nlrq.control

Set algorithmic parameters for nlrq (nonlinear quantile regression function)

 environment 

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

nlrq.control function

<dl> <dt>maxiter</dt>
<dd>maximum number of allowed iterations</dd> <dt>k</dt>
<dd>the number of iterations of the Meketon algorithm to be calculated 
	in each step, usually 2 is reasonable, occasionally it may be helpful 
	to set k=1</dd> <dt>InitialStepSize</dt>
<dd>Starting value in <code>optim</code> to determine the step
	length of iterations. The default value of 1 is sometimes too optimistic.
	In such cases, the value 0 forces optim to just barely stick its toe in
	the water.</dd> <dt>big</dt>
<dd>a large scalar</dd> <dt>eps</dt>
<dd>tolerance for convergence of the algorithm</dd> <dt>beta</dt>
<dd>a shrinkage parameter which controls the recentering process 
	in the interior point algorithm.</dd></dl>

Arguments

Set control parameters for nlrq — nlrq.control

<dl>

 <dt>maxiter</dt>
<dd>maximum number of allowed iterations</dd>

 <dt>k</dt>
<dd>the number of iterations of the Meketon algorithm to be calculated 
	in each step, usually 2 is reasonable, occasionally it may be helpful 
	to set k=1</dd>

 <dt>InitialStepSize</dt>
<dd>Starting value in <code>optim</code> to determine the step
	length of iterations. The default value of 1 is sometimes too optimistic.
	In such cases, the value 0 forces optim to just barely stick its toe in
	the water.</dd>

 <dt>big</dt>
<dd>a large scalar</dd>

 <dt>eps</dt>
<dd>tolerance for convergence of the algorithm</dd>

 <dt>beta</dt>
<dd>a shrinkage parameter which controls the recentering process 
	in the interior point algorithm.</dd>

</dl>

Set control parameters for nlrq

nlrq.control: Set control parameters for nlrq

Description

Usage

Arguments

See Also