fuzzy.FDR.approx {BaySIC} | R Documentation |
For hypothesis tests with discrete reference distributions, obtains fuzzy rejection probabilites for a given level of false discovery rate control
fuzzy.FDR.approx(pprev, p, alpha, N)
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. |
This is a Monte Carlo implementation of the fuzzy FDR work developed by Kulinskaya et al. (2007)
Returns a vector of length l corresponding to the fuzzy rejection probabilities of the hypotheses represented in pprev
and p
, under FDR level alpha
Nicholas B. Larson
http://www.bgx.org.uk/alex/Kulinskaya-Lewin-resubmit.pdf