# FDR: False Discorvery Rate (FDR) In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

## Description

Compute the False Discovery Rate for a vector of p-values and alpha value.

## Usage

 ```1 2 3``` ```FDR(pvalues = NULL, alpha = 0.95, dep = 1) pvalue.FDR(pvalues = NULL, dep = 1) ```

## Arguments

 `pvalues` Vector of p-values `alpha` Alpha value (level of significance). `dep` Parameter dependence test. By default `dep = 1`, direct dependence between tests.

## Details

`FDR` method is used for multiple hypothesis testing to correct problems of multiple contrasts.
If `dep = 1`, the tests are positively correlated, for example when many tests are the same contrast.
If `dep < 1` the tests are negatively correlated.

## Value

Return:

• `out.FDR` `=TRUE`. If there are significative differences.

• `pv.FDR` p-value for False Discovery Rate test.

## Author(s)

Febrero-Bande, M. and Oviedo de la Fuente, M.

## References

Benjamini, Y., Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics. 29 (4): 1165-1188. DOI:10.1214/aos/1013699998.

Function used in `fanova.RPm`

## Examples

 ```1 2 3 4 5 6``` ``` p=seq(1:50)/1000 FDR(p) pvalue.FDR(p) FDR(p,alpha=0.9999) FDR(p,alpha=0.9) FDR(p,alpha=0.9,dep=-1) ```

### Example output

```Loading required package: fda

Attaching package: 'fda'

The following object is masked from 'package:graphics':

matplot