Nothing
context("glmnet")
skip_if_not_installed("modeltests")
library(modeltests)
skip_if_not_installed("glmnet")
library(glmnet)
set.seed(27)
x <- matrix(rnorm(100 * 20), 100, 20)
y <- rnorm(100)
g <- sample(1:4, 100, replace = TRUE)
fit <- glmnet(x, y)
fit2 <- glmnet(x, g, family = "multinomial")
cv_fit <- cv.glmnet(x, y)
cv_fit2 <- cv.glmnet(x, g, family = "multinomial")
test_that("glmnet tidier arguments", {
check_arguments(tidy.glmnet)
check_arguments(glance.glmnet)
check_arguments(tidy.cv.glmnet)
check_arguments(glance.cv.glmnet)
})
test_that("tidy.glmnet", {
td <- tidy(fit)
tdz <- tidy(fit, return_zeros = TRUE)
check_tidy_output(td)
check_tidy_output(tdz)
check_dims(td, expected_cols = 5)
check_dims(tdz, expected_cols = 5)
expect_true(all(td$estimate != 0))
expect_true(any(tdz$estimate == 0))
# multinomial
td2 <- tidy(fit2)
td2z <- tidy(fit2, return_zeros = TRUE)
check_tidy_output(td2)
check_tidy_output(td2z)
expect_true(all(td2$estimate != 0))
expect_true(any(td2z$estimate == 0))
# regression tests
expect_true(is.numeric(td$step) && !any(is.na(td$step)))
expect_true(is.numeric(td2$step) && !any(is.na(td2$step)))
})
test_that("glance.glmnet", {
gl <- glance(fit)
gl2 <- glance(fit2)
check_glance_outputs(gl, gl2)
})
test_that("tidy.cv.glmnet", {
td <- tidy(cv_fit)
check_tidy_output(td)
check_dims(td, expected_cols = 6)
# multinomial
td2 <- tidy(cv_fit2)
check_tidy_output(td2)
check_dims(td2, expected_cols = 6)
})
test_that("glance.cv.glmnet", {
gl <- glance(cv_fit)
gl2 <- glance(cv_fit2)
check_glance_outputs(gl, gl2)
})
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