Nothing
testvc2level <-
function(y,cluster,npermut,N,p,smat,m1,n1,weight){
# y = Matrix Nxp (N obs, p=dimension) of (maybe standardized; signs or ranks) data
# cluster = List of indices (each element gives the indices of the obs in a level 1 cluster). From the "preparecluster" function.
# npermut = Number of permutations for the test
# N = Total number of observations
# p = Dimension
# smat = Transformation matrix to achieve affine-equivariance (from the "ystand" function)
# m1 = Number of level 1 clusters
# n1 = Number of observations in each level 1 cluster (vector m1X1)
# weight = weights used to compute the dependence matrix
# "observation" gives equal weight to each observation
# "pair" gives equal weight to each pair of observations
# "cluster" gives equal weight to each cluster
pvalmax=0
stato=statvc2level(y,cluster,m1,n1,p,weight)
statoriginal=(smat %*% stato[[1]] %*% t(smat))/stato[[2]]
eig=eigen(statoriginal)
maxo=max(eig$values)
if(maxo<=0)
{
pvalmax=99
return(c(pvalmax,maxo))
}
for(i in 1:npermut)
{
clusterperm=relist(sample(1:N),cluster)
statp=statvc2level(y,clusterperm,m1,n1,p,weight)
statperm=(smat %*% statp[[1]] %*% t(smat))/statp[[2]]
maxperm=max(eigen(statperm)$values)
pvalmax=pvalmax+(maxperm>maxo)
}
c(pvalmax/npermut,maxo)
}
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