test_that('gls execution', {
skip_if_not_installed("nlme")
skip_on_cran()
set.seed(2452)
gls_mod <-
nlme::gls(depr_score ~ week + imipramine,
data = riesby_tr,
correlation = nlme::corSymm(form = ~ 1 | subject))
set.seed(2452)
expect_error(
ps_mod <-
linear_reg() %>%
set_engine("gls", correlation = nlme::corSymm(form = ~ 1 | subject)) %>%
fit(depr_score ~ week + imipramine, data = riesby_tr),
regex = NA
)
expect_equal(
gls_mod$modelStruct,
ps_mod$fit$modelStruct,
ignore_formula_env = TRUE
)
expect_equal(
coef(ps_mod$fit),
coef(gls_mod)
)
gls_pred <- predict(gls_mod, riesby_te, level = 0)
ps_pred <- predict(ps_mod, riesby_te)
expect_equal(as.numeric(gls_pred), ps_pred$.pred)
})
test_that('mode specific package dependencies', {
expect_identical(
get_from_env(paste0("linear_reg", "_pkgs")) %>%
dplyr::filter(engine == "gls", mode == "classification") %>%
dplyr::pull(pkg),
list()
)
expect_identical(
get_from_env(paste0("linear_reg", "_pkgs")) %>%
dplyr::filter(engine == "gls", mode == "regression") %>%
dplyr::pull(pkg),
list(c("nlme", "multilevelmod"))
)
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.