VSLCMmodel-class

<dl class="dl-horizontal">
 <dt>g</dt><dd>numeric. Number of components.</dd>
 <dt>omega</dt><dd>logical. Vector indicating if each variable is irrelevant (1) or not (0) to the clustering.</dd>
 <dt>names.relevant</dt><dd>character. Names of the relevant variables.</dd>
 <dt>names.irrelevant</dt><dd>character. Names of the irrelevant variables.</dd>
</dl>

Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.

Mohammed Sedki

VarSelLCM

Variable Selection for Model-Based Clustering of Mixed-Type Data
Set with Missing Values

VSLCMmodel-class function

<dl class='dl-horizontal'>
 <dt>g</dt><dd>numeric. Number of components.</dd>
 <dt>omega</dt><dd>logical. Vector indicating if each variable is irrelevant (1) or not (0) to the clustering.</dd>
 <dt>names.relevant</dt><dd>character. Names of the relevant variables.</dd>
 <dt>names.irrelevant</dt><dd>character. Names of the irrelevant variables.</dd>
</dl>

VSLCMmodel-class: Constructor of `'>VSLCMmodel` class

Description

Arguments

Examples