fix issue #1501

DESCRIPTION

@@ -2,8 +2,8 @@ Package: brms
 Encoding: UTF-8
 Type: Package
 Title: Bayesian Regression Models using 'Stan'
-Version: 2.19.6
-Date: 2023-05-17
+Version: 2.19.7
+Date: 2023-07-12
 Authors@R:
     c(person("Paul-Christian", "Bürkner", email = "paul.buerkner@gmail.com",
              role = c("aut", "cre")),

NEWS.md

@@ -14,6 +14,8 @@ and incompatible with the newly implemented global shrinkage prior framework.
 * No longer support multiple deprecated prior options for categorical and
 multivariate models after around 3 years of deprecation. (#1420)
 * Deprecate argument `newdata` of `get_refmodel.brmsfit()`. (#1502)
+* Disallow binomial models without `trials` argument after several years
+of deprecation. (#1501)
 
 ### Bug Fixes
 

R/data-response.R

@@ -173,32 +173,18 @@ data_response.brmsterms <- function(x, data, check_response = TRUE,
   # data for addition arguments of the response
   if (has_trials(x$family) || is.formula(x$adforms$trials)) {
     if (!length(x$adforms$trials)) {
-      if (is_multinomial(x$family)) {
-        stop2("Specifying 'trials' is required in multinomial models.")
-      }
-      trials <- round(max(out$Y, na.rm = TRUE))
-      if (isTRUE(is.finite(trials))) {
-        message("Using the maximum response value as the number of trials.")
-        warning2(
-          "Using 'binomial' families without specifying 'trials' ",
-          "on the left-hand side of the model formula is deprecated."
-        )
-      } else if (!is.null(basis$trials)) {
-        trials <- max(basis$trials)
-      } else {
-        stop2("Could not compute the number of trials.")
-      }
-    } else if (is.formula(x$adforms$trials)) {
-      trials <- get_ad_values(x, "trials", "trials", data)
-      if (!is.numeric(trials)) {
-        stop2("Number of trials must be numeric.")
-      }
-      if (any(!is_wholenumber(trials) | trials < 0)) {
-        stop2("Number of trials must be non-negative integers.")
-      }
-    } else {
+      stop2("Specifying 'trials' is required for this model.")
+    }
+    if (!is.formula(x$adforms$trials)) {
       stop2("Argument 'trials' is misspecified.")
     }
+    trials <- get_ad_values(x, "trials", "trials", data)
+    if (!is.numeric(trials)) {
+      stop2("Number of trials must be numeric.")
+    }
+    if (any(!is_wholenumber(trials) | trials < 0)) {
+      stop2("Number of trials must be non-negative integers.")
+    }
     if (length(trials) == 1L) {
       trials <- rep(trials, nrow(data))
     }

R/make_standata.R

@@ -210,12 +210,6 @@ standata_basis.brmsterms <- function(x, data, ...) {
   }
   # old levels are required to select the right indices for new levels
   out$levels <- get_levels(tidy_meef(x, data), tidy_ranef(x, data))
-  if (has_trials(x$family)) {
-    # trials should not be computed based on new data
-    datr <- data_response(x, data, check_response = FALSE, internal = TRUE)
-    # partially match via $ to be independent of the response suffix
-    out$trials <- datr$trials
-  }
   if (is_binary(x$family) || is_categorical(x$family)) {
     y <- model.response(model.frame(x$respform, data, na.action = na.pass))
     out$resp_levels <- levels(as.factor(y))

tests/testthat/tests.make_standata.R

@@ -162,8 +162,6 @@ test_that("make_standata returns correct values for addition terms", {
                as.array(c(rep(1:0, 4), 0)))
   expect_equal(make_standata(y | cens(c4, y + 2) ~ 1, data = dat)$rcens,
                as.array(c(rep(0, 5), dat$y[6:9] + 2)))
-  sdata <- suppressWarnings(make_standata(s ~ 1, dat, family = "binomial"))
-  expect_equal(sdata$trials, as.array(rep(9, 9)))
   expect_equal(make_standata(s | trials(10) ~ 1, dat,
                              family = "binomial")$trials,
                as.array(rep(10, 9)))

tests/testthat/tests.posterior_epred.R

@@ -65,10 +65,12 @@ test_that("posterior_epred helper functions run without errors", {
   expect_equal(dim(SW(posterior_epred(fit, summary = FALSE))), c(ndraws, nobs))
 
   # pseudo binomial model
+  old_formula <- fit$formula$formula
+  fit$formula$formula <- update(fit$formula$formula, . | trials(100) ~ .)
   fit$autocor <- brms:::cor_empty()
   fit$family <- fit$formula$family <- binomial()
   expect_equal(dim(SW(posterior_epred(fit, summary = FALSE))), c(ndraws, nobs))
-  
+
   # pseudo beta-binomial model
   fit$family <- fit$formula$family <- beta_binomial()
   expect_equal(dim(SW(posterior_epred(fit, summary = FALSE))), c(ndraws, nobs))
@@ -82,6 +84,7 @@ test_that("posterior_epred helper functions run without errors", {
   expect_equal(dim(SW(posterior_epred(fit, summary = FALSE))), c(ndraws, nobs))
 
   # pseudo hurdle poisson model
+  fit$formula$formula <- old_formula
   fit$family <- fit$formula$family <- hurdle_poisson()
   fit$formula <- bf(count ~ Trt*Age + mo(Exp) + offset(Age) + (1+Trt|visit),
                     family = family(fit))