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. The 'nlrq' package for nonlinear quantile regression
Annexes
This package was developed by R. Koenker and Ph. Grosjean and it is
now available as part of the official distribution of the R software.
Description file
Package: nlrq
Version: 0.1-1
Date: 2000/05/14
Title: Nonlinear quantile regression
Author: Roger Koenker ,
Philippe Grosjean
Maintainer: Roger Koenker
Depends: R (>= 1.2.3)
Description: Nonlinear quantile regression routines
License: GPL version 2 or later
URL: http://www.econ.uiuc.edu/~roger/research/nlrq/nlrq.html
This package contains functions and methods for nonlinear quantile regression
coef.nlrq extract coefficients
deviance.nlrq deviance at solution
fitted.nlrq response of the fitted model
formula.nlrq formula used in the nlrq object
nlrq nonlinear quantile regression
nlrq.control construct a control list for using with nlrq
predict.nlrq predict data according to the model
residuals.nlrq extract residuals
summary.nlrq display summary of an nlrq object
tau.nlrq quantile used in the nlrq object
Online manual pages
nlrq package:nlrq R Documentation
Function to compute nonlinear quantile regression estimates
Description:
Usage:
This function implements an R version of an interior point method
for computing the solution to quantile regression problems which
are nonlinear in the parameters. The algorithm is based on
interior point ideas described in Koenker and Park (1994).
nlrq(formula, data=parent.frame(), start, tau=0.5, control, trace=FALSE)
Arguments:
formula: formula for model in nls format; accept self-starting models
data: an optional data frame in which to evaluate the variables in
`formula'
start: a named list or named numeric vector of starting estimates
tau: a vector of quantiles to be estimated
control: an optional list of control settings. See `nlrq.control' for
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