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VarSelLCM (version 2.1.3.1)

Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

Description

Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here ). 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.

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Version

Install

install.packages('VarSelLCM')

Monthly Downloads

585

Version

2.1.3.1

License

GPL (>= 2)

Maintainer

Mohammed Sedki

Last Published

October 14th, 2020

Functions in VarSelLCM (2.1.3.1)

VarSelLCM-package

Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

VSLCMmodel-class

Constructor of VSLCMmodel class

VarSelImputation

Imputation of missing values

VSLCMpartitions-class

Constructor of VSLCMpartitions class

VSLCMresults-class

Constructor of VSLCMresults class

Statlog (Heart) Data Set

Extract the partition or the probabilities of classification

VSLCMparamContinuous-class

Constructor of VSLCMparamContinuous class

Extract the parameters

Prediction of the cluster memberships

Shiny app for analyzing results from VarSelCluster

Plots of an instance of VSLCMresults

VSLCMparamInteger-class

Constructor of VSLCMparamInteger class

Extract the parameters

Extract the partition or the probabilities of classification

Variable selection and clustering.

VSLCMstrategy-class

Constructor of VSLCMstrategy class

Print function.

Summary function.

Adjusted Rand Index

VSLCMcriteria-class

Constructor of VSLCMcriteria class

VSLCMdata-class

Constructor of VSLCMdata class

VSLCMparam-class

Constructor of VSLCMparam class

VSLCMparamCategorical-class

Constructor of VSLCMparamCategorical class