Nothing
test_that("algorithm performance test", {
skip_on_cran()
skip_on_ci()
skip_if_not(
find_proglang("julia"),
message = "skip test as julia installation not found."
)
# convergence in probability of boottest p values when B -> infinity
seed <- 1230123
data1 <<- fwildclusterboot:::create_data(
N = 1000,
N_G1 = 15,
icc1 = 0.5,
N_G2 = 20,
icc2 = 0.2,
numb_fe1 = 10,
numb_fe2 = 10,
seed = seed,
weights = 1:N / N
)
mc_res_p <- matrix(NA, 250, 2)
mc_res_cl <- matrix(NA, 250, 2)
mc_res_cu <- matrix(NA, 250, 2)
bootstrap_iter <- c(199, 999)
for (B in seq_along(bootstrap_iter)) {
x <- bootstrap_iter[B]
for (mc_iter in 1:250) {
fit <- lm(proposition_vote ~ treatment, data = data1)
boot1 <- boottest(fit,
param = "treatment",
B = x,
clustid = "group_id1"
)
boot2 <- boottest(fit,
param = "treatment",
B = x,
clustid = "group_id1"
)
mc_res_p[mc_iter, B] <- pval(boot1) - pval(boot2)
mc_res_cl[mc_iter, B] <- (confint(boot1) - confint(boot2))[1]
mc_res_cu[mc_iter, B] <- (confint(boot1) - confint(boot2))[2]
}
}
# test that differences get smaller
expect_equal(sort(abs(diff(
colMeans(mc_res_p)
)), decreasing = TRUE), abs(diff(colMeans(mc_res_p))))
expect_equal(sort(abs(diff(
colMeans(mc_res_cl)
)), decreasing = TRUE), abs(diff(colMeans(mc_res_cl))))
expect_equal(sort(abs(diff(
colMeans(mc_res_cu)
)), decreasing = TRUE), abs(diff(colMeans(mc_res_cu))))
})
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