get_variance_distribution() returns NULL for brms negative binomial model

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",

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)

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

R/compute_variances.R

@@ -1,4 +1,4 @@
 .compute_variances <- function(model,
                                component,
                                name_fun = NULL,
                                name_full = NULL,
@@ -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")
 
@@ -497,7 +505,7 @@
 # fixed effects variance ------------------------------------------------------
 #
 # This is in line with Nakagawa et al. 2017, Suppl. 2
 # see https://royalsocietypublishing.org/action/downloadSupplement?doi=10.1098%2Frsif.2017.0213&file=rsif20170213supp2.pdf
 #
 # However, package MuMIn differs and uses "fitted()" instead, leading to minor
 # deviations
@@ -525,7 +533,7 @@
 # variance associated with a random-effects term (Johnson 2014) --------------
 #
 # This is in line with Nakagawa et al. 2017, Suppl. 2, and package MuMIm
 # see https://royalsocietypublishing.org/action/downloadSupplement?doi=10.1098%2Frsif.2017.0213&file=rsif20170213supp2.pdf
 # ----------------------------------------------------------------------------
 .compute_variance_random <- function(model, terms, mixed_effects_info) {
   if (is.null(terms)) {
@@ -566,7 +574,7 @@
 # (also call obersvation level variance).
 #
 # This is in line with Nakagawa et al. 2017, Suppl. 2, and package MuMIm
 # see https://royalsocietypublishing.org/action/downloadSupplement?doi=10.1098%2Frsif.2017.0213&file=rsif20170213supp2.pdf
 #
 # We need:
 # - the overdispersion parameter / sigma
@@ -581,7 +589,7 @@
                                            faminfo,
                                            model_null = NULL,
                                            revar_null = NULL,
                                            name,
                                            approx_method = "lognormal",
                                            model_component = NULL,
                                            verbose = TRUE) {
@@ -762,7 +770,7 @@
 # different methods, which are now also implemented here.
 #
 # This is in line with Nakagawa et al. 2017, Suppl. 2, and package MuMIm
 # see https://royalsocietypublishing.org/action/downloadSupplement?doi=10.1098%2Frsif.2017.0213&file=rsif20170213supp2.pdf
 #
 # There may be small deviations to Nakagawa et al. for the null-model, which
 # despite being correctly re-formulated in "null_model()", returns slightly
@@ -778,7 +786,7 @@
                                      sig,
                                      model_null = NULL,
                                      revar_null = NULL,
                                      name,
                                      approx_method = "lognormal",
                                      model_component = NULL,
                                      verbose = TRUE) {
@@ -1204,7 +1212,7 @@
   if (length(rs) > length(fe)) rs <- rs[seq_along(fe)]
   if (length(fe) > length(rs)) fe <- fe[seq_along(rs)]
 
   all(mapply(function(r, f) all(r %in% f), rs, fe, SIMPLIFY = TRUE))
 }
 
 

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

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),

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),