library(testthat)
test_that("Tests that ShRF is working correctly", {
context('S-RF')
set.seed(1423614230)
feat <- iris[, -1]
tr <- rbinom(nrow(iris), 1, .5)
yobs <- iris[, 1]
sl <- S_RF(
feat = feat,
tr = tr,
yobs = yobs,
mu.forestry =
list(
relevant.Variable = 1:ncol(feat),
ntree = 20,
replace = TRUE,
sample.fraction = 0.9,
mtry = ncol(feat),
nodesizeSpl = 1,
nodesizeAvg = 3,
splitratio = .5,
middleSplit = FALSE
))
expect_equal(EstimateCate(sl, feat)[1], 0.0491662, tolerance = 1e-4)
set.seed(432)
cate_problem <-
simulate_causal_experiment(
ntrain = 400,
ntest = 100,
dim = 20,
alpha = .1,
feat_distribution = "normal",
testseed = 543,
trainseed = 234
)
sl <- S_RF(
feat = cate_problem$feat_tr,
yobs = cate_problem$Yobs_tr,
tr = cate_problem$W_tr,
mu.forestry =
list(
relevant.Variable = 1:ncol(feat),
ntree = 20,
replace = TRUE,
sample.fraction = 0.9,
mtry = ncol(feat),
nodesizeSpl = 1,
nodesizeAvg = 3,
splitratio = .5,
middleSplit = FALSE))
expect_equal(mean((
EstimateCate(sl, cate_problem$feat_te) - cate_problem$tau_te
) ^ 2),
31.19558,
tolerance = 1)
})
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