int_dia: Comprehensive Diagnostic Modeling Pipeline

In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Comprehensive Diagnostic Modeling Pipeline

Description

Executes a complete diagnostic modeling workflow including single models, bagging, stacking, and voting ensembles across training and multiple test datasets. Returns structured results with AUROC values for visualization.

Usage

int_dia(
  ...,
  model_names = NULL,
  tune = TRUE,
  n_estimators = 10,
  seed = 123,
  positive_label_value = 1,
  negative_label_value = 0,
  new_positive_label = "Positive",
  new_negative_label = "Negative"
)

Arguments

...	Data frames for analysis. The first is the training dataset; all subsequent arguments are test datasets.
model_names	Character vector specifying which models to use. If NULL (default), uses all registered models.
tune	Logical, enable hyperparameter tuning. Default TRUE.
n_estimators	Integer, number of bootstrap samples for bagging. Default 10.
seed	Integer for reproducibility. Default 123.
positive_label_value	Value representing positive class. Default 1.
negative_label_value	Value representing negative class. Default 0.
new_positive_label	Factor level name for positive class. Default "Positive".
new_negative_label	Factor level name for negative class. Default "Negative".

Value

A list containing all_results, auroc_matrix, model_categories, dataset_names.

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