rq.wfit

Weight the data and then call the chosen fitting algorithm.

 regression

Estimation and inference methods for models for conditional quantile functions:
Linear and nonlinear parametric and non-parametric (total variation penalized) models
for conditional quantiles of a univariate response and several methods for handling
censored survival data. Portfolio selection methods based on expected shortfall
risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press,
<doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression,
CRC Press, <doi:10.1201/9781315120256>.

Roger Koenker

quantreg

Quantile Regression

Stephen Portnoy

Blaise Melly

Achim Zeileis

Philip Grosjean

Cleve Moler

Yousef Saad

Victor Chernozhukov

Ivan Fernandez-Val

Martin Maechler

rq.wfit function

<dl>
 
<dt>x</dt>
<dd>the design matrix</dd>
 
<dt>y</dt>
<dd>the response variable</dd>
 
<dt>tau</dt>
<dd>the quantile desired, if tau lies outside (0,1) the whole process 
 is estimated.</dd>
 
<dt>weights</dt>
<dd>weights used in the fitting</dd>
 
<dt>method</dt>
<dd>method of computation: "br" is Barrodale and Roberts exterior point 
 "fn" is the Frisch-Newton interior point method.</dd>
 
<dt>...</dt>
<dd>Optional arguments passed to fitting routine.</dd>
 
</dl>

Arguments

Function to choose method for Weighted Quantile Regression — rq.wfit

Function to choose method for Weighted Quantile Regression

rq.wfit: Function to choose method for Weighted Quantile Regression

Description

Usage

Arguments

See Also