Nothing
wald.coef<-function(mat,H,indices, tolval=10*.Machine$double.eps, tolsym=1000*.Machine$double.eps)
{
# Function wald.coef
# Computes the value of Wald statistic for testing the significance of the omited variables
# in a generalized linear model
# error checking
# mat and indices
if (sum(!(as.integer(indices) == indices)) > 0) stop("\n The variable indices must be integers")
p <- dim(mat)[2]
validmat(mat,p,tolval,tolsym)
# checks on r and H
validnovcrit(mat,criterion="WALD",H,r=1,p,tolval,tolsym)
# Computing the criterion value
tr <- function(mat){sum(diag(mat))}
waldallvar <- tr(solve(mat,H))
wald.1d <- function(mat,H,indices){ waldallvar - tr(solve(mat[indices,indices],H[indices,indices])) }
dimension<-length(dim(indices))
if (dimension > 1){
wald.2d<-function(mat,H,subsets){
apply(subsets,1,function(indices){
wald.1d(mat,H,indices)})
}
if (dimension > 2) {
wald.3d<-function(mat,H,array3d){
apply(array3d,3,function(subsets){wald.2d(mat,H,subsets)})
}
output<-wald.3d(mat,H=H,indices)
}
if (dimension == 2) {output<-wald.2d(mat=mat,H,indices)}
}
if (dimension < 2) {output<-wald.1d(mat,H,indices)}
output
}
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