VSLCMparam-class

<dl>
 <dt>pi</dt>
<dd>numeric. Proportions of the mixture components.</dd> <dt>paramContinuous</dt>
<dd>VSLCMparamContinuous. Parameters of the continuous variables.</dd> <dt>paramInteger</dt>
<dd>VSLCMparamInteger. Parameters of the integer variables.</dd> <dt>paramCategorical</dt>
<dd>VSLCMparamCategorical. Parameters of the categorical 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

Matthieu Marbac

VSLCMparam-class function

<dl>
 <dt>pi</dt>
<dd>numeric. Proportions of the mixture components.</dd>

 <dt>paramContinuous</dt>
<dd>VSLCMparamContinuous. Parameters of the continuous variables.</dd>

 <dt>paramInteger</dt>
<dd>VSLCMparamInteger. Parameters of the integer variables.</dd>

 <dt>paramCategorical</dt>
<dd>VSLCMparamCategorical. Parameters of the categorical variables.</dd>


</dl>

VSLCMparam-class: Constructor of `VSLCMparam` class

Description

Arguments

Examples