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VarSelLCM (version 2.0.1)

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

Description

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.

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Version

Install

install.packages('VarSelLCM')

Monthly Downloads

392

Version

2.0.1

License

GPL (>= 2)

Maintainer

Mohammed Sedki

Last Published

October 16th, 2017

Functions in VarSelLCM (2.0.1)

VSLCMcriteria-class

Constructor of ['>VSLCMcriteria] class

VSLCMdataCategorical-class

Constructor of ['>VSLCMdataCategorical] class

VSLCMpartitions-class

Constructor of ['>VSLCMpartitions] class

VSLCMresultsCategorical-class

Constructor of ['>VSLCMresultsCategorical] class

VSLCMdataContinuous-class

Constructor of ['>VSLCMdataContinuous] class

VSLCMdataInteger-class

Constructor of ['>VSLCMdataInteger] class

VarSelCluster

This function performs the variable selection and the maximum likelihood estimation of the Latent Class Model

VarSelLCM-package

Variable Selection in model-based clustering managed by the Latent Class Model for analysis mixed-type data with missing values.

VSLCMparamCategorical-class

Constructor of ['>VSLCMparamCategorical] class

VSLCMparamContinuous-class

Constructor of ['>VSLCMparamContinuous] class

VSLCMresultsContinuous-class

Constructor of ['>VSLCMresultsContinuous] class

VSLCMresultsInteger-class

Constructor of ['>VSLCMresultsInteger] class

VSLCMparamInteger-class

Constructor of ['>VSLCMparamInteger] class

VSLCMparamMixed-class

Constructor of ['>VSLCMparamMixed] class

VSLCMresultsMixed-class

Constructor of ['>VSLCMresultsMixed] class

VSLCMstrategy-class

Constructor of ['>VSLCMstrategy] class

VSLCMdataMixed-class

Constructor of ['>VSLCMdataMixed] class

n: le nombre d'observations.
d: le nombre de variables.
withContinuous: boolien qui indique si variables continues ou pas.
withInteger: boolien qui indique si variables entieres ou pas.
withCategorica: boolien qui indique si variables categorielles ou pas.
dataContinuous: objet de la calsse VSLCMdataContinuous pour la partie continue des donnees.
dataInteger: objet de la classe VSLCMdataInteger pour la partie entiere des donnees.
dataCategorical: objet de la classe VSLCMdataCategorical pour la partie categorielle des donnees.
var.names: caracteres contenant les noms des variables.

VSLCMmodel-class

Constructor of ['>VSLCMmodel] class

heart

Heart desease data set

summary

Summary function.