plotComparativeEmpiricalScore: Plot Comparative Empirical Score for Multiple Methods

In predomics/predomicspkg: Interpretable Prediction in Omics Data

Plot Comparative Empirical Score for Multiple Methods

Description

This function generates a plot comparing the empirical scores (such as AUC or accuracy) across multiple methods for different values of the sparse parameter (k sparse). The plot includes lines representing each method and points indicating the empirical score at each k sparse value. Horizontal lines indicate major thresholds (e.g., AUC = 0.5 or accuracy = majority class).

Usage

plotComparativeEmpiricalScore(
  digested.results,
  ylim = c(0.5, 1),
  score = "auc_",
  main = ""
)

Arguments

digested.results	A list containing the empirical results of the models, including performance scores for various methods.
ylim	A numeric vector of length 2 specifying the limits for the y-axis. Default is 'c(0.5, 1)'.
score	A string specifying which score to visualize, e.g., "auc_" or "accuracy_". Default is '"auc_"'.
main	A string specifying the title of the plot. Default is an empty string.

Details

The function plots empirical scores (such as AUC or accuracy) for different methods across various values of k sparse. It handles multiple methods and adds horizontal lines to indicate important thresholds, such as AUC = 0.5 or the majority class in classification tasks.

The plot is created using ggplot2, and different methods can be assigned different colors and point shapes. If no empirical data is provided, a blank plot with axis labels and horizontal lines for thresholds is displayed.

Value

A ggplot object visualizing the comparative empirical scores across multiple methods.

Author(s)

Edi Prifti (IRD)

Examples

# Assuming digested.results contains the performance scores for methods
plotComparativeEmpiricalScore(digested.results, ylim = c(0.5, 1), score = "auc_", main = "Comparison of AUC across Methods")

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