MixBonf.p.adjust: The adjusted p-values for Mixed Bonferroni single-step FWER...
In MHTdiscrete: Multiple Hypotheses Testing for Discrete Data

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

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

1	MixBonf.p.adjust(pc, pd, pd.set, alpha, make.decision)

`pc`	numeric vector of the available p-values (possibly with `NA`s) for the continuous test statistics. Any other R is coerced by `as.numeric`. Same as in `p.adjust`.
`pd`	numeric vector of the available p-values (possibly with `NA`s) for the discrete test statistics. Any other R is coerced by `as.numeric`. Same as in `p.adjust`.
`pd.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 for discrete data.
`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) if make.decision = FALSE, or a list including original p-values, adjusted p-values and decision rules if make.decision = TRUE.

The arguments include three parts, the available p-values need to be reorganized in advance. Gather all available p-values for continuous data as pc, and all available p-values for discrete data as pd. The attainable p-value refers to the element of domain set of p-value for the corresponding hypothesis for discrete test statistics, the p-value can only take finite values bewtween 0 and 1, that is, the attainable p-values for discrete case are finite and countable, so we can assign them in a finite list pd.set. The function returns the adjusted p-values with the first part for continuous data of the same length as pc, and second part for discrete data of the same length as pd

Yalin Zhu

Zhu, Y., & Guo, W. (2017). Familywise error rate controlling procedures for discrete data arXiv preprint arXiv:1711.08147.

Tarone.p.adjust, MBonf.p.adjust, p.adjust.

pd <- c(pbinom(1,8,0.5),pbinom(1,5,0.75)); pc <- c(0.04, 0.1)
pd.set <-list(pbinom(0:8,8,0.5),pbinom(0:5,5,0.75))
MixBonf.p.adjust(pc,pd,pd.set)
## Compare with the traditional Bonferroni adjustment
p.adjust(c(pc,pd),method = "bonferroni")