lad.fit

This function is a switcher among various numerical fitting functions
 (<code>lad.fit.BR</code>, and <code>lad.fit.EM</code>). The argument <code>method</code>
 does the switching: <code>"BR"</code> for <code>lad.fit.BR</code>, etc. This should
 usually not be used directly unless by experienced users.

regression

L1 estimation for linear regression using Barrodale and Roberts' method
<doi:10.1145/355616.361024> and the EM algorithm <doi:10.1023/A:1020759012226>.
Estimation of mean and covariance matrix using the multivariate Laplace distribution,
density, distribution function, quantile function and random number generation
for univariate and multivariate Laplace distribution <doi:10.1080/03610929808832115>.
Implementation of Naik and Plungpongpun <doi:10.1007/0-8176-4487-3_7> for the
Generalized spatial median estimator is included.

Felipe Osorio

L1pack

Routines for L1 Estimation

Tymoteusz Wolodzko

lad.fit function

<dl> <dt>x</dt>
<dd>design matrix of dimension \(n\times p\).</dd> <dt>y</dt>
<dd>vector of observations of length \(n\).</dd> <dt>method</dt>
<dd>currently, methods <code>"BR"</code> (default), and <code>"EM"</code> are supported.</dd> <dt>tol</dt>
<dd>the relative tolerance for the iterative algorithm. Default is <code>tol = 1e-7</code>.</dd> <dt>maxiter</dt>
<dd>The maximum number of iterations for the <code>EM</code> method. Default to 200.</dd></dl>

Arguments

Fitter functions for least absolute deviation (LAD) regression — lad.fit

<dl>

 <dt>x</dt>
<dd>design matrix of dimension \(n\times p\).</dd>

 <dt>y</dt>
<dd>vector of observations of length \(n\).</dd>

 <dt>method</dt>
<dd>currently, methods <code>"BR"</code> (default), and <code>"EM"</code> are supported.</dd>

 <dt>tol</dt>
<dd>the relative tolerance for the iterative algorithm. Default is <code>tol = 1e-7</code>.</dd>

 <dt>maxiter</dt>
<dd>The maximum number of iterations for the <code>EM</code> method. Default to 200.</dd>

</dl>

lad.fit: Fitter functions for least absolute deviation (LAD) regression

Description

Usage

Value

Arguments

See Also

Examples