plotPerf {netDx} | R Documentation

## Plots various measures of predictor performance for binary classifiers

### Description

Plots various measures of predictor performance for binary classifiers

### Usage

    plotPerf(resList = NULL, inFiles, predClasses)

### Arguments

`resList` | (list) list of prediction results. If provided, the method will ignore inDir
`inFiles` | (char) path to predictionResults.txt files. A vector, each with absolute paths to predictionResults.txt
`predClasses` | (char) vector of class names.

### Details

Plots individual and average ROC/PR curves. mean+/-SEM performance for a predictor run using nested cross-validation or a similar repeated design. predictionResults.txt contains a (data.frame)

### Value

(list) each key corresponds to an input file in inDir. Value is a list with: 1) stats: 'stats' component of perfCalc 2) rocCurve: ROCR performance object for ROC curve 3) prCurve: ROCR performance object for PR curve 4) auroc: Area under ROC curve 5) aupr: Area under PR curve 6) accuracy: Accuracy

Side effect of plotting in a 2x2 format: 1) mean+/-SEM AUROC 2) mean+/-SEM AUPR 3) ROC curve for all runs plus average 4) PR curve for all runs plus average

### Examples

    inDir <- system.file("extdata","example_output",package='netDx')
    inFiles <- paste(rep(inDir,3), sprintf("rng%i",seq_len(3)),"predictionResults.txt",
     sep=getFileSep())
    resList <- list()
    for (k in seq_len(length(inFiles))) {
    	resList[[k]] <- read.delim(inFiles[k],sep="\t",header=TRUE,as.is=TRUE)
    }
    plotPerf(resList, predClasses = c('LumA','notLumA'))

[Package netDx version 1.0.3 Index]