MixBonf.p.adjust {MHTdiscrete}

The adjusted p-values for Mixed Bonferroni single-step FWER controlling procedure.

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

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

Arguments

pc

numeric vector of the available p-values (possibly with NAs) 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 NAs) 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 \alpha

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

Author(s)

Yalin Zhu

References

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

See Also

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

Examples

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")