PFER {sharp}

Per Family Error Rate

Description

Computes the Per Family Error Rate upper-bound of a stability selection model using the methods proposed by Meinshausen and Bühlmann (2010) or Shah and Samworth (2013). In stability selection, the PFER corresponds to the expected number of stably selected features that are not relevant to the outcome (i.e. False Positives).

Usage

PFER(q, pi, N, K, PFER_method = "MB")

Arguments

q

average number of features selected by the underlying algorithm.

pi

threshold in selection proportions.

N

total number of features.

K

number of resampling iterations.

PFER_method

method used to compute the upper-bound of the expected number of False Positives (or Per Family Error Rate, PFER). If PFER_method="MB", the method proposed by Meinshausen and Bühlmann (2010) is used. If PFER_method="SS", the method proposed by Shah and Samworth (2013) under the assumption of unimodality is used.

Value

The estimated upper-bound in PFER.

References

Meinshausen N, Bühlmann P (2010). “Stability selection.” Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4), 417-473. doi:10.1111/j.1467-9868.2010.00740.x.

Shah RD, Samworth RJ (2013). “Variable selection with error control: another look at stability selection.” Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(1), 55-80. doi:10.1111/j.1467-9868.2011.01034.x.

See Also

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

Examples

# Computing PFER for 10/50 selected features and threshold of 0.8
pfer_mb <- PFER(q = 10, pi = 0.8, N = 50, K = 100, PFER_method = "MB")
pfer_ss <- PFER(q = 10, pi = 0.8, N = 50, K = 100, PFER_method = "SS")