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

#### Documented in fk.test

```fk.test <-
function(x,y,alternative = c("two.sided", "less", "greater"),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
#
zed = c(abs(x-median(x)),abs(y-median(y)))
n1 = length(x)
n2 = length(y)
n = n1 + n2
cvec  = c(rep(0,n1),rep(1,n2))
rz = rank(zed)/(n+1)
v = getScores(fkscores,rz)
num = sum(cvec*v)
sigphi = sqrt(((n1*n2)/n)*var(v))
ts = num/sigphi
see = alternative[1]
if(see == "two.sided"){pval = 2*(1 - pnorm(abs(ts)))}
if(see == "greater"){pval = 1 - pnorm(ts)}
if(see == "less"){pval = pnorm(ts)}

#
#      Estimation and CI
#
xs = abs(x - median(x))
xs = xs[xs!=0]
xstarl = log(xs)
ys = abs(y - median(y))
ys = ys[ys!=0]
ystarl = log(ys)
n1s = length(xs)
n2s = length(ys)
ns = n1s + n2s
tc = abs(qt(((1-conf.level)/2),ns-2))
cvec  = c(rep(0,n1s),rep(1,n2s))
zed = c(xstarl,ystarl)
fitz = rfit(zed~cvec,scores=fkscores)
sumf = summary(fitz)
delta = coef(sumf)[2,1]
se = coef(sumf)[2,2]
lb = delta - tc*se
ub = delta + tc*se
eta = exp(delta)
ci = c(exp(lb),exp(ub))
res<-list(estimate=eta,conf.int=ci,statistic=ts,p.value=pval,conf.level=conf.level)
res\$call<-match.call()
class(res)<-list('rank.test')
res
}
```
kloke/npsm documentation built on May 18, 2017, 9:42 p.m.