Skip to content
This repository has been archived by the owner on Oct 3, 2021. It is now read-only.

dirkschumacher/PValueAdjust.jl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Build Status Coverage Status PValueAdjust

PValueAdjust.jl

This package has been deprecated in favor of MultipleTesting.jl. Please use that package instead.

Some methods to adjust p-values for multiple comparisons in Julia. The various methods can be called using the function padjust. padjust takes an array of p-values and a second method parameter and returns an array of adjusted p-values. Please refer to the documentation of the corresponding function in R or to Wikipedia (FWER, FDR), if you want to know more on this topic.

Current stable version is 2.0.0.

In case you find any bugs please post an issue here or send a pull request. Make sure you write a test for your contribution.

Install

This package has been deprecated in favor of MultipleTesting.jl. Please use that package instead.

Methods

Control the familywise error rate (FWER)

Bonferroni

julia > pValues = [0.05, 0.03, 0.01, 0.5]
julia > padjust(pValues, Bonferroni)
4-element Array{Float64,1}:
 0.2 
 0.12
 0.04
 1.0

Hochberg

julia > pValues = [0.05, 0.03, 0.01, 0.5]
julia > padjust(pValues, Hochberg)
4-element Array{Float64,1}:
 0.1 
 0.09
 0.04
 0.5

Holm

Also known as the Holm–Bonferroni method.

julia > pValues = [0.05, 0.03, 0.01, 0.5]
julia > padjust(pValues, Holm)
4-element Array{Float64,1}:
 0.1 
 0.09
 0.04
 0.5

Control the false discovery rate (FDR)

Benjamini-Hochberg

julia > pValues = [0.05, 0.03, 0.01, 0.5]
julia > padjust(pValues, BenjaminiHochberg)
4-element Array{Float64,1}:
 0.0666667
 0.06     
 0.04     
 0.5

Benjamini-Hochberg-Yekutieli

julia > pValues = [0.05, 0.03, 0.01, 0.5]
julia > padjust(pValues, BenjaminiYekutieli)
4-element Array{Float64,1}:
 0.138889 
 0.125    
 0.0833333
 1.0

Versioning

This package uses Semantic Versioning 2.0.

References

Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57, 289–300.

Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165–1188.

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

Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70.

About

[deprecated] P-value adjustment methods for multiple testing correction

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages