docs

R/check_normality.R

@@ -58,7 +58,7 @@ check_normality.default <- function(x, ...) {
 
   if (!insight::model_info(x)$is_linear) {
     insight::format_alert(
-      "Checking normality of residuals is only appropriate for linear models."
+      "Checking normality of residuals is only appropriate for linear models. It is recommended to use `simulate_residuals()` and `check_residuals()` to check generalized linear (mixed) models for uniformity of residuals." # nolint
     )
     return(NULL)
   }
@@ -87,7 +87,7 @@ check_normality.glm <- function(x, ...) {
 
   insight::format_alert(
     "There's no formal statistical test for normality for generalized linear model.",
-    "Please use `plot()` on the return value of this function: `plot(check_normality(model))`"
+    "Instead, please use `simulate_residuals()` and `check_residuals()` to check for uniformity of residuals."
   )
   invisible(out)
 }

R/check_residuals.R

@@ -12,6 +12,7 @@
 #' @param ... Passed down to [`stats::ks.test()`].
 #'
 #' @details Uniformity of residuals is checked using a Kolmogorov-Smirnov test.
+#' There is a `plot()` method to visualize the distribution of the residuals.
 #'
 #' @seealso [`simulate_residuals()`]
 #'

R/simulate_residuals.R

@@ -18,7 +18,8 @@
 #' @details This function is a small wrapper around [`DHARMa::simulateResiduals()`].
 #' It basically only sets `plot = FALSE` and adds an additional class attribute
 #' (`"performance_sim_res"`), which allows using the DHARMa object in own plotting
-#' functions in the **see** package. See also `vignette("DHARMa")`.
+#' functions in the **see** package. See also `vignette("DHARMa")`. There is a
+#' `plot()` method to visualize the distribution of the residuals.
 #'
 #' @references
 #'