summary

summary,VSLCMresultsContinuous-method

summary,VSLCMresultsInteger-method

summary,VSLCMresultsCategorical-method

summary,VSLCMresultsMixed-method

instance of <code><a href="/link/?package=VarSelLCM&version=2.0.1" vslcmresultscontinuous'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMresultsContinuous</code>, <code><a href="/link/?package=VarSelLCM&version=2.0.1" vslcmresultsinteger'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMresultsInteger</code>, <code><a href="/link/?package=VarSelLCM&version=2.0.1" vslcmresultscategorical'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMresultsCategorical</code> or <code><a href="/link/?package=VarSelLCM&version=2.0.1" vslcmresultsmixed'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMresultsMixed</code>..

object

This function gives the summary of an instance of <code><a href="/link/?package=VarSelLCM&version=2.0.1" vslcmresultscontinuous'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMresultsContinuous</code>, <code><a href="/link/?package=VarSelLCM&version=2.0.1" vslcmresultsinteger'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMresultsInteger</code>, <code><a href="/link/?package=VarSelLCM&version=2.0.1" vslcmresultscategorical'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMresultsCategorical</code> or <code><a href="/link/?package=VarSelLCM&version=2.0.1" vslcmresultsmixed'="" data-mini-rdoc="VarSelLCM::"></a>'>VSLCMresultsMixed</code>.

Variable Selection for model-based clustering managed by the Latent
Class Model. This model analyses mixed-type data (data with continuous and/
or count and/or categorical variables) with missing values (missing at random)
by assuming independence between classes. The one-dimensional marginals of
the components follow standard distributions for facilitating both the model
interpretation and the model selection. The variable selection is led by an
alternated optimization procedure for maximizing the Maximum Integrated
Complete-data Likelihood criterion. The maximum likelihood inference is done
by an EM algorithm for the selected model. This package also performs the
imputation of missing values by taking the expectation of the missing values
conditionally on the model, its parameters and on the observed variables.

Mohammed Sedki

VarSelLCM

Variable Selection for Model-Based Clustering of Continuous,
Count, Categorical or Mixed-Type Data Set with Missing Values

summary function

instance of <code><a href='<a href='VSLCMresultsContinuous'></a>'>VSLCMresultsContinuous</a></code>, <code><a href='<a href='VSLCMresultsInteger'></a>'>VSLCMresultsInteger</a></code>, <code><a href='<a href='VSLCMresultsCategorical'></a>'>VSLCMresultsCategorical</a></code> or <code><a href='<a href='VSLCMresultsMixed'></a>'>VSLCMresultsMixed</a></code>..

This function gives the summary of an instance of <code><a href='<a href='VSLCMresultsContinuous'></a>'>VSLCMresultsContinuous</a></code>, <code><a href='<a href='VSLCMresultsInteger'></a>'>VSLCMresultsInteger</a></code>, <code><a href='<a href='VSLCMresultsCategorical'></a>'>VSLCMresultsCategorical</a></code> or <code><a href='<a href='VSLCMresultsMixed'></a>'>VSLCMresultsMixed</a></code>.

summary: Summary function.

Description

Usage

Arguments