FDP | R Documentation |
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).
FDP(selprop, PFER, pi)
selprop |
matrix or vector of selection proportions. |
PFER |
Per Family Error Rate. |
pi |
threshold in selection proportions. |
The estimated upper-bound in FDP.
Other stability metric functions:
ConsensusScore()
,
PFER()
,
StabilityMetrics()
,
StabilityScore()
# 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.