.runThisTest <- Sys.getenv("RunAllggeffectsTests") == "yes" if (.runThisTest) { if (suppressWarnings( requiet("testthat") && requiet("ggeffects") && requiet("lme4") && requiet("glmmTMB") )) { # glmer ---- data(efc_test) fit <- glmer( negc7d ~ c12hour + e42dep + c161sex + c172code + (1 | grp), data = efc_test, family = binomial(link = "logit") ) test_that("ggpredict, glmer", { pr <- ggpredict(fit, "c12hour") expect_equal( pr$predicted, c(0.34217, 0.34406, 0.34596, 0.34787, 0.34978, 0.3517, 0.35362, 0.35554, 0.35747, 0.35941, 0.36135, 0.36329, 0.36524, 0.36719, 0.36915, 0.37111, 0.37307, 0.37504, 0.37702, 0.37899, 0.38098, 0.38296, 0.38495, 0.38694, 0.38894, 0.39094, 0.39295, 0.39496, 0.39697, 0.39898, 0.401, 0.40302, 0.40505, 0.40708, 0.40911), tolerance = 1e-3, ignore_attr = TRUE) expect_s3_class(ggpredict(fit, "c12hour"), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex")), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code")), "data.frame") expect_s3_class(ggpredict(fit, "c12hour", type = "re"), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), type = "re"), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), type = "re"), "data.frame") }) test_that("ggeffect, glmer", { pr <- ggeffect(fit, "c12hour") expect_equal( pr$predicted, c(0.34217, 0.34406, 0.34596, 0.34787, 0.34978, 0.3517, 0.35362, 0.35554, 0.35747, 0.35941, 0.36135, 0.36329, 0.36524, 0.36719, 0.36915, 0.37111, 0.37307, 0.37504, 0.37702, 0.37899, 0.38098, 0.38296, 0.38495, 0.38694, 0.38894, 0.39094, 0.39295, 0.39496, 0.39697, 0.39898, 0.401, 0.40302, 0.40505, 0.40708, 0.40911), tolerance = 1e-3, ignore_attr = TRUE) expect_s3_class(ggeffect(fit, "c12hour"), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour", "c161sex")), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour", "c161sex", "c172code")), "data.frame") }) test_that("ggemmeans, glmer", { pr <- ggemmeans(fit, "c12hour") expect_equal( pr$predicted, c(0.34217, 0.34406, 0.34596, 0.34787, 0.34978, 0.3517, 0.35362, 0.35554, 0.35747, 0.35941, 0.36135, 0.36329, 0.36524, 0.36719, 0.36915, 0.37111, 0.37307, 0.37504, 0.37702, 0.37899, 0.38098, 0.38296, 0.38495, 0.38694, 0.38894, 0.39094, 0.39295, 0.39496, 0.39697, 0.39898, 0.401, 0.40302, 0.40505, 0.40708, 0.40911), tolerance = 1e-3, ignore_attr = TRUE) expect_s3_class(ggemmeans(fit, "c12hour"), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex")), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex", "c172code")), "data.frame") }) m <- insight::download_model("merMod_5") dd <<- insight::get_data(m, source = "frame") test_that("ggpredict, glmer.nb", { expect_s3_class(ggpredict(m, "f1"), "data.frame") expect_s3_class(ggpredict(m, "f1", type = "re"), "data.frame") expect_s3_class(ggpredict(m, c("f1", "f2")), "data.frame") expect_s3_class(ggpredict(m, c("f1", "f2"), type = "re"), "data.frame") expect_message(ggemmeans(m, "f1")) expect_s3_class(ggemmeans(m, c("f1", "f2")), "data.frame") }) test_that("ggpredict, glmer.nb-simulate", { expect_s3_class(ggpredict(m, c("f1", "f2"), type = "sim"), "data.frame") }) data(cbpp) cbpp$trials <- cbpp$size - cbpp$incidence m1 <- glmer(cbind(incidence, trials) ~ period + (1 | herd), data = cbpp, family = binomial) m2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) test_that("ggpredict, glmer, cbind", { expect_s3_class(ggpredict(m1, "period"), "data.frame") expect_s3_class(ggpredict(m2, "period"), "data.frame") expect_s3_class(ggpredict(m1, "period", type = "re"), "data.frame") expect_s3_class(ggpredict(m2, "period", type = "re"), "data.frame") expect_s3_class(ggemmeans(m1, "period"), "data.frame") expect_s3_class(ggemmeans(m2, "period"), "data.frame") }) test_that("compare, glmer, cbind", { p1 <- ggpredict(m1, "period") p2 <- ggemmeans(m1, "period") expect_equal(p1$predicted[1], p2$predicted[1], tolerance = 1e-3) }) } }