dot-run_replicated_cv_ris: Run replicated K-fold CV with random splits, matching the...

In pense: Penalized Elastic Net S/MM-Estimator of Regression

Run replicated K-fold CV with random splits, matching the global estimates to the CV estimates by Kendall's tau-b computed on the robustness weights.

Description

Run replicated K-fold CV with random splits, matching the global estimates to the CV estimates by Kendall's tau-b computed on the robustness weights.

Usage

.run_replicated_cv_ris(
  std_data,
  cv_k,
  cv_repl,
  cv_est_fun,
  global_ests,
  min_similarity = 0,
  par_cluster = NULL,
  rho_opts,
  handler_args = list()
)

Arguments

std_data	standardized full data set (standardized by .standardize_data)
cv_k	number of folds per CV split
cv_repl	number of CV replications.
cv_est_fun	function taking the standardized training set and the indices of the left-out observations and returns a list of estimates. The function always needs to return the same number of estimates!
global_ests	estimates computed on all observations.
min_similarity	minimum (average) similarity for CV solutions to be considered (between 0 and 1). If no CV solution satisfies this lower bound, the best CV solution will be used regardless of similarity.
par_cluster	parallel cluster to parallelize computations.
rho_opts	rho function options.
handler_args	additional arguments to the handler function.

pense documentation built on Jan. 27, 2026, 5:06 p.m.