skip_on_cran()
skip_on_os(c("mac", "solaris"))
skip_if_not_installed("lme4")
skip_if_not_installed("datawizard")
skip_if_not_installed("marginaleffects")
# lmer ----
data(efc, package = "ggeffects")
efc$grp <- datawizard::to_factor(efc$e15relat)
fit <- lme4::lmer(neg_c_7 ~ c12hour + e42dep + c161sex + c172code + (1 | grp), data = efc)
test_that("validate ggpredict lmer against predict", {
nd <- data_grid(fit, "e42dep")
pr <- predict(fit, newdata = nd, re.form = NA)
predicted <- ggpredict(fit, "e42dep")
expect_equal(predicted$predicted, pr, tolerance = 1e-3, ignore_attr = TRUE)
})
test_that("validate ggpredict lmer against marginaleffects", {
out1 <- suppressWarnings(marginaleffects::predictions(
fit,
variables = "e42dep",
newdata = marginaleffects::datagrid(fit),
re.form = NULL
))
out1 <- out1[order(out1$e42dep), ]
out2 <- ggpredict(
fit,
"e42dep",
condition = c(grp = "child"),
type = "random",
interval = "confidence"
)
expect_equal(
out1$estimate,
out2$predicted,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_equal(
out1$estimate - stats::qt(0.975, df = 826) * out1$std.error,
out2$conf.low,
tolerance = 1e-4,
ignore_attr = TRUE
)
})
test_that("ggpredict, lmer", {
expect_s3_class(ggpredict(fit, "c12hour"), "data.frame")
expect_s3_class(ggpredict(fit, c("c12hour", "c161sex")), "data.frame")
expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code")), "data.frame")
expect_s3_class(ggpredict(fit, "c12hour", type = "random"), "data.frame")
expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), type = "random"), "data.frame")
expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), type = "random"), "data.frame")
})
test_that("ggpredict, lmer", {
pr <- ggpredict(fit, "c12hour")
expect_equal(pr$std.error[1:5], c(0.2911, 0.2852, 0.2799, 0.2752, 0.2713), tolerance = 1e-3)
pr <- ggpredict(fit, c("c12hour", "c161sex", "c172code"), type = "random")
expect_equal(pr$std.error[1:5], c(3.5882, 3.58185, 3.58652, 3.58162, 3.57608), tolerance = 1e-3)
# validate against predict
pr <- ggpredict(fit, "c12hour")
nd <- data_grid(fit, "c12hour")
pr2 <- suppressWarnings(predict(
fit,
newdata = nd,
se.fit = TRUE,
re.form = NA,
allow.new.levels = TRUE
))
expect_equal(pr$std.error[1:5], pr2$se.fit[1:5], tolerance = 1e-3, ignore_attr = TRUE)
expect_equal(
pr$conf.low,
pr2$fit - qt(0.975, ggeffects:::.get_df(fit)) * pr2$se.fit,
tolerance = 1e-3,
ignore_attr = TRUE
)
pr <- ggpredict(fit, "c12hour", type = "random")
expect_equal(pr$conf.low[1:5], c(4.26939, 4.3036, 4.3377, 4.37168, 4.40554), tolerance = 1e-3)
})
test_that("ggpredict, lmer-simulate", {
expect_s3_class(ggpredict(fit, "c12hour", type = "simulate"), "data.frame")
expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), type = "simulate"), "data.frame")
expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), type = "simulate"), "data.frame")
})
test_that("ggeffect, lmer", {
skip_if_not_installed("effects")
expect_s3_class(ggeffect(fit, "c12hour"), "data.frame")
expect_s3_class(ggeffect(fit, c("c12hour", "c161sex")), "data.frame")
expect_s3_class(ggeffect(fit, c("c12hour", "c161sex", "c172code")), "data.frame")
})
test_that("ggeffect, lmer", {
skip_if_not_installed("emmeans")
data(efc, package = "ggeffects")
efc$cluster <- as.factor(efc$e15relat)
efc <- datawizard::standardise(efc, c("c160age", "e42dep"), append = "_z")
m <- lme4::lmer(
neg_c_7 ~ c160age_z * e42dep_z + c161sex + (1 | cluster),
data = efc
)
p1 <- ggpredict(m, terms = c("c160age_z", "e42dep_z [-1.17,2.03]"))
p2 <- ggemmeans(m, terms = c("c160age_z", "e42dep_z [-1.17,2.03]"))
expect_equal(p1$predicted[1], p2$predicted[1], tolerance = 1e-3)
})
test_that("ggeffect, lmer", {
skip_if_not_installed("emmeans")
data(efc, package = "ggeffects")
efc$cluster <- as.factor(efc$e15relat)
efc <- datawizard::to_factor(efc, c("e42dep", "c172code", "c161sex"))
efc$c172code[efc$c172code == "intermediate level of education"] <- NA
m <- suppressMessages(lme4::lmer(
neg_c_7 ~ c172code + e42dep + c161sex + (1 | cluster),
data = efc
))
expect_s3_class(ggpredict(m, terms = "e42dep"), "data.frame")
expect_s3_class(ggemmeans(m, terms = "e42dep"), "data.frame")
p1 <- ggpredict(m, terms = "e42dep")
p2 <- ggemmeans(m, terms = "e42dep")
p3 <- ggemmeans(m, terms = "e42dep", condition = c(c161sex = "Male", c172code = "low level of education"))
expect_equal(p1$predicted[1], 8.902934, tolerance = 1e-3)
expect_equal(p2$predicted[1], 9.742945, tolerance = 1e-3)
expect_equal(p1$predicted[1], p3$predicted[1], tolerance = 1e-3)
})
test_that("ggeffect, lmer", {
skip_if_not_installed("effects")
skip_if_not_installed("emmeans")
data(sleepstudy, package = "lme4")
m <- suppressWarnings(lme4::lmer(
log(Reaction) ~ Days + I(Days^2) + (1 + Days + exp(Days) | Subject),
data = sleepstudy
))
p1 <- ggpredict(m, terms = "Days", verbose = FALSE)
p2 <- ggemmeans(m, terms = "Days", verbose = FALSE)
p3 <- ggeffect(m, terms = "Days")
expect_message(expect_message(ggemmeans(m, terms = "Days"), "polynomial"), "log-transformed")
expect_equal(p1$predicted[1], 253.5178, tolerance = 1e-3)
expect_equal(p2$predicted[1], 253.5178, tolerance = 1e-3)
expect_equal(p3$predicted[1], 5.535434, tolerance = 1e-3)
expect_s3_class(
ggpredict(m, terms = c("Days", "Subject [sample=5]"), type = "random", verbose = FALSE),
"data.frame"
)
})
test_that("ggpredict, sample random effects levels", {
N <- 18 # Number of people
Nt <- 9 # Number of trials
set.seed(123)
d <- data.frame(
Subject = factor(sprintf("%03d", 1:N)), # create subject numbers
subj_b0 = rnorm(n = N, mean = 250, sd = 20), # create random intercepts
subj_b1 = rnorm(n = N, mean = 10, sd = 6) # create random slopes
)
d <- do.call(rbind, replicate(10, d, simplify = FALSE))
d$Days <- rep(0:Nt, 18)
d$Y <- d$subj_b0 + d$Days * d$subj_b1 + rnorm(n = N * (Nt + 1), sd = 15)
fit <- lme4::lmer(Y ~ 1 + Days + (1 + Days | Subject), data = d)
set.seed(123)
p <- ggpredict(fit, terms = c("Days", "Subject [sample=8]"), type = "random")
expect_identical(
p$group,
structure(
c(
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L
),
levels = c("015", "014", "003", "010", "002", "006", "011", "005"),
class = "factor"
)
)
d$Subject <- rep(factor(1:N), 10)
fit <- lme4::lmer(Y ~ 1 + Days + (1 + Days | Subject), data = d)
set.seed(123)
p <- ggpredict(fit, terms = c("Days", "Subject [sample=8]"), type = "random")
expect_identical(
p$group,
structure(
c(
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L
),
levels = c("2", "3", "5", "6", "10", "11", "14", "15"),
class = "factor"
)
)
})
test_that("ggpredict, smooth plot message", {
data("sleepstudy", package = "lme4")
# mixed model with lme4
m_lmer <- lme4::lmer(Reaction ~ poly(Days, 2) + (1 | Subject),
data = sleepstudy
)
expect_message(ggpredict(m_lmer, terms = "Days"), regex = "Model contains")
expect_silent(ggpredict(m_lmer, terms = "Days", verbose = FALSE))
})
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