From fd780f27fa90216f9c5c9e1d9cef985e059f8a3f Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Wed, 24 Jul 2024 11:55:26 +0200
Subject: [PATCH] `get_variance_distribution()` returns `NULL` for `brms`
 negative binomial model (#911)

* `get_variance_distribution()` returns `NULL` for `brms` negative binomial model
Fixes #776

* add test

* detect panelr as mixed model

* add test

* fix test

* fix test

* test
---
 DESCRIPTION                        |  2 +-
 NAMESPACE                          |  2 ++
 NEWS.md                            |  2 ++
 R/compute_variances.R              |  8 ++++++++
 R/is_mixed_model.R                 |  8 ++++++++
 tests/testthat/test-get_variance.R | 10 ++++++++++
 tests/testthat/test-panelr.R       |  5 +++++
 7 files changed, 36 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d7ff0ae54..187f66def 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: insight
 Title: Easy Access to Model Information for Various Model Objects
-Version: 0.20.2.9
+Version: 0.20.2.10
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
diff --git a/NAMESPACE b/NAMESPACE
index f0f42c97c..c328b677a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1048,6 +1048,8 @@ S3method(is_converged,merMod)
 S3method(is_mixed_model,afex_aov)
 S3method(is_mixed_model,default)
 S3method(is_mixed_model,marginaleffects)
+S3method(is_mixed_model,wbgee)
+S3method(is_mixed_model,wbm)
 S3method(is_nullmodel,afex_aov)
 S3method(is_nullmodel,default)
 S3method(link_function,BBmm)
diff --git a/NEWS.md b/NEWS.md
index ebf59ee22..d5905da03 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -4,6 +4,8 @@
 
 * `get_df()` now supports more model classes.
 
+* `get_variance()` gives an informative error if no mixed model is provided.
+
 ## Bug fixes
 
 * Fixed issues in `find_response()` for *brms* models with `mi()` function in
diff --git a/R/compute_variances.R b/R/compute_variances.R
index 82f2d0bf4..c0e21f454 100644
--- a/R/compute_variances.R
+++ b/R/compute_variances.R
@@ -14,6 +14,14 @@
   ## Revisions and adaption to more complex models and other packages
   ## by Daniel Lüdecke
 
+  # sanity check - only proceed for mixed models
+  if (!is_mixed_model(model)) {
+    if (verbose) {
+      format_warning("This function only works for mixed models, i.e. models with random effects.")
+    }
+    return(NULL)
+  }
+
   # needed for singularity check
   check_if_installed("performance", reason = "to check for singularity")
 
diff --git a/R/is_mixed_model.R b/R/is_mixed_model.R
index e3d9cb3d6..c3df0bb0a 100644
--- a/R/is_mixed_model.R
+++ b/R/is_mixed_model.R
@@ -35,3 +35,11 @@ is_mixed_model.afex_aov <- function(x) {
 is_mixed_model.marginaleffects <- function(x) {
   is_mixed_model(attributes(x)$model)
 }
+
+#' @export
+is_mixed_model.wbm <- function(x) {
+  TRUE
+}
+
+#' @export
+is_mixed_model.wbgee <- is_mixed_model.wbm
diff --git a/tests/testthat/test-get_variance.R b/tests/testthat/test-get_variance.R
index 984b632ac..0014f0e43 100644
--- a/tests/testthat/test-get_variance.R
+++ b/tests/testthat/test-get_variance.R
@@ -35,6 +35,16 @@ v4 <- suppressWarnings(get_variance(fm4))
 v5 <- suppressWarnings(get_variance(fm5))
 v6 <- suppressWarnings(get_variance(fm6))
 
+test_that("error for non-mixed", {
+  skip_if_not_installed("glmmTMB")
+  data(mtcars)
+  expect_warning(
+    get_variance(glmmTMB::glmmTMB(mpg ~ gear, data = mtcars)),
+    regex = "This function only works for mixed models"
+  )
+  expect_silent(get_variance(glmmTMB::glmmTMB(mpg ~ gear, data = mtcars), verbose = FALSE))
+})
+
 test_that("get_variance-1", {
   expect_equal(v1$var.intercept,
     c(Subject = 612.10016),
diff --git a/tests/testthat/test-panelr.R b/tests/testthat/test-panelr.R
index 637b54e69..c03a9969e 100644
--- a/tests/testthat/test-panelr.R
+++ b/tests/testthat/test-panelr.R
@@ -10,6 +10,11 @@ test_that("model_info", {
   expect_true(model_info(m2)$is_linear)
 })
 
+test_that("id_mixed", {
+  expect_true(is_mixed_model(m1))
+  expect_true(is_mixed_model(m2))
+})
+
 test_that("find_predictors", {
   expect_identical(
     find_predictors(m1),