Improve binned_residuals()

NEWS.md

@@ -1,5 +1,15 @@
 # performance 0.10.7
 
+## Breaking changes
+
+* `binned_residuals()` gains a few new arguments to control the residuals used
+  for the test, as well as different options to calculate confidence intervals
+  (namely, `ci_type`, `residuals`, `ci` and `iterations`). The default values
+  to compute binned residuals have changed. Default residuals are now "deviance"
+  residuals (and no longer "response" residuals). Default confidence intervals
+  are now "exact" intervals (and no longer based on Gaussian approximation).
+  Use `ci_type = "gaussian"` and `residuals = "response"` to get the old defaults.
+
 ## Changes to functions
 
 * `binned_residuals()` - like `check_model()` - gains a `show_dots` argument to

R/binned_residuals.R

@@ -11,6 +11,19 @@
 #' @param n_bins Numeric, the number of bins to divide the data. If
 #'   `n_bins = NULL`, the square root of the number of observations is
 #'   taken.
+#' @param ci Numeric, the confidence level for the error bounds.
+#' @param ci_type Character, the type of error bounds to calculate. Can be
+#'   `"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
+#'   `"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
 #'   to `FALSE` for models with many observations, if generating the plot is too
 #'   time-consuming. By default, `show_dots = NULL`. In this case `binned_residuals()`
@@ -62,12 +75,24 @@
 #' }
 #'
 #' @export
-binned_residuals <- function(model, term = NULL, n_bins = NULL, show_dots = NULL, ...) {
-  fv <- stats::fitted(model)
+binned_residuals <- function(model,
+                             term = NULL,
+                             n_bins = NULL,
+                             show_dots = NULL,
+                             ci = 0.95,
+                             ci_type = c("exact", "gaussian", "boot"),
+                             residuals = c("deviance", "pearson", "response"),
+                             iterations = 1000,
+                             ...) {
+  # match arguments
+  ci_type <- match.arg(ci_type)
+  residuals <- match.arg(residuals)
+
+  fitted_values <- stats::fitted(model)
   mf <- insight::get_data(model, verbose = FALSE)
 
   if (is.null(term)) {
-    pred <- fv
+    pred <- fitted_values
   } else {
     pred <- mf[[term]]
   }
@@ -78,7 +103,20 @@ binned_residuals <- function(model, term = NULL, n_bins = NULL, show_dots = NULL
     show_dots <- is.null(n) || n <= 1e5
   }
 
-  y <- .recode_to_zero(insight::get_response(model, verbose = FALSE)) - fv
+  # make sure response is 0/1 (and numeric)
+  y0 <- .recode_to_zero(insight::get_response(model, verbose = FALSE))
+
+  # calculate residuals
+  y <- switch(residuals,
+    response = y0 - fitted_values,
+    pearson = .safe((y0 - fitted_values) / sqrt(fitted_values * (1 - fitted_values))),
+    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)))
 
@@ -95,24 +133,32 @@ binned_residuals <- function(model, term = NULL, n_bins = NULL, show_dots = NULL
     n <- length(items)
     sdev <- stats::sd(y[items], na.rm = TRUE)
 
-    data.frame(
+    conf_int <- 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
+        # center CIs around point estimate
+        out <- out - (min(out) - ybar) - (diff(out) / 2)
+        out
+      },
+      boot = .boot_binned_ci(y[items], ci, iterations)
+    )
+    names(conf_int) <- c("CI_low", "CI_high")
+
+    d0 <- data.frame(
       xbar = xbar,
       ybar = ybar,
       n = n,
       x.lo = model.range[1],
       x.hi = model.range[2],
-      se = stats::qnorm(0.975) * sdev / sqrt(n),
-      ci_range = sdev / sqrt(n)
+      se = stats::qnorm((1 + ci) / 2) * sdev / sqrt(n)
     )
+    cbind(d0, rbind(conf_int))
   }))
 
   d <- do.call(rbind, d)
   d <- d[stats::complete.cases(d), ]
 
-  # CIs
-  d$CI_low <- d$ybar - stats::qnorm(0.975) * d$ci_range
-  d$CI_high <- d$ybar + stats::qnorm(0.975) * d$ci_range
-
   gr <- abs(d$ybar) > abs(d$se)
   d$group <- "yes"
   d$group[gr] <- "no"
@@ -129,6 +175,21 @@ binned_residuals <- function(model, term = NULL, n_bins = NULL, show_dots = NULL
 }
 
 
+# utilities ---------------------------
+
+.boot_binned_ci <- function(x, ci = 0.95, iterations = 1000) {
+  x <- x[!is.na(x)]
+  n <- length(x)
+  out <- vector("numeric", iterations)
+  for (i in seq_len(iterations)) {
+    out[i] <- sum(x[sample.int(n, n, replace = TRUE)])
+  }
+  out <- out / n
+
+  quant <- stats::quantile(out, c((1 - ci) / 2, (1 + ci) / 2))
+  c(CI_low = quant[1L], CI_high = quant[2L])
+}
+
 
 # methods -----------------------------
 

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.
@@ -48,7 +49,7 @@
 #'
 #' @details For Bayesian models from packages **rstanarm** or **brms**,
 #' models will be "converted" to their frequentist counterpart, using
 #' [`bayestestR::bayesian_as_frequentist`](https://easystats.github.io/bayestestR/reference/convert_bayesian_as_frequentist.html).
 #' A more advanced model-check for Bayesian models will be implemented at a
 #' later stage.
 #'
@@ -76,7 +77,7 @@
 #' 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).
 #'
 #' @section Homogeneity of Variance:
 #' This plot checks the assumption of equal variance (homoscedasticity). The
@@ -185,11 +186,11 @@
   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) {
@@ -202,7 +203,7 @@
   }
 
   # try to find sensible default for "type" argument
-  suggest_dots <- (minfo$is_bernoulli || minfo$is_count || minfo$is_ordinal || minfo$is_categorical || minfo$is_multinomial)
+  suggest_dots <- (minfo$is_bernoulli || minfo$is_count || minfo$is_ordinal || minfo$is_categorical || minfo$is_multinomial) # nolint
   if (missing(type) && suggest_dots) {
     type <- "discrete_interval"
   }
@@ -330,7 +331,7 @@
 
 # compile plots for checks of linear models  ------------------------
 
-.check_assumptions_linear <- function(model, model_info, verbose = TRUE) {
+.check_assumptions_linear <- function(model, model_info, verbose = TRUE, ...) {
   dat <- list()
 
   dat$VIF <- .diag_vif(model, verbose = verbose)
@@ -340,13 +341,13 @@
   dat$NCV <- .diag_ncv(model, verbose = verbose)
   dat$HOMOGENEITY <- .diag_homogeneity(model, verbose = verbose)
   dat$OUTLIERS <- check_outliers(model, method = "cook")
-  if (!is.null(dat$OUTLIERS)) {
-    threshold <- attributes(dat$OUTLIERS)$threshold$cook
-  } else {
+  if (is.null(dat$OUTLIERS)) {
     threshold <- NULL
+  } else {
+    threshold <- attributes(dat$OUTLIERS)$threshold$cook
   }
   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")
@@ -357,23 +358,23 @@
 
 # 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$VIF <- .diag_vif(model, verbose = verbose)
   dat$QQ <- .diag_qq(model, verbose = verbose)
   dat$HOMOGENEITY <- .diag_homogeneity(model, verbose = verbose)
   dat$REQQ <- .diag_reqq(model, level = 0.95, model_info = model_info, verbose = verbose)
   dat$OUTLIERS <- check_outliers(model, method = "cook")
-  if (!is.null(dat$OUTLIERS)) {
-    threshold <- attributes(dat$OUTLIERS)$threshold$cook
-  } else {
+  if (is.null(dat$OUTLIERS)) {
     threshold <- NULL
+  } else {
+    threshold <- attributes(dat$OUTLIERS)$threshold$cook
   }
   dat$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)
@@ -388,7 +389,7 @@
 
 # compile plots for checks of Bayesian models  ------------------------
 
-.check_assumptions_stan <- function(model) {
+.check_assumptions_stan <- function(model, ...) {
   if (inherits(model, "brmsfit")) {
     # check if brms can be loaded