Nothing
se_repeated = function(se_s, coefficients, theta_s) {
se = sqrt(stats::median(se_s^2 + (theta_s - coefficients)^2))
return(se)
}
sample_splitting = function(k, data) {
resampling = mlr3::ResamplingCV$new()
resampling$param_set$values$folds = k
dummy_task = mlr3::Task$new("dummy_resampling", "regr", data)
resampling = resampling$instantiate(dummy_task)
n_iters = resampling$iters
train_ids = lapply(1:n_iters, function(x) resampling$train_set(x))
test_ids = lapply(1:n_iters, function(x) resampling$test_set(x))
return(list(train_ids = train_ids, test_ids = test_ids))
}
draw_bootstrap_weights = function(bootstrap, n_rep_boot, n_obs) {
if (bootstrap == "Bayes") {
weights = stats::rexp(n_rep_boot * n_obs, rate = 1) - 1
} else if (bootstrap == "normal") {
weights = stats::rnorm(n_rep_boot * n_obs)
} else if (bootstrap == "wild") {
weights = stats::rnorm(n_rep_boot * n_obs) / sqrt(2) + (stats::rnorm(n_rep_boot * n_obs)^2 - 1) / 2
} else {
stop("invalid boot method")
}
weights = matrix(weights, nrow = n_rep_boot, ncol = n_obs, byrow = TRUE)
return(weights)
}
functional_bootstrap = function(theta, se, psi, psi_a, k, smpls,
n_rep_boot, weights) {
score = psi
J = mean(psi_a)
boot_coef = matrix(NA_real_, nrow = 1, ncol = n_rep_boot)
boot_t_stat = matrix(NA_real_, nrow = 1, ncol = n_rep_boot)
for (i in seq(n_rep_boot)) {
boot_coef[1, i] = mean(weights[i, ] * 1 / J * score)
boot_t_stat[1, i] = boot_coef[1, i] / se
}
res = list(boot_coef = boot_coef, boot_t_stat = boot_t_stat)
return(res)
}
trim_vec = function(values, trimming_threshold) {
if (trimming_threshold > 0) {
values[values < trimming_threshold] = trimming_threshold
values[values > 1 - trimming_threshold] = 1 - trimming_threshold
}
return(values)
}
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