clusterCategoricalNames

clusterValidCategoricalNames

In a Categorical mixture model, we can build 4 models:<ol>
<li>The proportions can be equal or free</li>
<li>The probabilities can be equal or free 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

clusterCategoricalNames function

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

Arguments

Create a vector of Categorical mixture model names. — clusterCategoricalNames

<dl>

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


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


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

</dl>

Model Name	Proportions	Probabilities between variables
categorical_p_pjk	Equal	Free
categorical_p_pk	Equal	Equal
categorical_pk_pjk	Free	Free
categorical_pk_pk	Free	Equal

clusterCategoricalNames: Create a vector of Categorical mixture model names.

Description

Usage

Value

Arguments

Details

Examples