fuzzy.FDR.approx: Generate Approximate Fuzzy Rejection Probabilites

In BaySIC: Bayesian Analysis of Significantly Mutated Genes in Cancer

Description

For hypothesis tests with discrete reference distributions, obtains fuzzy rejection probabilites for a given level of false discovery rate control

Usage

fuzzy.FDR.approx(pprev, p, alpha, N)

Arguments

pprev	numeric vector of p-values of length l, corresponding to strict inequality of test statistic values in a one-sided test (i.e., P(T>t))
p	length l numeric vector of p-values corresponding to traditional one-sided test (i.e., P(T≥q t)).
alpha	FDR level of interest (under Benjamini-Hochberg FDR procedure)
N	Number of Monte Carlo samples used to generate fuzzy rejection probability approximations.

Details

This is a Monte Carlo implementation of the fuzzy FDR work developed by Kulinskaya et al. (2007)

Value

Returns a vector of length l corresponding to the fuzzy rejection probabilities of the hypotheses represented in pprev and p, under FDR level alpha

Author(s)

Nicholas B. Larson

References

http://www.bgx.org.uk/alex/Kulinskaya-Lewin-resubmit.pdf

BaySIC documentation built on May 2, 2019, 10:29 a.m.