bagging_pro: Train a Bagging Prognostic Model

In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Train a Bagging Prognostic Model

Description

Implements a Bagging (Bootstrap Aggregating) ensemble for prognostic models. It trains multiple base models on bootstrapped samples of the training data and aggregates their predictions.

Usage

bagging_pro(
  data,
  base_model_name,
  n_estimators = 10,
  subset_fraction = 0.632,
  tune_base_model = FALSE,
  time_unit = "day",
  years_to_evaluate = c(1, 3, 5),
  seed = 456
)

Arguments

data	A data frame for training. The first column must be the sample ID, the second column the event status (0/1), the third column the time, and subsequent columns the features.
base_model_name	A character string, the name of the base prognostic model to use (e.g., "lasso_pro", "rsf_pro"). This model must be registered.
n_estimators	An integer, the number of base models to train.
subset_fraction	A numeric value between 0 and 1, the fraction of samples to bootstrap for each base model.
tune_base_model	Logical, whether to enable tuning for each base 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

initialize_modeling_system_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()

  bagging_lasso_results <- bagging_pro(
    data = train_pro,
    base_model_name = "lasso_pro",
    n_estimators = 3, # Small number for example speed
    subset_fraction = 0.8,
    years_to_evaluate = c(1, 3)
  )
  print_model_summary_pro("Bagging (Lasso)", bagging_lasso_results)
}

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