Nothing
test_that("slim handles vector y with missing values", {
set.seed(1)
x <- matrix(rnorm(80), nrow = 20, ncol = 4)
y <- rnorm(20)
y[c(3, 7)] <- NA_real_
fit <- NULL
utils::capture.output({
fit <- slim(x, y, nlambda = 2, method = "lasso", verbose = FALSE, max.ite = 2000)
})
expect_s3_class(fit, "slim")
expect_equal(nrow(fit$X), 18L)
expect_equal(nrow(fit$Y), 18L)
expect_equal(ncol(fit$beta), fit$nlambda)
expect_false(anyNA(fit$X))
expect_false(anyNA(fit$Y))
})
test_that("predict.slim validates input shape and returns prediction matrix", {
set.seed(2)
x <- matrix(rnorm(120), nrow = 30, ncol = 4)
y <- rnorm(30)
fit <- slim(x, y, nlambda = 3, method = "lasso", verbose = FALSE, max.ite = 2000)
bad_newdata <- matrix(rnorm(30), nrow = 10, ncol = 3)
bad_pred <- NULL
utils::capture.output({
bad_pred <- predict.slim(fit, bad_newdata, lambda.idx = 1, Y.pred.idx = 1)
})
expect_null(bad_pred)
good_pred <- NULL
utils::capture.output({
good_pred <- predict.slim(fit, x, lambda.idx = 1:2, Y.pred.idx = 1:2)
})
expect_type(good_pred, "list")
expect_equal(dim(good_pred$Y.pred), c(nrow(x), 2L))
})
test_that("slim rejects non-positive or non-finite lambda values", {
set.seed(3)
x <- matrix(rnorm(60), nrow = 15, ncol = 4)
y <- rnorm(15)
fit <- NULL
utils::capture.output({
fit <- slim(x, y, lambda = c(0.2, 0, 0.1), method = "lasso", verbose = FALSE)
})
expect_null(fit)
})
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