if (requiet("nlme") && requiet("lme4")) {
data("sleepstudy")
data(Orthodont)
m1 <- lme(Reaction ~ Days,
random = ~ 1 + Days | Subject,
data = sleepstudy
)
m2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~1)
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)
}
m3 <- lme(Reaction ~ Days,
random = ~ 1 | mygrp / mysubgrp,
data = sleepstudy
)
# from easystats/insight/482
cr <- corAR1(form = ~ 1 | Mare)
m4 <- lme(follicles ~ Time, Ovary, correlation = cr)
test_that("nested_varCorr", {
skip_on_cran()
expect_equal(
insight:::.get_nested_lme_varcorr(m3)$mysubgrp[1, 1],
7.508310765,
tolerance = 1e-3
)
expect_equal(
insight:::.get_nested_lme_varcorr(m3)$mygrp[1, 1],
0.004897827,
tolerance = 1e-2
)
})
test_that("model_info", {
expect_true(model_info(m1)$is_linear)
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = "Days"))
expect_identical(find_predictors(m2), list(conditional = c("age", "Sex")))
expect_identical(
find_predictors(m1, effects = "all"),
list(conditional = "Days", random = "Subject")
)
expect_identical(find_predictors(m2, effects = "all"), list(conditional = c("age", "Sex")))
expect_identical(find_predictors(m1, flatten = TRUE), "Days")
expect_identical(
find_predictors(m1, effects = "random"),
list(random = "Subject")
)
})
test_that("find_response", {
expect_identical(find_response(m1), "Reaction")
expect_identical(find_response(m2), "distance")
})
test_that("get_response", {
expect_equal(get_response(m1), sleepstudy$Reaction)
})
test_that("find_random", {
expect_equal(find_random(m1), list(random = "Subject"))
expect_null(find_random(m2))
})
test_that("get_random", {
expect_equal(get_random(m1), data.frame(Subject = sleepstudy$Subject), ignore_attr = TRUE)
expect_warning(get_random(m2))
})
test_that("link_inverse", {
expect_equal(link_inverse(m1)(0.2), 0.2, tolerance = 1e-5)
})
test_that("get_data", {
expect_equal(nrow(get_data(m1)), 180)
expect_equal(colnames(get_data(m1)), c("Reaction", "Days", "Subject"))
expect_equal(colnames(get_data(m2)), c("distance", "age", "Sex"))
})
test_that("get_df", {
expect_equal(get_df(m1, type = "residual"), c(161, 161), ignore_attr = TRUE)
expect_equal(get_df(m1, type = "normal"), Inf, ignore_attr = TRUE)
expect_equal(get_df(m1, type = "wald"), c(161, 161), ignore_attr = TRUE)
expect_equal(get_df(m2, type = "residual"), c(80, 80, 25), ignore_attr = TRUE)
expect_equal(get_df(m2, type = "normal"), Inf, ignore_attr = TRUE)
expect_equal(get_df(m3, type = "residual"), c(98, 76), ignore_attr = TRUE)
expect_equal(get_df(m3, type = "normal"), Inf, ignore_attr = TRUE)
})
test_that("find_formula", {
expect_length(find_formula(m1), 2)
expect_equal(
find_formula(m1),
list(
conditional = as.formula("Reaction ~ Days"),
random = as.formula("~1 + Days | Subject")
),
ignore_attr = TRUE
)
expect_length(find_formula(m2), 2)
expect_equal(
find_formula(m2),
list(
conditional = as.formula("distance ~ age + Sex"),
random = as.formula("~1")
),
ignore_attr = TRUE
)
expect_length(find_formula(m4), 2)
expect_equal(
find_formula(m4),
list(
conditional = as.formula("follicles ~ Time"),
correlation = as.formula("~1 | Mare")
),
ignore_attr = TRUE
)
})
test_that("find_variables", {
expect_equal(
find_variables(m1),
list(
response = "Reaction",
conditional = "Days",
random = "Subject"
)
)
expect_equal(
find_variables(m1, flatten = TRUE),
c("Reaction", "Days", "Subject")
)
expect_equal(
find_variables(m2),
list(
response = "distance",
conditional = c("age", "Sex")
)
)
})
test_that("n_obs", {
expect_equal(n_obs(m1), 180)
})
test_that("linkfun", {
expect_false(is.null(link_function(m1)))
})
test_that("find_parameters", {
expect_equal(
find_parameters(m1),
list(
conditional = c("(Intercept)", "Days"),
random = 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)", "age", "SexFemale"),
random = c("(Intercept)")
)
)
})
test_that("find_algorithm", {
expect_equal(
find_algorithm(m1),
list(algorithm = "REML", optimizer = "nlminb")
)
})
test_that("get_variance", {
skip_on_cran()
expect_equal(
get_variance(m1),
list(
var.fixed = 908.95336262308865116211,
var.random = 1698.06593646939654718153,
var.residual = 654.94240352794997761521,
var.distribution = 654.94240352794997761521,
var.dispersion = 0,
var.intercept = c(Subject = 612.07951112963326067984),
var.slope = c(Subject.Days = 35.07130179308116169068),
cor.slope_intercept = c(Subject = 0.06600000000000000311)
),
tolerance = 1e-3
)
})
test_that("find_statistic", {
expect_identical(find_statistic(m1), "t-statistic")
expect_identical(find_statistic(m2), "t-statistic")
expect_identical(find_statistic(m3), "t-statistic")
})
test_that("Issue #658", {
requiet("nlme")
models <- lapply(
c("", " + Sex"),
function(x) {
lme(as.formula(paste0("distance ~ age", x)),
random = ~1,
data = Orthodont
)
}
)
dat <- lapply(models, get_data)
form <- lapply(models, find_formula)
expect_s3_class(form[[1]], "insight_formula")
expect_s3_class(form[[2]], "insight_formula")
expect_s3_class(dat[[1]], "data.frame")
expect_s3_class(dat[[2]], "data.frame")
})
}
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