\name{summary.rq}
\alias{summary.rq}
\title{
Summary method for Quantile Regression
}
\description{
Returns a summary list for a quantile regression fit. A null value
will be returned if printing is invoked.
}
\usage{
summary.rq(object, se="nid", covariance=TRUE, hs = TRUE, ...)
}
\arguments{
\item{object}{
This is an object of class \code{"rq"} produced by a call to \code{rq()}.
}
\item{se}{
specifies the method used to compute standard standard errors. There
are currently three available methods:
\enumerate{
\item \code{"iid"} which presumes that the errors are iid and computes
an estimate of the asymptotic covariance matrix as in KB(1978).
\item \code{"nid"} which presumes local (in \code{tau})
linearity (in \code{x}) of the
the conditional quantile functions and computes a Huber
sandwich estimate using a local estimate of the sparsity.
\item \code{"ker"} which uses a kernel estimate of the sandwich
as proposed by Powell(1990).
}
}
\item{covariance}{
logical flag to indicate whether the full covariance matrix of the
estimated parameters should be returned.
}
\item{hs}{
Use Hall Sheather bandwidth for sparsity estimation
If false revert to Bofinger bandwidth.
}
\item{...}{
Optional arguments to summary
}
}
\value{
a list is returned with the following components
\item{coefficients}{
a p by 4 matrix consisting of the coefficients, their estimated standard
errors, their t-statistics, and their associated p-values.
}
\item{cov}{
the estimated covariance matrix for the coefficients in the model,
provided that \code{cov=TRUE} in the called sequence.
}
\item{Hinv}{
inverse of the estimated Hessian matrix returned if \code{cov=TRUE} and
\code{se != "iid"}.
}
\item{J}{
Outer product of gradient matrix returned if \code{cov=TRUE} and \code{se
!= "iid"}. The Huber sandwich is \code{cov = Hinv \%*\% J \%*\% Hinv}.
}}
\references{
Koenker, R. (2000) \emph{Quantile Regression}.
}
\seealso{
\code{\link{rq}}
\code{\link{bandwidth.rq}}
}
\examples{
data(stackloss)
y <- stack.loss
x <- stack.x
summary(rq(y ~ x, method="fn")) # Compute se's for fit using "nid" method.
summary(rq(y ~ x, ci=FALSE),se="ker")
# default "br" alg, and compute kernel method se's
}
\keyword{regression}
% Converted by Sd2Rd version 0.3-3.