Controlling the Generalized Familywise Error Rate

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Description

This library collects some procedures controlling the Generalized Familywise Error Rate: Lehmannn and Romano (2005), Guo and Romano (2007) (single step and stepdown), Finos and Farcomeni (2009).

Usage

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kfweLR(p, k = 1, alpha = 0.01, disp = TRUE)
kfweGR(p, k = 1, alpha = 0.01, disp = TRUE,
SD=TRUE, const = 10, alpha.prime = getAlpha(k = k, s = length(p), 
                    alpha = alpha, const = const))
kfweOrd(p, k = 1, alpha = 0.01, ord = NULL, 
    alpha.prime = alpha, J = qnbinom(alpha,k,alpha.prime), 
    disp = TRUE, GD=FALSE)
                                                  
getAlpha (s, k = 1, alpha = 0.01, const = 10)

Arguments

p

vector of p-values of length s

s

number of p-values (i.e. hypotheses)

k

number of allowed errors in kFWE controls

alpha

global significance level

ord

the vector of values based on which the p-values have to be ordered

const

Bigger is better (more precise but slower)

J

number of allowed jumps befor stopping

disp

diplay output? TRUE/FALSE

SD

Step-down version of the procedure? (TRUE/FALSE) the step-down version is uniformly more powerful than the single step one.

alpha.prime

univariate alpha for single step Guo and Romano procedure

GD

Logic value. Should the correction for general dependence be applied? (See reference below for further details)

Value

kfweOrd, kfweLR, kfweGR, kfweGR.SD return a vector of kFWE-adjusted p-values. It respect the order of input vector of p-values p.

getAlpha returns the alpha for Guo and Romano procedure.

Author(s)

L. Finos and A. Farcomeni

References

For Lehmann and Romano procedure see:

Lehmann and Romano (2005) Generalizations of the Familywise Error Rate, Annals of Statistics, 33, 1138-1154.

For Guo and Romano procedure see:

Guo and Romano (2007) A Generalized Sidak-Holm procedure and control of genralized error rates under independence, Statistical Applications in Genetics and Molecular Biology, 6, 3.

For Ordinal procedure see:

Finos and Farcomeni (2010) k-FWER control without multiplicity correction, with application to detection of genetic determinants of multiple sclerosis in Italian twins. Biometrics (Articles online in advance of print: DOI 10.1111/j.1541-0420.2010.01443.x)

Examples

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set.seed(13)
y=matrix(rnorm(3000),3,1000)+2                      #create toy data
p=apply(y,2,function(y) t.test(y)$p.value)          #compute p-values
M2=apply(y^2,2,mean)                                #compute ordering criterion

kord=kfweOrd(p,k=5,ord=M2)                          #ordinal procedure
kgr=kfweGR(p,k=5)                                   #Guo and Romano

kord=kfweOrd(p,k=5,ord=M2,GD=TRUE)                  #ordinal procedure (any dependence)
klr=kfweLR(p,k=5)                                   #Lehaman and Romano (any dependence)

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