Blanchard-Roquain (2009) 2-stage adaptive step-up...

Description

Blanchard-Roquain (2009) 2-stage adaptive step-up

Usage

1
twostageBR(pValues, alpha, lambda=1, silent=FALSE)

Arguments

pValues

the used p-values (assumed to be independent)

alpha

the level at which the FDR should be controlled.

lambda

parameter of the procedure, should belong to (0, 1/alpha) (lambda=1 default)

silent

if true any output on the console will be suppressed.

Details

This is an adaptive linear step-up procedure where the proportion of true nulls is estimated using the Blanchard-Roquain 1-stage procedure with parameter lambda, via the formula

estimated pi_0 = ( m - R(alpha,lambda) + 1) / ( m*( 1 - lambda * alpha ) )

where R(alpha,lambda) is the number of hypotheses rejected by the BR 1-stage procedure, alpha is the level at which FDR should be controlled and lambda an arbitrary parameter belonging to (0, 1/alpha) with default value 1. This procedure controls FDR at the desired level when the p-values are independent.

Value

A list containing:

rejected

A logical vector indicating which hypotheses are rejected

errorControl

A Mutoss S4 class of type errorControl, containing the type of error controlled by the function and the level alpha.

Author(s)

GillesBlanchard

References

Blanchard, G. and Roquain, E. (2009) Adaptive False Discovery Rate Control under Independence and Dependence Journal of Machine Learning Research 10:2837-2871.

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