computePseudoROCCurves: Compute Pseudo-ROC Curves

In funkycells: Functional Data Analysis for Multiplexed Cell Images

Compute Pseudo-ROC Curves

Description

An receiver operating characteristic (ROC) curve is a curve showing the performance of a classification model at all classification thresholds. True ROC can only be computed for two-options, but we can consider each classification, i.e. prediction, correct or incorrect and overlay the curves. Note this means the lines may cover each other and be difficult to see.

Usage

computePseudoROCCurves(trueOutcomes, modelPercents)

Arguments

trueOutcomes	Vector of the true results
modelPercents	Data.frame with columns named after the true outcomes, giving the percent of selecting that outcome. This is what is returned predict.RandomForest_PC with type='all' in object PredPerc[-1] (first column is the predictions).

Details

This function requires the package 'pROC' to be installed.

Value

ggplot object containing the ROC curves.

Examples

percents <- data.frame(c(0.980, 0.675, 0.878, 0.303, 0.457, 0.758,
                         0.272, 0.524, 0.604, 0.342, 0.214, 0.569,
                         0.279, 0.128, 0.462, 0.098, 0.001, 0.187),
                       c(0.005, 0.160, 0.100, 0.244, 0.174, 0.143,
                         0.652, 0.292, 0.040, 0.312, 0.452, 0.168,
                         0.173, 0.221, 0.281, 0.029, 0.005, 0.057),
                       c(0.015, 0.165, 0.022, 0.453, 0.369, 0.099,
                         0.076, 0.084, 0.156, 0.346, 0.334, 0.263,
                         0.548, 0.651, 0.257, 0.873, 0.994, 0.756))
colnames(percents) <- c('0','1','2')
proc <- computePseudoROCCurves(c(0, 0, 0, 0, 0, 0,
                                 1, 1, 1, 1, 1, 1,
                                 2, 2, 2, 2, 2, 2),
                               percents)

funkycells documentation built on Aug. 9, 2023, 5:10 p.m.