fdr.control {GeneTS} | R Documentation |

## Controlling the False Discovery Rate in Multiple Testing

### Description

`fdr.control`

controls the False Discovery Rate (FDR) at a
given level Q using the algorithms described in Benjamini and Hochberg (1995)
and Storey (2002). The FDR is the expected proportion
of false positives (erroneous rejections) among the significant tests (rejections).
For a given vector of p-values and the desired FDR level Q the corresponding p-value
cut-off and the q-values for each hypothesis (see Storey, 2002) are computed.

### Usage

fdr.control(p, Q=0.05, eta0=1.0, robust=FALSE)

### Arguments

`p` |
vector of p-values |

`Q` |
desired FDR level |

`eta0` |
proportion of null p-values (default: eta0=1). |

`robust` |
use small sample approximation for estimating q-values (default: robust=FALSE) |

### Details

Notes:

- the default settings correspond to the step-up procedure to control the FDR
by Benjamini and Hochberg (1995).
- q-values for each hypothesis are computed as defined in Storey (2002).
- small sample approximation for q-value (robust=TRUE) is from Storey (2002).
- default eta0=0 is safe but also most conservative choice (for other possibilities
see
`fdr.estimate.eta0`

).

### Value

A list object with the following components:

`qvalues` |
a vector with the q-values for each hypothesis. |

`significant` |
a vector with a TRUE/FALSE value for each hypothesis |

`num.significant` |
number of significant hypotheses. |

`pvalue.cutoff` |
cutoff level for the individual p-values to obtain the
desired control of FDR.
Hypotheses whose corresponding p-values are below or equal to this
cuttoff level are rejected (i.e. significant). |

### Author(s)

Konstantinos Fokianos (http://www.ucy.ac.cy/~fokianos/) and
Korbinian Strimmer (http://www.stat.uni-muenchen.de/~strimmer/).

Adapted in part from S-PLUS code by Y. Benjamini (http://www.math.tau.ac.il/~roee/FDR_Splus.txt)
and R code from J.D. Storey (http://faculty.washington.edu/~jstorey/).

### References

Benjamini, Y., and Y. Hochberg (1995) Controlling the false
discovery rate: a practical and powerful approach to multiple testing.
*J. Roy. Statist. Soc. B*, **57**, 289–300.

Storey, J. D. (2002) A direct approach to false
discovery rates.
*J. Roy. Statist. Soc. B.*, **64**, 479–498.

### See Also

`fdr.estimate.eta0`

.

### Examples

# load GeneTS library
library(GeneTS)
# load data set
data(caulobacter)
# how many genes and how many samples?
dim(caulobacter)
# p-values from Fisher's g test
pval.caulobacter <- fisher.g.test(caulobacter)
# FDR test on the level 0.05
fdr.control(pval.caulobacter, Q = 0.05)

[Package

*GeneTS* version 2.3

Index]