\name{ranks}
\alias{ranks}
\title{
Quantile Regression Ranks
}
\description{
Function to compute ranks from the dual (regression rankscore) process.
}
\usage{
ranks(v, score="wilcoxon", tau=0.5)
}
\arguments{
\item{v}{
  object of class \code{"rq.process"} generated by \code{rq()}
}
\item{score}{
  The score function desired.  Currently  implemented score  functions  
  are \code{"wilcoxon"}, \code{"normal"}, and \code{"sign"}
  which are asymptotically optimal  for  
  the  logistic,  Gaussian  and Laplace location shift models respectively.
  Also implemented are the \code{"tau"} which generalizes sign scores to an
  arbitrary quantile, and \code{"interquartile"} which is appropriate
  for tests of scale shift.
}
\item{tau}{
  the optional value of \code{tau} if the \code{"tau"} score function is used.
}}
\value{
The function returns two components. One is the ranks,  the
other is a scale factor which is the \eqn{L_2} norm of the score
function.  All score functions should be normalized to have mean zero.
}
\details{
  See GJKP(1993) for further details.
}
\references{
  Gutenbrunner, C., J. Jureckova,  Koenker, R. and  Portnoy,
  S. (1993)  Tests  of linear hypotheses  based on regression
  rank scores, \emph{Journal of  Nonparametric  Statistics},  (2), 307--331.
}
\seealso{
  \code{\link{rq}}, \code{\link{rrs.test}}
}
\examples{
data(stackloss)
ranks(rq(stack.loss ~ stack.x, tau=-1))
}
\keyword{regression}
% Converted by Sd2Rd version 0.3-3.