Alpha Correction R Package

library(alpha-correction-bh)

Introduction

This package provides functions for calculating alpha corrections for a list of p-values according to the Benjamini-Hochberg alpha correction.

Reference: Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: series B (Methodological), 57(1), 289-300.

For a sorted list containing m p-values indexed from 1 to m, the alpha for each p-value p is computed as:

                      alpha(i) = (p_value(i)/m)Q

where:

• i is the index of the p-value in list l (1 to m),
• p_value(i) is the p_value at index i, and
• Q is the false discovery rate, which is 0.05 by default.

Installation

Install the package using dev-tools directly from github or from cran.

devtools::install_github('pcla-code/alpha.correction.bh')

Dependencies

This library uses knitr to render tables.

Usage

Import the package:

library(alpha-correction-bh)

library(knitr)

And call the get_alphas_bh function, passing your p_values and, optionally, Q:

get_alphas_bh(p_values, Q)

Use this function to calculate corrected values for a list of p-values and a given false discovery rate Q.

If you do not provide Q, a default value of 0.05 will be used.

Output Options

You can customize the output of the function using the following two options:

1. output - valid values are:

   • print - print the data frame to the console only

   • data_frame - return the data frame only

   • both - print the data frame to the console and return it. This is the default behavior.

2. include_is_significant_column - valid values are:
   • TRUE - The is significant? column is included. This is the default behavior.
   • FALSE - The is significant? column is not included.

Example 1:

get_alphas_bh(list(0.08,0.01,0.039))

Output:

p-value	alpha	is significant?
0.08	0.05	NO
0.01	0.017	YES
0.039	0.033	NO

Example 2:

get_alphas_bh(list(0.08,0.01,0.039), .07)

Output:

p-value	alpha	is significant?
0.08	0.07	NO
0.01	0.023	YES
0.039	0.047	YES

Documentation

To read the documentation of the function, execute the following in R:

?get_alphas_bh

You can also read the vignette here.