skip_if_not_installed("parsnip")

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)
# })