rd

Author: strengejacke

DESCRIPTION

@@ -159,7 +159,7 @@ Suggests:
     withr (>= 3.0.0)
 Encoding: UTF-8
 Language: en-US
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 Roxygen: list(markdown = TRUE)
 Config/testthat/edition: 3
 Config/testthat/parallel: true

NAMESPACE

@@ -28,7 +28,9 @@ S3method(as.data.frame,r2_nakagawa)
 S3method(as.double,check_outliers)
 S3method(as.double,item_omega)
 S3method(as.double,performance_roc)
+S3method(check_autocorrelation,DHARMa)
 S3method(check_autocorrelation,default)
+S3method(check_autocorrelation,performance_simres)
 S3method(check_collinearity,BFBayesFactor)
 S3method(check_collinearity,MixMod)
 S3method(check_collinearity,afex_aov)

R/check_autocorrelation.R

@@ -56,19 +56,18 @@ check_autocorrelation.default <- function(x, nsim = 1000, ...) {
 }
 
 
-#' @rdname check_autocorrelation
 #' @export
 check_autocorrelation.performance_simres <- function(x, ...) {
   insight::check_if_installed("DHARMa")
-  
+
   # Use DHARMa's temporal autocorrelation test
   # This requires the residuals to be ordered by time
   # DHARMa::testTemporalAutocorrelation expects a DHARMa object
   result <- DHARMa::testTemporalAutocorrelation(x, plot = FALSE, ...)
-  
+
   # Extract p-value from the result
   p.val <- result$p.value
-  
+
   class(p.val) <- c("check_autocorrelation", "see_check_autocorrelation", class(p.val))
   p.val
 }
@@ -81,7 +80,9 @@ check_autocorrelation.DHARMa <- check_autocorrelation.performance_simres
 
 #' @export
 plot.check_autocorrelation <- function(x, ...) {
-  insight::format_warning("There is currently no `plot()` method for `check_autocorrelation()`.")
+  insight::format_warning(
+    "There is currently no `plot()` method for `check_autocorrelation()`."
+  )
 }
 
 

R/check_overdispersion.R

@@ -78,7 +78,11 @@ check_overdispersion <- function(x, ...) {
 check_overdispersion.default <- function(x, ...) {
   .is_model_valid(x)
   insight::format_error(
-    paste0("`check_overdisperion()` not yet implemented for models of class `", class(x)[1], "`.")
+    paste0(
+      "`check_overdisperion()` not yet implemented for models of class `",
+      class(x)[1],
+      "`."
+    )
   )
 }
 
@@ -122,7 +126,9 @@ print.check_overdisp <- function(x, digits = 3, ...) {
   }
 
   x$p_value <- pval <- round(x$p_value, digits = digits)
-  if (x$p_value < 0.001) x$p_value <- "< 0.001"
+  if (x$p_value < 0.001) {
+    x$p_value <- "< 0.001"
+  }
 
   maxlen <- max(
     nchar(x$dispersion_ratio),
@@ -132,12 +138,27 @@ print.check_overdisp <- function(x, digits = 3, ...) {
 
   insight::print_color("# Overdispersion test\n\n", "blue")
   if (is.null(x$chisq_statistic)) {
-    cat(sprintf(" dispersion ratio = %s\n", format(x$dispersion_ratio, justify = "right", width = maxlen)))
-    cat(sprintf("          p-value = %s\n\n", format(x$p_value, justify = "right", width = maxlen)))
+    cat(sprintf(
+      " dispersion ratio = %s\n",
+      format(x$dispersion_ratio, justify = "right", width = maxlen)
+    ))
+    cat(sprintf(
+      "          p-value = %s\n\n",
+      format(x$p_value, justify = "right", width = maxlen)
+    ))
   } else {
-    cat(sprintf("       dispersion ratio = %s\n", format(x$dispersion_ratio, justify = "right", width = maxlen)))
-    cat(sprintf("  Pearson's Chi-Squared = %s\n", format(x$chisq_statistic, justify = "right", width = maxlen)))
-    cat(sprintf("                p-value = %s\n\n", format(x$p_value, justify = "right", width = maxlen)))
+    cat(sprintf(
+      "       dispersion ratio = %s\n",
+      format(x$dispersion_ratio, justify = "right", width = maxlen)
+    ))
+    cat(sprintf(
+      "  Pearson's Chi-Squared = %s\n",
+      format(x$chisq_statistic, justify = "right", width = maxlen)
+    ))
+    cat(sprintf(
+      "                p-value = %s\n\n",
+      format(x$p_value, justify = "right", width = maxlen)
+    ))
   }
 
   if (pval > 0.05) {
@@ -161,7 +182,10 @@ check_overdispersion.glm <- function(x, verbose = TRUE, ...) {
   obj_name <- insight::safe_deparse_symbol(substitute(x))
 
   # for certain distributions, simulated residuals are more accurate
-  use_simulated <- info$is_bernoulli || info$is_binomial || (!info$is_count && !info$is_binomial) || info$is_negbin
+  use_simulated <- info$is_bernoulli ||
+    info$is_binomial ||
+    (!info$is_count && !info$is_binomial) ||
+    info$is_negbin
 
   # model classes not supported in DHARMa
   not_supported <- c("fixest", "glmx")
@@ -247,7 +271,13 @@ check_overdispersion.merMod <- function(x, ...) {
 
   # for certain distributions, simulated residuals are more accurate
   # Note: now including Poisson models for mixed models (see #595, #643)
-  use_simulated <- info$family == "genpois" || info$is_zero_inflated || info$is_bernoulli || info$is_binomial || (!info$is_count && !info$is_binomial) || info$is_negbin || info$is_poisson # nolint
+  use_simulated <- info$family == "genpois" ||
+    info$is_zero_inflated ||
+    info$is_bernoulli ||
+    info$is_binomial ||
+    (!info$is_count && !info$is_binomial) ||
+    info$is_negbin ||
+    info$is_poisson
 
   if (use_simulated) {
     return(check_overdispersion(simulate_residuals(x, ...), object_name = obj_name, ...))

R/check_zeroinflation.R

@@ -91,8 +91,11 @@ check_zeroinflation.default <- function(x, tolerance = 0.05, ...) {
   # for models with zero-inflation component, negative binomial families,
   # or Poisson mixed models, we use simulate_residuals()
   # Note: now including Poisson mixed models (see #595, #643)
-  use_simulated <- model_info$is_zero_inflated || model_info$is_negbin || model_info$family == "genpois" || (model_info$is_mixed && model_info$is_poisson) # nolint
-  
+  use_simulated <- model_info$is_zero_inflated ||
+    model_info$is_negbin ||
+    model_info$family == "genpois" ||
+    (model_info$is_mixed && model_info$is_poisson)
+
   if (!inherits(x, not_supported) && use_simulated) {
     if (missing(tolerance)) {
       tolerance <- 0.1
@@ -120,17 +123,23 @@ check_zeroinflation.default <- function(x, tolerance = 0.05, ...) {
 
 #' @rdname check_zeroinflation
 #' @export
-check_zeroinflation.performance_simres <- function(x,
-                                                   tolerance = 0.1,
-                                                   alternative = "two.sided",
-                                                   ...) {
+check_zeroinflation.performance_simres <- function(
+  x,
+  tolerance = 0.1,
+  alternative = "two.sided",
+  ...
+) {
   alternative <- insight::validate_argument(
     alternative,
     c("two.sided", "less", "greater")
   )
 
   # compute test results
-  result <- .simres_statistics(x, statistic_fun = function(i) sum(i == 0), alternative = alternative)
+  result <- .simres_statistics(
+    x,
+    statistic_fun = function(i) sum(i == 0),
+    alternative = alternative
+  )
 
   structure(
     class = "check_zi",