VSLCMparamContinuous-class

<dl>
 <dt>pi</dt>
<dd>numeric. Proportions of the mixture components.</dd> <dt>mu</dt>
<dd>matrix. Mean for each component (column) and each variable (row).</dd> <dt>sd</dt>
<dd>matrix. Standard deviation for each component (column) and each variable (row).</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

VSLCMparamContinuous-class function

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

 <dt>mu</dt>
<dd>matrix. Mean for each component (column) and each variable (row).</dd>

 <dt>sd</dt>
<dd>matrix. Standard deviation for each component (column) and each variable (row).</dd>


</dl>

VSLCMparamContinuous-class: Constructor of `VSLCMparamContinuous` class

Description

Arguments

Examples