plotAUCg: Plot AUC with ROC Curve and Confidence Intervals

In predomics/predomicspkg: Interpretable Prediction in Omics Data

Plot AUC with ROC Curve and Confidence Intervals

Description

This function generates a ROC (Receiver Operating Characteristic) curve for a given model or score, along with the corresponding AUC (Area Under the Curve) value and its confidence intervals. Optionally, it can also display the intercept point on the curve.

Usage

plotAUCg(mod = NULL, score, y, main = "", ci = TRUE, show.intercept = TRUE)

Arguments

mod	An optional model object. If provided, the function will use 'mod$score_' as the predicted scores. If not provided, the 'score' argument must be supplied.
score	A numeric vector containing the predicted scores (either provided directly or obtained from 'mod').
y	A numeric or factor vector containing the true class labels. The labels should be binary (e.g., 1 and -1).
main	A string representing the title of the plot. Default is an empty string.
ci	A logical value indicating whether to compute and display the confidence intervals for the AUC. Default is 'TRUE'.
show.intercept	A logical value indicating whether to display the intercept point on the ROC curve. Default is 'TRUE'.

Details

The function computes the ROC curve and the AUC using the 'pROC' package. If the 'mod' object is provided, the function will use 'mod$score_' as the predicted score. The plot includes the ROC curve, AUC, confidence intervals, and optionally the intercept point. The intercept is represented as a red '+' symbol on the plot.

Value

A 'ggplot' object representing the ROC curve with AUC and its confidence intervals.

Author(s)

Edi Prifti (IRD)

Examples

## Not run: 
# Assuming `mod` is a trained model and `y` is the true labels
plotAUCg(mod, y, main = "ROC Curve with AUC", ci = TRUE)

## End(Not run)

predomics/predomicspkg documentation built on Dec. 11, 2024, 11:06 a.m.