clusterPoissonNames

clusterValidPoissonNames

In a Poisson mixture model, we can build 4 models:<ol>
<li>The proportions can be equal or free</li>
<li>The means can be equal, free or proportional for all the variables</li>
</ol>

Algorithms and methods for model-based clustering and classification.
It supports various types of data: continuous, categorical and counting
and can handle mixed data of these types. It can fit Gaussian (with diagonal
covariance structure), gamma, categorical and Poisson models. The algorithms
also support missing values.

Serge Iovleff

MixAll

Clustering and Classification using Model-Based Mixture Models

Parmeet Bathia

clusterPoissonNames function

<dl><dt>prop</dt>
<dd>A character string equal to "equal", "free" or "all". Default is "all".</dd>
<dt>mean</dt>
<dd>A character string equal to "equal", "free", "proportional or "all". Default is "all".</dd>
<dt>names</dt>
<dd>a vector of character</dd></dl>

Arguments

Create a vector of Poisson mixture model names. — clusterPoissonNames

<dl>

<dt>prop</dt>
<dd>A character string equal to "equal", "free" or "all". Default is "all".</dd>


<dt>mean</dt>
<dd>A character string equal to "equal", "free", "proportional or "all". Default is "all".</dd>


<dt>names</dt>
<dd>a vector of character</dd>

</dl>

Model Name	Proportions	Mean between variables
poisson_p_ljk	Equal	Free
poisson_p_lk	Equal	Equal
poisson_p_ljlk	Equal	Proportional
poisson_pk_ljk	Free	Free
poisson_pk_lk	Free	Equal
poisson_pk_ljlk	Free	Proportional

clusterPoissonNames: Create a vector of Poisson mixture model names.

Description

Usage

Value

Arguments

Details

Examples