Nothing
set.seed(1071)
n = 2000; p = 10
X = matrix(rnorm(n*p), n, p)
W = rbinom(n, 1, 0.4 + 0.2 * (X[,1] > 0))
Y = pmax(X[,1], 0) * W + X[,2] + pmin(X[,3], 0) + rnorm(n)
test_that(" Double ML selection works with glmnet", {
double_glmnet <- double_ML(X, Y, W, method = c("glmnet"),
k.fld = 4,
simulations = 50,
show.progress = FALSE,
lambda.set.Y = 1,
lambda.set.W = 1,
Z.trans = TRUE,
parallelize = FALSE)
expect_lte(double_glmnet[[1]], 0.55)
expect_gte(double_glmnet[[1]], 0.35)
expect_true(is.list(double_glmnet))
expect_true(is.data.frame(double_glmnet[[3]]))
})
test_that(" Double ML selection works with ols", {
double_ols <- double_ML(X, Y, W, method = c("ols"),
show.progress = FALSE,
k.fld = 4,
simulations = 50)
expect_lte(double_ols[[1]], 0.55)
expect_gte(double_ols[[1]], 0.35)
expect_true(is.list(double_ols))
expect_true(is.data.frame(double_ols[[3]]))
})
test_that(" Double ML selection works with random forests", {
skip_on_cran()
skip_on_travis()
double_rf <- double_ML(X, Y, W, method = c("randomforest"),
k.fld = 2, simulations = 10,
tree.n = 200,
show.progress = FALSE,
tune = FALSE)
double_rf_tune <- double_ML(X, Y, W, method = c("randomforest"),
k.fld = 2, simulations = 10,
tree.n = 200,
show.progress = FALSE,
tune = TRUE)
expect_lte(double_rf[[1]], 0.55)
expect_gte(double_rf[[1]], 0.20)
expect_true(is.list(double_rf))
expect_true(is.data.frame(double_rf[[3]]))
expect_lte(double_rf_tune[[1]], 0.55)
expect_gte(double_rf_tune[[1]], 0.20)
expect_true(is.list(double_rf_tune))
expect_true(is.data.frame(double_rf_tune[[3]]))
})
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