Implements a Bagging (Bootstrap Aggregating) ensemble for diagnostic models. It trains multiple base models on bootstrapped samples of the training data and aggregates their predictions by averaging probabilities.
Usage
bagging_dia(
data,
base_model_name,
n_estimators = 50,
subset_fraction = 0.632,
tune_base_model = FALSE,
threshold_strategy = "default",
specific_threshold_value = 0.5,
positive_label_value = 1,
negative_label_value = 0,
new_positive_label = "Positive",
new_negative_label = "Negative",
seed = 456
)Arguments
- data
A data frame where the first column is the sample ID, the second is the outcome label, and subsequent columns are features.
- base_model_name
A character string, the name of the base diagnostic model to use (e.g., "rf", "lasso"). 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.
- threshold_strategy
A character string (e.g., "f1", "youden", "default") or a numeric value (0-1) for determining the evaluation threshold for the ensemble.
- specific_threshold_value
A numeric value between 0 and 1. Only used if
threshold_strategyis "numeric".- positive_label_value
A numeric or character value in the raw data representing the positive class.
- negative_label_value
A numeric or character value in the raw data representing the negative class.
- new_positive_label
A character string, the desired factor level name for the positive class (e.g., "Positive").
- new_negative_label
A character string, the desired factor level name for the negative class (e.g., "Negative").
- seed
An integer, for reproducibility.
Examples
# \donttest{
# This example assumes your package includes a dataset named 'train_dia'.
# If not, create a toy data frame first.
if (exists("train_dia")) {
initialize_modeling_system_dia()
bagging_rf_results <- bagging_dia(
data = train_dia,
base_model_name = "rf",
n_estimators = 5, # Reduced for a quick example
threshold_strategy = "youden",
positive_label_value = 1,
negative_label_value = 0,
new_positive_label = "Case",
new_negative_label = "Control"
)
print_model_summary_dia("Bagging (RF)", bagging_rf_results)
}
#> Diagnostic modeling system already initialized.
#> Running Bagging model: Bagging_dia (base: rf)
#>
#> --- Bagging (RF) Model (on Training Data) Metrics ---
#> Ensemble Type: Bagging (Base: rf, Estimators: 5)
#>
#> AUROC: 0.9999 (95% CI: 0.9997 - 1.0000)
#> AUPRC: 1.0000
#> Accuracy: 0.9942
#> F1: 0.9968
#> Precision: 1.0000
#> Recall: 0.9936
#> Specificity: 1.0000
#> --------------------------------------------------
# }