if (requiet("ordinal")) {
data(wine, package = "ordinal")
m1 <- clm(rating ~ temp * contact, data = wine)
test_that("model_info", {
expect_true(model_info(m1)$is_ordinal)
expect_false(model_info(m1)$is_multinomial)
expect_false(model_info(m1)$is_linear)
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = c("temp", "contact")))
expect_identical(find_predictors(m1, flatten = TRUE), c("temp", "contact"))
expect_null(find_predictors(m1, effects = "random"))
})
test_that("find_random", {
expect_null(find_random(m1))
})
test_that("get_random", {
expect_warning(get_random(m1))
})
test_that("find_response", {
expect_identical(find_response(m1), "rating")
})
test_that("get_response", {
expect_equal(get_response(m1), wine$rating)
})
test_that("get_predictors", {
expect_equal(colnames(get_predictors(m1)), c("temp", "contact"))
})
test_that("link_inverse", {
expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
})
test_that("get_data", {
expect_equal(nrow(get_data(m1)), 72)
expect_equal(colnames(get_data(m1)), c("rating", "temp", "contact"))
})
test_that("find_formula", {
expect_length(find_formula(m1), 1)
expect_equal(
find_formula(m1),
list(conditional = as.formula("rating ~ temp * contact")),
ignore_attr = TRUE
)
})
test_that("find_terms", {
expect_equal(find_terms(m1), list(
response = "rating",
conditional = c("temp", "contact")
))
expect_equal(
find_terms(m1, flatten = TRUE),
c("rating", "temp", "contact")
)
})
test_that("n_obs", {
expect_equal(n_obs(m1), 72)
})
test_that("linkfun", {
expect_false(is.null(link_function(m1)))
})
test_that("find_parameters", {
expect_equal(
find_parameters(m1),
list(
conditional = c(
"1|2",
"2|3",
"3|4",
"4|5",
"tempwarm",
"contactyes",
"tempwarm:contactyes"
)
)
)
expect_equal(nrow(get_parameters(m1)), 7)
expect_equal(
get_parameters(m1)$Parameter,
c(
"1|2",
"2|3",
"3|4",
"4|5",
"tempwarm",
"contactyes",
"tempwarm:contactyes"
)
)
})
test_that("is_multivariate", {
expect_false(is_multivariate(m1))
})
test_that("find_statistic", {
expect_identical(find_statistic(m1), "z-statistic")
})
test_that("get_predicted", {
nd <- wine
nd$rating <- NULL
x <- as.data.frame(get_predicted(m1))
y <- as.data.frame(get_predicted(m1, predict = NULL, type = "prob"))
z <- predict(m1, type = "prob", newdata = nd, se.fit = TRUE)
expect_true(all(c("Row", "Response", "Predicted", "SE") %in% colnames(x)))
expect_equal(x, y)
for (i in 1:5) {
expect_equal(x$Predicted[x$Response == i], unname(z$fit[, i]), ignore_attr = FALSE)
expect_equal(x$SE[x$Response == i], unname(z$se.fit[, i]), ignore_attr = FALSE)
}
x <- as.data.frame(get_predicted(m1, predict = "classification"))
y <- as.data.frame(get_predicted(m1, predict = NULL, type = "class"))
z <- predict(m1, type = "class", newdata = nd)
expect_equal(x, y)
expect_equal(as.character(x$Predicted), as.character(z$fit), ignore_attr = FALSE)
# we use a hack to handle in-formula factors
tmp <- wine
tmp$rating <- as.numeric(tmp$rating)
tmp <- clm(factor(rating) ~ temp * contact, data = tmp)
expect_s3_class(get_predicted(tmp), "get_predicted")
})
}
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