simss` function for customization.
+#'
+#' @return A `ggplot` object illustrating:
+#'   - Power (y-axis) vs. Total Sample Size (x-axis) for individual endpoints and comparators.
+#'   - Error bars representing the 95% confidence interval of the power estimates.
+#'   - A dashed horizontal line indicating the target power for comparison.
+#'   - Faceted panels for each comparator, making it easy to compare results across different groups.
+#'
+#' @details
+#' The plot dynamically adjusts to exclude unnecessary components, such as redundant endpoints or comparators with insufficient data, ensuring clarity and simplicity.
+#' The `ggplot2` framework is used for visualizations, allowing further customization if needed.
+#'
+#' @author
+#' Johanna Muñoz \email{[email protected]}
 #'
-#' @return Plot of power vs total sample size per comparator and total of comparators
 #' @importFrom data.table .SD
 #' @export plot.simss
 #'
@@ -14,7 +27,7 @@ plot.simss <- function(x, ...){
 
   table.test <- x$table.test
   nsim <- as.numeric(x$param.d["nsim"])
-  tpower<-as.numeric(x$param.d["power"])
+  tpower <- as.numeric(x$param.d["power"])
   # Calculate totaly test across all comparators= power
   qnam <- colnames(table.test)[grep("^totaly",colnames(table.test))]
 

man/plot.simss.Rd

@@ -126,12 +126,13 @@ Key Considerations:
 
 We proceed similarly to the initial example, modifying the `list_comparator` to include multiple comparators. In this case, the list contains two elements specifying the simultaneous comparison between SB2 vs. RemEU and SB2 vs. RemUSA. This approach ensures that the equivalence assessment accounts for both reference products.
 
+Additionally, we must specify equivalence boundaries for each comparison individually. These boundaries define the acceptable range for equivalence and are provided separately for each comparator.
 
 ```{r}
-list_comparator <- list("EU" = c("SB2", "EU_Remicade"),
-                        "US" = c("SB2", "USA_Remicade"))
-list_lequi.tol <- list("EU" = 0.8, "US" = 0.8)     # Lower equivalence boundary
-list_uequi.tol <- list("EU" = 1/0.8, "US" = 1/0.8) # Upper equivalence boundary
+list_comparator <- list("EMA" = c("SB2", "EU_Remicade"),
+                        "FDA" = c("SB2", "USA_Remicade"))
+list_lequi.tol <- list("EMA" = 0.8, "FDA" = 0.8)     # Lower equivalence boundary
+list_uequi.tol <- list("EMA" = 1/0.8, "FDA" = 1/0.8) # Upper equivalence boundary
 ```
 
 ## Computing Sample Size