CLC-methods

CLC,REBMIX-method

CLC,REBMVNORM-method

Returns the classification likelihood criterion at <code>pos</code>.

information criterion

Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.

Marko Nagode

rebmix

Finite Mixture Modeling, Clustering & Classification

Branislav Panic

Jernej Klemenc

Simon Oman

CLC-methods function

<dl><dt>x</dt>
<dd>see Methods section below.</dd><dt>pos</dt>
<dd>a desired row number in <code>x@summary</code> for which the information criterion is calculated. The default value is <code>1</code>.</dd><dt>...</dt>
<dd>currently not used.</dd></dl>

Arguments

<dl>
<dt><code>signature(x = "REBMIX")</code></dt>
<dd>an object of class <code>REBMIX</code>.</dd><dt><code>signature(x = "REBMVNORM")</code></dt>
<dd>an object of class <code>REBMVNORM</code>.</dd>
</dl>

Methods

Author

Classification Likelihood Criterion — CLC-methods

<dl>

<dt>x</dt>
<dd>see Methods section below.</dd>

<dt>pos</dt>
<dd>a desired row number in <code>x@summary</code> for which the information criterion is calculated. The default value is <code>1</code>.</dd>

<dt>...</dt>
<dd>currently not used.</dd>

</dl>

<dl>
<dt><code>signature(x = "REBMIX")</code></dt>
<dd>an object of class <code>REBMIX</code>.</dd>

<dt><code>signature(x = "REBMVNORM")</code></dt>
<dd>an object of class <code>REBMVNORM</code>.</dd>


</dl>

CLC-methods: Classification Likelihood Criterion

Description

Usage

Arguments

Methods

Author

References