model-methods

model


Recall that a model is defined by $\left(K,S\right)$ where $K$ is the number of clusters and $S$ that indicates the clustering variable. This method retrieves a list of model  $\left(K,S\right)$ from a <code>modelKS</code> object.


methods

An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.

Wilson Toussile

ClustMMDD

Variable Selection in Clustering by Mixture Models for Discrete
Data

model-methods function

<dl>
<dt><code>signature(object = "modelKS")</code></dt><dd>Retrieve a list of model  $\left(K,S\right)$ from a <code>modelKS</code> object.</dd> </dl>

model-methods: Retrieve a list of model $\left(K,S\right)$ from a `modelKS` object.

Description

Arguments

Methods

See Also

Examples