check for NULL

tests/testthat/test-GLMMadaptive.R

@@ -5,6 +5,9 @@ skip_if_not_installed("lme4")
 m <- download_model("GLMMadaptive_zi_2")
 m2 <- download_model("GLMMadaptive_zi_1")
 
+skip_if(is.null(m))
+skip_if(is.null(m2))
+
 data(cbpp, package = "lme4")
 tmp <<- cbpp
 m3 <- GLMMadaptive::mixed_model(
@@ -68,7 +71,7 @@ test_that("find_predictors", {
   )
   expect_identical(
     find_predictors(m, effects = "all")$zero_inflated_random,
-    c("persons")
+    "persons"
   )
   expect_identical(find_predictors(m, effects = "random")$random, "persons")
   expect_identical(
@@ -167,15 +170,14 @@ test_that("clean_names", {
 
 test_that("find_formula", {
   expect_length(find_formula(m), 4)
-  expect_identical(
-    names(find_formula(m)),
+  expect_named(
+    find_formula(m),
     c(
       "conditional",
       "random",
       "zero_inflated",
       "zero_inflated_random"
-    ),
-    ignore_attr = TRUE
+    )
   )
 })
 

tests/testthat/test-brms.R

@@ -1,3 +1,4 @@
+skip_on_cran()
 skip_if_offline()
 skip_if_not_installed("brms")
 
@@ -12,34 +13,38 @@ m6 <- insight::download_model("brms_corr_re1")
 m7 <- suppressWarnings(insight::download_model("brms_mixed_8"))
 m8 <- insight::download_model("brms_ordinal_1")
 
+all_loaded <- !vapply(list(m1, m2, m3, m4, m5, m6, m7, m8), is.null, TRUE)
+skip_if(!all(all_loaded))
+
 # Tests -------------------------------------------------------------------
 test_that("get_predicted.brmsfit: ordinal dv", {
   skip_if_not_installed("bayestestR")
+  skip_if_not_installed("rstantools")
 
   pred1 <- get_predicted(m8, ci = 0.95)
   pred2 <- get_predicted(m8, ci_method = "hdi", ci = 0.95)
-  expect_true(inherits(pred1, "get_predicted"))
-  expect_true(inherits(pred1, "data.frame"))
+  expect_s3_class(pred1, "get_predicted")
+  expect_s3_class(pred1, "data.frame")
   expect_true(all(c("Row", "Response") %in% colnames(pred1)))
 
   # ci_method changes intervals but not se or predicted
   pred1 <- data.frame(pred1)
   pred2 <- data.frame(pred2)
-  expect_equal(pred1$Row, pred2$Row)
-  expect_equal(pred1$Response, pred2$Response)
-  expect_equal(pred1$Predicted, pred2$Predicted)
-  expect_equal(pred1$SE, pred2$SE)
+  expect_equal(pred1$Row, pred2$Row, ignore_attr = TRUE)
+  expect_equal(pred1$Response, pred2$Response, ignore_attr = TRUE)
+  expect_equal(pred1$Predicted, pred2$Predicted, ignore_attr = TRUE)
+  expect_equal(pred1$SE, pred2$SE, ignore_attr = TRUE)
   expect_false(mean(pred1$CI_low == pred2$CI_low) > 0.1) # most CI bounds are different
   expect_false(mean(pred1$CI_high == pred2$CI_high) > 0.1) # most CI bounds are different
 
   # compare to manual predictions
   pred3 <- get_predicted(m8, centrality_function = stats::median, ci = 0.95)
   manual <- rstantools::posterior_epred(m8)
   manual <- apply(manual[, , 1], 2, median)
-  expect_equal(pred3$Predicted[1:32], manual)
+  expect_equal(pred3$Predicted[1:32], manual, ignore_attr = TRUE)
   manual <- rstantools::posterior_epred(m8)
   manual <- apply(manual[, , 1], 2, mean)
-  expect_equal(pred1$Predicted[1:32], manual)
+  expect_equal(pred1$Predicted[1:32], manual, ignore_attr = TRUE)
 })
 
 test_that("find_statistic", {
@@ -51,8 +56,8 @@ test_that("find_statistic", {
 })
 
 test_that("n_parameters", {
-  expect_equal(n_parameters(m1), 65)
-  expect_equal(n_parameters(m1, effects = "fixed"), 5)
+  expect_identical(n_parameters(m1), 65L)
+  expect_identical(n_parameters(m1, effects = "fixed"), 5L)
 })
 
 test_that("model_info", {
@@ -161,36 +166,36 @@ test_that("find_predictors", {
 })
 
 test_that("find_response", {
-  expect_equal(find_response(m1, combine = TRUE), "count")
-  expect_equal(
+  expect_identical(find_response(m1, combine = TRUE), "count")
+  expect_identical(
     find_response(m2, combine = TRUE),
     c(SepalLength = "Sepal.Length", SepalWidth = "Sepal.Width")
   )
-  expect_equal(find_response(m3, combine = TRUE), c("r", "n"))
-  expect_equal(find_response(m1, combine = FALSE), "count")
-  expect_equal(
+  expect_identical(find_response(m3, combine = TRUE), c("r", "n"))
+  expect_identical(find_response(m1, combine = FALSE), "count")
+  expect_identical(
     find_response(m2, combine = FALSE),
     c(SepalLength = "Sepal.Length", SepalWidth = "Sepal.Width")
   )
-  expect_equal(find_response(m3, combine = FALSE), c("r", "n"))
-  expect_equal(find_response(m4, combine = FALSE), "count")
-  expect_equal(
+  expect_identical(find_response(m3, combine = FALSE), c("r", "n"))
+  expect_identical(find_response(m4, combine = FALSE), "count")
+  expect_identical(
     find_response(m5, combine = TRUE),
     c(count = "count", count2 = "count2")
   )
 })
 
 test_that("get_response", {
   expect_length(get_response(m1), 236)
-  expect_equal(ncol(get_response(m2)), 2)
-  expect_equal(
+  expect_identical(ncol(get_response(m2)), 2L)
+  expect_identical(
     colnames(get_response(m2)),
     c("Sepal.Length", "Sepal.Width")
   )
-  expect_equal(ncol(get_response(m3)), 2)
-  expect_equal(colnames(get_response(m3)), c("r", "n"))
+  expect_identical(ncol(get_response(m3)), 2L)
+  expect_identical(colnames(get_response(m3)), c("r", "n"))
   expect_length(get_response(m4), 250)
-  expect_equal(colnames(get_response(m5)), c("count", "count2"))
+  expect_identical(colnames(get_response(m5)), c("count", "count2"))
 })
 
 test_that("find_variables", {
@@ -264,16 +269,16 @@ test_that("find_variables", {
 })
 
 test_that("n_obs", {
-  expect_equal(n_obs(m1), 236)
-  expect_equal(n_obs(m2), 150)
-  expect_equal(n_obs(m3), 10)
-  expect_equal(n_obs(m4), 250)
-  expect_equal(n_obs(m5), 250)
+  expect_identical(n_obs(m1), 236L)
+  expect_identical(n_obs(m2), 150L)
+  expect_identical(n_obs(m3), 10L)
+  expect_identical(n_obs(m4), 250L)
+  expect_identical(n_obs(m5), 250L)
 })
 
 
 test_that("find_random", {
-  expect_equal(find_random(m5), list(
+  expect_identical(find_random(m5), list(
     count = list(
       random = "persons",
       zero_inflated_random = "persons"
@@ -283,8 +288,8 @@ test_that("find_random", {
       zero_inflated_random = "persons"
     )
   ))
-  expect_equal(find_random(m5, flatten = TRUE), "persons")
-  expect_equal(find_random(m6, flatten = TRUE), "id")
+  expect_identical(find_random(m5, flatten = TRUE), "persons")
+  expect_identical(find_random(m6, flatten = TRUE), "id")
 })
 
 
@@ -302,7 +307,7 @@ test_that("get_data", {
 
 
 test_that("find_paramaters", {
-  expect_equal(
+  expect_identical(
     find_parameters(m1),
     list(
       conditional = c(
@@ -316,7 +321,7 @@ test_that("find_paramaters", {
     )
   )
 
-  expect_equal(
+  expect_identical(
     find_parameters(m2),
     structure(
       list(
@@ -343,7 +348,7 @@ test_that("find_paramaters", {
     )
   )
 
-  expect_equal(
+  expect_identical(
     find_parameters(m4),
     list(
       conditional = c("b_Intercept", "b_child", "b_camper"),
@@ -353,7 +358,7 @@ test_that("find_paramaters", {
     )
   )
 
-  expect_equal(
+  expect_identical(
     find_parameters(m5, effects = "all"),
     structure(
       list(
@@ -380,7 +385,7 @@ test_that("find_paramaters", {
 })
 
 test_that("find_paramaters", {
-  expect_equal(
+  expect_identical(
     colnames(get_parameters(m4)),
     c(
       "b_Intercept",
@@ -391,11 +396,11 @@ test_that("find_paramaters", {
       "b_zi_camper"
     )
   )
-  expect_equal(
+  expect_identical(
     colnames(get_parameters(m4, component = "zi")),
     c("b_zi_Intercept", "b_zi_child", "b_zi_camper")
   )
-  expect_equal(
+  expect_identical(
     colnames(get_parameters(m4, effects = "all")),
     c(
       "b_Intercept", "b_child", "b_camper", "r_persons[1,Intercept]",
@@ -405,14 +410,14 @@ test_that("find_paramaters", {
       "r_persons__zi[4,Intercept]", "sd_persons__zi_Intercept"
     )
   )
-  expect_equal(
+  expect_identical(
     colnames(get_parameters(m4, effects = "random", component = "conditional")),
     c(
       "r_persons[1,Intercept]", "r_persons[2,Intercept]", "r_persons[3,Intercept]",
       "r_persons[4,Intercept]", "sd_persons__Intercept"
     )
   )
-  expect_equal(
+  expect_identical(
     colnames(get_parameters(m5, effects = "random", component = "conditional")),
     c(
       "r_persons__count[1,Intercept]", "r_persons__count[2,Intercept]",
@@ -423,7 +428,7 @@ test_that("find_paramaters", {
     )
   )
 
-  expect_equal(
+  expect_identical(
     colnames(get_parameters(m5, effects = "all", component = "all")),
     c(
       "b_count_Intercept", "b_count_child", "b_count_camper", "r_persons__count[1,Intercept]",
@@ -468,7 +473,7 @@ test_that("is_multivariate", {
 })
 
 test_that("find_terms", {
-  expect_equal(
+  expect_identical(
     find_terms(m2),
     list(
       SepalLength = list(
@@ -484,7 +489,7 @@ test_that("find_terms", {
 })
 
 test_that("find_algorithm", {
-  expect_equal(
+  expect_identical(
     find_algorithm(m1),
     list(
       algorithm = "sampling",

tests/testthat/test-format_table.R

@@ -4,6 +4,8 @@ skip_if_not_installed("bayestestR")
 
 # test for bayesian models -----------------
 m1 <- insight::download_model("stanreg_glm_1")
+skip_if(is.null(m1))
+
 set.seed(123)
 x <- suppressWarnings(as.data.frame(bayestestR::describe_posterior(m1, test = c("pd", "bf"))))
 

tests/testthat/test-gam.R

@@ -28,6 +28,9 @@ m1 <- mgcv::gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat2)
 m2 <- download_model("gam_zi_1")
 m3 <- download_model("gam_mv_1")
 
+skip_if(is.null(m2))
+skip_if(is.null(m3))
+
 test_that("model_info", {
   expect_true(model_info(m1)$is_linear)
   expect_true(model_info(m2)$is_count)

tests/testthat/test-get_loglikelihood.R

@@ -94,10 +94,12 @@ test_that("get_loglikelihood - (g)lmer", {
   expect_equal(as.numeric(ll), as.numeric(ll2))
 
   model <- download_model("lmerMod_1")
+  skip_if(is.null(model))
   expect_equal(get_loglikelihood(model, estimator = "REML"), logLik(model, REML = TRUE), tolerance = 0.01, ignore_attr = TRUE)
   expect_equal(get_loglikelihood(model, estimator = "ML"), logLik(model, REML = FALSE), tolerance = 0.01, ignore_attr = TRUE)
 
   model <- download_model("merMod_1")
+  skip_if(is.null(model))
   expect_equal(get_loglikelihood(model, estimator = "REML"), logLik(model, REML = FALSE), tolerance = 0.01, ignore_attr = TRUE)
   expect_equal(get_loglikelihood(model, estimator = "ML"), logLik(model, REML = FALSE), tolerance = 0.01, ignore_attr = TRUE)
 })

tests/testthat/test-mvrstanarm.R

@@ -4,6 +4,7 @@ skip_if_not_installed("rstanarm")
 
 data("pbcLong", package = "rstanarm")
 m1 <- download_model("stanmvreg_1")
+skip_if(is.null(m1))
 
 test_that("clean_names", {
   expect_identical(

tests/testthat/test-rstanarm.R

@@ -16,6 +16,10 @@ m1 <- insight::download_model("stanreg_merMod_5")
 m2 <- insight::download_model("stanreg_glm_6")
 m3 <- insight::download_model("stanreg_glm_1")
 
+skip_if(is.null(m1))
+skip_if(is.null(m2))
+skip_if(is.null(m3))
+
 data("puzzles", package = "BayesFactor")
 m4 <- suppressWarnings(
   stan_glm(
@@ -38,6 +42,7 @@ m5 <- suppressWarnings(
   )
 )
 m6 <- insight::download_model("stanreg_gamm4_1")
+skip_if(is.null(m6))
 
 m7 <- suppressWarnings(
   stan_lm(mpg ~ wt + qsec + am,

tests/testthat/test-spatial.R

@@ -16,6 +16,7 @@ d <- data.frame(
 dat <<- d
 
 m1 <- download_model("glmmTMB_spatial_1")
+skip_if(is.null(m1))
 
 test_that("find_weights", {
   expect_null(find_weights(m1))

tests/testthat/test-vgam.R

@@ -6,6 +6,9 @@ data("hunua", package = "VGAM")
 m1 <- download_model("vgam_1")
 m2 <- download_model("vgam_2")
 
+skip_if(is.null(m1))
+skip_if(is.null(m2))
+
 test_that("model_info", {
   expect_true(model_info(m1)$is_binomial)
   expect_true(model_info(m2)$is_binomial)