FDP {sharp}

False Discovery Proportion

Description

Computes the False Discovery Proportion (upper-bound) as a ratio of the PFER (upper-bound) over the number of stably selected features. In stability selection, the FDP corresponds to the expected proportion of stably selected features that are not relevant to the outcome (i.e. proportion of False Positives among stably selected features).

Usage

FDP(selprop, PFER, pi)

Arguments

selprop

matrix or vector of selection proportions.

PFER

Per Family Error Rate.

pi

threshold in selection proportions.

Value

The estimated upper-bound in FDP.

See Also

Other stability metric functions: ConsensusScore(), PFER(), StabilityMetrics(), StabilityScore()

Examples

# Simulating set of selection proportions
selprop <- round(runif(n = 20), digits = 2)

# Computing the FDP with a threshold of 0.8
fdp <- FDP(PFER = 3, selprop = selprop, pi = 0.8)