docs, minor fixes

R/binned_residuals.R

@@ -13,9 +13,15 @@
 #'   taken.
 #' @param ci Numeric, the confidence level for the error bounds.
 #' @param ci_type Character, the type of error bounds to calculate. Can be
-#'   `"gaussian"` (default), `"exact"` or `"boot"`.
+#'   `"exact"` (default), `"gaussian"` or `"boot"`. `"exact"` calculates the
+#'   error bounds based on the exact binomial distribution, using [`binom.test()`].
+#'   `"gaussian"` uses the Gaussian approximation, while `"boot"` uses a simple
+#'   bootstrap method, where confidence intervals are calculated based on the
+#'   quantiles of the bootstrap distribution.
 #' @param residuals Character, the type of residuals to calculate. Can be
-#'   `"response"` (default), `"pearson"` or `"deviance"`.
+#'   `"deviance"` (default), `"pearson"` or `"response"`. It is recommended to
+#'   use `"response"` only for those models where other residuals are not
+#'   available.
 #' @param iterations Integer, the number of iterations to use for the
 #'   bootstrap method. Only used if `ci_type = "boot"`.
 #' @param show_dots Logical, if `TRUE`, will show data points in the plot. Set
@@ -74,8 +80,8 @@ binned_residuals <- function(model,
                              n_bins = NULL,
                              show_dots = NULL,
                              ci = 0.95,
-                             ci_type = c("gaussian", "exact", "boot"),
-                             residuals = c("response", "pearson", "deviance"),
+                             ci_type = c("exact", "gaussian", "boot"),
+                             residuals = c("deviance", "pearson", "response"),
                              iterations = 1000,
                              ...) {
   # match arguments
@@ -106,6 +112,11 @@ binned_residuals <- function(model,
     deviance = .safe(stats::residuals(model, type = "deviance"))
   )
 
+  # make sure we really have residuals
+  if (is.null(y)) {
+    insight::format_error("Could not calculate residuals. Try using `residuals = \"response\"`.")
+  }
+
   if (is.null(n_bins)) n_bins <- round(sqrt(length(pred)))
 
   breaks.index <- floor(length(pred) * (1:(n_bins - 1)) / n_bins)
@@ -124,7 +135,7 @@ binned_residuals <- function(model,
     r <- switch(ci_type,
       gaussian = stats::qnorm(c((1 - ci) / 2, (1 + ci) / 2), mean = ybar, sd = sdev / sqrt(n)),
       exact = {
-        out <- stats:::binom.test(sum(y0[items]), n)$conf.int
+        out <- stats::binom.test(sum(y0[items]), n)$conf.int
         out <- out - (min(out) - ybar) - (diff(out) / 2)
         out
       },
@@ -138,8 +149,7 @@ binned_residuals <- function(model,
       n = n,
       x.lo = model.range[1],
       x.hi = model.range[2],
-      se = stats::qnorm((1 + ci) / 2) * sdev / sqrt(n),
-      ci_range = sdev / sqrt(n)
+      se = stats::qnorm((1 + ci) / 2) * sdev / sqrt(n)
     )
     cbind(d0, rbind(r))
   }))

R/check_model.R

@@ -35,7 +35,8 @@
 #'   tries to guess whether performance will be poor due to a very large model
 #'   and thus automatically shows or hides dots.
 #' @param verbose If `FALSE` (default), suppress most warning messages.
-#' @param ... Currently not used.
+#' @param ... Arguments passed down to the individual check functions, especially
+#'   to `check_predictions()` and `binned_residuals()`.
 #' @inheritParams check_predictions
 #'
 #' @return The data frame that is used for plotting.
@@ -185,11 +186,11 @@ check_model.default <- function(x,
   ca <- tryCatch(
     {
       if (minfo$is_bayesian) {
-        suppressWarnings(.check_assumptions_stan(x))
+        suppressWarnings(.check_assumptions_stan(x, ...))
       } else if (minfo$is_linear) {
-        suppressWarnings(.check_assumptions_linear(x, minfo, verbose))
+        suppressWarnings(.check_assumptions_linear(x, minfo, verbose, ...))
       } else {
-        suppressWarnings(.check_assumptions_glm(x, minfo, verbose))
+        suppressWarnings(.check_assumptions_glm(x, minfo, verbose, ...))
       }
     },
     error = function(e) {
@@ -346,7 +347,7 @@ check_model.model_fit <- function(x,
     threshold <- NULL
   }
   dat$INFLUENTIAL <- .influential_obs(model, threshold = threshold)
-  dat$PP_CHECK <- .safe(check_predictions(model))
+  dat$PP_CHECK <- .safe(check_predictions(model, ...))
 
   dat <- insight::compact_list(dat)
   class(dat) <- c("check_model", "see_check_model")
@@ -357,7 +358,7 @@ check_model.model_fit <- function(x,
 
 # compile plots for checks of generalized linear models  ------------------------
 
-.check_assumptions_glm <- function(model, model_info, verbose = TRUE) {
+.check_assumptions_glm <- function(model, model_info, verbose = TRUE, ...) {
   dat <- list()
 
   dat$VIF <- .diag_vif(model, verbose = verbose)
@@ -371,9 +372,9 @@ check_model.model_fit <- function(x,
     threshold <- NULL
   }
   dat$INFLUENTIAL <- .influential_obs(model, threshold = threshold)
-  dat$PP_CHECK <- .safe(check_predictions(model))
+  dat$PP_CHECK <- .safe(check_predictions(model, ...))
   if (isTRUE(model_info$is_binomial)) {
-    dat$BINNED_RESID <- binned_residuals(model)
+    dat$BINNED_RESID <- binned_residuals(model, ...)
   }
   if (isTRUE(model_info$is_count)) {
     dat$OVERDISPERSION <- .diag_overdispersion(model)