https://github.com/uscbiostats/fdrci

Permutation-Based FDR Point and Confidence Interval Estimation
https://github.com/uscbiostats/fdrci

permutation-test r-package rstats

Last synced: about 2 months ago
JSON representation

Permutation-Based FDR Point and Confidence Interval Estimation

• Host: GitHub
• URL: https://github.com/uscbiostats/fdrci
• Owner: USCbiostats
• Created: 2018-02-23T17:40:15.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-10-17T23:50:12.000Z (about 2 years ago)
• Last Synced: 2023-03-01T04:36:16.252Z (almost 2 years ago)
• Topics: permutation-test, r-package, rstats
• Language: R
• Homepage: https://uscbiostats.github.io/fdrci
• Size: 386 KB
• Stars: 3
• Watchers: 4
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

README

        
---

output: github_document

---

[![Travis build status](https://travis-ci.org/USCbiostats/fdrci.svg?branch=master)](https://travis-ci.org/USCbiostats/fdrci)

[![Build status](https://ci.appveyor.com/api/projects/status/jykjw0b9f5rc9g7w?svg=true)](https://ci.appveyor.com/project/gvegayon/fdrci)

```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# fdrci

> FDR functions for permutation-based estimators, including pi0 as well as FDR

             confidence intervals. The confidence intervals account for dependencies between

             tests by the incorporation of an overdispersion parameter, which is estimated

             from the permuted data. --- From the package description

## Installation

You can install fdrci from GitHub with:

```{r gh-installation, eval = FALSE}

# install.packages("devtools")

devtools::install_github("USCbiostats/fdrci")

```

## Example

This is a basic example which shows you how to solve a common problem:

```{r example}

library(fdrci)

ss = 100

nvar = 100

X = as.data.frame(matrix(rnorm(ss*nvar),nrow=ss,ncol=nvar))

Y = as.data.frame(matrix(rnorm(ss*nvar),nrow=ss,ncol=nvar))

nperm = 10

myanalysis = function(X,Y){

	ntests = ncol(X)

	rslts = as.data.frame(matrix(NA,nrow=ntests,ncol=2))

	names(rslts) = c("ID","pvalue")

	rslts[,"ID"] = 1:ntests

	for(i in 1:ntests){

		fit = cor.test(X[,i],Y[,i],na.action="na.exclude",

			alternative="two.sided",method="pearson")

		rslts[i,"pvalue"] = fit$p.value

	}

	return(rslts)

} # End myanalysis

# Generate observed results

obs = myanalysis(X,Y)

# Generate permuted results

perml = vector('list',nperm)

for(p_ in 1:nperm){

	X1 = X[order(runif(nvar)),]

	perml[[p_]] = myanalysis(X1,Y)

}

# FDR results table

myfdrtbl = fdrTbl(obs$pvalue,perml,"pvalue",nvar,0.5,3)

myfdrtbl1 = fdrTbl(obs$pvalue,perml,"pvalue",n.col,0.5,3,correct="BH")

fdrTbl(obs$pvalue, NULL, "pvalue",n.col,0.5,3,meff = TRUE, seff = TRUE, mymat = X, nperms = 5)

# Plot results

FDRplot(myfdrtbl,0,3,annot="A. An Example")

```