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

Description

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

Usage

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

Arguments

 `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

Value

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`.

Note

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

References

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`.
 ```1 2 3 4 5``` ```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") ```