README.Rmd

---
output: github_document
---

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<!-- README.md is generated from README.Rmd. Please edit that file -->

```{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")
```