VSLCMresults-class

<dl>
 <dt>data</dt>
<dd>VSLCMdata. Results relied to the data.</dd> <dt>criteria</dt>
<dd>VSLCMcriteria. Results relied to the information criteria.</dd> <dt>partitions</dt>
<dd>VSLCMpartitions. Results relied to the partitions.</dd> <dt>model</dt>
<dd>VSLCMmodel. Results relied to the selected model.</dd> <dt>strategy</dt>
<dd>VSLCMstrategy. Results relied to the tune parameters.</dd> <dt>param</dt>
<dd>VSLCMparam. Results relied to the parameters.</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

VSLCMresults-class function

<dl>
 <dt>data</dt>
<dd>VSLCMdata. Results relied to the data.</dd>

 <dt>criteria</dt>
<dd>VSLCMcriteria. Results relied to the information criteria.</dd>

 <dt>partitions</dt>
<dd>VSLCMpartitions. Results relied to the partitions.</dd>

 <dt>model</dt>
<dd>VSLCMmodel. Results relied to the selected model.</dd>

 <dt>strategy</dt>
<dd>VSLCMstrategy. Results relied to the tune parameters.</dd>

 <dt>param</dt>
<dd>VSLCMparam. Results relied to the parameters.</dd>


</dl>

VSLCMresults-class: Constructor of `VSLCMresults` class

Description

Arguments

Examples