plotROC: Receiver Operator Characteristic (ROC) plot

In puma: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0)

Description

Plots a Receiver Operator Characteristic (ROC) curve.

Usage

plotROC(
	scoresList
,	truthValues
,	includedProbesets=1:length(truthValues)
,	legendTitles=1:length(scoresList)
,	main = "PUMA ROC plot"
,	lty = 1:length(scoresList)
,	col = rep(1,length(scoresList))
,	lwd = rep(1,length(scoresList))
,	yaxisStat = "tpr"
,	xaxisStat = "fpr"
,	downsampling = 100
,	showLegend = TRUE
,	showAUC = TRUE
,	...
)

Arguments

scoresList	A list, each element of which is a numeric vector of scores.
truthValues	A boolean vector indicating which scores are True Positives.
includedProbesets	A vector of indices indicating which scores (and truthValues) are to be used in the calculation. The default is to use all, but a subset can be used if, for example, you only want a subset of the probesets which are not True Positives to be treated as False Positives.
legendTitles	Vector of names to appear in legend.
main	Main plot title
lty	Line types.
col	Colours.
lwd	Line widths.
yaxisStat	Character string identifying what is to be plotted on the y-axis. The default is "tpr" for True Positive Rate. See performance function from ROCR package.
xaxisStat	Character string identifying what is to be plotted on the x-axis. The default is "fpr" for False Positive Rate. See performance function from ROCR package.
downsampling	See details for plot.performance from the ROCR package.
showLegend	Boolean. Should legend be displayed?
showAUC	Boolean. Should AUC values be included in legend?
...	Other parameters to be passed to plot.

Value

This function has no return value. The output is the plot created.

Author(s)

Richard D. Pearson

See Also

Related method calcAUC

Examples

if(FALSE){
	class1a <- rnorm(1000,0.2,0.1)
	class2a <- rnorm(1000,0.6,0.2)
	class1b <- rnorm(1000,0.3,0.1)
	class2b <- rnorm(1000,0.5,0.2)
	scores_a <- c(class1a, class2a)
	scores_b <- c(class1b, class2b)
	scores <- list(scores_a, scores_b)
	classElts <- c(rep(FALSE,1000), rep(TRUE,1000))
	plotROC(scores, classElts)
}

puma documentation built on Nov. 8, 2020, 11:08 p.m.