find_variables() & brms: Distributional component variables missing

Fixes #896

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: insight
 Title: Easy Access to Model Information for Various Model Objects
-Version: 0.20.2.11
+Version: 0.20.2.12
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -8,6 +8,9 @@
 
 ## Bug fixes
 
+* Fixed issue in `find_formula()`, `find_predictors()` and `find_variables()`
+  for models from package *brms* with custom formulas.
+
 * Fixed issues in `find_response()` for *brms* models with `mi()` function in
   the response variable.
 

R/find_formula.R

@@ -1578,6 +1578,33 @@ find_formula.model_fit <- function(x, verbose = TRUE, ...) {
   f_bias <- f$pforms$bias
   f_bs <- f$pforms$bs
 
+  # brms formulas can also have custom names, based on variable names, e.g.:
+  # brm(
+  #   bf(carb ~ gear * vs) + lf(disc ~ 0 + mo(cyl)),
+  #   data = mtcars,
+  #   family = cumulative("probit"),
+  # )
+  # the lf() part is in "f$pforms" with name "disc".
+  #
+  # we therefore need to check whether we have additional names not yet covered
+  # by the above exceptions.
+
+  # auxiliary names
+  auxiliary_names <- c(
+    "sigma", "mu", "nu", "shape", "beta", "phi", "hu", "ndt", "zoi", "coi",
+    "kappa", "bias", "bs", "zi"
+  )
+
+  # check if any further pforms exist
+  if (!all(names(f$pforms) %in% auxiliary_names)) {
+    custom_names <- setdiff(names(f$pforms), auxiliary_names)
+    if (length(custom_names)) {
+      f_custom <- f$pforms[custom_names]
+    }
+  } else {
+    f_custom <- NULL
+  }
+
   f_sigmarandom <- NULL
   f_betarandom <- NULL
 
@@ -1633,7 +1660,7 @@ find_formula.model_fit <- function(x, verbose = TRUE, ...) {
   }
 
 
-  compact_list(list(
+  compact_list(c(list(
     conditional = f_cond,
     random = f_random,
     zero_inflated = f_zi,
@@ -1653,7 +1680,7 @@ find_formula.model_fit <- function(x, verbose = TRUE, ...) {
     zero_one_inflated = f_zoi,
     conditional_one_inflated = f_coi,
     kappa = f_kappa
-  ))
+  ), f_custom))
 }
 
 

R/find_predictors.R

@@ -87,9 +87,9 @@ find_predictors.default <- function(x,
 
   # filter formulas, depending on requested effects and components
   if (is_mv) {
-    f <- lapply(f, function(.x) .prepare_predictors(x, .x, elements))
+    f <- lapply(f, function(.x) .prepare_predictors(x, .x, elements, component))
   } else {
-    f <- .prepare_predictors(x, f, elements)
+    f <- .prepare_predictors(x, f, elements, component)
   }
 
   # random effects are returned as list, so we need to unlist here
@@ -292,8 +292,10 @@ find_predictors.afex_aov <- function(x,
 }
 
 
-.prepare_predictors <- function(x, f, elements) {
-  f <- f[names(f) %in% elements]
+.prepare_predictors <- function(x, f, elements, component = "all") {
+  if (!inherits(x, "brmsfit") && component != "all") {
+    f <- f[names(f) %in% elements]
+  }
 
   # from conditional model, remove response
   if (object_has_names(f, "conditional")) {