Draft plotting simulated residuals

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: see
 Title: Model Visualisation Toolbox for 'easystats' and 'ggplot2'
-Version: 0.8.2.4
+Version: 0.8.2.5
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -75,6 +75,7 @@ Imports:
 Suggests:
     brms,
     curl,
+    DHARMa,
     emmeans,
     factoextra,
     ggdist,
@@ -118,3 +119,4 @@ Config/Needs/website:
     r-lib/pkgdown,
     easystats/easystatstemplate
 Config/rcmdcheck/ignore-inconsequential-notes: true
+Remotes: easystats/performance#643

NAMESPACE

@@ -38,6 +38,7 @@ S3method(plot,see_check_model)
 S3method(plot,see_check_normality)
 S3method(plot,see_check_outliers)
 S3method(plot,see_check_overdisp)
+S3method(plot,see_check_residuals)
 S3method(plot,see_compare_parameters)
 S3method(plot,see_compare_performance)
 S3method(plot,see_effectsize_table)
@@ -64,6 +65,7 @@ S3method(plot,see_parameters_sem)
 S3method(plot,see_parameters_simulate)
 S3method(plot,see_performance_pp_check)
 S3method(plot,see_performance_roc)
+S3method(plot,see_performance_simres)
 S3method(plot,see_point_estimate)
 S3method(plot,see_rope)
 S3method(plot,see_si)

R/data_plot.R

@@ -148,6 +148,11 @@
   obj_name <- attr(x, "object_name", exact = TRUE)
   dat <- NULL
 
+  # for simulated residuals, we save all necessary information in the object
+  if (inherits(x, "performance_simres")) {
+    return(x$fittedModel)
+  }
+
   if (!is.null(obj_name)) {
     # first try, parent frame
     dat <- tryCatch(get(obj_name, envir = parent.frame()), error = function(e) NULL)

R/plot.check_model.R

@@ -9,14 +9,14 @@
 #'
 #' @return A ggplot2-object.
 #'
 #' @seealso See also the vignette about [`check_model()`](https://easystats.github.io/performance/articles/check_model.html).
 #'
 #' @examplesIf require("performance") && require("patchwork")
 #' model <- lm(qsec ~ drat + wt, data = mtcars)
 #' plot(check_model(model))
 #'
 #' @export
 plot.see_check_model <- function(x,
                                  style = theme_lucid,
                                  colors = NULL,
                                  type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
@@ -41,7 +41,7 @@
   plot_type <- attr(x, "type")
   model_class <- attr(x, "model_class")
 
   if (missing(type) && !is.null(plot_type) && plot_type %in% c("density", "discrete_dots", "discrete_interval", "discrete_both")) {
     type <- plot_type
   } else {
     type <- match.arg(type)
@@ -165,19 +165,33 @@
   }
 
   if ("QQ" %in% names(x) && !is.null(x$QQ) && any(c("qq", "all") %in% check)) {
-    p$QQ <- .plot_diag_qq(
-      x$QQ,
-      size_point,
-      size_line,
-      alpha_level = alpha_level,
-      detrend = detrend,
-      theme_style = style,
-      colors = colors,
-      dot_alpha_level = dot_alpha_level,
-      show_dots = TRUE, # qq-plots w/o dots makes no sense
-      model_info = model_info,
-      model_class = model_class
-    )
+    if (inherits(x$QQ, "performance_simres")) {
+      p$QQ <- plot(
+        x$QQ,
+        size_line = size_line,
+        size_point = size_point,
+        alpha = alpha_level,
+        dot_alpha = dot_alpha_level,
+        colors = colors,
+        detrend = detrend,
+        style = style
+
+      )
+    } else {
+      p$QQ <- .plot_diag_qq(
+        x$QQ,
+        size_point,
+        size_line,
+        alpha_level = alpha_level,
+        detrend = detrend,
+        theme_style = style,
+        colors = colors,
+        dot_alpha_level = dot_alpha_level,
+        show_dots = TRUE, # qq-plots w/o dots makes no sense
+        model_info = model_info,
+        model_class = model_class
+      )
+    }
   }
 
   if ("NORM" %in% names(x) && !is.null(x$NORM) && any(c("normality", "all") %in% check)) {

R/plot.parameters_model.R

@@ -36,7 +36,7 @@
 #' result
 #' plot(result)
 #' @export
 plot.see_parameters_model <- function(x,
                                       show_intercept = FALSE,
                                       size_point = 0.8,
                                       size_text = NA,
@@ -60,7 +60,7 @@
 
   # Clean up column names
   if (!any(grepl("Coefficient", colnames(x), fixed = TRUE))) {
     colnames(x)[which.min(match(colnames(x), c("Median", "Mean", "Map", "MAP", model_attributes$coefficient_name)))] <- "Coefficient"
   }
 
   if (!any(grepl("Parameter", colnames(x), fixed = TRUE))) {
@@ -130,7 +130,7 @@
     if (!is.null(cleaned_parameters)) {
       x <- merge(x, cleaned_parameters, sort = FALSE)
       x$Parameter <- x$Cleaned_Parameter
       if (all(x$Group == "")) {
         x$Group <- NULL
       } else {
         x <- x[!startsWith(x$Group, "SD/Cor"), , drop = FALSE]
@@ -149,7 +149,7 @@
   x <- .fix_facet_names(x)
 
   if (is_bayesian && "Group" %in% colnames(x)) {
     x$Effects[x$Group != ""] <- paste0(x$Effects[x$Group != ""], " (", x$Group[x$Group != ""], ")")
   }
 
   # remember components
@@ -165,7 +165,7 @@
   is_meta_bma <- !is.null(mc) && any(mc %in% c("meta_random", "meta_fixed", "meta_bma"))
 
   # minor fixes for Bayesian models
   if (!is.null(mc) && !is.null(cp) && any(mc %in% c("stanreg", "stanmvreg", "brmsfit")) && length(cp) == length(x$Parameter)) {
     x$Parameter <- cp
   }
 
@@ -595,28 +595,28 @@
 
 .meta_measure <- function(meta_measure) {
   switch(meta_measure,
-    "MD" = "Raw Mean Difference",
-    "SMDH" = ,
-    "SMD" = "Standardized Mean Difference",
-    "ROM" = "Log transformed Ratio of Means",
-    "D2ORL" = ,
-    "D2ORN" = "Transformed Standardized Mean Difference",
-    "UCOR" = ,
-    "COR" = "Raw Correlation Coefficient",
-    "ZCOR" = "Z transformed Correlation Coefficient",
-    "PHI" = "Phi Coefficient",
-    "RR" = "Log Risk Ratio",
-    "OR" = "Log Odds Ratio",
-    "RD" = "Risk Difference",
-    "AS" = "Root transformed Risk Difference",
-    "PETO" = "Peto's Log Odds Ratio",
-    "PBIT" = "Standardized Mean Difference (Probit-transformed)",
-    "OR2DL" = ,
-    "OR2DN" = "Standardized Mean Difference (Odds Ratio-transformed)",
-    "IRR" = "Log Incidence Rate Ratio",
-    "IRD" = "Incidence Rate Difference",
-    "IRSD" = "Square Root transformed Incidence Rate Difference",
-    "GEN" = "Generic Estimate",
+    MD = "Raw Mean Difference",
+    SMDH = ,
+    SMD = "Standardized Mean Difference",
+    ROM = "Log transformed Ratio of Means",
+    D2ORL = ,
+    D2ORN = "Transformed Standardized Mean Difference",
+    UCOR = ,
+    COR = "Raw Correlation Coefficient",
+    ZCOR = "Z transformed Correlation Coefficient",
+    PHI = "Phi Coefficient",
+    RR = "Log Risk Ratio",
+    OR = "Log Odds Ratio",
+    RD = "Risk Difference",
+    AS = "Root transformed Risk Difference",
+    PETO = "Peto's Log Odds Ratio",
+    PBIT = "Standardized Mean Difference (Probit-transformed)",
+    OR2DL = ,
+    OR2DN = "Standardized Mean Difference (Odds Ratio-transformed)",
+    IRR = "Log Incidence Rate Ratio",
+    IRD = "Incidence Rate Difference",
+    IRSD = "Square Root transformed Incidence Rate Difference",
+    GEN = "Generic Estimate",
     "Estimate"
   )
 }

R/plot.performance_simres.R

@@ -0,0 +1,133 @@
+#' Plot method for check model for (non-)normality of residuals
+#'
+#' The `plot()` method for the `performance::check_residuals()` resp.
+#' `performance::simulate_residuals()` function.
+#'
+#' @param transform Function to transform the residuals. If `NULL` (default),
+#' no transformation is applied and uniformly distributed residuals are expected.
+#' See argument `quantileFuntion` in `?DHARMa:::residuals.DHARMa` for more details.
+#'
+#' @inheritParams plot.see_check_normality
+#' @inheritParams plot.see_check_model
+#'
+#' @return A ggplot2-object.
+#'
+#' @seealso See also the vignette about [`check_model()`](https://easystats.github.io/performance/articles/check_model.html).
+#'
+#' @examplesIf insight::check_if_installed("performance", "0.10.9.7") && require("glmmTMB") && require("qqplotr") && require("DHARMa")
+#' data(Salamanders, package = "glmmTMB")
+#' model <- glmmTMB::glmmTMB(
+#'   count ~ mined + spp + (1 | site),
+#'   family = poisson(),
+#'   data = Salamanders
+#' )
+#' simulated_residuals <- performance::simulate_residuals(model)
+#' plot(simulated_residuals)
+#'
+#' # or
+#' simulated_residuals <- performance::simulate_residuals(model)
+#' result <- performance::check_residuals(simulated_residuals)
+#' plot(result)
+#'
+#' @export
+plot.see_performance_simres <- function(x,
+                                        size_line = 0.8,
+                                        size_point = 1,
+                                        alpha = 0.2,
+                                        dot_alpha = 0.8,
+                                        colors = c("#3aaf85", "#1b6ca8"),
+                                        detrend = FALSE,
+                                        transform = NULL,
+                                        style = theme_lucid,
+                                        ...) {
+  dp <- list(min = 0, max = 1, lower.tail = TRUE, log.p = FALSE)
+
+  # need DHARMa to be installed
+  insight::check_if_installed("DHARMa")
+
+  # extract data, if from check_residuals
+  if (inherits(x, "see_check_residuals")) {
+    x <- attributes(x)$data
+  }
+
+  if (is.null(transform)) {
+    res <- stats::residuals(x)
+  } else {
+    res <- stats::residuals(x, quantileFunction = transform)
+  }
+
+  # base plot information
+  gg_init <- ggplot2::ggplot(
+    data.frame(scaled_residuals = res),
+    ggplot2::aes(sample = .data$scaled_residuals)
+  )
+
+  # when we have package qqplotr, we can add confidence bands
+  if (requireNamespace("qqplotr", quietly = TRUE)) {
+    qq_stuff <- list(
+      qqplotr::stat_qq_band(
+        distribution = "unif",
+        dparams = list(min = 0, max = 1),
+        alpha = alpha,
+        detrend = detrend
+      ),
+      qqplotr::stat_qq_line(
+        distribution = "unif",
+        dparams = dp,
+        size = size_line,
+        colour = colors[1],
+        detrend = detrend
+      ),
+      qqplotr::stat_qq_point(
+        distribution = "unif",
+        dparams = dp,
+        size = size_point,
+        alpha = dot_alpha,
+        colour = colors[2],
+        detrend = detrend
+      )
+    )
+    if (detrend) {
+      y_lab <- "Sample Quantile Deviations"
+    } else {
+      y_lab <- "Sample Quantiles"
+    }
+  } else {
+    insight::format_alert("For confidence bands, please install `qqplotr`.")
+    qq_stuff <- list(
+      ggplot2::geom_qq(
+        shape = 16,
+        stroke = 0,
+        distribution = stats::qunif,
+        dparams = dp,
+        size = size_point,
+        colour = colors[2]
+      ),
+      ggplot2::geom_qq_line(
+        linewidth = size_line,
+        colour = colors[1],
+        na.rm = TRUE,
+        distribution = stats::qunif,
+        dparams = dp
+      )
+    )
+    y_lab <- "Sample Quantiles"
+  }
+
+  gg_init +
+    qq_stuff +
+    ggplot2::labs(
+      title = "Uniformity of Residuals",
+      subtitle = "Dots should fall along the line",
+      x = "Standard Uniform Distribution Quantiles",
+      y = y_lab
+    ) +
+    style(
+      base_size = 10,
+      plot.title.space = 3,
+      axis.title.space = 5
+    )
+}
+
+#' @export
+plot.see_check_residuals <- plot.see_performance_simres