VSLCMparam-class

<dl class="dl-horizontal">
 <dt>pi</dt><dd>numeric. Proportions of the mixture components.</dd>
 <dt>paramContinuous</dt><dd><a href="/link/?package=VarSelLCM&version=2.1.3.1" vslcmparamcontinuous'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMparamContinuous. Parameters of the continuous variables.</dd>
 <dt>paramInteger</dt><dd><a href="/link/?package=VarSelLCM&version=2.1.3.1" vslcmparaminteger'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMparamInteger. Parameters of the integer variables.</dd>
 <dt>paramCategorical</dt><dd><a href="/link/?package=VarSelLCM&version=2.1.3.1" vslcmparamcategorical'="" data-mini-rdoc="VarSelLCM::"></a>'>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

VSLCMparam-class function

<dl class='dl-horizontal'>
 <dt>pi</dt><dd>numeric. Proportions of the mixture components.</dd>
 <dt>paramContinuous</dt><dd><a href='<a href='VSLCMparamContinuous'></a>'>VSLCMparamContinuous</a>. Parameters of the continuous variables.</dd>
 <dt>paramInteger</dt><dd><a href='<a href='VSLCMparamInteger'></a>'>VSLCMparamInteger</a>. Parameters of the integer variables.</dd>
 <dt>paramCategorical</dt><dd><a href='<a href='VSLCMparamCategorical'></a>'>VSLCMparamCategorical</a>. Parameters of the categorical variables.</dd>
</dl>

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

Description

Arguments

Examples