glmRob.misclass.control

Allows users to set parameters for <code>glmRob</code>.

robust

regression

Methods for robust statistics, a state of the art in the early
2000s, notably for robust regression and robust multivariate analysis.

Valentin Todorov

Port of the S+ "Robust Library"

Jiahui Wang

Ruben Zamar

Alfio Marazzi

Victor Yohai

Matias Salibian-Barrera

Ricardo Maronna

Eric Zivot

David Rocke

Doug Martin

Martin Maechler

Kjell Konis

glmRob.misclass.control function

<dl>
<dt>mc.gamma</dt>
<dd>a real number between 0 and 1 that represents the probability of
misclassification of a response variable.</dd>
<dt>mc.maxit</dt>
<dd>maximum number of iterations.</dd>
<dt>mc.trc</dt>
<dd>a logical value indicating whether a trace of the current parameter values is printed to the screen while the algorithm iterates.</dd>
<dt>mc.tol</dt>
<dd>convergence threshold.</dd>
<dt>mc.initial</dt>
<dd>a vector of initial values to start the iterations. If ommited, the coeficients resulting from a non-robust glm fit are used.</dd>
<dt>...</dt>
<dd>additional arguments are ignored.</dd></dl>

Arguments

Control for Misclassification Robust GLM Estimator — glmRob.misclass.control

<dl>


<dt>mc.gamma</dt>
<dd>a real number between 0 and 1 that represents the probability of
misclassification of a response variable.</dd>


<dt>mc.maxit</dt>
<dd>maximum number of iterations.</dd>


<dt>mc.trc</dt>
<dd>a logical value indicating whether a trace of the current parameter values is printed to the screen while the algorithm iterates.</dd>


<dt>mc.tol</dt>
<dd>convergence threshold.</dd>


<dt>mc.initial</dt>
<dd>a vector of initial values to start the iterations. If ommited, the coeficients resulting from a non-robust glm fit are used.</dd>


<dt>...</dt>
<dd>additional arguments are ignored.</dd>

</dl>

Control for Misclassification Robust GLM Estimator

glmRob.misclass.control: Control for Misclassification Robust GLM Estimator

Description

Usage

Value

Arguments

See Also