MHoch.p.adjust: The adjusted p-values for Modified Hochberg step-up 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 all attaianble p-values.

Usage

 `1` ```MHoch.p.adjust(p, p.set, alpha, make.decision) ```

Arguments

 `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.. `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`).

Yalin Zhu

References

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

Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75: 800-803.

`Roth.p.adjust`, `p.adjust`.
 ```1 2 3 4 5 6 7``` ```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)) MHoch.p.adjust(p,p.set) ## Compare with the traditional Hochberg adjustment p.adjust(p,method = "hochberg") ## Compare with the Roth adjustment Roth.p.adjust(p,p.set) ```