Nothing
context("Enhanced family arg")
# to avoid issues with 32-bit Solaris checks on CRAN
skip_on_os("solaris")
test_that("enhanced family arg works with glmnet", {
x <- as.matrix(mtcars[-1])
y <- mtcars$mpg
mod0.1 <- glmnet::glmnet(x, y)
mod0.2 <- glmnet::glmnet(x, y, family="gaussian")
mod0.3 <- glmnet::glmnet(x, y, family=gaussian)
mod0.4 <- glmnet::glmnet(x, y, family=gaussian())
mod1.1 <- glmnet(mpg ~ ., data=mtcars)
mod1.2 <- glmnet(mpg ~ ., data=mtcars, family="gaussian")
mod1.3 <- glmnet(mpg ~ ., data=mtcars, family=gaussian)
mod1.4 <- glmnet(mpg ~ ., data=mtcars, family=gaussian())
expect_s3_class(mod1.1, "glmnet.formula")
expect_s3_class(mod1.2, "glmnet.formula")
expect_s3_class(mod1.3, "glmnet.formula")
expect_s3_class(mod1.4, "glmnet.formula")
expect_equal(mod0.1$beta, mod1.1$beta)
expect_equal(mod0.2$beta, mod1.2$beta)
expect_equal(mod0.3$beta, mod1.3$beta)
expect_equal(mod0.4$beta, mod1.4$beta)
})
test_that("enhanced family arg works with cv.glmnet", {
data(Boston, package="MASS")
x <- as.matrix(Boston[-1])
y <- Boston$crim
set.seed(12345)
mod0.1 <- glmnet::cv.glmnet(x, y, alpha=0.5)
set.seed(12345)
mod0.2 <- glmnet::cv.glmnet(x, y, alpha=0.5, family="gaussian")
set.seed(12345)
mod0.3 <- glmnet::cv.glmnet(x, y, alpha=0.5, family=gaussian)
set.seed(12345)
mod0.4 <- glmnet::cv.glmnet(x, y, alpha=0.5, family=gaussian())
set.seed(12345)
mod1.1 <- cv.glmnet(crim ~ ., data=Boston, alpha=0.5)
set.seed(12345)
mod1.2 <- cv.glmnet(crim ~ ., data=Boston, alpha=0.5, family="gaussian")
set.seed(12345)
mod1.3 <- cv.glmnet(crim ~ ., data=Boston, alpha=0.5, family=gaussian)
set.seed(12345)
mod1.4 <- cv.glmnet(crim ~ ., data=Boston, alpha=0.5, family=gaussian())
expect_equal(mod0.1$beta, mod1.1$beta)
expect_equal(mod0.2$beta, mod1.2$beta)
expect_equal(mod0.3$beta, mod1.3$beta)
expect_equal(mod0.4$beta, mod1.4$beta)
})
test_that("enhanced family arg works with cva.glmnet", {
data(Boston, package="MASS")
set.seed(12345)
mod1.1 <- cva.glmnet(medv ~ ., data=Boston)
set.seed(12345)
mod1.2 <- cva.glmnet(medv ~ ., data=Boston, family="gaussian")
set.seed(12345)
mod1.3 <- cva.glmnet(medv ~ ., data=Boston, family=gaussian)
set.seed(12345)
mod1.4 <- cva.glmnet(medv ~ ., data=Boston, family=gaussian())
w <- 5
expect_equal(coef(mod1.1, which=w), coef(mod1.2, which=w))
expect_equal(coef(mod1.3, which=w), coef(mod1.4, which=w))
})
test_that("enhanced family arg works with relaxed glmnet", {
x <- as.matrix(mtcars[-1])
y <- mtcars$mpg
mod0.1 <- glmnet::glmnet(x, y, alpha=0.5, relax=TRUE)
mod0.2 <- glmnet::glmnet(x, y, family="gaussian", alpha=0.5, relax=TRUE)
mod0.3 <- glmnet::glmnet(x, y, family=gaussian, alpha=0.5, relax=TRUE)
mod0.4 <- glmnet::glmnet(x, y, family=gaussian(), alpha=0.5, relax=TRUE)
mod1.1 <- glmnet(mpg ~ ., data=mtcars, alpha=0.5, relax=TRUE)
mod1.2 <- glmnet(mpg ~ ., data=mtcars, family="gaussian", alpha=0.5, relax=TRUE)
mod1.3 <- glmnet(mpg ~ ., data=mtcars, family=gaussian, alpha=0.5, relax=TRUE)
mod1.4 <- glmnet(mpg ~ ., data=mtcars, family=gaussian(), alpha=0.5, relax=TRUE)
expect_equivalent(mod0.1$beta, mod1.1$beta)
expect_equivalent(mod0.2$beta, mod1.2$beta)
expect_equivalent(mod0.3$beta, mod1.3$beta)
expect_equivalent(mod0.4$beta, mod1.4$beta)
})
test_that("enhanced family arg works with relaxed cv.glmnet", {
data(Boston, package="MASS")
x <- as.matrix(Boston[-1])
y <- Boston$crim
set.seed(12345)
mod0.1 <- glmnet::cv.glmnet(x, y, alpha=0.5, gamma=(0:3)/3, relax=TRUE)
set.seed(12345)
mod0.2 <- glmnet::cv.glmnet(x, y, family="gaussian", alpha=0.5, gamma=(0:3)/3, relax=TRUE)
set.seed(12345)
mod0.3 <- glmnet::cv.glmnet(x, y, family=gaussian, alpha=0.5, gamma=(0:3)/3, relax=TRUE)
set.seed(12345)
mod0.4 <- glmnet::cv.glmnet(x, y, family=gaussian(), alpha=0.5, gamma=(0:3)/3, relax=TRUE)
set.seed(12345)
mod1.1 <- cv.glmnet(crim ~ ., data=Boston, alpha=0.5, gamma=(0:3)/3, relax=TRUE)
set.seed(12345)
mod1.2 <- cv.glmnet(crim ~ ., data=Boston, family="gaussian", alpha=0.5, gamma=(0:3)/3, relax=TRUE)
set.seed(12345)
mod1.3 <- cv.glmnet(crim ~ ., data=Boston, family=gaussian, alpha=0.5, gamma=(0:3)/3, relax=TRUE)
set.seed(12345)
mod1.4 <- cv.glmnet(crim ~ ., data=Boston, family=gaussian(), alpha=0.5, gamma=(0:3)/3, relax=TRUE)
expect_equal(mod0.1$glmnet.fit$beta, mod1.1$glmnet.fit$beta)
expect_equal(mod0.2$glmnet.fit$beta, mod1.2$glmnet.fit$beta)
expect_equal(mod0.3$glmnet.fit$beta, mod1.3$glmnet.fit$beta)
expect_equal(mod0.4$glmnet.fit$beta, mod1.4$glmnet.fit$beta)
})
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