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

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

Arguments

p	numeric vector of p-values (possibly with NAs). 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).

Author(s)

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.

See Also

Roth.p.adjust, p.adjust.

Examples

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)

MHTdiscrete documentation built on May 1, 2019, 10:23 p.m.