Issue plotting lme4 random effects from model_parameters

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: see
 Title: Model Visualisation Toolbox for 'easystats' and 'ggplot2'
-Version: 0.9.0.2
+Version: 0.9.0.3
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -121,3 +121,4 @@ Config/testthat/edition: 3
 Config/testthat/parallel: true
 Config/Needs/website: easystats/easystatstemplate
 Config/rcmdcheck/ignore-inconsequential-notes: true
+Remotes: easystats/performance, easystats/parameters

NEWS.md

@@ -5,6 +5,9 @@
 - `plot()` for `p_function()` now checks the values of the `size_length`
   argument, to give an informative error message when the input is not valid.
 
+- `plot()` for `model_parameters()` now also plots group-levels of random effects
+  (i.e. for mixed models, when `model_parameters(x, ..., group_level = TRUE)`).
+
 # see 0.9.0
 
 ## Changes

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,
@@ -53,6 +53,16 @@
   # retrieve settings ----------------
   model_attributes <- attributes(x)[!names(attributes(x)) %in% c("names", "row.names", "class")]
 
+  # user wants to plot random effects (group levels)?
+  if (isFALSE(model_attributes$ignore_group) &&
+      isTRUE(model_attributes$mixed_model) &&
+      !"brmsfit" %in% model_attributes$model_class) {
+    if (missing(show_intercept)) {
+      show_intercept <- TRUE
+    }
+    return(.group_level_plot(x, size_point, size_text, sort, n_columns, show_intercept, show_labels, ...))
+  }
+
   # show intercept for intercept-only models
   if (insight::is_model(x) && insight::is_nullmodel(x)) {
     show_intercept <- TRUE
@@ -60,7 +70,7 @@
 
   # 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 +140,7 @@
     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 +159,7 @@
   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 +175,7 @@
   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
   }
 
@@ -177,9 +187,9 @@
 
     if (is_bootstrap || isTRUE(is_bayesian)) {
       if (is_bootstrap) {
         data <- model_attributes$boot_samples
       } else {
         data <- as.data.frame(.retrieve_data(x))
       }
 
       # MCMC or bootstrapped models
@@ -187,7 +197,7 @@
         insight::format_error("Could not retrieve parameter simulations.")
       }
 
       data <- datawizard::reshape_longer(
         data,
         names_to = "Parameter",
         rows_to = "Iteration",
@@ -461,21 +471,21 @@
   # values we can use as breaks and labels for the scale...
 
   if (exponentiated_coefs && log_scale) {
-    range <- 2^(-24:16)
-    x_low <- which.min(min_ci > range) - 1
-    x_high <- which.max(max_ci < range)
+    axis_range <- 2^(-24:16)
+    x_low <- which.min(min_ci > axis_range) - 1
+    x_high <- which.max(max_ci < axis_range)
 
     if (add_values) {
       # add some space to the right panel for text
       new_range <- pretty(2 * max_ci)
-      x_high <- which.max(max(new_range) < range)
+      x_high <- which.max(max(new_range) < axis_range)
     }
 
     p <- p + scale_x_continuous(
       trans = "log",
-      breaks = range[x_low:x_high],
-      limits = c(range[x_low], range[x_high]),
-      labels = sprintf("%g", range[x_low:x_high])
+      breaks = axis_range[x_low:x_high],
+      limits = c(axis_range[x_low], axis_range[x_high]),
+      labels = sprintf("%g", axis_range[x_low:x_high])
     )
   }
 
@@ -526,32 +536,145 @@
 
   if (isTRUE(is_meta)) {
     measure <- .meta_measure(meta_measure)
-    p + labs(
+    p + ggplot2::labs(
       y = "",
       x = measure,
       colour = "CI"
     )
+  } else if (isTRUE(axis_title_in_facet)) {
+    p + ggplot2::labs(
+      y = parameter_label,
+      x = NULL,
+      colour = "CI"
+    )
   } else {
-    if (isTRUE(axis_title_in_facet)) {
-      p + labs(
-        y = parameter_label,
-        x = NULL,
-        colour = "CI"
-      )
-    } else {
-      p + labs(
-        y = parameter_label,
-        x = ifelse(is.null(coefficient_name),
-          ifelse(exponentiated_coefs, "Exp(Estimate)", "Estimate"), # nolint
-          coefficient_name
-        ),
-        colour = "CI"
-      )
-    }
+    p + ggplot2::labs(
+      y = parameter_label,
+      x = ifelse(is.null(coefficient_name),
+        ifelse(exponentiated_coefs, "Exp(Estimate)", "Estimate"), # nolint
+        coefficient_name
+      ),
+      colour = "CI"
+    )
   }
 }
 
 
+.group_level_plot <- function(x, size_point, size_text, sort, n_columns, show_intercept, show_labels, ...) {
+  # filter random effects
+  x <- x[x$Effects == "random", ]
+  # remove intercept?
+  if (isFALSE(show_intercept) && length(.is_intercept(x$Parameter)) > 0L) {
+    x <- x[!.is_intercept(x$Parameter), ]
+  }
+  # prepare group variable
+  x$Group <- paste(x$Group, x$Parameter, sep = ": ")
+
+  # define columns
+  if (is.null(n_columns)) {
+    n_columns <- 1
+  }
+
+  # define text size
+  if (is.null(size_text) || is.na(size_text)) {
+    size_text <- 4
+  }
+
+  # for now, we fix the NULL to 0. Maybe we could exp() random parameters
+  # for logistic regression and similar models, but currently only link-scale
+  y_intercept <- 0
+
+  # plot setup for regular model parameters
+  x$colored <- factor(x$Coefficient < y_intercept, levels = c(FALSE, TRUE))
+  if (all(x$colored == "TRUE")) {
+    color_scale <- scale_color_material(reverse = TRUE)
+    fill_scale <- scale_fill_material(reverse = TRUE)
+  } else {
+    color_scale <- scale_color_material()
+    fill_scale <- scale_fill_material()
+  }
+
+  # create text string for estimate and CI
+  x$Estimate_CI <- sprintf(
+    "%.2f %s",
+    x$Coefficient,
+    insight::format_ci(x$CI_low, x$CI_high, ci = NULL, digits = 2, zap_small = TRUE)
+  )
+
+  # handle sorting
+  if (isTRUE(sort) || (!is.null(sort) && sort == "ascending")) {
+    x$Level <- factor(x$Level, levels = rev(unique(x$Level)[order(x$Coefficient)]))
+  } else if (!is.null(sort) && sort == "descending") {
+    x$Level <- factor(x$Level, levels = unique(x$Level)[order(x$Coefficient)])
+  } else {
+    # sort coefficients as they appear in the classical summary output by default
+    x$Level <- factor(x$Level, levels = rev(unique(x$Level)))
+  }
+
+  # find min/max range based on CI
+  min_ci <- min(x$CI_low, na.rm = TRUE)
+  max_ci <- max(x$CI_high, na.rm = TRUE)
+
+  # here we check if all facets have the same scale. If so, we set the scales
+  # to fixed, otherwise we set them to free_y (in facet_wrap). This removes
+  # a redundant scale for plots with identical scales.
+  check_scales <- split(x$Level, x$Group)
+  if (isTRUE(identical(unname(check_scales[-length(check_scales)]), unname(check_scales[-1])))) {
+    facet_scales <- "fixed"
+  } else {
+    facet_scales <- "free_y"
+  }
+
+  p <- ggplot2::ggplot(
+    x,
+    ggplot2::aes(
+      x = .data$Coefficient,
+      y = .data$Level,
+      xmin = .data$CI_low,
+      xmax = .data$CI_high,
+      colour = .data$colored,
+      fill = .data$colored
+    )
+  ) +
+    ggplot2::geom_vline(
+      ggplot2::aes(xintercept = y_intercept),
+      linetype = "dotted",
+      colour = "black"
+    ) +
+    theme_modern(legend.position = "none") +
+    color_scale +
+    fill_scale +
+    ggplot2::geom_errorbarh(
+      height = 0,
+      linewidth = size_point
+    ) +
+    ggplot2::geom_point(
+      size = 4 * size_point,
+      colour = "white",
+      shape = 21
+    ) +
+    ggplot2::facet_wrap(~Group, ncol = n_columns, scales = facet_scales)
+
+  # add coefficients and CIs?
+  if (isTRUE(show_labels)) {
+    # add some space to the right panel for text
+    space_factor <- (n_columns^(size_text / 1.2)) * sqrt(ceiling(diff(c(min_ci, max_ci))) / 5)
+    new_range <- pretty(c(min_ci, max_ci + space_factor))
+
+    # expand scale range and add numbers to the right border
+    if (!any(is.infinite(new_range)) && !anyNA(new_range)) {
+      p <- p +
+        ggplot2::geom_text(
+          mapping = ggplot2::aes(label = .data$Estimate_CI, x = Inf),
+          colour = "black", hjust = "inward", size = size_text
+        ) +
+        ggplot2::xlim(c(min(new_range), max(new_range)))
+    }
+  }
+
+  p
+}
+
 
 .funnel_plot <- function(x, size_point = 3, meta_measure = NULL) {
   max_y <- max(pretty(max(x$SE) * 105)) / 100