skip_on_os(c("mac", "solaris"))
skip_on_cran()
skip_if_not_installed("lme4")
skip_if_not_installed("glmmTMB")
skip_if_not_installed("emmeans")
skip_if_not_installed("effects")
skip_if_not_installed("withr")
withr::with_options(
list(ggeffects_warning_bias_correction = FALSE),
test_that("ggpredict, lme4::glmer", {
data(efc_test, package = "ggeffects")
fit <- lme4::glmer(
negc7d ~ c12hour + e42dep + c161sex + c172code + (1 | grp),
data = efc_test,
family = binomial(link = "logit")
)
pr <- ggpredict(fit, "c12hour", verbose = FALSE)
expect_equal(
pr$predicted,
c(0.34217, 0.34406, 0.34596, 0.34787, 0.34978, 0.3517, 0.35362,
0.35554, 0.35747, 0.35941, 0.36135, 0.36329, 0.36524, 0.36719,
0.36915, 0.37111, 0.37307, 0.37504, 0.37702, 0.37899, 0.38098,
0.38296, 0.38495, 0.38694, 0.38894, 0.39094, 0.39295, 0.39496,
0.39697, 0.39898, 0.401, 0.40302, 0.40505, 0.40708, 0.40911),
tolerance = 1e-3,
ignore_attr = TRUE
)
expect_message(ggpredict(fit, "c12hour"), "prettified")
expect_silent(ggpredict(fit, "c12hour", verbose = FALSE))
expect_s3_class(ggpredict(fit, "c12hour", verbose = FALSE), "data.frame")
expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), verbose = FALSE), "data.frame")
expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), verbose = FALSE), "data.frame")
expect_s3_class(
ggpredict(fit, "c12hour", type = "random", verbose = FALSE),
"data.frame"
)
expect_s3_class(
ggpredict(fit, c("c12hour", "c161sex"), type = "random", verbose = FALSE),
"data.frame"
)
expect_s3_class(
ggpredict(fit, c("c12hour", "c161sex", "c172code"), type = "random", verbose = FALSE),
"data.frame"
)
})
)
test_that("ggpredict, lme4::glmer, conf int, validate against predict", {
data(efc_test)
fit <- lme4::glmer(
negc7d ~ c12hour + e42dep + c161sex + c172code + (1 | grp),
data = efc_test,
family = binomial(link = "logit")
)
nd <- data_grid(fit, "c12hour")
pr <- ggpredict(fit, "c12hour", verbose = FALSE)
pr2 <- suppressWarnings(predict(
fit,
newdata = nd,
se.fit = TRUE,
re.form = NA,
allow.new.levels = TRUE,
type = "link"
))
expect_equal(
pr$predicted,
plogis(pr2$fit),
tolerance = 1e-3,
ignore_attr = TRUE
)
expect_equal(
pr$conf.low,
plogis(pr2$fit - qt(0.975, Inf) * pr2$se.fit),
tolerance = 1e-3,
ignore_attr = TRUE
)
})
test_that("ggeffect, lme4::glmer", {
data(efc_test)
fit <- lme4::glmer(
negc7d ~ c12hour + e42dep + c161sex + c172code + (1 | grp),
data = efc_test,
family = binomial(link = "logit")
)
pr <- ggeffect(fit, "c12hour")
expect_equal(
pr$predicted,
c(0.34217, 0.34406, 0.34596, 0.34787, 0.34978, 0.3517, 0.35362,
0.35554, 0.35747, 0.35941, 0.36135, 0.36329, 0.36524, 0.36719,
0.36915, 0.37111, 0.37307, 0.37504, 0.37702, 0.37899, 0.38098,
0.38296, 0.38495, 0.38694, 0.38894, 0.39094, 0.39295, 0.39496,
0.39697, 0.39898, 0.401, 0.40302, 0.40505, 0.40708, 0.40911),
tolerance = 1e-3,
ignore_attr = TRUE
)
expect_equal(
pr$conf.low,
c(
0.24901, 0.25138, 0.25363, 0.25576, 0.25777, 0.25965, 0.2614,
0.26302, 0.2645, 0.26585, 0.26706, 0.26814, 0.26909, 0.2699,
0.27059, 0.27115, 0.2716, 0.27192, 0.27214, 0.27225, 0.27226,
0.27217, 0.272, 0.27173, 0.27139, 0.27096, 0.27047, 0.26991,
0.26928, 0.2686, 0.26786, 0.26707, 0.26623, 0.26534, 0.26441
),
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, lme4::glmer", {
data(efc_test)
fit <- lme4::glmer(
negc7d ~ c12hour + e42dep + c161sex + c172code + (1 | grp),
data = efc_test,
family = binomial(link = "logit")
)
pr <- ggemmeans(fit, "c12hour", verbose = FALSE)
expect_equal(
pr$predicted,
c(0.34217, 0.34406, 0.34596, 0.34787, 0.34978, 0.3517, 0.35362,
0.35554, 0.35747, 0.35941, 0.36135, 0.36329, 0.36524, 0.36719,
0.36915, 0.37111, 0.37307, 0.37504, 0.37702, 0.37899, 0.38098,
0.38296, 0.38495, 0.38694, 0.38894, 0.39094, 0.39295, 0.39496,
0.39697, 0.39898, 0.401, 0.40302, 0.40505, 0.40708, 0.40911),
tolerance = 1e-3,
ignore_attr = TRUE
)
expect_s3_class(ggemmeans(fit, "c12hour", verbose = FALSE), "data.frame")
expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex"), verbose = FALSE), "data.frame")
expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex", "c172code"), verbose = FALSE), "data.frame")
})
withr::with_environment(
new.env(),
test_that("ggpredict, lme4::glmer.nb", {
m <- insight::download_model("merMod_5")
dd <- insight::get_data(m, source = "frame")
expect_s3_class(ggpredict(m, "f1"), "data.frame")
expect_message(
expect_s3_class(ggpredict(m, "f1", type = "random"), "data.frame"),
regex = "It seems that"
)
expect_s3_class(ggpredict(m, c("f1", "f2")), "data.frame")
expect_message(
expect_s3_class(ggpredict(m, c("f1", "f2"), type = "random"), "data.frame"),
regex = "It seems that"
)
expect_message(ggemmeans(m, "f1"))
expect_s3_class(ggemmeans(m, c("f1", "f2")), "data.frame")
expect_s3_class(ggpredict(m, c("f1", "f2"), type = "simulate"), "data.frame")
})
)
test_that("ggpredict, lme4::glmer, cbind", {
data(cbpp, package = "lme4")
cbpp$trials <- cbpp$size - cbpp$incidence
m1 <- lme4::glmer(cbind(incidence, trials) ~ period + (1 | herd), data = cbpp, family = binomial)
m2 <- lme4::glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial)
expect_s3_class(ggpredict(m1, "period"), "data.frame")
expect_s3_class(ggpredict(m2, "period"), "data.frame")
expect_message(
expect_s3_class(ggpredict(m1, "period", type = "random"), "data.frame"),
regex = "It seems that"
)
expect_message(
expect_s3_class(ggpredict(m2, "period", type = "random"), "data.frame"),
regex = "It seems that"
)
expect_s3_class(ggemmeans(m1, "period"), "data.frame")
expect_s3_class(ggemmeans(m2, "period"), "data.frame")
p1 <- ggpredict(m1, "period")
p2 <- ggemmeans(m1, "period")
expect_equal(p1$predicted[1], p2$predicted[1], tolerance = 1e-3)
})
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