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".