FDR: False Discorvery Rate (FDR)

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/FDR.R

Description

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

Usage

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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:

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.

See Also

Function used in fanova.RPm

Examples

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 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
Loading required package: splines
Loading required package: Matrix

Attaching package: 'fda'

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

    matplot

Loading required package: MASS
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-28. For overview type 'help("mgcv-package")'.
Loading required package: rpart
[1] TRUE
[1] 0.05
[1] FALSE
[1] TRUE
[1] FALSE

fda.usc documentation built on Feb. 18, 2020, 1:07 a.m.