Built-in Datasets

E2E includes example datasets for both diagnostic and prognostic modeling:

Built-in Models

models_dia()

Trains base classification models for diagnostic tasks. Parameters:

• data (required): Data frame with sample names (column 1), outcomes 0/1 (column 2), features (columns 3+)

• model (required): Character vector of model names or “all_dia” for all models

• tune: Logical (default FALSE). Whether to perform hyperparameter tuning

• threshold_choices: Threshold selection method

  • “default” (default): Fixed 0.5 threshold
  • “f1”: Optimize F1 score
  • “youden”: Optimize Youden index
    
  • Numeric value (0-1): Custom threshold

• seed: Integer (default 123). Random seed for reproducibility

bagging_dia()

Bootstrap aggregating ensemble method. Parameters:

• data (required): Training data frame

• base_model_name (required): Base model name (e.g., “xb”, “rf”)

• n_estimators: Integer (default 50). Number of base models

• subset_fraction: Numeric (default 0.632). Bootstrap sampling fraction

• tune_base_model: Logical (default FALSE). Tune base models

• threshold_choices: Same as models_dia()

• seed: Integer (default 123). Random seed

voting_dia()

Voting ensemble combining multiple models. Parameters:

• results_all_models (required): Output from models_dia()

• data (required): Training data

• type: Voting type

  • “soft” (default): Weighted probability averaging
  • “hard”: Majority voting

• weight_metric: String (default “AUROC”). Metric for soft voting weights

• top: Integer (default 5). Number of top models to use

• threshold_choices: Same as models_dia()

• seed: Integer (default 123). Random seed

stacking_dia()

Stacking ensemble with meta-model. Parameters:

• results_all_models (required): Output from models_dia()

• data (required): Training data

• meta_model_name (required): Meta-model name (e.g., “lasso”, “gbm”)

• top: Integer (default 5). Number of top base models

• tune_meta: Logical (default FALSE). Tune meta-model

• threshold_choices: Same as models_dia()

• seed: Integer (default 123). Random seed

imbalance_dia()

Handles imbalanced datasets using EasyEnsemble-like algorithm. Parameters:

• data (required): Imbalanced training data

• base_model_name (required): Base model for balanced subsets

• n_estimators: Integer (default 10). Number of balanced subsets

• tune_base_model: Logical (default FALSE). Tune base models

• threshold_choices: Same as models_dia()

• seed: Integer (default 123). Random seed

apply_dia()

Applies trained model to new data. Parameters:

• trained_model_object (required): Trained model object from E2E functions

• new_data (required): New data for prediction (sample IDs in column 1)

• label_col_name: String (default NULL). True label column name if available

evaluate_predictions_dia()

Evaluates model predictions. Parameters:

• prediction_df (required): Prediction data frame from apply_dia()

• threshold_choices: Same as models_dia()

models_pro()

Trains base survival models. Parameters:

• data (required): Data frame with sample ID, survival status, time, features

• model (required): Model names or “all_pro” for all models

• tune: Logical (default FALSE). Hyperparameter tuning

• time_unit: String (default “day”). Time unit (“day”, “month”, “year”)

• years_to_evaluate: Numeric vector (default c(1,3,5)). Time points for time-dependent AUROC

• seed: Integer (default 789). Random seed

stacking_pro()

Stacking ensemble for survival analysis. Parameters:

• results_all_models (required): Output from models_pro()

• data (required): Training data

• meta_model_name (required): Meta-model name

• top: Integer (default 3). Number of top base models

• tune_meta: Logical (default FALSE). Tune meta-model

• time_unit: String (default “day”). Time unit

• years_to_evaluate: Numeric vector (default c(1,3,5)). Evaluation time points

• seed: Integer (default 789). Random seed

bagging_pro()

Bootstrap aggregating for survival analysis. Parameters:

• data (required): Training data

• base_model_name (required): Base model name

• n_estimators: Integer (default 10). Number of base models

• subset_fraction: Numeric (default 0.632). Bootstrap sampling fraction

• tune_base_model: Logical (default FALSE). Tune base models

• time_unit: String (default “day”). Time unit

• years_to_evaluate: Numeric vector (default c(1,3,5)). Evaluation time points

• seed: Integer (default 456). Random seed

apply_pro()

Applies trained survival model to new data. Parameters:

• trained_model_object (required): Trained model object

• new_data (required): New data with same structure as training data

• time_unit: String (default “day”). Time unit

evaluate_predictions_pro()

Evaluates survival model predictions. Parameters:

• prediction_df (required): Prediction data frame from apply_pro()

• years_to_evaluate: Numeric vector (default c(1,3,5)). Evaluation time points

figure_dia()

Creates diagnostic model evaluation plots. Parameters:

• type (required): Plot type
  • “roc”: ROC curve
  • “prc”: Precision-recall curve
  • “matrix”: Confusion matrix
• data (required): Model results object

figure_pro()

Creates prognostic model evaluation plots. Parameters:

• type (required): Plot type

  • “km”: Kaplan-Meier survival curves
  • “tdroc”: Time-dependent ROC curves

• data (required): Model results object

• time_unit: String (default “days”). Time unit for axis labels

figure_shap()

Creates SHAP interpretation plots. Parameters:

• data (required): Model results with sample_score data frame

• raw_data (required): Original feature data

• target_type (required): Data type

  • “diagnosis”: Features start from column 3
  • “prognosis”: Features start from column 4

register_model_dia() / register_model_pro()

Registers custom algorithms.

Usage: 1. Define custom function following E2E conventions 2. Register with register_model_dia("model_name", custom_function) 3. Use registered model in E2E workflows