skip_on_cran()
skip_on_os(c("mac", "solaris"))
skip_if_not_installed("marginaleffects")
skip_if_not_installed("glmmTMB")
skip_if_not_installed("lme4")
skip_if_not_installed("datawizard")
test_that("ggpredict", {
# sample data set
data(efc, package = "ggeffects")
d <- datawizard::to_factor(efc, select = c("c161sex", "c172code", "c175empl"))
d <- datawizard::recode_values(
d,
select = "c160age",
recode = list(`1` = "min:40", `2` = 41:64, `3` = "65:max")
)
d <- datawizard::data_rename(
d,
pattern = c("c161sex", "c160age", "quol_5", "c175empl"),
replacement = c("gender", "age", "qol", "employed")
)
d <- datawizard::data_modify(d, age = factor(age, labels = c("-40", "41-64", "65+")))
m_null1 <- glmmTMB::glmmTMB(qol ~ e42dep + (1 | gender:employed:age), data = d)
m_null2 <- lme4::lmer(qol ~ e42dep + (1 | gender:employed:age), data = d)
# warning when margin is not empirical
expect_warning(
predict_response(m_null1, c("gender", "employed", "age")),
regex = "All focal terms"
)
expect_warning(
predict_response(m_null2, c("gender", "employed", "age")),
regex = "All focal terms"
)
expect_silent(
predict_response(m_null1, c("gender", "employed", "age"), type = "random")
)
expect_silent(
predict_response(m_null1, c("gender", "employed", "age"), margin = "empirical")
)
expect_silent(
predict_response(m_null2, c("gender", "employed", "age"), type = "random")
)
expect_silent(
predict_response(m_null2, c("gender", "employed", "age"), margin = "empirical")
)
predictions <- predict_response(m_null1, c("gender", "employed", "age"), margin = "empirical")
expect_silent(test_predictions(predictions))
expect_warning(
test_predictions(m_null1, c("gender", "employed", "age")),
regex = "All focal terms are included"
)
})
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