Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
kml and kml3d: R Packages to Cluster Longitudinal Data | Genolini | Journal of Statistical Software
Authors: Christophe Genolini, Xavier Alacoque, Mariane Sentenac, Catherine Arnaud
Title: kml and kml3d: R Packages to Cluster Longitudinal Data

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).

Page views:: 1953. Submitted: 2012-11-20. Published: 2015-06-01.
Paper: kml and kml3d: R Packages to Cluster Longitudinal Data     Download PDF (Downloads: 2990)
kml_2.3.tar.gz: R source package Download (Downloads: 87; 37KB)
kml3d_2.3.tar.gz: R source package Download (Downloads: 107; 155KB)
v65i04.R: R example code from the paper Download (Downloads: 93; 1KB)

DOI: 10.18637/jss.v065.i04

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.