#376

NAMESPACE

@@ -198,6 +198,7 @@ S3method(performance_mse,negbinmfx)
 S3method(performance_mse,poissonirr)
 S3method(performance_mse,poissonmfx)
 S3method(performance_mse,probitmfx)
+S3method(plot,binned_residuals)
 S3method(plot,check_collinearity)
 S3method(plot,check_distribution)
 S3method(plot,check_distribution_numeric)

R/binned_residuals.R

@@ -33,12 +33,9 @@
 #'   of residuals along the x-axis is a signal to consider taking the logarithm
 #'   of the predictor (cf. Gelman and Hill 2007, pp. 97-98).
 #'
-#' @note Since `binned_residuals()` returns a data frame, the default
-#'   action for the result is *printing*. However, the `print()`-method for
-#'   `binned_residuals()` actually creates a plot. For further
-#'   modifications of the plot, use `print()` and add ggplot-layers to the
-#'   return values, e.g. `print(binned_residuals(model)) +
-#'   see::scale_color_pizza()`.
+#' @note `binned_residuals()` returns a data frame, however, the `print()`
+#'   method only returns a short summary of the result. The data frame itself
+#'   is used for plotting. The `plot()` method, in turn, creates a ggplot-object.
 #'
 #' @references
 #' Gelman, A., & Hill, J. (2007). Data analysis using regression and
@@ -103,19 +100,12 @@ binned_residuals <- function(model, term = NULL, n_bins = NULL, ...) {
 
   resid_ok <- sum(d$group == "yes") / length(d$group)
 
-  if (resid_ok < .8) {
-    insight::print_color(sprintf("Warning: Probably bad model fit. Only about %g%% of the residuals are inside the error bounds.\n", round(100 * resid_ok)), "red")
-  } else if (resid_ok < .95) {
-    insight::print_color(sprintf("Warning: About %g%% of the residuals are inside the error bounds (~95%% or higher would be good).\n", round(100 * resid_ok)), "yellow")
-  } else {
-    insight::print_color(sprintf("Ok: About %g%% of the residuals are inside the error bounds.\n", round(100 * resid_ok)), "green")
-  }
-
   add.args <- lapply(match.call(expand.dots = FALSE)$`...`, function(x) x)
   size <- if ("size" %in% names(add.args)) add.args[["size"]] else 2
   color <- if ("color" %in% names(add.args)) add.args[["color"]] else c("#d11141", "#00aedb")
 
   class(d) <- c("binned_residuals", "see_binned_residuals", class(d))
+  attr(d, "resid_ok") <- resid_ok
   attr(d, "resp_var") <- insight::find_response(model)
   attr(d, "term") <- term
   attr(d, "geom_size") <- size

R/check_model.R

@@ -52,8 +52,7 @@
 #' plots are helpful to check model assumptions, they do not necessarily
 #' indicate so-called "lack of fit", e.g. missed non-linear relationships or
 #' interactions. Thus, it is always recommended to also look at
-#' [effect
-#' plots, including partial residuals](https://strengejacke.github.io/ggeffects/articles/introduction_partial_residuals.html).
+#' [effect plots, including partial residuals](https://strengejacke.github.io/ggeffects/articles/introduction_partial_residuals.html).
 #'
 #' @section Residuals for (Generalized) Linear Models:
 #' Plots that check the normality of residuals (QQ-plot) or the homogeneity of
@@ -126,6 +125,7 @@ check_model.default <- function(x,
   attr(ca, "detrend") <- detrend
   attr(ca, "colors") <- colors
   attr(ca, "theme") <- theme
+  attr(ca, "model_info") <- minfo
   ca
 }
 
@@ -243,6 +243,9 @@ check_model.model_fit <- function(x,
     threshold <- NULL
   }
   dat$INFLUENTIAL <- .influential_obs(model, threshold = threshold)
+  if (isTRUE(model_info$is_binomial)) {
+    dat$BINNED_RESID <- binned_residuals(model)
+  }
 
   dat <- datawizard::compact_list(dat)
   class(dat) <- c("check_model", "see_check_model")

R/print-methods.R

@@ -503,13 +503,29 @@ print.icc_decomposed <- function(x, digits = 2, ...) {
 
 #' @export
 print.binned_residuals <- function(x, ...) {
-  insight::check_if_installed("see", "to plot binned residuals")
+  resid_ok <- attributes(x)$resid_ok
+
+  if (!is.null(resid_ok)) {
+    if (resid_ok < .8) {
+      insight::print_color(sprintf("Warning: Probably bad model fit. Only about %g%% of the residuals are inside the error bounds.\n", round(100 * resid_ok)), "red")
+    } else if (resid_ok < .95) {
+      insight::print_color(sprintf("Warning: About %g%% of the residuals are inside the error bounds (~95%% or higher would be good).\n", round(100 * resid_ok)), "yellow")
+    } else {
+      insight::print_color(sprintf("Ok: About %g%% of the residuals are inside the error bounds.\n", round(100 * resid_ok)), "green")
+    }
+  }
+}
 
+
+#' @export
+plot.binned_residuals <- function(x, ...) {
+  insight::check_if_installed("see", "to plot binned residuals")
   NextMethod()
 }
 
 
 
+
 #' @export
 print.performance_hosmer <- function(x, ...) {
   insight::print_color("# Hosmer-Lemeshow Goodness-of-Fit Test\n\n", "blue")