First updates around documentation

R/BMCPMod.R

@@ -185,6 +185,8 @@ getContr <- function (
 
 #' @title getCritProb
 #' 
+#' @description This function calculates multiplicity adjusted 
+#' 
 #' @param mods An object of class "Mods" as specified in the Dosefinding package.
 #' @param dose_levels vector containing the different dosage levels.
 #' @param dose_weights Vector specifying weights for the different doses
@@ -197,15 +199,17 @@ getCritProb <- function (
 
   mods,
   dose_levels,
-  dose_weights,
+  dose_weights =NULL,
+  se_new_trial = NULL,
   alpha_crit_val = 0.025
 
 ) {
 
-  contr <- DoseFinding::optContr(
-    models = mods,
-    doses  = dose_levels,
-    w      = dose_weights)
+  contr <- getContr(mods  = mods,
+           dose_levels    = dose_levels ,
+           dose_weights   = dose_weights,
+           se_new_trial   = se_new_trial,
+           alpha_crit_val = alpha_crit_val)
 
   crit_prob <- stats::pnorm(DoseFinding::critVal(
     corMat      = contr$corMat,
@@ -317,13 +321,14 @@ addSignificance <- function (
 
 #' @title performBayesianMCP
 #' 
-#' @description performs bayesian MCP Test step.
+#' @description performs bayesian MCP Test step, as described in Fleischer et al. (Bayesian MCPMod. Pharmaceutical Statistics. 2022; 21(3): 654-670.) 
+#' Tests for a dose-response effect using a model-based multiple contrast test based on the (provided) posterior distribution. In particular for every dose-response candidate is calculated that the contrast  is bigger than 0 given the data observed
 #' 
-#' @param posterior_list a getPosterior object
+#' @param posterior_list a getPosterior object with information about the (mixture) posterior distribution per dose group 
 #' @param contr a getContrMat object, contrast matrix to be used for the testing step.
 #' @param crit_prob_adj a getCritProb object, specifying the critical value to be used for the testing (on the probability scale)
 #' 
-#' @return b_mcp test result 
+#' @return b_mcp test result, with information about p-values for the individual dose-response shapes   
 #' 
 #' @export
 performBayesianMCP <- function(

R/posterior.R

@@ -1,15 +1,16 @@
 
 #' @title getPosterior
 #' 
-#' @description Either the patient level data or both the mu_hat as well as the sd_hat must to be provided.
+#' @description Either the patient level data or both mu_hat as well as sd_hat must to be provided. If patient level data is provided mu_hat and se_hat are calculated within the function using a linear model.
+#' This function calculates the posterior for every dose group independently via the RBesT function postmix.
 #' 
 #' @param prior_list prior_list object
 #' @param data dataframe containing the information of dose and response. Default NULL
 #' Also a simulateData object can be provided.
-#' @param mu_hat vector of estimated mean values
-#' @param se_hat vector of estimated standard deviations.
-#' @param calc_ess tbd. Default NULL
-#'
+#' @param mu_hat vector of estimated mean values (per dose group).
+#' @param se_hat vector of estimated standard deviations (per dose group).
+#' @param calc_ess boolean variable, indicating whether effective sample size should be calculated. Default FALSE
+#' @return posterior_list, a posterior list object is returned with information about (mixture) posterior distribution per dose group
 #' @export
 getPosterior <- function(
   prior_list,
@@ -99,10 +100,11 @@ getPosteriorI <- function(
 
 #' @title getESS
 #' 
-#' @description blubber
+#' @description This function calculates the effective sample size for every dose group via the RBesT function ess.
 #' 
-#' @param post_list blubb
+#' @param post_list a posterior list object, for which the effective sample size (per dose group) should be calculated
 #'
+#' @return a vector of the effective sample sizes (per dose group) 
 #' @export
 getESS <- function (