# R/fkk.test.r In kloke/npsm: Package for Nonparametric Statistical Methods using R

#### Documented in fkk.test

```fkk.test = function(y,ind,conf.level = 0.95){
fkscores = new("scores",phi=function(u)
{
(qnorm((u+1)/2))^2 - 1
}
,Dphi=function(u)
{
qnorm((u+1)/2)/dnorm(qnorm((u+1)/2))
})
myscores = fkscores
#
#      Test
#
xmat = model.matrix(~as.factor(ind) - 1)
uind <- unique(ind)
k <- length(uind)
yu = c(0)
yua = c(0)
iu = c(0)
ni = c(0)
for(j in 1:k){
vec = y[xmat[,j]==1]
yu = c(yu,vec)
yua = c(yua,vec - median(vec))
iu = c(iu,rep(j,length(vec)))
}
np1=length(yu)
yu = yu[2:np1]
yua = yua[2:np1]
iu = iu[2:np1]
regdata = cbind(yu,iu)
n = np1 - 1
rua = rank(abs(yua))/(n+1)
v = getScores(myscores,(1:n)/(n+1))
sv = sum(v^2)
yuav = getScores(myscores,rua)
ts = 0
nc = c(0)
for(j in 1:k){
vec = yuav[xmat[,j]==1]
ni = length(vec)
nc = c(nc,ni)
ts = ts + sum(vec)^2/ni
}
ts = ((n-1)/sv)*ts
pval = 1 - pchisq(ts,k-1)

nc  = nc[2:(k+1)]
yua = c(0)
iu = c(0)
ni = c(0)
for(j in 1:k){
vec = y[xmat[,j]==1]
vec = vec - median(vec)
vec = vec[vec[]!=0]
yua = c(yua,vec)
iu = c(iu,rep(j,length(vec)))
}
np1=length(yua)
yua = yua[2:np1]
iu = iu[2:np1]
n = np1 - 1
zed = log(abs(yua))

xm = model.matrix(~as.factor(iu))
xmat = xm[,2:k]
fitz = rfit(zed~xmat,scores=myscores)
sumf = summary(fitz)
delta = coef(sumf)[2:k,1]
se = coef(sumf)[2:k,2]
tc = abs(qt(((1-conf.level)/2),n-k))
lb = delta - tc*se
ub = delta + tc*se
eta = exp(delta)
ci = cbind(exp(lb),exp(ub))

cwts = rep(1,nc[1])
for(i in 2:k){
cwts = c(cwts,rep(1/eta[i-1],nc[i]))
}

res<-list(statistic=ts,p.value=pval,estimate=eta,conf.int=ci,cwts=cwts,conf.level=conf.level)
class(res)<-c('fkk.test')
res
}
```
kloke/npsm documentation built on May 18, 2017, 9:42 p.m.