kmmNames

kmmValidModelNames

kmmValidKernelNames

In a Kernel mixture model, sssumptions on the proportions
and standard deviations give rise to 4 models:<ol>
<li>Proportions can be equal or free.</li>
<li>Standard deviations are equal or free for all clusters.</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

kmmNames function

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

Arguments

Create a vector of Kernel mixture model (KMM) names. — kmmNames

<dl>

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


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


<dt>names</dt>
<dd>a vector of character with the names to check</dd>

</dl>

Model Name	Proportions	s. d. between clusters
kmm_p_sk	equal	Free
kmm_p_s	equal	Equal
kmm_pk_sk	equal	Free
kmm_pk_s	equal	Equal

kmmNames: Create a vector of Kernel mixture model (KMM) names.

Description

Usage

Value

Arguments

Details

Examples