calcAUC: Calculate Area Under Curve (AUC) for a standard ROC plot.

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

Description

Calculates the AUC values for one or more ROC plots.

Usage

calcAUC(scores, truthValues, includedProbesets = 1:length(truthValues))

Arguments

scores	A vector of scores. This could be, e.g. one of the columns of the statistics of a DEResult object.
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.

Value

A single number which is the AUC value.

Author(s)

Richard D. Pearson

See Also

Related methods plotROC and numFP.

Examples

#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)
#classElts <- c(rep(FALSE,1000), rep(TRUE,1000))
#print(calcAUC(scores_a, classElts))
#print(calcAUC(scores_b, classElts))

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