# Roth.p.adjust: The adjusted p-values for Roth's step-up FWER controlling... 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` ```Roth.p.adjust(p, p.set, digits, 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.. `digits` minimal number of significant digits for the adjusted p-values, the default value is 4, see `print.default`. `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

Roth, A. J. (1999). Multiple comparison procedures for discrete test statistics. Journal of statistical planning and inference, 82: 101-117.

`MHoch.p.adjust`, `p.adjust`.

## Examples

 ```1 2 3``` ```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)) Roth.p.adjust(p,p.set,digits=5) ```

### Example output

```[1] 0.04096 0.04096 0.04096
```

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