stacking_pro: Train a Stacking Prognostic Model

In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Train a Stacking Prognostic Model

Description

Implements a Stacking ensemble for prognostic models. It trains multiple base models and uses their predictions to train a meta-model.

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	A list of results from models_pro(), containing trained base model objects and their evaluation metrics.
data	A data frame for training the meta-model. The first column must be ID, second event status (0/1), third time, and subsequent columns features.
meta_model_name	A character string, the name of the meta-model to use (e.g., "lasso_pro", "gbm_pro"). This model must be registered.
top	An integer, the number of top-performing base models (ranked by C-index) to select for the stacking ensemble.
tune_meta	Logical, whether to enable tuning for the meta-model.
time_unit	A character string, the unit of time in the third column of data.
years_to_evaluate	A numeric vector of specific years at which to calculate time-dependent AUROC for evaluation.
seed	An integer, for reproducibility.

Value

A list containing the model_object, sample_score, and evaluation_metrics.

See Also

models_pro, evaluate_model_pro

Examples

# NOTE: This example requires the 'train_pro' dataset.
if (requireNamespace("E2E", quietly = TRUE) &&
"train_pro" %in% utils::data(package = "E2E")$results[,3]) {
  data(train_pro, package = "E2E")
  initialize_modeling_system_pro()

  # First, generate results for base models
  base_model_results <- models_pro(data = train_pro, model = c("lasso_pro", "rsf_pro"))

  # Then, create the stacking ensemble
  stacking_lasso_results <- stacking_pro(
    results_all_models = base_model_results,
    data = train_pro,
    meta_model_name = "lasso_pro",
    top = 3,
    years_to_evaluate = c(1, 3)
  )
  print_model_summary_pro("Stacking (Lasso)", stacking_lasso_results)
}

E2E documentation built on Aug. 27, 2025, 1:09 a.m.