if (requiet("nnet") && requiet("MASS")) {
data("birthwt")
void <- capture.output({
m1 <- nnet::multinom(low ~ age + lwt + race + smoke, data = birthwt)
})
test_that("model_info", {
expect_true(model_info(m1)$is_binomial)
expect_false(model_info(m1)$is_linear)
})
test_that("n_parameters", {
expect_equal(n_parameters(m1), 5)
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = c("age", "lwt", "race", "smoke")))
expect_identical(
find_predictors(m1, flatten = TRUE),
c("age", "lwt", "race", "smoke")
)
expect_null(find_predictors(m1, effects = "random"))
})
test_that("find_response", {
expect_identical(find_response(m1), "low")
})
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)), 189)
expect_equal(
colnames(get_data(m1)),
c("low", "age", "lwt", "race", "smoke")
)
})
test_that("find_formula", {
expect_length(find_formula(m1), 1)
expect_equal(
find_formula(m1),
list(conditional = as.formula("low ~ age + lwt + race + smoke")),
ignore_attr = TRUE
)
})
test_that("find_terms", {
expect_equal(find_terms(m1), list(
response = "low",
conditional = c("age", "lwt", "race", "smoke")
))
expect_equal(
find_terms(m1, flatten = TRUE),
c("low", "age", "lwt", "race", "smoke")
)
})
test_that("n_obs", {
expect_equal(n_obs(m1), 189)
})
test_that("linkfun", {
expect_false(is.null(link_function(m1)))
})
test_that("find_parameters", {
expect_equal(
find_parameters(m1),
list(conditional = c(
"(Intercept)", "age", "lwt", "race", "smoke"
))
)
expect_equal(nrow(get_parameters(m1)), 5)
expect_equal(
get_parameters(m1)$Parameter,
c("(Intercept)", "age", "lwt", "race", "smoke")
)
})
test_that("find_statistic", {
expect_identical(find_statistic(m1), "t-statistic")
})
test_that("get_predicted", {
void <- capture.output({
# binary outcome
m1 <- nnet::multinom(low ~ age + lwt + race + smoke, data = birthwt)
# multinomial outcome
m2 <- nnet::multinom(ftv ~ age + lwt + race + smoke, data = birthwt)
})
# binary outcomes produces an atomic vector
x <- get_predicted(m1, predict = "classification")
expect_true(is.atomic(x) && !is.null(x) && is.null(dim(x)))
expect_true(all(levels(x) %in% c("0", "1")))
x <- get_predicted(m1, predict = "expectation")
expect_true(is.atomic(x) && !is.null(x) && is.null(dim(x)))
x <- get_predicted(m1, predict = NULL, type = "class")
expect_true(is.atomic(x) && !is.null(x) && is.null(dim(x)))
expect_true(all(levels(x) %in% c("0", "1")))
x <- get_predicted(m1, predict = NULL, type = "probs")
expect_true(is.atomic(x) && !is.null(x) && is.null(dim(x)))
# multinomial outcomes depends on predict type
x <- get_predicted(m2, predict = "classification")
expect_true(is.atomic(x) && !is.null(x) && is.null(dim(x)))
expect_true(all(levels(x) %in% as.character(0:6)))
x <- get_predicted(m2, predict = "expectation")
expect_s3_class(x, "data.frame")
expect_true(all(c("Row", "Response", "Predicted") %in% colnames(x)))
x <- get_predicted(m2, predict = NULL, type = "class")
expect_true(is.atomic(x) && !is.null(x) && is.null(dim(x)))
expect_true(all(levels(x) %in% as.character(0:6)))
x <- get_predicted(m2, predict = NULL, type = "probs")
expect_s3_class(x, "data.frame")
expect_true(all(c("Row", "Response", "Predicted") %in% colnames(x)))
})
}
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