| miclust-package {miclust} | R Documentation |
miclust-package: integrating multiple imputation with cluster analysis
Description
Cluster analysis with selection of the final number of clusters and an optional variable selection procedure. The package is designed to integrate the results of multiply imputed data sets while accounting for the uncertainty that the imputations introduce in the final results. See ‘Procedure’ below for further details on how the tool works.
Procedure
The tool consists of a two-step procedure. In the first step,
the user provides the data to be analyzed. They can be a single data.frame or a
list of data.frames including the raw data and the imputed data sets. In the
latter case, getdata needs to by used first to get data prepared. In the
second step, the miclust performs k-means clustering with selection of
the final number of clusters and an optional (backward or forward) variable
selection procedure. Specific summary and plot methods are provided
to summarize and visualize the impact of the imputations on the results.
Authors
Jose Barrera-Gomez (maintainer, <jose.barrera@isglobal.org>) and Xavier Basagana.
References
The methodology used in the package is described in
Basagana X, Barrera-Gomez J, Benet M, Anto JM, Garcia-Aymerich J. A Framework for Multiple Imputation in Cluster Analysis. American Journal of Epidemiology. 2013;177(7):718-725.