bayesian_as_frequentist() fails for brms 0 + Intercept formula (#654

)

* `bayesian_as_frequentist()` fails for brms `0 + Intercept` formula
Fixes #646

* add test

* simplify

* add test

* fix

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: bayestestR
 Title: Understand and Describe Bayesian Models and Posterior Distributions
-Version: 0.13.2.4
+Version: 0.13.2.5
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",
@@ -76,6 +76,7 @@ Suggests:
     BayesFactor (>= 0.9.12-4.4),
     bayesQR,
     bayesplot,
+    betareg,
     BH,
     blavaan,
     bridgesampling,
@@ -88,6 +89,7 @@ Suggests:
     ggplot2,
     glmmTMB,
     httr,
+    httr2,
     KernSmooth,
     knitr,
     lavaan,

NEWS.md

@@ -13,6 +13,11 @@
 * `bayesian_as_frequentist()` now supports more model families from Bayesian
   models that can be successfully converted to their frequentists counterparts.
 
+## Bug fixes
+
+* Fixed issue in `bayesian_as_frequentist()` for *brms* models with `0 + Intercept`
+  specification in the model formula.
+
 # bayestestR 0.13.2
 
 ## Breaking Changes

R/convert_bayesian_to_frequentist.R

@@ -47,6 +47,14 @@ convert_bayesian_as_frequentist <- function(model, data = NULL, REML = TRUE) {
   model_formula <- insight::find_formula(model)
   model_family <- insight::get_family(model)
 
+  # fix exception: The 0 + Intercept syntax in brms can be used to facilitate
+  # prior specification for the intercept, but but it leads to issues where it
+  # wrongly can be believed that Intercept is a variable and not a special term.
+  f_string <- insight::safe_deparse(model_formula$conditional)
+  if (grepl("0 + Intercept", f_string, fixed = TRUE)) {
+    model_formula$conditional <- stats::as.formula(gsub("0 + Intercept", "1", f_string, fixed = TRUE))
+  }
+
   if (inherits(model_family, "brmsfamily")) {
     insight::check_if_installed("glmmTMB")
     # exception: ordbetareg()
@@ -200,12 +208,7 @@ convert_bayesian_as_frequentist <- function(model, data = NULL, REML = TRUE) {
   }
 
   fixed_formula <- paste(as.character(formula$conditional)[c(2, 1, 3)], collapse = " ")
-  cond_formula <- stats::as.formula(paste(
-    fixed_formula, random_formula,
-    sep = " + "
-  ))
-
-  cond_formula
+  stats::as.formula(paste(fixed_formula, random_formula, sep = " + "))
 }
 
 #' @rdname convert_bayesian_as_frequentist

tests/testthat/test-bayesian_as_frequentist.R

@@ -1,8 +1,9 @@
+skip_on_cran()
+skip_if_offline()
+skip_if_not_or_load_if_installed("httr2")
+
 test_that("rstanarm to freq", {
-  skip_on_cran()
-  skip_if_offline()
   skip_if_not_or_load_if_installed("rstanarm")
-  skip_if_not_or_load_if_installed("httr")
 
   set.seed(333)
   m <- insight::download_model("stanreg_glm_1")
@@ -14,11 +15,8 @@ test_that("rstanarm to freq", {
 
 
 test_that("rstanarm to freq", {
-  skip_on_cran()
-  skip_if_offline()
   skip_if_not_or_load_if_installed("rstanarm")
   skip_if_not_or_load_if_installed("lme4")
-  skip_if_not_or_load_if_installed("httr")
 
   set.seed(333)
   m <- insight::download_model("stanreg_lmerMod_1")
@@ -30,12 +28,9 @@ test_that("rstanarm to freq", {
 
 
 test_that("brms beta to freq", {
-  skip_on_cran()
-  skip_if_offline()
   skip_if_not_or_load_if_installed("brms")
   skip_if_not_or_load_if_installed("glmmTMB")
   skip_if_not_or_load_if_installed("lme4")
-  skip_if_not_or_load_if_installed("httr")
   skip_if_not_or_load_if_installed("betareg")
 
   set.seed(333)
@@ -53,13 +48,10 @@ test_that("brms beta to freq", {
 
 
 test_that("ordbetareg to freq", {
-  skip_on_cran()
-  skip_if_offline()
   skip_if_not_or_load_if_installed("brms")
   skip_if_not_or_load_if_installed("ordbetareg")
   skip_if_not_or_load_if_installed("glmmTMB")
   skip_if_not_or_load_if_installed("lme4")
-  skip_if_not_or_load_if_installed("httr")
   skip_if_not_or_load_if_installed("datawizard")
 
   set.seed(333)
@@ -75,3 +67,28 @@ test_that("ordbetareg to freq", {
 
   expect_equal(lme4::fixef(m1), lme4::fixef(m2), tolerance = 1e-1)
 })
+
+
+test_that("brms 0 + Intercept to freq", {
+  skip_if_not_or_load_if_installed("brms")
+
+  set.seed(333)
+  data(mtcars)
+  m <- brms::brm(qsec ~ 0 + Intercept + mpg, data = mtcars, refresh = 0)
+  m1 <- lm(qsec ~ mpg, data = mtcars)
+  m2 <- convert_bayesian_as_frequentist(m)
+
+  expect_equal(coef(m1), coef(m2), tolerance = 1e-2)
+})
+
+
+test_that("brms Interaction terms to freq", {
+  skip_if_not_or_load_if_installed("brms")
+
+  set.seed(333)
+  m <- brms::brm(qsec ~ mpg * as.factor(am), data = mtcars, refresh = 0)
+  m1 <- lm(qsec ~ mpg * as.factor(am), data = mtcars)
+  m2 <- convert_bayesian_as_frequentist(m)
+
+  expect_equal(coef(m1), coef(m2), tolerance = 1e-2)
+})