.runThisTest <- Sys.getenv("RunAllinsightTests") == "yes"
if (.runThisTest && requiet("parsnip")) {
data(mtcars)
m <- parsnip::linear_reg()
m <- parsnip::set_engine(m, "lm")
m <- parsnip::set_mode(m, "regression")
m <- parsnip::fit(m, mpg ~ am + vs, data = mtcars)
test_that("find_formula", {
expect_equal(
find_formula(m),
list(conditional = as.formula("mpg ~ am + vs")),
ignore_attr = TRUE
)
})
# test_that("model_info", {
# expect_true(model_info(m1)$is_poisson)
# expect_true(model_info(m1)$is_count)
# expect_false(model_info(m1)$is_negbin)
# expect_false(model_info(m1)$is_binomial)
# expect_false(model_info(m1)$is_linear)
# })
#
# test_that("loglik", {
# expect_equal(get_loglikelihood(m1), logLik(m1), ignore_attr = TRUE)
# })
#
# test_that("get_df", {
# expect_equal(get_df(m1), df.residual(m1), ignore_attr = TRUE)
# expect_equal(get_df(m1, type = "model"), attr(logLik(m1), "df"), ignore_attr = TRUE)
# })
#
#
# test_that("find_predictors", {
# expect_identical(find_predictors(m1), list(conditional = c("mined", "cover", "sample")))
# expect_identical(
# find_predictors(m1, flatten = TRUE),
# c("mined", "cover", "sample")
# )
# expect_null(find_predictors(m1, effects = "random"))
# })
#
# test_that("find_random", {
# expect_null(find_random(m1))
# })
#
# test_that("get_random", {
# expect_warning(get_random(m1))
# })
#
# test_that("find_response", {
# expect_identical(find_response(m1), "count")
# })
#
# test_that("get_response", {
# expect_equal(get_response(m1), Salamanders$count)
# })
#
# test_that("get_predictors", {
# expect_equal(colnames(get_predictors(m1)), c("mined", "cover", "sample"))
# })
#
# test_that("link_inverse", {
# expect_equal(link_inverse(m1)(0.2), exp(0.2), tolerance = 1e-5)
# })
#
# test_that("linkfun", {
# expect_equal(link_function(m1)(0.2), -1.609438, tolerance = 1e-4)
# })
#
# test_that("get_data", {
# expect_equal(nrow(get_data(m1)), 644)
# expect_equal(
# colnames(get_data(m1)),
# c("count", "mined", "cover", "sample")
# )
# })
#
# test_that("get_call", {
# expect_equal(class(get_call(m1)), "call")
# })
#
#
#
# test_that("find_variables", {
# expect_equal(
# find_variables(m1),
# list(
# response = "count",
# conditional = c("mined", "cover", "sample")
# )
# )
# expect_equal(
# find_variables(m1, flatten = TRUE),
# c("count", "mined", "cover", "sample")
# )
# })
#
# test_that("n_obs", {
# expect_equal(n_obs(m1), 644)
# })
#
# test_that("find_parameters", {
# expect_equal(
# find_parameters(m1),
# list(
# conditional = c("(Intercept)", "minedno", "log(cover)", "sample")
# )
# )
# expect_equal(nrow(get_parameters(m1)), 4)
# expect_equal(
# get_parameters(m1)$Parameter,
# c("(Intercept)", "minedno", "log(cover)", "sample")
# )
# })
#
# test_that("is_multivariate", {
# expect_false(is_multivariate(m1))
# })
#
# test_that("find_terms", {
# expect_equal(
# find_terms(m1),
# list(
# response = "count",
# conditional = c("mined", "log(cover)", "sample")
# )
# )
# })
#
# test_that("find_algorithm", {
# expect_equal(find_algorithm(m1), list(algorithm = "ML"))
# })
#
# test_that("find_statistic", {
# expect_identical(find_statistic(m1), "z-statistic")
# })
#
# test_that("get_statistic", {
# expect_equal(get_statistic(m1)$Statistic, c(-10.7066515607315, 18.1533878215937, -1.68918157150882, 2.23541768590273), tolerance = 1e-4)
# })
}
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