Documentation for get_auc_curve_with_rf_model

Function: get_auc_curve_with_rf_model

Purpose

The function get_auc_curve_with_rf_model is designed to train a Random Forest model using a provided dataset, optionally from an SQLite database. It computes and visualizes the ROC curve along with the AUC (Area Under the Curve) metric. The function offers various options for handling data preprocessing, including hyperparameter tuning, imputation, and undersampling, and outputs the model performance via the ROC curve.

Input Parameters

The function accepts the following parameters:

Parameter	Type	Description
Data	data.frame	Input data frame for training. If NULL, the function fetches data from the database.
path_db	string	Path to the SQLite database. Used to fetch study data if Data is NULL.
rat_studies	logical	Whether to filter for rat studies (default is FALSE).
studyid_metadata	data.frame	Metadata associated with study IDs.
fake_study	logical	Whether to use fake study IDs for data simulation (default is FALSE).
use_xpt_file	logical	Whether to use an XPT file for input data (default is FALSE).
Round	logical	Whether to round numerical values (default is FALSE).
Impute	logical	Whether to perform imputation on missing values (default is FALSE).
best.m	numeric	The ‘mtry’ hyperparameter for Random Forest (optional).
reps	numeric	Number of repetitions for cross-validation (numeric value).
holdback	numeric	Fraction value (e.g., 0.75) for holdback during cross-validation.
Undersample	logical	Whether to perform undersampling (default is FALSE).
hyperparameter_tuning	logical	Whether to perform hyperparameter tuning (default is FALSE).
error_correction_method	string	Method for error correction: “Flip”, “Prune”, or “None”.
output_individual_scores	logical	Whether to output individual scores (default is TRUE).
output_zscore_by_USUBJID	logical	Whether to output z-scores by subject ID (default is FALSE).

Output

The function does not return any explicit values. However, it generates the following outputs:

1. AUC Value: The AUC of the ROC curve is printed to the console.
2. ROC Curve Plot: A ROC curve is displayed, showing the model’s performance with the computed AUC value.
3. Performance Metrics: Other performance metrics (e.g., True Positive Rate, False Positive Rate) are computed but not returned directly.

Key Steps

1. Data Generation or Fetching:
   • If Data is not provided, the function fetches the data either from the SQLite database or generates synthetic data (if fake_study is TRUE).
   • If use_xpt_file is TRUE, it fetches data from the specified XPT files.
2. Data Preprocessing:
   • The function performs data preprocessing, including imputation (if Impute is TRUE), rounding (if Round is TRUE), and undersampling (if Undersample is TRUE).
   • It harmonizes the liver scores and prepares the data for machine learning.
3. Model Training:
   • The function then prepares the data for Random Forest (RF) modeling, tuning hyperparameters if hyperparameter_tuning is enabled.
   • A Random Forest model is trained using the prepared data, and predictions are generated.
4. AUC Calculation and Plotting:
   • The model’s performance is evaluated by computing the AUC (Area Under the Curve) and plotting the ROC curve.
   • The AUC is printed to the console, and the ROC curve is displayed with the calculated AUC value.
5. Error Correction and Hyperparameter Tuning:
   • If specified, the function applies an error correction method (error_correction_method) and performs hyperparameter tuning to optimize the model.