The clustering of variables is a strategy for deciphering the underlying structure of a data set. Adopting an exploratory data analysis point of view, the Clustering of Variables around Latent Variables (CLV) approach has been proposed by Vigneau and Qannari (2003). Based on a family of optimization criteria, the CLV approach is adaptable to many situations. In particular, constraints may be introduced in order to take account of additional information about the observations and/or the variables. In this paper, the CLV method is depicted and the R package ClustVarLV including a set of functions developed so far within this framework is introduced. Considering successively different types of situations, the underlying CLV criteria are detailed and the various functions of the package are illustrated using real case studies.
cluster, ClustVarLV, ClustOfVar, clere, biclust, pvclust, Hmisc, FactoMineR, plsgenomics, Rcpp, ClustVarLV
Multivariate, Cluster, Psychometrics, Environmetrics, HighPerformanceComputing, Bayesian, ClinicalTrials, Econometrics, Graphics, NumericalMathematics, OfficialStatistics, ReproducibleResearch, SocialSciences
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For attribution, please cite this work as
Vigneau, et al., "ClustVarLV: An R Package for the Clustering of Variables Around Latent Variables", The R Journal, 2015
BibTeX citation
@article{RJ-2015-026,
author = {Vigneau, Evelyne and Chen, Mingkun and Qannari, El Mostafa},
title = {ClustVarLV: An R Package for the Clustering of Variables Around Latent Variables},
journal = {The R Journal},
year = {2015},
note = {https://doi.org/10.32614/RJ-2015-026},
doi = {10.32614/RJ-2015-026},
volume = {7},
issue = {2},
issn = {2073-4859},
pages = {134-148}
}