R/qse.R

In musto101/wilcox_R: What the package does (short line)

qse <-
function(x,q=.5,op=3){
#
#  Compute the standard error of qth sample quantile estimator
#  based on the single order statistic, x sub ([qn+.5]) (See Ch 3)
#
#  Store the data in vector
#  x, and the desired quantile in q
#  The default value for q is .5
#
# op=1 Use Rosenblatt's shifted histogram
# op=2 Use expected frequency curve
# op=3 Use adaptive kernel density estimator
#
y <- sort(x)
n <- length(x)
iq <- floor(q * n + 0.5)
qest <- y[iq]
fhat<-NA
if(op==1)fhat<-kerden(x,q)
if(op==2)fhat<-rdplot(x,pts=qest,pyhat=TRUE,plotit=FALSE)
if(op==3)fhat<-akerd(x,pts=qest,pyhat=TRUE,plotit=FALSE)
if(is.na(fhat[1]))stop("Something wrong, op should be 1 or 2 or 3")
qse<-1/(2*sqrt(length(x))*fhat)
qse
}