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NonParametric.VUS.var <-
function(x,y,z,alpha=0.05,NBOOT=100,FisherZ=FALSE)
{
######################################################################################################################################################
################This function provide variance on nonparametric estimate of VUS by bootstrapping sampling##############
########
####1. Input:
###(1)x,y,z:vectors are the test marker measurements for the 3 ordinal group (D-,D0,D+)
###(2)alpha: provide bootstrap basic (1-alpha)% confidence interval, alpha=0.05 gives (5%,97.5%) CI
###(3)NBOOT: number of bootstrapping
###(4)FisherZ: default=FALSE, if TRUE, calculate the variance and quantile of VUS on the logit scale
####2. Output: (1) variance on nonparametric VUS estimate from bootstrapping (2) bootstrap basic (1-alpha)% quantile interval
######################################################################################################################################################
#cat("It may take a minute to obtain bootstrapping variance\n")
boot.VUS <- sapply(1:NBOOT,function(jj)
{
new.x <- bootSample(x,jj)##use jj as seed for random number generation
new.y <- bootSample(y,jj+1)##use jj+1 as seed for random number generation
new.z <- bootSample(z,jj+2)##use jj+2 as seed for random number generation
res0 <- NonParametric.VUS(x=new.x,y=new.y,z=new.z,FisherZ=FisherZ)
res0$estimate
})
###caculate variance
var0 <- var(boot.VUS,na.rm=T)
prob0 <- alpha/2
###CI
lower <- quantile(boot.VUS,prob=prob0)
upper <- quantile(boot.VUS,prob=1-prob0)
CI <- c(lower,upper)
names(CI) <- c(paste(alpha/2*100,"%",sep=""),paste(100-alpha/2*100,"%",sep=""))
return(list(variance=var0,CI=CI))
}
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