draft

NAMESPACE

@@ -70,7 +70,6 @@ S3method(check_normality,htest)
 S3method(check_normality,lmerModLmerTest)
 S3method(check_normality,merMod)
 S3method(check_normality,numeric)
-S3method(check_normality,performance_simres)
 S3method(check_outliers,BFBayesFactor)
 S3method(check_outliers,character)
 S3method(check_outliers,data.frame)
@@ -106,6 +105,9 @@ S3method(check_overdispersion,poissonmfx)
 S3method(check_predictions,BFBayesFactor)
 S3method(check_predictions,default)
 S3method(check_predictions,lme)
+S3method(check_residuals,DHARMa)
+S3method(check_residuals,default)
+S3method(check_residuals,performance_simres)
 S3method(check_singularity,MixMod)
 S3method(check_singularity,clmm)
 S3method(check_singularity,cpglmm)

R/check_normality.R

@@ -92,25 +92,6 @@ check_normality.glm <- function(x, ...) {
   invisible(out)
 }
 
-# DHARMa -------------------
-
-#' @export
-check_normality.performance_simres <- function(x, ...) {
-  # check for normality of residuals
-  res <- stats::residuals(x, quantileFunction = stats::qnorm)
-  p.val <- suppressWarnings(stats::ks.test(stats::residuals(x), "punif"))$p.value
-
-  attr(p.val, "data") <- res
-  attr(p.val, "object_name") <- NULL
-  attr(p.val, "effects") <- "fixed"
-  attr(p.val, "type") <- "residuals"
-  attr(p.val, "model") <- x$fittedModel
-  attr(p.val, "model_info") <- insight::model_info(x$fittedModel)
-  class(p.val) <- unique(c("check_normality", "see_check_normality", "performance_simres", class(p.val)))
-
-  p.val
-}
-
 # numeric -------------------
 
 #' @export

R/check_residuals.R

@@ -5,22 +5,46 @@
 #' misspecification problems, such as over/underdispersion, zero-inflation, and
 #' residual spatial and temporal autocorrelation.
 #'
-#' @param x An object returned by [`simulated_residuals()`].
-#' @param ... Passed down to [`DHARMa::testUniformity()`]
+#' @param x An object returned by [`simulate_residuals()`] or
+#' [`DHARMa::simulateResiduals()`].
+#' @param alternative A character string specifying the alternative hypothesis.
+#' See [`stats::ks.test()`] for details.
+#' @param ... Passed down to [`stats::ks.test()`]
+#'
+#' @examplesIf require("DHARMa")
+#' dat <- DHARMa::createData(sampleSize = 100, overdispersion = 0.5, family = poisson())
+#' m <- glm(observedResponse ~ Environment1, family = poisson(), data = dat)
+#' res <- simulate_residuals(m)
+#' check_residuals(res)
 #'
 #' @export
 check_residuals <- function(x, ...) {
-  insight::check_if_installed("DHARMa")
-  # TODO: This should be an S3 method instead of using ifelse
-  if (inherits(x, c("performance_simres", "DHARMa"))) {
-    # tests if the overall distribution conforms to expectations; equivalent to:
-    # ks.test(residuals(simulated_residuals), "punif")
-    DHARMa::testUniformity(x, plot = FALSE, ...)
-  } else {
-    insight::format_error("Unsupported input.")
-  }
+  UseMethod("check_residuals")
+}
+
+#' @export
+check_residuals.default <- function(x, ...) {
+  insight::format_error("`check_residuals()` only works with objects returned by `simulate_residuals()` by `DHARMa::simulateResiduals()`.") # nolint
+}
+
+#' @rdname check_residuals
+#' @export
+check_residuals.performance_simres <- function(x,
+                                               alternative = c("two.sided", "less", "greater"),
+                                               ...) {
+  alternative <- match.arg(alternative)
+  stats::ks.test(
+    stats::residuals(simulated_residuals),
+    "punif",
+    alternative = alternative,
+    ...
+  )
 }
 
+#' @export
+check_residuals.DHARMa <- check_residuals.performance_simres
+
+
 # methods ------------------------------
 
 # TODO: Add print method

R/simulate_residuals.R

@@ -4,6 +4,7 @@
 #' @description to do.
 #'
 #' @param x A model object.
+#' @param iterations Number of simulations to run.
 #' @param ... Arguments passed on to [`DHARMa::simulateResiduals()`].
 #'
 #' @return Simulated residuals.
@@ -19,12 +20,12 @@
 #' - Dunn, P. K., & Smyth, G. K. (1996). Randomized Quantile Residuals. Journal
 #'   of Computational and Graphical Statistics, 5(3), 236. \doi{10.2307/1390802}
 #'
-#' @examples
+#' @examplesIf require("DHARMa")
 #' m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
 #' simulate_residuals(m)
 #'
 #' @export
-simulate_residuals <- function(x, ...) {
+simulate_residuals <- function(x, iterations = 250, ...) {
   insight::check_if_installed("DHARMa")
   # TODO (low priority): Note that DHARMa::simulateResiduals(x, ...) does its own checks for whether
   # or not the model passed to it is supported, do we want to use this or do our
@@ -34,7 +35,7 @@ simulate_residuals <- function(x, ...) {
   # extracting the residuals from it because the object contains important stuff
   # in it that we'll want to pass onto other functions later, such as passing
   # the fitted model into check_model().
-  out <- DHARMa::simulateResiduals(x, ...)
+  out <- DHARMa::simulateResiduals(x, n = iterations, plot = FALSE, ...)
   class(out) <- c("performance_simres", class(out))
   out
 }
@@ -46,8 +47,12 @@ print.performance_simres <- function(x, ...) {
   # TODO (low priority): We can probably just base this off of the print method
   # DHARMa uses, but with an easystats style. For now we can just stick with
   # DHARMa's method.
-  cat(
-    "Simulated residuals from a model of class", class(x$fittedModel),
-    "based on", x$nSim, "simulations."
+  msg <- paste0(
+    "Simulated residuals from a model of class `", class(x$fittedModel),
+    "` based on ", x$nSim, " simulations. Use `check_residuals()` to check ",
+    "uniformity of residuals. It is recommended to refer to `?DHARMa::simulateReisudals`",
+    " and `vignette(\"DHARMa\")` for more information about different settings",
+    " in particular situations or for particular models."
   )
+  cat(insight::format_message(msg))
 }