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

 FDR R Documentation

## False Discorvery Rate (FDR)

### Description

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

### Usage

```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

``` 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)
```

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.