lintr

tests/testthat/test-bayesfactor_models.R

@@ -17,9 +17,13 @@ test_that("bayesfactor_models BIC", {
   BFM3 <- bayesfactor_models(mo2, mo3, mo4, mo1, denominator = mo1)
   BFM4 <<- bayesfactor_models(mo2, mo3, mo4, mo5, mo1, denominator = mo1)
 
-  expect_equal(BFM1, BFM2)
-  expect_equal(BFM1, BFM3)
-  expect_equal(BFM1, bayesfactor_models(list(mo2 = mo2, mo3 = mo3, mo4 = mo4, mo1 = mo1), denominator = 4))
+  expect_equal(BFM1, BFM2, tolerance = 1e-4, ignore_attr = TRUE)
+  expect_equal(BFM1, BFM3, tolerance = 1e-4, ignore_attr = TRUE)
+  expect_equal(
+    BFM1,
+    bayesfactor_models(list(mo2 = mo2, mo3 = mo3, mo4 = mo4, mo1 = mo1), denominator = 4),
+    tolerance = 1e-4, ignore_attr = TRUE
+  )
 
   # only on same data!
   expect_warning(bayesfactor_models(mo1, mo2, mo4_e))
@@ -105,11 +109,13 @@ test_that("bayesfactor_models STAN", {
   set.seed(333)
   suppressMessages({
     expect_warning(
-      stan_models <- bayesfactor_models(stan_bf_0, stan_bf_1)
+      {
+        stan_models <- bayesfactor_models(stan_bf_0, stan_bf_1)
+      }
     )
   })
   expect_s3_class(stan_models, "bayesfactor_models")
-  expect_equal(length(stan_models$log_BF), 2)
+  expect_length(stan_models$log_BF, 2)
   expect_equal(stan_models$log_BF[2], log(bridge_BF$bf), tolerance = 0.1)
 })
 
@@ -143,7 +149,9 @@ test_that("bayesfactor_models BRMS", {
   set.seed(444)
   suppressWarnings(suppressMessages(
     expect_message(
-      bfm <- bayesfactor_models(stan_brms_model_0, stan_brms_model_1),
+      {
+        bfm <- bayesfactor_models(stan_brms_model_0, stan_brms_model_1)
+      },
       regexp = "marginal"
     )
   ))
@@ -163,7 +171,12 @@ test_that("bayesfactor_models BRMS", {
     silent = 2
   )
 
-  suppressWarnings(expect_message(bfmwc <- bayesfactor_models(stan_brms_model_0wc, stan_brms_model_1wc), regexp = NA))
+  suppressWarnings(expect_message(
+    {
+      bfmwc <- bayesfactor_models(stan_brms_model_0wc, stan_brms_model_1wc)
+    },
+    regexp = NA
+  ))
   expect_equal(bfmwc$log_BF, bfm$log_BF, tolerance = 0.01)
 })
 
@@ -179,7 +192,8 @@ test_that("bayesfactor_inclusion | BayesFactor", {
   BF_ToothGrowth <- BayesFactor::anovaBF(len ~ dose * supp, ToothGrowth)
   expect_equal(
     bayesfactor_inclusion(BF_ToothGrowth),
-    bayesfactor_inclusion(bayesfactor_models(BF_ToothGrowth))
+    bayesfactor_inclusion(bayesfactor_models(BF_ToothGrowth)),
+    tolerance = 1e-4, ignore_attr = TRUE
   )
 })
 

tests/testthat/test-brms.R

@@ -10,14 +10,14 @@ test_that("brms", {
   expect_s3_class(hdi(model), "data.frame")
   expect_s3_class(ci(model), "data.frame")
   expect_s3_class(rope(model, verbose = FALSE), "data.frame")
-  expect_true(inherits(equivalence_test(model), "equivalence_test"))
+  expect_s3_class(equivalence_test(model), "equivalence_test")
   expect_s3_class(map_estimate(model), "data.frame")
   expect_s3_class(p_map(model), "data.frame")
   expect_s3_class(p_direction(model), "data.frame")
 
-  expect_identical(colnames(hdi(model)), c("Parameter", "CI", "CI_low", "CI_high", "Effects", "Component"))
-  expect_identical(colnames(hdi(model, effects = "all")), c("Parameter", "CI", "CI_low", "CI_high", "Effects", "Component"))
-  expect_equal(nrow(equivalence_test(model)), 2L)
+  expect_named(hdi(model), c("Parameter", "CI", "CI_low", "CI_high", "Effects", "Component"))
+  expect_named(hdi(model, effects = "all"), c("Parameter", "CI", "CI_low", "CI_high", "Effects", "Component"))
+  expect_identical(nrow(equivalence_test(model)), 2L)
 
   out <- describe_posterior(model, effects = "all", component = "all", centrality = "mean")
   suppressWarnings({

tests/testthat/test-weighted_posteriors.R

@@ -41,7 +41,7 @@ test_that("weighted_posteriors for BayesFactor (intercept)", {
   BFmods <- regressionBF(y ~ x1 + x2, data = dat, progress = FALSE)
 
   res <- weighted_posteriors(BFmods)
-  expect_equal(attr(res, "weights")$weights, c(1032, 805, 1388, 775))
+  expect_equal(attr(res, "weights")$weights, c(1032, 805, 1388, 775), ignore_attr = TRUE)
 
   wHDI <- hdi(res[c("x1", "x2")], ci = 0.9)
   expect_equal(wHDI$CI_low, c(-0.519, -0.640), tolerance = 0.01)
@@ -64,7 +64,7 @@ test_that("weighted_posteriors for nonlinear BayesFactor", {
 
   res <- weighted_posteriors(BFS)
 
-  expect_equal(attributes(res)$weights$weights, c(113, 3876, 11))
+  expect_equal(attributes(res)$weights$weights, c(113, 3876, 11), ignore_attr = TRUE)
 })
 
 test_that("weighted_posteriors vs posterior_average", {
@@ -87,7 +87,9 @@ test_that("weighted_posteriors vs posterior_average", {
   )
 
   set.seed(444)
-  expect_warning(res_BT <- weighted_posteriors(fit1, fit2))
+  expect_warning({
+    res_BT <- weighted_posteriors(fit1, fit2)
+  })
 
   set.seed(444)
   res_brms <- brms::posterior_average(fit1, fit2, weights = "bma", missing = 0)
@@ -96,8 +98,8 @@ test_that("weighted_posteriors vs posterior_average", {
   res_BT1 <- eti(res_BT)
   res_brms1 <- eti(res_brms)
 
-  expect_equal(res_BT1$Parameter, res_brms1$Parameter)
-  expect_equal(res_BT1$CI, res_brms1$CI)
-  expect_equal(res_BT1$CI_low, res_brms1$CI_low)
-  expect_equal(res_BT1$CI_high, res_brms1$CI_high)
+  expect_equal(res_BT1$Parameter, res_brms1$Parameter, tolerance = 1e-4)
+  expect_equal(res_BT1$CI, res_brms1$CI, tolerance = 1e-4)
+  expect_equal(res_BT1$CI_low, res_brms1$CI_low, tolerance = 1e-4)
+  expect_equal(res_BT1$CI_high, res_brms1$CI_high, tolerance = 1e-4)
 })