.runThisTest <- Sys.getenv("RunAllinsightTests") == "yes"
if (.runThisTest && requiet("lme4")) {
data(sleepstudy)
set.seed(123)
sleepstudy$mygrp <- sample(1:5, size = 180, replace = TRUE)
sleepstudy$mysubgrp <- NA
for (i in 1:5) {
filter_group <- sleepstudy$mygrp == i
sleepstudy$mysubgrp[filter_group] <-
sample(1:30, size = sum(filter_group), replace = TRUE)
}
m1 <- lme4::lmer(Reaction ~ Days + (1 + Days | Subject),
data = sleepstudy
)
m2 <- suppressMessages(
lme4::lmer(Reaction ~ Days + (1 | mygrp / mysubgrp) + (1 | Subject),
data = sleepstudy
)
)
test_that("model_info", {
expect_true(model_info(m1)$is_linear)
expect_true(model_info(m2)$is_linear)
})
test_that("loglik", {
expect_equal(get_loglikelihood(m1, estimator = "REML"), logLik(m1), ignore_attr = TRUE)
expect_equal(get_loglikelihood(m2, estimator = "REML"), logLik(m2), ignore_attr = TRUE)
expect_equal(get_loglikelihood(m1), logLik(m1), ignore_attr = TRUE)
expect_equal(get_loglikelihood(m2), logLik(m2), ignore_attr = TRUE)
expect_equal(get_loglikelihood(m1, estimator = "ML"), logLik(m1, REML = FALSE), ignore_attr = TRUE)
expect_equal(get_loglikelihood(m2, estimator = "ML"), logLik(m2, REML = FALSE), ignore_attr = TRUE)
})
test_that("get_df", {
expect_equal(get_df(m1), df.residual(m1), ignore_attr = TRUE)
expect_equal(get_df(m2), df.residual(m2), ignore_attr = TRUE)
expect_equal(get_df(m1, type = "model"), attr(logLik(m1), "df"), ignore_attr = TRUE)
expect_equal(get_df(m2, type = "model"), attr(logLik(m2), "df"), ignore_attr = TRUE)
})
test_that("get_df", {
expect_equal(
get_df(m1, type = "residual"),
df.residual(m1),
ignore_attr = TRUE
)
expect_equal(
get_df(m1, type = "normal"),
Inf,
ignore_attr = TRUE
)
expect_equal(
get_df(m1, type = "wald"),
df.residual(m1),
ignore_attr = TRUE
)
expect_equal(
get_df(m1, type = "satterthwaite"),
c(`(Intercept)` = 16.99973, Days = 16.99998),
ignore_attr = TRUE,
tolerance = 1e-4
)
expect_equal(
as.vector(get_df(m1, type = "kenward")),
c(17, 17),
ignore_attr = TRUE,
tolerance = 1e-4
)
if (requiet("pbkrtest")) {
expect_equal(
as.vector(get_df(m1, type = "kenward")),
c(pbkrtest::get_Lb_ddf(m1, c(1, 0)), pbkrtest::get_Lb_ddf(m1, c(0, 1))),
ignore_attr = TRUE,
tolerance = 1e-4
)
expect_equal(
unique(as.vector(get_df(m2, type = "kenward"))),
c(pbkrtest::get_Lb_ddf(m2, c(1, 0)), pbkrtest::get_Lb_ddf(m2, c(0, 1))),
ignore_attr = TRUE,
tolerance = 1e-4
)
}
})
test_that("n_parameters", {
expect_equal(n_parameters(m1), 2)
expect_equal(n_parameters(m2), 2)
expect_equal(n_parameters(m1, effects = "random"), 2)
expect_equal(n_parameters(m2, effects = "random"), 3)
})
test_that("find_offset", {
data(mtcars)
model_off <- lmer(log(mpg) ~ disp + (1 | cyl), offset = log(wt), data = mtcars)
expect_identical(find_offset(model_off), "wt")
model_off <- lmer(log(mpg) ~ disp + (1 | cyl) + offset(log(wt)), data = mtcars)
expect_identical(find_offset(model_off), "wt")
})
test_that("find_predictors", {
expect_equal(
find_predictors(m1, effects = "all"),
list(conditional = "Days", random = "Subject")
)
expect_equal(
find_predictors(m1, effects = "all", flatten = TRUE),
c("Days", "Subject")
)
expect_equal(
find_predictors(m1, effects = "fixed"),
list(conditional = "Days")
)
expect_equal(
find_predictors(m1, effects = "fixed", flatten = TRUE),
"Days"
)
expect_equal(
find_predictors(m1, effects = "random"),
list(random = "Subject")
)
expect_equal(
find_predictors(m1, effects = "random", flatten = TRUE),
"Subject"
)
expect_equal(
find_predictors(m2, effects = "all"),
list(
conditional = "Days",
random = c("mysubgrp", "mygrp", "Subject")
)
)
expect_equal(
find_predictors(m2, effects = "all", flatten = TRUE),
c("Days", "mysubgrp", "mygrp", "Subject")
)
expect_equal(
find_predictors(m2, effects = "fixed"),
list(conditional = "Days")
)
expect_equal(find_predictors(m2, effects = "random"), list(random = c("mysubgrp", "mygrp", "Subject")))
expect_null(find_predictors(m2, effects = "all", component = "zi"))
expect_null(find_predictors(m2, effects = "fixed", component = "zi"))
expect_null(find_predictors(m2, effects = "random", component = "zi"))
})
test_that("find_random", {
expect_equal(find_random(m1), list(random = "Subject"))
expect_equal(find_random(m1, flatten = TRUE), "Subject")
expect_equal(find_random(m2), list(random = c("mysubgrp:mygrp", "mygrp", "Subject")))
expect_equal(find_random(m2, split_nested = TRUE), list(random = c("mysubgrp", "mygrp", "Subject")))
expect_equal(
find_random(m2, flatten = TRUE),
c("mysubgrp:mygrp", "mygrp", "Subject")
)
expect_equal(
find_random(m2, split_nested = TRUE, flatten = TRUE),
c("mysubgrp", "mygrp", "Subject")
)
})
test_that("find_response", {
expect_identical(find_response(m1), "Reaction")
expect_identical(find_response(m2), "Reaction")
})
test_that("get_response", {
expect_equal(get_response(m1), sleepstudy$Reaction)
})
test_that("link_inverse", {
expect_identical(link_inverse(m1)(0.2), 0.2)
expect_identical(link_inverse(m2)(0.2), 0.2)
})
test_that("get_data", {
expect_equal(colnames(get_data(m1)), c("Reaction", "Days", "Subject"))
expect_equal(colnames(get_data(m1, effects = "all")), c("Reaction", "Days", "Subject"))
expect_equal(colnames(get_data(m1, effects = "random")), "Subject")
expect_equal(
colnames(get_data(m2)),
c("Reaction", "Days", "mysubgrp", "mygrp", "Subject")
)
expect_equal(
colnames(get_data(m2, effects = "all")),
c("Reaction", "Days", "mysubgrp", "mygrp", "Subject")
)
expect_equal(colnames(get_data(m2, effects = "random")), c("mysubgrp", "mygrp", "Subject"))
})
test_that("find_formula", {
expect_length(find_formula(m1), 2)
expect_length(find_formula(m2), 2)
expect_equal(
find_formula(m1, component = "conditional"),
list(
conditional = as.formula("Reaction ~ Days"),
random = as.formula("~1 + Days | Subject")
),
ignore_attr = TRUE
)
expect_equal(
find_formula(m2, component = "conditional"),
list(
conditional = as.formula("Reaction ~ Days"),
random = list(
as.formula("~1 | mysubgrp:mygrp"),
as.formula("~1 | mygrp"),
as.formula("~1 | Subject")
)
),
ignore_attr = TRUE
)
})
test_that("find_terms", {
expect_identical(
find_terms(m1),
list(
response = "Reaction",
conditional = "Days",
random = c("Days", "Subject")
)
)
expect_identical(
find_terms(m1, flatten = TRUE),
c("Reaction", "Days", "Subject")
)
expect_identical(
find_terms(m2),
list(
response = "Reaction",
conditional = "Days",
random = c("mysubgrp", "mygrp", "Subject")
)
)
expect_identical(
find_terms(m2, flatten = TRUE),
c("Reaction", "Days", "mysubgrp", "mygrp", "Subject")
)
})
test_that("find_variables", {
expect_identical(
find_variables(m1),
list(
response = "Reaction",
conditional = "Days",
random = "Subject"
)
)
expect_identical(
find_variables(m1, flatten = TRUE),
c("Reaction", "Days", "Subject")
)
expect_identical(
find_variables(m2),
list(
response = "Reaction",
conditional = "Days",
random = c("mysubgrp", "mygrp", "Subject")
)
)
expect_identical(
find_variables(m2, flatten = TRUE),
c("Reaction", "Days", "mysubgrp", "mygrp", "Subject")
)
})
test_that("get_response", {
expect_identical(get_response(m1), sleepstudy$Reaction)
})
test_that("get_predictors", {
expect_identical(colnames(get_predictors(m1)), "Days")
expect_identical(colnames(get_predictors(m2)), "Days")
})
test_that("get_random", {
expect_identical(colnames(get_random(m1)), "Subject")
expect_identical(colnames(get_random(m2)), c("mysubgrp", "mygrp", "Subject"))
})
test_that("clean_names", {
expect_identical(clean_names(m1), c("Reaction", "Days", "Subject"))
expect_identical(
clean_names(m2),
c("Reaction", "Days", "mysubgrp", "mygrp", "Subject")
)
})
test_that("linkfun", {
expect_false(is.null(link_function(m1)))
expect_false(is.null(link_function(m2)))
})
test_that("find_parameters", {
expect_equal(
find_parameters(m1),
list(
conditional = c("(Intercept)", "Days"),
random = list(Subject = c("(Intercept)", "Days"))
)
)
expect_equal(nrow(get_parameters(m1)), 2)
expect_equal(get_parameters(m1)$Parameter, c("(Intercept)", "Days"))
expect_equal(
find_parameters(m2),
list(
conditional = c("(Intercept)", "Days"),
random = list(
`mysubgrp:mygrp` = "(Intercept)",
Subject = "(Intercept)",
mygrp = "(Intercept)"
)
)
)
expect_equal(nrow(get_parameters(m2)), 2)
expect_equal(get_parameters(m2)$Parameter, c("(Intercept)", "Days"))
expect_equal(
names(get_parameters(m2, effects = "random")),
c("mysubgrp:mygrp", "Subject", "mygrp")
)
})
test_that("is_multivariate", {
expect_false(is_multivariate(m1))
expect_false(is_multivariate(m2))
})
test_that("get_variance", {
expect_equal(
get_variance(m1),
list(
var.fixed = 908.9534,
var.random = 1698.084,
var.residual = 654.94,
var.distribution = 654.94,
var.dispersion = 0,
var.intercept = c(Subject = 612.1002),
var.slope = c(Subject.Days = 35.07171),
cor.slope_intercept = c(Subject = 0.06555124)
),
tolerance = 1e-1
)
expect_equal(get_variance_fixed(m1),
c(var.fixed = 908.9534),
tolerance = 1e-1
)
expect_equal(get_variance_random(m1),
c(var.random = 1698.084),
tolerance = 1e-1
)
expect_equal(
get_variance_residual(m1),
c(var.residual = 654.94),
tolerance = 1e-1
)
expect_equal(
get_variance_distribution(m1),
c(var.distribution = 654.94),
tolerance = 1e-1
)
expect_equal(get_variance_dispersion(m1),
c(var.dispersion = 0),
tolerance = 1e-1
)
expect_equal(
get_variance_intercept(m1),
c(var.intercept.Subject = 612.1002),
tolerance = 1e-1
)
expect_equal(
get_variance_slope(m1),
c(var.slope.Subject.Days = 35.07171),
tolerance = 1e-1
)
expect_equal(
get_correlation_slope_intercept(m1),
c(cor.slope_intercept.Subject = 0.06555124),
tolerance = 1e-1
)
if (.runThisTest) {
expect_equal(
suppressWarnings(get_variance(m2)),
list(
var.fixed = 889.3301,
var.residual = 941.8135,
var.distribution = 941.8135,
var.dispersion = 0,
var.intercept = c(
`mysubgrp:mygrp` = 0,
Subject = 1357.4257,
mygrp = 24.4064
)
),
tolerance = 1e-1
)
}
})
test_that("find_algorithm", {
expect_equal(
find_algorithm(m1),
list(algorithm = "REML", optimizer = "nloptwrap")
)
})
test_that("find_random_slopes", {
expect_equal(find_random_slopes(m1), list(random = "Days"))
expect_null(find_random_slopes(m2))
})
m3 <- lme4::lmer(Reaction ~ (1 + Days | Subject),
data = sleepstudy
)
m4 <- lme4::lmer(
Reaction ~ (1 |
mygrp / mysubgrp) + (1 | Subject),
data = sleepstudy
)
m5 <- lme4::lmer(Reaction ~ 1 + (1 + Days | Subject),
data = sleepstudy
)
m6 <- lme4::lmer(
Reaction ~ 1 + (1 | mygrp / mysubgrp) + (1 | Subject),
data = sleepstudy
)
test_that("find_formula", {
expect_equal(
find_formula(m3),
list(
conditional = as.formula("Reaction ~ 1"),
random = as.formula("~1 + Days | Subject")
),
ignore_attr = TRUE
)
expect_equal(
find_formula(m5),
list(
conditional = as.formula("Reaction ~ 1"),
random = as.formula("~1 + Days | Subject")
),
ignore_attr = TRUE
)
expect_equal(
find_formula(m4),
list(
conditional = as.formula("Reaction ~ 1"),
random = list(
as.formula("~1 | mysubgrp:mygrp"),
as.formula("~1 | mygrp"),
as.formula("~1 | Subject")
)
),
ignore_attr = TRUE
)
expect_equal(
find_formula(m6),
list(
conditional = as.formula("Reaction ~ 1"),
random = list(
as.formula("~1 | mysubgrp:mygrp"),
as.formula("~1 | mygrp"),
as.formula("~1 | Subject")
)
),
ignore_attr = TRUE
)
})
test_that("satterthwaite dof vs. emmeans", {
requiet("emmeans")
requiet("pbkrtest")
v1 <- get_varcov(m2, vcov = "kenward-roger")
v2 <- as.matrix(vcovAdj(m2))
expect_equal(v1, v2)
p1 <- get_predicted(m2, ci_method = "satterthwaite", ci = 0.95)
p1 <- data.frame(p1)
em1 <- ref_grid(
object = m2,
specs = ~Days,
at = list(Days = sleepstudy$Days),
lmer.df = "satterthwaite"
)
em1 <- confint(em1)
expect_equal(p1$CI_low, em1$lower.CL)
expect_equal(p1$CI_high, em1$upper.CL)
p2 <- get_predicted(m2, ci_method = "kenward-roger", ci = 0.95)
p2 <- data.frame(p2)
em2 <- ref_grid(
object = m2,
specs = ~Days,
at = list(Days = sleepstudy$Days),
lmer.df = "kenward-roger"
)
em2 <- confint(em2)
expect_equal(p2$CI_low, em2$lower.CL)
expect_equal(p2$CI_high, em2$upper.CL)
})
test_that("find_statistic", {
expect_identical(find_statistic(m1), "t-statistic")
expect_identical(find_statistic(m2), "t-statistic")
})
test_that("get_call", {
expect_equal(class(get_call(m1)), "call")
expect_equal(class(get_call(m2)), "call")
})
test_that("get_predicted_ci: warning when model matrix and varcovmat do not match", {
skip_if(getRversion() < "4.1.0")
mod <- suppressMessages(lmer(
weight ~ 1 + Time + I(Time^2) + Diet + Time:Diet + I(Time^2):Diet + (1 + Time + I(Time^2) | Chick),
data = ChickWeight
))
newdata <- ChickWeight[ChickWeight$Time %in% 0:10 & ChickWeight$Chick %in% c(1, 40), ]
newdata$Chick[newdata$Chick == "1"] <- NA
expect_warning(
get_predicted(mod, data = newdata, include_random = FALSE, ci = 0.95),
regexp = "levels"
)
# VAB: Not sure where these hard-coded values come from
# Related to Issue #693. Not sure if these are valid since we arbitrarily
# shrink the varcov and mm to be conformable. In some cases documented in
# Issue #556 of {marginaleffects}, we know that this produces incorrect
# results, so it's probably best to be conservative and not return results
# here.
known <- data.frame(
Predicted = c(37.53433, 47.95719, 58.78866, 70.02873, 81.67742, 93.73472),
SE = c(1.68687, 0.82574, 1.52747, 2.56109, 3.61936, 4.76178),
CI_low = c(34.22096, 46.33525, 55.78837, 64.99819, 74.56822, 84.38154),
CI_high = c(40.84771, 49.57913, 61.78894, 75.05927, 88.78662, 103.08789)
)
p <- suppressWarnings(get_predicted(mod, data = newdata, include_random = FALSE, ci = 0.95))
expect_equal(
head(data.frame(p)$Predicted),
known$Predicted,
tolerance = 1e-3
)
})
}
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