fix an issue in validating re terms

R/brmsframe.R

@@ -34,7 +34,6 @@ brmsframe.brmsterms <- function(x, data, frame = NULL, basis = NULL, ...) {
     # this must be a multivariate model
     stopifnot(is.list(frame))
     x$frame <- frame
-    x$frame$re <- subset2(x$frame$re, resp = x$resp)
   }
   data <- subset_data(data, x)
   x$frame$resp <- frame_resp(x, data = data)
@@ -51,6 +50,10 @@ brmsframe.brmsterms <- function(x, data, frame = NULL, basis = NULL, ...) {
       basis = basis$nlpars[[nlp]], ...
     )
   }
+  # If this is a multivariate model, retain only the subset of random effects
+  # belonging to the current response variable. Subsetting is performed here
+  # rather than earlier to allow for correct validation of 're' terms in stan_sp.
+  x$frame$re <- subset2(x$frame$re, resp = x$resp)
   class(x) <- c("brmsframe", class(x))
   x
 }

tests/testthat/tests.stancode.R

@@ -1127,7 +1127,7 @@ test_that("monotonic effects appear in the Stan code", {
   )
 })
 
-test_that("Stan code for re prectiro terms is correct", {
+test_that("Stan code for re predictor terms is correct", {
   dat <- data.frame(
     y = rnorm(100, mean = rep(1:10, each = 10)),
     x = rnorm(100), gr = rep(1:10, each = 10)
@@ -1141,7 +1141,7 @@ test_that("Stan code for re prectiro terms is correct", {
     bf(x ~ (1|gr) + re(gr, resp = "y")) +
     set_rescor(FALSE)
   scode <- make_stancode(bform, dat)
-  expect_match2(scode, 
+  expect_match2(scode,
     "mu_x[n] += (bsp_x[1]) * r_1_y_1[J_1_y[n]] + r_2_x_1[J_2_x[n]] * Z_2_x_1[n];"
   )
 
@@ -1158,7 +1158,7 @@ test_that("Stan code for re prectiro terms is correct", {
 
   bform <- bf(y ~ x + (1 + x|gr), sigma ~ re(gr, coef = "z"))
   expect_error(
-    make_stancode(bform, dat), 
+    make_stancode(bform, dat),
     "Cannot find all varying coefficients"
   )
 })