AWE-methods

AWE,REBMIX-method

AWE,REBMVNORM-method

Returns the approximate weight of evidence 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

AWE-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

Approximate Weight of Evidence Criterion — AWE-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>

AWE-methods: Approximate Weight of Evidence Criterion

Description

Usage

Arguments

Methods

Author

References