stacking_pro: Train Stacking Ensemble for Prognosis

In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Train Stacking Ensemble for Prognosis

Description

Implements a Stacking Ensemble (Super Learner). It uses the risk scores from top-performing base models as meta-features to train a second-level meta-learner.

Usage

stacking_pro(
  results_all_models,
  data,
  meta_model_name,
  top = 3,
  tune_meta = FALSE,
  time_unit = "day",
  years_to_evaluate = c(1, 3, 5),
  seed = 789
)

Arguments

results_all_models	List of results from models_pro().
data	Training data.
meta_model_name	Name of the meta-learner (e.g., "lasso_pro").
top	Integer. Number of top base models to include based on C-index.
tune_meta	Logical. Tune the meta-learner?
time_unit	Time unit.
years_to_evaluate	Evaluation years.
seed	Integer seed.

Value

A list containing the stacking object and evaluation results.

E2E documentation built on March 22, 2026, 1:07 a.m.