#' 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)
}
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