Nothing
cHollBivSym<-function(alpha,d.mat,method=NA, n.mc=10000){
outp<-list()
if(alpha>1||alpha<0||!is.numeric(alpha)){
cat('Error: Check alpha value! \n')
return(alpha)
}
outp$alpha<-alpha
outp$m<-outp$n<-m<-n<-nrow(d.mat)
outp$n.mc<-n.mc
outp$stat.name<-"Hollander A"
N<-outp$m+outp$n
if(!is.na(method)){
if(method=="Asymptotic"){
warning("Koziol's LSA was found to perform poorly so Asymptotic method not included.")
outp$method=NA
}
}
##When the user doesn't give us any indication of which method to use, try to pick one.
if(is.na(method)){
if(choose(N,outp$m)<=10000){
method<-"Exact"
}
if(choose(N,outp$m)>10000){
method<-"Monte Carlo"
}
}
#####################################################################
outp$method<-method
A.calc<-function(r.vec){
s.vec<-2*r.vec-1
T.vec<-s.vec%*%d.mat
A.obs<-sum(T.vec*T.vec)/n^2
return(A.obs)
}
if(outp$method=="Exact"){
possible.r<-expand.grid(lapply(1:n, function(i) (c(0,1))))
A.values<-apply(possible.r,1,A.calc)
A.dist<-sort(unique(A.values))
upper.calc<-function(cand){
mean(cand<=A.values)
}
upper.tails<-unlist(lapply(A.dist,upper.calc))
outp$cutoff.U<-A.dist[min(which(upper.tails<=alpha))]
outp$true.alpha.U<-upper.tails[min(which(upper.tails<=alpha))]
}
if(outp$method=="Monte Carlo"){
mc.dist<-numeric(n.mc)
for(i in 1:n.mc){
mc.r<-sample(0:1,outp$n,replace=T)
mc.dist[i]<-A.calc(mc.r)
}
A.dist<-sort(unique(mc.dist))
upper.calc<-function(cand){
mean(cand<=mc.dist)
}
upper.tails<-unlist(lapply(A.dist,upper.calc))
outp$cutoff.U<-A.dist[min(which(upper.tails<=alpha))]
outp$true.alpha.U<-upper.tails[min(which(upper.tails<=alpha))]
}
class(outp)="NSM3Ch5c"
outp
}
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