AIC,flexmix-method

AIC,stepFlexmix-method

Compute the Akaike Information Criterion.

methods

AIC-methods

A general framework for finite mixtures of regression
models using the EM algorithm is implemented. The E-step and all
data handling are provided, while the M-step can be supplied by the
user to easily define new models. Existing drivers implement
mixtures of standard linear models, generalized linear models and
model-based clustering.

Bettina Gruen

flexmix

Flexible Mixture Modeling

Friedrich Leisch

Deepayan Sarkar

Frederic Mortier

Nicolas Picard

AIC-methods function

<dl>
 <dt>object = flexmix:</dt>
<dd>Compute the AIC of a <code>flexmix</code> object</dd> <dt>object = stepFlexmix:</dt>
<dd>Compute the AIC of all
 models contained in the <code>stepFlexmix</code> object.</dd> 
</dl>

Methods

Methods for Function AIC — AIC-methods

<dl>
 <dt>object = flexmix:</dt>
<dd>Compute the AIC of a <code>flexmix</code> object</dd>

 <dt>object = stepFlexmix:</dt>
<dd>Compute the AIC of all
 models contained in the <code>stepFlexmix</code> object.</dd>

 
</dl>

AIC-methods: Methods for Function AIC

Description

Arguments

Methods