if (suppressWarnings( requiet("testthat") && requiet("ggeffects") && requiet("lme4") && requiet("sjlabelled") && requiet("sjmisc") )) { # glm, logistic regression ---- data(efc, package = "ggeffects") efc$neg_c_7d <- dicho(efc$neg_c_7) d <<- efc fit <- glm(neg_c_7d ~ c12hour + e42dep + c161sex + c172code, data = d, family = binomial(link = "logit")) m <- glm( cbind(incidence, size - incidence) ~ period, family = binomial, data = lme4::cbpp ) test_that("validate ggpredict glm against predict", { nd <- data_grid(fit, "c12hour [10, 50, 100]") pr <- predict(fit, newdata = nd, se.fit = TRUE, type = "link") expected <- stats::plogis(pr$fit + stats::qnorm(0.975) * pr$se.fit) predicted <- ggpredict(fit, "c12hour [10, 50, 100]") expect_equal(predicted$conf.high, expected, tolerance = 1e-3, ignore_attr = TRUE) expect_equal(predicted$predicted, stats::plogis(pr$fit), tolerance = 1e-3, ignore_attr = TRUE) }) test_that("validate ggpredict glm against predict 2", { nd <- data_grid(m, "period") pr <- predict(m, newdata = nd, se.fit = TRUE, type = "link") expected <- stats::plogis(pr$fit + stats::qnorm(0.975) * pr$se.fit) predicted <- ggpredict(m, "period") expect_equal(predicted$conf.high, expected, tolerance = 1e-3, ignore_attr = TRUE) expect_equal(predicted$predicted, stats::plogis(pr$fit), tolerance = 1e-3, ignore_attr = TRUE) }) test_that("ggpredict, glm", { expect_s3_class(ggpredict(fit, "c12hour", verbose = FALSE), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), verbose = FALSE), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), verbose = FALSE), "data.frame") }) test_that("ggeffect, glm", { expect_s3_class(ggeffect(fit, "c12hour", verbose = FALSE), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour", "c161sex"), verbose = FALSE), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour", "c161sex", "c172code"), verbose = FALSE), "data.frame") }) test_that("ggemmeans, glm", { expect_s3_class(ggemmeans(fit, "c12hour", verbose = FALSE), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex"), verbose = FALSE), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex", "c172code"), verbose = FALSE), "data.frame") }) p1 <- ggpredict(m, "period", verbose = FALSE) p2 <- ggeffect(m, "period", verbose = FALSE) p3 <- ggemmeans(m, "period", verbose = FALSE) test_that("ggeffects, glm", { expect_equal(p1$predicted[1], 0.2194245, tolerance = 1e-3) expect_equal(p2$predicted[1], 0.2194245, tolerance = 1e-3) expect_equal(p3$predicted[1], 0.2194245, tolerance = 1e-3) }) test_that("ggpredict, glm, robust", { expect_s3_class(ggpredict(fit, "c12hour", vcov.fun = "vcovHC", vcov.type = "HC1", verbose = FALSE), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), vcov.fun = "vcovHC", vcov.type = "HC1", verbose = FALSE), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), vcov.fun = "vcovHC", vcov.type = "HC1", verbose = FALSE), "data.frame") }) test_that("ggeffects, glm, robust", { expect_s3_class(ggpredict(m, "period", vcov.fun = "vcovHC", vcov.type = "HC1"), "data.frame") }) data(cbpp) cbpp$trials <- cbpp$size - cbpp$incidence d2 <<- cbpp m1 <- glmer(cbind(incidence, trials) ~ period + (1 | herd), data = d2, family = binomial) m2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = d2, family = binomial) m3 <- glm(cbind(incidence, trials) ~ period, data = d2, family = binomial) m4 <- glm(cbind(incidence, size - incidence) ~ period, data = d2, family = binomial) test_that("ggeffects, glm-matrix-columns", { expect_s3_class(ggpredict(m1, "period"), "data.frame") expect_s3_class(ggpredict(m2, "period"), "data.frame") expect_s3_class(ggpredict(m3, "period"), "data.frame") expect_s3_class(ggpredict(m4, "period"), "data.frame") expect_s3_class(ggemmeans(m1, "period"), "data.frame") expect_s3_class(ggemmeans(m2, "period"), "data.frame") expect_s3_class(ggemmeans(m3, "period"), "data.frame") expect_s3_class(ggemmeans(m4, "period"), "data.frame") }) }