Implements Bootstrap Aggregating (Bagging) for survival models. It trains multiple base models on bootstrapped subsets and averages the risk scores. This method reduces variance and improves stability.
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
Input data frame (ID, Status, Time, Features).
- base_model_name
Character string name of the base model (e.g., "rsf_pro").
- n_estimators
Integer. Number of bootstrap iterations.
- subset_fraction
Numeric (0-1). Fraction of data to sample in each iteration.
- tune_base_model
Logical. Whether to tune each base model (computationally expensive).
- time_unit
Time unit of the input data.
- years_to_evaluate
Numeric vector of years for time-dependent AUC evaluation.
- seed
Integer seed for reproducibility.