swh:1:snp:16c54c84bc54885e783d4424d714e5cc82f479a1
Tip revision: 4dfab3bdc8d05045a922f285e9ab1236f75bebbd authored by Roger Koenker on 08 August 1977, 00:00:00 UTC
version 3.33
version 3.33
Tip revision: 4dfab3b
rrs.test.Rd
\name{rrs.test}
\alias{rrs.test}
\title{
Quantile Regression Rankscore Test
}
\description{
Function to compute regression rankscore test of a linear hypothesis
based on the dual quantile regression process. A test of the
hypothesis,
is carried out by estimating the restricted model and constructing
a test based on the dual process under the restricted model. The
details of the test are described in GJKP(1993). The test has a
Rao-score, Lagrange-multiplier interpretation since in effect it
is based on the value of the gradient of unrestricted quantile regression
problem evaluated under the null. This function will eventually be
superseded by a more general \code{anova()} method for \code{rq}.
}
\usage{
rrs.test(x0, x1, y, v, score="wilcoxon")
}
\arguments{
\item{x0}{
the matrix of maintained regressors, a column of ones is
appended automatically.
}
\item{x1}{
matrix of covariates under test.
}
\item{y}{
response variable, may be omitted if \code{v} is provided.
}
\item{v}{
object of class \code{"rq.process"} generated e.g. by
\code{rq(y ~ x0, tau=-1)}
}
\item{score}{
Score function for test (see \code{\link{ranks}})
}
}
\value{
Test statistic \code{sn} is asymptotically Chi-squared with rank(X1) dfs.
The vector of ranks is also returned as component \code{rank}.
}
\details{
See GJKP(1993)
}
\references{
[1] Gutenbrunner, C., Jureckova, J., Koenker, R. and
Portnoy, S. (1993) Tests of linear hypotheses based on
regression rank scores. \emph{Journal of Nonparametric
Statistics}, (2), 307-331.
[2] Koenker, R. W. and d'Orey (1994). Remark on Alg. AS
229: Computing dual regression quantiles and regression
rank scores. \emph{Applied Statistics}, \bold{43}, 410-414.
}
\seealso{
\code{\link{rq}}, \code{\link{ranks}}
}
\examples{
# Test that covariates 2 and 3 belong in stackloss model using Wilcoxon scores.
data(stackloss)
rrs.test(stack.x[,1], stack.x[,2:3], stack.loss)
}
\keyword{regression}
% Converted by Sd2Rd version 0.3-3.