library(testthat)
library(LinearModel)
data(prostate, package = "ElemStatLearn")
X.mat <- data.matrix(subset(prostate, select = -c(train, lpsa)))
y.vec <- as.vector(data.matrix(subset(prostate, select = lpsa)))
fold.vec <- sample(rep(1:5,l=length(y.vec)))
max.iteration <- 5L
# LMSquareLossEarlyStoppingCV X.mat, y.vec, fold.vec, max.iteration
test_that(
"For valid inputs, your function returns an output of the expected type/dimension",
{
result.list <-
LMSquareLossEarlyStoppingCV(X.mat, y.vec, fold.vec, max.iteration)
expect_true(is.list(result.list))
}
)
test_that(
"For an invalid input, your function stops with an informative error message.",
{
expect_error(
result.list <-
LMSquareLossEarlyStoppingCV(as.data.frame(X.mat), y.vec, fold.vec, max.iteration),
"X.mat must be a numeric matrix.",
fixed = TRUE
)
expect_error(
result.list <-
LMSquareLossEarlyStoppingCV(X.mat, y.vec[-1], fold.vec, max.iteration),
"y.vec must be a numeric vector of the same number of rows as X.mat.",
fixed = TRUE
)
expect_error(
result.list <-
LMSquareLossEarlyStoppingCV(X.mat, y.vec, fold.vec[-1], max.iteration),
"fold.vec must be a numeric vector of length nrow(X.mat)",
fixed = TRUE
)
expect_error(
result.list <-
LMSquareLossEarlyStoppingCV(X.mat, y.vec, fold.vec, as.double(max.iteration)),
"Input max.iteration must be a greater than 1 integer scalar number.",
fixed = TRUE
)
}
)
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