FLXfit

FLXfit,list-method

This is the basic computing engine called by <code>flexmix</code>, it
 should usually not be used directly.

regression

cluster

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

FLXfit function

<dl> <dt>model</dt>
<dd>List of <code>FLXM</code> objects.</dd> <dt>concomitant</dt>
<dd>Object of class <code>FLXP</code>.</dd> <dt>control</dt>
<dd>Object of class <code>FLXcontrol</code>.</dd> <dt>weights</dt>
<dd>A numeric vector of weights to be used in the fitting
 process.</dd> <dt>postunscaled</dt>
<dd>Initial a-posteriori probabilities of the
 observations at the start of the EM algorithm.</dd> <dt>groups</dt>
<dd>List with components <code>group</code> which is a factor with
 optional grouping of observations and <code>groupfirst</code> which is a
 logical vector for the first observation of each group.</dd></dl>

Arguments

Friedrich Leisch and Bettina Gruen

Author

Fitter Function for FlexMix Models — FLXfit

<dl>

 <dt>model</dt>
<dd>List of <code>FLXM</code> objects.</dd>

 <dt>concomitant</dt>
<dd>Object of class <code>FLXP</code>.</dd>

 <dt>control</dt>
<dd>Object of class <code>FLXcontrol</code>.</dd>

 <dt>weights</dt>
<dd>A numeric vector of weights to be used in the fitting
 process.</dd>

 <dt>postunscaled</dt>
<dd>Initial a-posteriori probabilities of the
 observations at the start of the EM algorithm.</dd>

 <dt>groups</dt>
<dd>List with components <code>group</code> which is a factor with
 optional grouping of observations and <code>groupfirst</code> which is a
 logical vector for the first observation of each group.</dd>

</dl>

FLXfit: Fitter Function for FlexMix Models

Description

Usage

Value

Arguments

Author

See Also