skip_if_not_installed("lme4")
skip_if_not_installed("ordinal")
data(wine, package = "ordinal")
data(soup, package = "ordinal")
m1 <- ordinal::clmm(rating ~ temp + contact + (1 | judge), data = wine)
m2 <- ordinal::clmm(SURENESS ~ PROD + (1 | RESP) + (1 | RESP:PROD),
data = soup,
link = "probit",
threshold = "equidistant"
)
test_that("model_info", {
expect_true(model_info(m1)$is_ordinal)
expect_true(model_info(m2)$is_ordinal)
expect_true(model_info(m1)$is_logit)
expect_true(model_info(m2)$is_probit)
expect_false(model_info(m1)$is_multinomial)
expect_false(model_info(m1)$is_linear)
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = c("temp", "contact")))
expect_identical(
find_predictors(m1, effects = "all"),
list(
conditional = c("temp", "contact"),
random = "judge"
)
)
expect_identical(
find_predictors(m1, effects = "all", flatten = TRUE),
c("temp", "contact", "judge")
)
expect_identical(find_predictors(m2), list(conditional = "PROD"))
expect_identical(
find_predictors(m2, effects = "all"),
list(
conditional = "PROD",
random = c("RESP", "PROD")
)
)
expect_identical(
find_predictors(m2, effects = "all", flatten = TRUE),
c("PROD", "RESP")
)
})
test_that("find_random", {
expect_identical(find_random(m1), list(random = "judge"))
expect_identical(find_random(m2), list(random = c("RESP", "RESP:PROD")))
expect_identical(find_random(m2, split_nested = TRUE), list(random = c("RESP", "PROD")))
})
test_that("get_random", {
expect_equal(get_random(m1), wine[, "judge", drop = FALSE], ignore_attr = TRUE)
expect_equal(get_random(m2), soup[, c("RESP", "PROD"), drop = FALSE], ignore_attr = TRUE)
})
test_that("find_response", {
expect_identical(find_response(m1), "rating")
expect_identical(find_response(m2), "SURENESS")
})
test_that("get_response", {
expect_equal(get_response(m1), wine$rating, ignore_attr = TRUE)
expect_equal(get_response(m2), soup$SURENESS, ignore_attr = TRUE)
})
test_that("get_predictors", {
expect_identical(colnames(get_predictors(m1)), c("temp", "contact"))
expect_identical(colnames(get_predictors(m2)), "PROD")
})
test_that("link_inverse", {
expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
expect_equal(link_inverse(m2)(0.2), pnorm(0.2), tolerance = 1e-5)
})
test_that("get_data", {
expect_identical(nrow(get_data(m1)), 72L)
expect_named(get_data(m1), c("rating", "temp", "contact", "judge"))
expect_identical(nrow(get_data(m2)), 1847L)
expect_identical(colnames(get_data(m2)), c("SURENESS", "PROD", "RESP"))
})
test_that("find_formula", {
expect_length(find_formula(m1), 2)
expect_equal(
find_formula(m1),
list(
conditional = as.formula("rating ~ temp + contact"),
random = as.formula("~1 | judge")
),
ignore_attr = TRUE
)
expect_length(find_formula(m2), 2)
expect_equal(
find_formula(m2),
list(
conditional = as.formula("SURENESS ~ PROD"),
random = list(as.formula("~1 | RESP"), as.formula("~1 | RESP:PROD"))
),
ignore_attr = TRUE
)
})
test_that("find_terms", {
expect_identical(
find_terms(m1),
list(
response = "rating",
conditional = c("temp", "contact"),
random = "judge"
)
)
expect_identical(
find_terms(m1, flatten = TRUE),
c("rating", "temp", "contact", "judge")
)
expect_identical(
find_terms(m2),
list(
response = "SURENESS",
conditional = "PROD",
random = c("RESP", "PROD")
)
)
expect_identical(
find_terms(m2, flatten = TRUE),
c("SURENESS", "PROD", "RESP")
)
})
test_that("n_obs", {
expect_identical(n_obs(m1), 72)
expect_identical(n_obs(m2), 1847)
})
test_that("linkfun", {
expect_false(is.null(link_function(m1)))
expect_false(is.null(link_function(m2)))
})
test_that("find_parameters", {
expect_identical(
find_parameters(m1),
list(
conditional = c("1|2", "2|3", "3|4", "4|5", "tempwarm", "contactyes")
)
)
expect_identical(
find_parameters(m2),
list(conditional = c("threshold.1", "spacing", "PRODTest"))
)
})
test_that("is_multivariate", {
expect_false(is_multivariate(m1))
expect_false(is_multivariate(m2))
})
if (getRversion() > "3.6.3") {
skip_on_cran() ## FIXME: check on win-devel
test_that("get_variance", {
expect_equal(
get_variance(m1),
list(
var.fixed = 3.23207765938872,
var.random = 1.27946088209319,
var.residual = 3.28986813369645,
var.distribution = 3.28986813369645,
var.dispersion = 0,
var.intercept = c(judge = 1.27946088209319)
),
tolerance = 1e-4
)
expect_equal(
get_variance(m2),
list(
var.fixed = 0.132313576370902,
var.random = 0.193186321588604,
var.residual = 1,
var.distribution = 1,
var.dispersion = 0,
var.intercept = c(`RESP:PROD` = 0.148265480396059, RESP = 0.0449208411925493)
),
tolerance = 1e-4
)
})
}
test_that("find_statistic", {
expect_identical(find_statistic(m1), "z-statistic")
expect_identical(find_statistic(m2), "z-statistic")
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.