numFP: Number of False Positives for a given proportion of True...

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

Description

Often when evaluating a differential expression method, we are interested in how well a classifier performs for very small numbers of false positives. This method gives one way of calculating this, by determining the number of false positives for a set proportion of true positives.

Usage

numFP(scores, truthValues, TPRate = 0.5)

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.
TPRate	A number between 0 and 1 identify the proportion of true positives for which we wish to determine the number of false positives.

Value

An integer giving the number of false positives.

Author(s)

Richard D. Pearson

See Also

Related methods plotROC and 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)
classElts <- c(rep(FALSE,1000), rep(TRUE,1000))
print(numFP(scores_a, classElts))
print(numFP(scores_b, classElts))
}

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