Procedures controlling the k-FWER (Generalized Familywise Error Rate) {someKfwer} | R Documentation |
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).
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)
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 |
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.
L. Finos and A. Farcomeni
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
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