test_that("rstanarm", { skip_on_cran() skip_if_offline() skip_if_not_or_load_if_installed("rstanarm") skip_if_not_or_load_if_installed("httr") set.seed(333) model <- insight::download_model("stanreg_lm_1") expect_equal(rope_range(model, verbose = FALSE)[1], -0.602, tolerance = 0.1) model <- insight::download_model("stanreg_meanfield_lm_1") expect_equal(rope_range(model, verbose = FALSE)[1], -0.602, tolerance = 0.1) model <- insight::download_model("stanreg_fullrank_lm_1") expect_equal(rope_range(model, verbose = FALSE)[1], -0.602, tolerance = 0.1) model <- insight::download_model("stanreg_lmerMod_1") expect_equal(rope_range(model, verbose = FALSE)[1], -0.097, tolerance = 0.1) model <- insight::download_model("stanreg_glm_1") expect_equal(rope_range(model, verbose = FALSE)[1], -0.18, tolerance = 0.1) model <- insight::download_model("stanreg_merMod_1") expect_equal(rope_range(model, verbose = FALSE)[1], -0.18, tolerance = 0.1) model <- insight::download_model("stanreg_gamm4_1") expect_equal(rope_range(model, verbose = FALSE)[1], -0.043, tolerance = 0.1) model <- insight::download_model("stanreg_gam_1") invisible(capture.output( expect_warning(params <- describe_posterior(model, centrality = "all", test = "all", dispersion = TRUE )) )) expect_equal(c(nrow(params), ncol(params)), c(4, 22)) 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("equivalence_test" %in% class(equivalence_test(model))) 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_error(equivalence_test(model, range = c(0.1, 0.3, 0.5))) }) test_that("rstanarm", { skip_on_cran() skip_if_offline() skip_if_not_or_load_if_installed("rstanarm") skip_if_not_or_load_if_installed("httr") set.seed(333) model <- insight::download_model("stanreg_glm_3") out <- describe_posterior(model, effects = "all", component = "all", centrality = "mean") s <- summary(model) expect_identical(colnames(out), c( "Parameter", "Mean", "CI", "CI_low", "CI_high", "pd", "ROPE_CI", "ROPE_low", "ROPE_high", "ROPE_Percentage", "Rhat", "ESS" )) expect_equal(as.vector(s[1:4, 1, drop = TRUE]), out$Mean, tolerance = 1e-3) expect_equal(as.vector(s[1:4, 8, drop = TRUE]), out$Rhat, tolerance = 1e-1) }) test_that("rstanarm", { skip_on_cran() skip_if_offline() skip_if_not_or_load_if_installed("rstanarm") skip_if_not_or_load_if_installed("httr") set.seed(333) model <- insight::download_model("stanreg_merMod_3") out <- describe_posterior(model, effects = "all", component = "all", centrality = "mean") s <- summary(model) expect_identical(colnames(out), c( "Parameter", "Effects", "Mean", "CI", "CI_low", "CI_high", "pd", "ROPE_CI", "ROPE_low", "ROPE_high", "ROPE_Percentage", "Rhat", "ESS" )) expect_equal(as.vector(s[1:8, 1, drop = TRUE]), out$Mean, tolerance = 1e-3) expect_equal(as.vector(s[1:8, 8, drop = TRUE]), out$Rhat, tolerance = 1e-1) }) test_that("rstanarm", { skip_on_cran() skip_if_offline() skip_if_not_or_load_if_installed("rstanarm") skip_if_not_or_load_if_installed("httr") set.seed(333) model <- insight::download_model("stanmvreg_1") out <- describe_posterior(model, effects = "fixed", component = "all", centrality = "mean", test = NULL) s <- summary(model) expect_identical(colnames(out), c( "Parameter", "Response", "Mean", "CI", "CI_low", "CI_high", "Rhat", "ESS" )) expect_equal(as.vector(s[c(1:2, 5:7), 1, drop = TRUE]), out$Mean, tolerance = 1e-3) expect_equal(as.vector(s[c(1:2, 5:7), 10, drop = TRUE]), out$Rhat, tolerance = 1e-1) }) test_that("rstanarm", { skip_on_cran() skip_if_offline() skip_if_not_or_load_if_installed("rstanarm") skip_if_not_or_load_if_installed("httr") set.seed(333) model <- insight::download_model("stanmvreg_1") out <- describe_posterior( model, effects = "fixed", component = "all", centrality = "mean", test = NULL, priors = TRUE ) expect_identical(colnames(out), c( "Parameter", "Response", "Mean", "CI", "CI_low", "CI_high", "Rhat", "ESS", "Prior_Distribution", "Prior_Location", "Prior_Scale" )) expect_equal(nrow(out), 5) })