Let us consider variables $X_1,\dots,X_k$. They are highly dependent. We want to perform some test for any pair $(X_i,Y)$ and imply proper multiple-comparisons correction.
As i understand, Westfall & Young or minP/maxT corrections should be efficient in this task. But i can't find any general implementation in R. And code it from scratch is hard and dangerous, as for me.
Is there any implementation of this methods or any alternatives?
Edit 1: Practical example.
X1=c(0,1,1,0,1,1,0,1) X2=c(0,1,1,0,1,1,0,0) X3=c(0,1,1,0,1,1,1,1)` set.seed(16) Y=rnorm(8,mean = 3)*X1+rnorm(8) p_1=wilcox.test(Y[X1==0],Y[X1==1])$p.value p_2=wilcox.test(Y[X2==0],Y[X2==1])$p.value p_3=wilcox.test(Y[X3==0],Y[X3==1])$p.value p_vec=c(p_1,p_2,p_3) p.adjust(p_vec,method = "holm")
Holm's correction is too strict for such dependent tests. I wanted to permute Y and perform Westfall & Young correction.