VSLCMdataContinuous-class

<dl class="dl-horizontal">
 <dt>n</dt><dd>number of observations</dd>
 <dt>d</dt><dd>number of variables</dd>
 <dt>data</dt><dd>matrix of observations (one row = one observation)</dd>
 <dt>notNA</dt><dd>matrix of logical (1:observed, 0:unobserved)</dd>
 <dt>priors</dt><dd>hyper-parameters of the prior distributions</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

VSLCMdataContinuous-class function

<dl class='dl-horizontal'>
 <dt>n</dt><dd>number of observations</dd>
 <dt>d</dt><dd>number of variables</dd>
 <dt>data</dt><dd>matrix of observations (one row = one observation)</dd>
 <dt>notNA</dt><dd>matrix of logical (1:observed, 0:unobserved)</dd>
 <dt>priors</dt><dd>hyper-parameters of the prior distributions</dd>
</dl>

Constructor of <code><a href='<a href='VSLCMdataContinuous'></a>'>VSLCMdataContinuous</a></code> class — VSLCMdataContinuous-class

Constructor of <code><a href='<a href='VSLCMdataContinuous'></a>'>VSLCMdataContinuous</a></code> class

Constructor of <code><a href="/link/?package=VarSelLCM&version=2.1.1" vslcmdatacontinuous'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMdataContinuous</code> class

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

Description

Arguments

Examples