Uses a finite mixture model for performing the cluster analysis with variable selection of continuous data by assuming independence between classes. The package deals dataset with missing values by assuming that values are missing at random. The one-dimensional marginals of the components follow Gaussian distributions for facilitating both model interpretation and model selection. The variable selection is led by 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.
|Author||Matthieu Marbac and Mohammed Sedki|
|Date of publication||2015-06-10 19:00:07|
|Maintainer||Mohammed Sedki <firstname.lastname@example.org>|
|License||GPL (>= 2)|
banknote: Swiss banknotes data
print-methods: Print an object of class VSLCMresults
summary-methods: Produce result summary of a VSLCMresultsContinuous class
VarSelLCM-package: Variable Selection in model-based clustering managed by the...
VSLCMcriteria-class: Class '"VSLCMcriteria"'
VSLCMdataContinuous-class: Class '"VSLCMdataContinuous"'
VSLCMmodel-class: Class '"VSLCMmodel"'
VSLCMparametersContinuous-class: Class '"VSLCMparamContinuous"'
VSLCMpartitions-class: Class '"VSLCMpartitions"'
VSLCMresultsContinuous-class: Class '"VSLCMresultsContinuous"'
VSLCMstrategy-class: Class '"VSLCMstrategy"'
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.