kml and kml3d: R Packages to Cluster Longitudinal Data

Christophe Genolini, Xavier Alacoque, Mariane Sentenac and Catherine Arnaud

Journal of Statistical Software, 2015, vol. 065, issue i04

Abstract: Longitudinal studies are essential tools in medical research. In these studies, variables are not restricted to single measurements but can be seen as variable-trajectories, either single or joint. Thus, an important question concerns the identification of homogeneous patient trajectories.kml and kml3d are R packages providing an implementation of k-means designed to work specifically on trajectories (kml) or on joint trajectories (kml3d). They provide various tools to work on longitudinal data: imputation methods for trajectories (nine classic and one original), methods to define starting conditions in k-means (four classic and three original) and quality criteria to choose the best number of clusters (four classic and one original). In addition, they offer graphic facilities to “visualize” the trajectories, either in 2D (single trajectory) or 3D (joint-trajectories). The 3D graph representing the mean joint-trajectories of each cluster can be exported through LATEX in a 3D dynamic rotating PDF graph (Figures 1 and 9).

Date: 2015-06-01
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:065:i04

DOI: 10.18637/jss.v065.i04

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