Revision 67095c96090cdfc0479abf9916e8ddcbf8ca316e authored by Roger Koenker on 10 January 2021, 22:30:06 UTC, committed by cran-robot on 10 January 2021, 22:30:06 UTC
1 parent d4080a3
rq.fit.conquer.Rd
\name{rq.fit.conquer}
\alias{rq.fit.conquer}
\title{Optional Fitting Method for Quantile Regression}
\description{
This fitting method provides a link to the gradient descent
for convolution smoothed quantile regression problem implemented
in the \pkg{conquer} package of He et al (2020).}
\usage{
rq.fit.conquer (x, y, tau=0.5, kernel = c("Gaussian", "uniform",
"parabolic", "triangular"), h = 0, tol = 1e-04,
iteMax = 5000, ci = FALSE, alpha = 0.05, B = 200)
}
\arguments{
\item{x}{design matrix usually supplied via rq(), expected to
have a intercept as the first column }
\item{y}{ response vector usually supplied via rq() }
\item{tau}{ quantile of interest }
\item{kernel}{A character string specifying the choice of
kernel function. Default is "Gaussian". Other choices are
"uniform", "parabolic" or "triangular".}
\item{h}{The bandwidth parameter for kernel smoothing of the QR
objective function. Default is max{((log(n) + p) / n)^0.4, 0.05}.
The default is used if the input value is less than 0.05.}
\item{tol}{Tolerance level of the gradient descent
algorithm. The gradient descent algorithm terminates when the
maximal entry of the gradient is less than "tol". Default is
1e-05.}
\item{iteMax}{Maximum number of iterations. Default is 5000.}
\item{ci}{A logical flag. Default is FALSE. If "ci =
TRUE", then three types of confidence intervals (percentile,
pivotal and normal) will be constructed via multiplier
bootstrap. This option is subsumed in normal use by the
\code{summary.rq} functionality.}
\item{alpha}{Nominal level for confidence intervals, may be passed
via the call to \code{summary}}
\item{B}{Number of bootstrap replications. May be passed via summary.}
}
\details{
See documentation in the \pkg{conquer} package.
}
\value{
Returns an object of class "rq".
}
\references{
Xuming He and Xiaoou Pan and Kean Ming Tan and Wen-Xin Zhou, (2020)
conquer: Convolution-Type Smoothed Quantile Regression,
\url{https://CRAN.R-project.org/package=conquer}}
\seealso{\code{\link{rq}}}
\keyword{regression}
Computing file changes ...