Fix for compare_parameters(effects = "random") (#338)

* Fix for compare_parameters(effects = "random")

* fix

* add test

* lintr, fix test

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: see
 Title: Model Visualisation Toolbox for 'easystats' and 'ggplot2'
-Version: 0.8.3.5
+Version: 0.8.3.6
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -11,6 +11,10 @@
 * `plot()` for `check_predictions()` now supports Bayesian regression models from
   *brms* and *rstanarm*.
 
+## Bug fixes
+
+* Corrected order of models for `plot.compare_parameters()`.
+
 # see 0.8.3
 
 ## Major changes

R/plot.compare_parameters.R

@@ -75,9 +75,13 @@ plot.see_compare_parameters <- function(x,
     x$Component <- factor(x$Component, levels = unique(x$Component))
   }
 
-
+  # show/hide intercepts
   if (!show_intercept) {
     x <- x[!.is_intercept(x$Parameter), ]
+    # sanity check - any data left?
+    if (nrow(x) == 0) {
+      insight::format_warning("No data left after removing intercepts. Returning empty plot. Try `show_intercept = TRUE`.") # nolint
+    }
   }
 
   if (isTRUE(sort) || (!is.null(sort) && sort == "ascending")) {
@@ -123,19 +127,19 @@ plot.see_compare_parameters <- function(x,
   # largest data points that are within this range. Thereby we have the pretty
   # values we can use as breaks and labels for the scale...
   if (exponentiated_coefs) {
-    range <- 2^(-24:16)
-    x_low <- which.min(min(x$CI_low) > range) - 1L
-    x_high <- which.max(max(x$CI_high) < range)
+    exp_range <- 2^(-24:16)
+    x_low <- which.min(min(x$CI_low) > exp_range) - 1L
+    x_high <- which.max(max(x$CI_high) < exp_range)
     if (add_values) {
       # add some space to the right panel for text
       new_range <- pretty(2 * max(x$CI_high))
-      x_high <- which.max(max(new_range) < range)
+      x_high <- which.max(max(new_range) < exp_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 = exp_range[x_low:x_high],
+      limits = c(exp_range[x_low], exp_range[x_high]),
+      labels = sprintf("%g", exp_range[x_low:x_high])
     )
   }
 
@@ -188,7 +192,7 @@ plot.see_compare_parameters <- function(x,
 
 #' @export
 data_plot.see_compare_parameters <- function(x, ...) {
-  col_coefficient <- which(grepl("^(Coefficient|Log-Odds|Log-Mean|Odds Ratio|Risk Ratio|IRR)\\.", colnames(x)))
+  col_coefficient <- grep("^(Coefficient|Log-Odds|Log-Mean|Odds Ratio|Risk Ratio|IRR)\\.", colnames(x))
   col_ci_low <- which(startsWith(colnames(x), "CI_low."))
   col_ci_high <- which(startsWith(colnames(x), "CI_high."))
   col_p <- which(startsWith(colnames(x), "p."))
@@ -210,15 +214,22 @@ data_plot.see_compare_parameters <- function(x, ...) {
     values_to = "CI_high",
     columns = colnames(x)[col_ci_high]
   )["CI_high"]
+  dataplot <- cbind(out1, out2, out3)
+
+  # if we have effects = "random", we probably don't have p-values. so
+  # check if this column exists, and if not, we skip it...
+  if (length(col_p) != 0) {
+    out4 <- .reshape_to_long(
+      x,
+      values_to = "p",
+      columns = colnames(x)[col_p]
+    )["p"]
+    dataplot <- cbind(dataplot, out4)
+  }
 
-  out4 <- .reshape_to_long(
-    x,
-    values_to = "p",
-    columns = colnames(x)[col_p]
-  )["p"]
-
-  dataplot <- cbind(out1, out2, out3, out4)
   dataplot$group <- gsub("(.*)\\.(.*)", "\\2", dataplot$group)
+  # make factor, so order in legend is preserved
+  dataplot$group <- factor(dataplot$group, levels = unique(dataplot$group))
 
   rownames(dataplot) <- NULL
 

tests/testthat/test-plot.compare_parameters.R

@@ -0,0 +1,24 @@
+test_that("`plot()` for compare_parameters", {
+  skip_if_not_installed("glmmTMB")
+  skip_if_not_installed("lme4")
+  skip_if_not_installed("parameters")
+  gdat <- readRDS(system.file("vignette_data", "gophertortoise.rds", package = "glmmTMB"))
+  form <- shells ~ prev + offset(log(Area)) + factor(year) + (1 | Site)
+  gmod_glmer <- lme4::glmer(form, family = poisson, data = gdat)
+  gprior <- data.frame(
+    prior = "gamma(1e8, 2.5)",
+    class = "theta",
+    coef = "",
+    stringsAsFactors = FALSE
+  )
+  gmod_glmmTMB <- glmmTMB::glmmTMB(form, family = poisson, priors = gprior, data = gdat)
+
+  cp <- parameters::compare_parameters(gmod_glmer, gmod_glmmTMB, effects = "random")
+  expect_warning(plot(cp), "No data left")
+
+  skip_if_not_installed("vdiffr")
+  vdiffr::expect_doppelganger(
+    title = "plot.compare_parameters works",
+    fig = plot(cp, show_intercept = TRUE)
+  )
+})