Roth.p.adjust: The adjusted p-values for Roth's step-up FWER controlling...
In allenzhuaz/MHTdiscrete: Multiple Hypotheses Testing for Discrete Data

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

The function for calculating the adjusted p-values based on original available p-values and all attaianble p-values.

1	Roth.p.adjust(p, p.set, digits, alpha, make.decision)

`p`	numeric vector of p-values (possibly with `NA`s). Any other R is coerced by `as.numeric`. Same as in `p.adjust`.
`p.set`	a list of numeric vectors, where each vector is the vector of all attainable p-values containing the available p-value for the corresponding hypothesis..
`digits`	minimal number of significant digits for the adjusted p-values, the default value is 4, see `print.default`.
`alpha`	significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.
`make.decision`	logical; if `TRUE`, then the output include the decision rules compared adjusted p-values with significant level α

A numeric vector of the adjusted p-values (of the same length as p).

Yalin Zhu

Roth, A. J. (1999). Multiple comparison procedures for discrete test statistics. Journal of statistical planning and inference, 82: 101-117.

MHoch.p.adjust, p.adjust.

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p <- c(pbinom(1,8,0.5),pbinom(1,5,0.75),pbinom(1,6,0.6))
p.set <-list(pbinom(0:8,8,0.5),pbinom(0:5,5,0.75),pbinom(0:6,6,0.6))
Roth.p.adjust(p,p.set,digits=5)