Procedures controlling the k-FWER (Generalized Familywise Error Rate) {someKfwer}R Documentation

Controlling the Generalized Familywise Error Rate

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

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, J = qnbinom(alpha, k, alpha), disp = TRUE)

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

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 (2009) k-FWER control without multiplicity correction, with application to detection of genetic determinants of multiple sclerosis in Italian twins. University of Padua, Dept Statistical Science. Working Paper #7

Examples

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
klr=kfweLR(p,k=5)           #Lehaman and Romano
kgr=kfweGR(p,k=5)           #Guo and Romano

[Package someKfwer version 1.0 Index]