https://github.com/cran/quantreg
Tip revision: 470402e079549c584851dc1a2189d061adb01734 authored by Roger Koenker on 08 August 1977, 00:00:00 UTC
version 3.34
version 3.34
Tip revision: 470402e
rq.fit.fn.Rd
\name{rq.fit.fn}
\alias{rq.fit.fn}
\title{
Quantile Regression Fitting via Interior Point Methods
}
\description{
This is a lower level routine called by \code{rq()} to compute quantile
regression methods using the Frisch-Newton algorithm.
}
\usage{
rq.fit.fn(x, y, tau=0.5, beta=0.99995, eps=1e-06)
}
\arguments{
\item{x}{
The design matrix
}
\item{y}{
The response vector
}
\item{tau}{
The quantile of interest, must lie in (0,1)
}
\item{beta}{
technical step length parameter -- alter at your own risk!
}
\item{eps}{
tolerance parameter for convergence. In cases of multiple optimal solutions
there may be some discrepancy between solutions produced by method
\code{"fn"} and method \code{"br"}. This is due to the fact that
\code{"fn"} tends to converge to a point near the centroid of the
solution set, while \code{"br"} stops at a vertex of the set.
}
}
\value{
returns an object of class \code{"rq"}, which can be passed to
\code{\link{summary.rq}} to obtain standard errors, etc.
}
\details{
The details of the algorithm are explained in Koenker and Portnoy (1997).
The basic idea can be traced back to the log-barrier methods proposed by
Frisch in the 1950's for constrained optimization. But the current
implementation is based on proposals by Mehrotra and others in the
recent (explosive) literature on interior point methods for solving linear
programming problems. This version of the algorithm is designed for
fairly large problems, for very large problems see \code{rq.fit.pfn}.
}
\references{
Koenker, R. and S. Portnoy (1997).
The Gaussian Hare and the Laplacian Tortoise:
Computability of squared-error vs. absolute-error estimators, with discussion,
\emph{Statistical Science}, \bold{12}, 279-300.
}
\seealso{
\code{\link{rq}}, \code{\link{rq.fit.br}},
\code{\link{rq.fit.pfn}}
}
\keyword{regression}
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