FSFDR.arbidept.p.adjust: Adjusted P-values for Fixed Sequence FDR Controlling...
In FixSeqMTP: Fixed Sequence Multiple Testing Procedures

Description Usage Arguments Details Value Author(s) References See Also Examples

Given a set of pre-ordered p-values and accuracy for the result, returns adjusted p-values using the generalized fixed sequence multiple testing procedures under arbitrary dependence (See Theorem 3.1 and 4.1 in Lynch et al. (2016)). The function also provides an option to make decisions given a pre-specified significant level α.

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

`p`	numeric vector of p-values (possibly with `NA`s). Any other R is coerced by `as.numeric`. Same as in `p.adjust`.
`alpha`	significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.
`k`	pre-specified number of acceptances allowed in the testing procedure (cannot exceed the length of `p`)
`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

The generalized fixed sequence FDR controlling procedure stops on the k-th acceptances and automatically accepts the rest of hypotheses, where k is a pre-specified positive integer. When k=1, the generalized procedure becomes conventional one (Theorem 3.1 in Lynch et al. (2016)), which stops testing once one acceptance appears. This method strongly controls FDR under arbitrary dependence.

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

Yalin Zhu

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.

FSFWER.arbidept.p.adjust for fixed sequence FWER controlling procedures.

## 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.arbidept.p.adjust(p = Pval, alpha = 0.05)
## generalized fixed sequence procedure allowing stop at 5th acceptance
FSFDR.arbidept.p.adjust(p = Pval, alpha = 0.05, k=5)