FixSeqMTP (version 0.1.2)

FSFDR.indept.cv: Critical Values for Fixed Sequence FDR Controlling Procedure under Independence

Description

Given a set of pre-ordered p-values and accuracy for the result, return the corresponding critical values using the generalized fixed sequence FDR controlling procedure under independence for true nulls (See Theorem 3.2 and 4.2 in Lynch et al. (2016)). The function also provides an option to make decisions given a pre-specified significant level $\alpha$.

Usage

FSFDR.indept.cv(p, k=1, alpha = 0.05, tol = 1e-6, make.decision = TRUE)

Arguments

p

numeric vector of p-values (possibly with NAs). Any other R is coerced by as.numeric. Same as in p.adjust.

k

pre-specified number of acceptances allowed in the testing procedure (cannot exceed the length of p)

alpha

significant level used to calculate the critical values to make decisions, the default value is 0.05.

tol

desired accuracy. The default value is 1e-6 .

make.decision

logical; if TRUE (default), then the output include the decision rules compared adjusted p-values with significant level $alpha$

Value

A numeric vector of the critical values (of the same length as p) if make.decision = FALSE, or a data frame including original p-values, critical values and decision rules if make.decision = TRUE.

References

Lynch, G., Guo, W., Sarkar, S. K., & Finner, H. (2016). The Control of the False Discovery Rate in Fixed Sequence Multiple Testing. arXiv preprint arXiv:1611.03146.

See Also

FSFWER.arbidept.cv for fixed sequence FWER controlling procedures.

Examples

## generate a pre-ordered pvalue vector for 50 hypotheses, where 80% are true nulls
set.seed(1234); m <- 50; pi0 <- 0.8; m0 <- m*pi0; m1 <- m-m0
mu <- c(4*0.9^(1:m1), rep(0,m0))
Zstat <- rnorm(n = m, mean = mu)
Pval <- 1-pnorm(Zstat)
## conventional fixed sequence procedure
FSFDR.indept.cv(p = Pval, alpha = 0.05)
## generalized fixed sequence procedure allowing stop at 5th acceptance
FSFDR.indept.cv(p = Pval, alpha = 0.05, k=5)