Error: check_model() not implemented for models of class lm yet

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.10.5.4
+Version: 0.10.5.5
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -7,6 +7,11 @@
   the previous method, which used `parallel::detectCores()`. Now you should
   set the number of cores via `options(mc.cores = 4)`.
 
+## Bug fixes
+
+* Fixed issues is `check_model()` for models that used data sets with
+  variables of class `"haven_labelled"`.
+
 # performance 0.10.5
 
 ## Changes to functions

R/check_model_diagnostics.R

@@ -85,7 +85,7 @@
 
 # prepare data for random effects QQ plot ----------------------------------
 
 .diag_reqq <- function(model, level = 0.95, model_info, verbose = TRUE) {
   # check if we have mixed model
   if (!model_info$is_mixed) {
     return(NULL)
@@ -153,10 +153,10 @@
 # prepare data for normality of residuals plot ----------------------------------
 
 .diag_norm <- function(model, verbose = TRUE) {
-  r <- try(stats::residuals(model), silent = TRUE)
+  r <- try(as.numeric(stats::residuals(model)), silent = TRUE)
 
   if (inherits(r, "try-error")) {
     insight::format_alert(sprintf("Non-normality of residuals could not be computed. Cannot extract residuals from objects of class '%s'.", class(model)[1]))
     return(NULL)
   }
 
@@ -174,7 +174,7 @@
 
   if (inherits(model, "lm", which = TRUE) == 1) {
     cook_levels <- round(stats::qf(0.5, s$fstatistic[2], s$fstatistic[3]), 2)
   } else if (!is.null(threshold)) {
     cook_levels <- threshold
   } else {
     cook_levels <- c(0.5, 1)
@@ -183,7 +183,7 @@
   n_params <- tryCatch(model$rank, error = function(e) insight::n_parameters(model))
 
   infl <- stats::influence(model, do.coef = FALSE)
-  resid <- insight::get_residuals(model)
+  resid <- as.numeric(insight::get_residuals(model))
 
   std_resid <- tryCatch(stats::rstandard(model, infl), error = function(e) resid)
 
@@ -212,8 +212,8 @@
   ncv <- tryCatch(
     {
       data.frame(
-        x = stats::fitted(model),
-        y = stats::residuals(model)
+        x = as.numeric(stats::fitted(model)),
+        y = as.numeric(stats::residuals(model))
       )
     },
     error = function(e) {
@@ -223,7 +223,7 @@
 
   if (is.null(ncv)) {
     if (verbose) {
       insight::format_alert(sprintf("Non-constant error variance could not be computed. Cannot extract residuals from objects of class '%s'.", class(model)[1]))
     }
     return(NULL)
   }
@@ -264,7 +264,7 @@
 
   if (is.null(r)) {
     if (verbose) {
       insight::format_alert(sprintf("Homogeneity of variance could not be computed. Cannot extract residual variance from objects of class '%s'.", class(model)[1]))
     }
     return(NULL)
   }
@@ -327,7 +327,7 @@
     }
     d$Prob <- stats::predict(model, type = ptype)
     d$Disp <- insight::get_sigma(model)
     d$V <- d$Predicted * (1 + d$Predicted / d$Disp) * (1 - d$Prob) * (1 + d$Predicted * (1 + d$Predicted / d$Disp) * d$Prob)
     d$StdRes <- insight::get_residuals(model, type = "pearson")
   }
 
@@ -343,7 +343,7 @@
     }
     d$Prob <- stats::predict(model, type = ptype)
     d$Disp <- stats::predict(model, type = "disp")
     d$V <- d$Predicted * (1 + d$Predicted / d$Disp) * (1 - d$Prob) * (1 + d$Predicted * (1 + d$Predicted / d$Disp) * d$Prob)
     d$StdRes <- insight::get_residuals(model, type = "pearson")
   }