R/mean_comparisons.check_model_bh_GxE.R

In priviere/PPBstats: An R package to perform analysis found within PPB programmes

#' Get mean comparisons from \code{\link{check_model.fit_model_bh_GxE}} object
#' 
#' @description
#' \code{mean_comparisons} performs mean comparisons from object coming from \code{\link{check_model.fit_model_bh_GxE}}
#'
#' @param x outputs from \code{\link{check_model.fit_model_bh_GxE}}
#'  
#' @param parameter parameter on which the mean comparison is done. 
#' The possible values are "alpha", "beta" and "theta"
#' 
#' @param alpha level of type one error. 0.05 (5\%) by default
#' 
#' @param type type of comparisons
#' \itemize{
#'  \item type = 1 for comparison two by two
#'  \item type = 2 for comparison to a specific threshold
#' }
#' 
#' @param get.at.least.X.groups For type = 1. 
#' If there are only one group with alpha, the minimum number of groups wanted with a higher type one error (i.e. lower confidence). 
#' If NULL, nothing is done.
#' 
#' @param precision For type = 1. The precision of the alpha with the correspondong groups from get.at.least.X.groups. The smaller the better, but the smaller the more time consuming due to computing matters
#' 
#' @param threshold For type = 2. The threshold to which a parameter is different
#' 
#' @param p.adj For all except type = 2. 
#' NULL for no adjustement of the type one error. 
#' p.adj can be "soft.bonf". 
#' 
#' p.adj = "soft.bonf" for a soft bonferonni correction to take into account multiple comparisons (alpha / nb of parameters)..
#' The comparisons is based on the probability of having a common distribution for each pair of parameter.
#' When there is only one group with the value of alpha, the function (via \code{get.at.least.X.groups argument}) returns at least X groups with a new value of alpha.
#' 
#' @param ... further arguments passed to or from other methods#' 
#' 
#' @details 
#' S3 method.
#' For more details, see in the book : https://priviere.github.io/PPBstats_book/intro-agro.html#section-bayes
#' 
#' @return 
#' A list of two elements:
#'    \itemize{
#'     \item mean.comparisons: a dataframe with the following columns : parameter, median, groups, number of groups, alpha (type one error), alpha.correction (correction used), entry, environment, location and year.
#'     \item Mpvalue : a square matrix with pvalue computed for each pair of parameter.
#'    }
#'  
#' @author Pierre Riviere
#' 
#' @seealso 
#' \itemize{
#'  \item \code{\link{mean_comparisons}}
#'  \item \code{\link{plot.PPBstats}}
#'  \item \code{\link{plot.mean_comparisons_model_bh_GxE}}
#' }
#' 
#' @export
#' 
mean_comparisons.check_model_bh_GxE <- function(
  x, 
  parameter,
  alpha = 0.05,
  type = 1,
  get.at.least.X.groups = 2,
  precision = 0.0005,
  threshold = 1,
  p.adj = "soft.bonf",
  ...
  ){
  
  # 1. Error message
  match.arg(parameter, c("alpha", "beta", "theta"), several.ok = FALSE)
  match.arg(p.adj, c("soft.bonf"), several.ok = FALSE)
  
  # 2. Get square matrice with pvalue or vector with pvalue ----------
  MCMC_par = function(MCMC, parameter, type, threshold, alpha, p.adj, precision, get.at.least.X.groups){
    MCMC_par = MCMC[,grep(paste("^", parameter, "\\[", sep = ""), colnames(MCMC))]
    out = get_mean_comparisons_and_Mpvalue(MCMC_par, parameter, type, threshold, alpha, p.adj, precision, get.at.least.X.groups) 
    return(out)
  }
  
  out <- MCMC_par(x$MCMC, parameter, type, threshold, alpha,
                  p.adj, precision, get.at.least.X.groups)

  # return results
  class(out) <- c("PPBstats", "mean_comparisons_model_bh_GxE")
  return(out)
}