Nothing
pUmbrPU<-function(x,g=NA,method=NA, n.mc=10000){
##From kruskal.test()##
if (is.list(x)) {
if (length(x) < 2L)
stop("'x' must be a list with at least 2 elements")
DNAME <- deparse(substitute(x))
x <- lapply(x, function(u) u <- u[complete.cases(u)])
k <- length(x)
l <- sapply(x, "length")
if (any(l == 0))
stop("all groups must contain data")
g <- factor(rep(1:k, l))
x <- unlist(x)
}
else {
if (length(x) != length(g))
stop("'x' and 'g' must have the same length")
DNAME <- paste(deparse(substitute(x)), "and", deparse(substitute(g)))
OK <- complete.cases(x, g)
x <- x[OK]
g <- g[OK]
if (!all(is.finite(g)))
stop("all group levels must be finite")
g <- factor(g)
l<-as.numeric(table(g))
k <- nlevels(g)
if (k < 2)
stop("all observations are in the same group")
}
N <- length(x)
#####################
outp<-list()
outp$n<-l
outp$stat.name<-paste("Mack-Wolfe Peak Unknown A*(p-hat)")
outp$n.mc<-n.mc
outp$ties<- (length(x) != length(unique(x)))
##When the user doesn't give us any indication of which method to use, try to pick one.
if(is.na(method)){
if(factorial(sum(outp$n))/prod(factorial(outp$n))<=10000){
method<-"Exact"
}
if(factorial(sum(outp$n))/prod(factorial(outp$n))>10000){
method<-"Monte Carlo"
}
}
#####################################################################
outp$method<-method
cumulative.sizes<-cumsum(outp$n)
peak.picker<-function(obs.data){
tmp<-numeric(k)
for(i in 1:k){
first<-obs.data[max(1,cumulative.sizes[i-1]+1):cumulative.sizes[i]]
second<-obs.data[-(max(1,cumulative.sizes[i-1]+1):cumulative.sizes[i])]
options(warn = (-1));
tmp[i]<-(wilcox.test(first,second)$statistic-(outp$n[i]*(cumulative.sizes[k]-outp$n[i])/2))/sqrt(
outp$n[i]*(cumulative.sizes[k]-outp$n[i])*(cumulative.sizes[k]+1)/12)
options(warn = (0));
}
initial.peak<-peak<-which.max(tmp)
#check for multiple peaks;
if(1-sum(tmp[-peak]==tmp[peak])){
return(peak)
}
for(i in (initial.peak+1):k){
if(tmp[i]==tmp[initial.peak]){
peak<-c(peak,i)
}
}
return(peak)
}
A.star.calc<-function(obs.data,peak){
N1<-cumulative.sizes[peak]
N2<-(cumulative.sizes[k]-max(0,cumulative.sizes[peak-1]))
exp.Ap<-(N1^2+N2^2-sum(outp$n^2)-outp$n[peak]^2)/4
var.Ap<-1/72*(2*(N1^3+N2^3)+3*(N1^2+N2^2)-sum(outp$n^2*(2*outp$n+3))
-outp$n[peak]^2*(2*outp$n[peak]+3)+12*outp$n[peak]*N1*N2-12*outp$n[peak]^2*cumulative.sizes[k])
U.vec<-numeric((peak*(peak-1)+(k-peak+1)*(k-peak))/2)
U.calc<-function(i,j){
wilcox.test(obs.data[g==levels(g)[i]],obs.data[g==levels(g)[j]])$statistic
}
count<-0
if(peak>1){
for(i in 2:peak){
for(j in 1:(i-1)) {
count<-count+1
options(warn = (-1));
U.vec[count]<-U.calc(i,j)
options(warn = (0));
}
}
}
if(peak<k){
for(i in peak:(k-1)){
for(j in (i+1):k) {
count<-count+1
options(warn = (-1));
U.vec[count]<-U.calc(i,j)
options(warn = (0));
}
}
}
(sum(U.vec)-exp.Ap)/sqrt(var.Ap)
}
PU.calc<-function(obs.data){
tmp.peak<-peak.picker(obs.data)
num.peak<-length(tmp.peak)
tmp.stat<-numeric(num.peak)
if(num.peak==1){
tmp.stat<-A.star.calc(obs.data,tmp.peak)
}
if(num.peak>1){
for(i in 1:num.peak){
tmp.stat[i]<-A.star.calc(obs.data,tmp.peak[i])
}
}
mean(tmp.stat)
}
est.peak<-peak.picker(x)
outp$extra<-paste("Estimated Peak Group(s):",est.peak)
outp$obs.stat<-PU.calc(x)
possible.ranks<-as.numeric(rank(x))
if(outp$method=="Asymptotic"){
warning("The asymptotic distribution for this statistic is unknown!")
outp$method=="Monte Carlo"
}
if(outp$method=="Exact"){
possible.orders<-multComb(l)
possible.perms<-t(apply(possible.orders,1,function(x) possible.ranks[x]))
A.stats<-apply(possible.perms,1,PU.calc)
A.tab<-table(A.stats)
A.vals<-round(as.numeric(names(A.tab)),5)
A.probs<-as.numeric(A.tab)/sum(A.tab)
outp$p.val<-sum(A.probs[A.vals>=round(outp$obs.stat,5)])
}
if(outp$method=="Monte Carlo"){
outp$p.val<-0
for(i in 1:n.mc){
mc.perm<-sample(possible.ranks)
if(PU.calc(mc.perm)>=outp$obs.stat){
outp$p.val=outp$p.val+1/n.mc
}
}
}
class(outp)<-"NSM3Ch6p"
outp
}
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