Nothing
rm.coef<-function(mat, indices)
{
# Computes the matrix correlation between data matrices and their
# regression on a subset of their variables. Expected input is a
# variance-covariance (or correlation) matrix.
# error checking
if (sum(!(as.integer(indices) == indices)) > 0) stop("\n The variable indices must be integers")
if (!is.matrix(mat)) {
stop("Data is missing or is not given in matrix form")}
if (dim(mat)[1] != dim(mat)[2]) {
mat<-cor(mat)
warning("Data must be given as a covariance or correlation matrix. \n It has been assumed that you wanted the correlation matrix of the \n data matrix which was supplied.")
}
tr<-function(mat){sum(diag(mat))}
rm.1d<-function(mat,indices){
sqrt(tr((mat %*% mat)[indices,indices] %*% solve(mat[indices,indices]))/tr(mat))
}
dimension<-length(dim(indices))
if (dimension > 1){
rm.2d<-function(mat,subsets){
apply(subsets,1,function(indices){rm.1d(mat,indices)})
}
if (dimension > 2) {
rm.3d<-function(mat,array3d){
apply(array3d,3,function(subsets){rm.2d(mat,subsets)})
}
output<-rm.3d(mat,indices)
}
if (dimension == 2) {output<-rm.2d(mat,indices)}
}
if (dimension < 2) {output<-rm.1d(mat,indices)}
output
}
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