check_model.fit_model_anova.R

#' Check if the classic anova model went well 
#'
#' @description
#' \code{check_model.fit_model_anova} computes tests to assess if the model went well. 
#' It is important to run this step before going ahead with the analysis otherwise you may make mistakes in the interpretation of the results.
#' 
#' @param x outputs from \code{\link{model_anova}}
#' 
#' @details
#' S3 method.
#' The different test apply to the model are explained in the book \href{https://priviere.github.io/PPBstats_book/intro-agro.html#section-freq}{here}.
#' 
#' @return It returns a list with the following elements:
#' 
#' \itemize{
#'  \item model_anova the output from the model
#'  \item data_ggplot a list containing information for ggplot:
#'  \itemize{
#'   \item data_ggplot_residuals a list containing :
#'    \itemize{
#'     \item data_ggplot_normality
#'     \item data_ggplot_skewness_test
#'     \item data_ggplot_kurtosis_test
#'     \item data_ggplot_shapiro_test
#'     \item data_ggplot_qqplot
#'     }
#'   \item data_ggplot_variability_repartition_pie
#'   \item data_ggplot_var_intra
#'  }
#' }
#' 
#' @author Pierre Riviere
#'
#' @seealso 
#' \itemize{
#' \item \code{\link{check_model}}
#' \item \code{\link{plot.check_model_anova}}
#' \item \code{\link{mean_comparisons}}
#' \item \code{\link{mean_comparisons.check_model_anova}}
#' }
#'  
#' @export
#'  
check_model.fit_model_anova <- function(
  x
){
  model = x$ANOVA$model
  
  out = c(list("info" = x$info, "model_anova" = x), "data_ggplot" = list(check_freq_anova(model)))
  
  class(out) <- c("PPBstats", "check_model_anova")
  
  return(out)
}