plotPerf: Plots various measures of predictor performance for binary...
In BaderLab/netDx: Network-based patient classifier

Description Usage Arguments Details Value Examples

Plots various measures of predictor performance for binary classifiers

1	plotPerf(resList = NULL, inFiles, predClasses, plotSEM = FALSE)

`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.
`plotSEM`	(logical) metric for error bars. If set to TRUE, plots SEM; else plots SD.

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)

(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 or (mean+/-SD) AUROC 2) mean+/-SEM or (mean+/-SD) AUPR 3) ROC curve for all runs plus average 4) PR curve for all runs plus average

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'))