kml3d: K-means for joint Longitudinal data
KmL3D is an implementation of k-means specifically design
to cluster joint trajectories (longitudinal data on several
variable-trajectories). Like KmL, it provides facilities to
deal with missing value, compute several quality criterion
(Calinski and Harabatz, Ray and Turie, Davies and Bouldin,
BIC,...) and propose a graphical interface for choosing the
'best' number of clusters. In addition, the 3D graph
representing the mean joint-trajectories of each cluster can be
exported through LaTeX in a 3D dynamic rotating PDF graph.
| Version: |
2.1.2 |
| Depends: |
methods, clv, rgl, misc3d, longitudinalData (≥ 2.1.2), kml (≥
2.1.2) |
| Published: |
2012-11-19 |
| Author: |
Christophe Genolini [cre, aut], Bruno Falissard [ctb],
Jean-Baptiste Pingault [ctb] |
| Maintainer: |
Christophe Genolini <christophe.genolini at u-paris10.fr> |
| License: |
GPL (≥ 2) |
| URL: |
http:www.r-project.org |
| NeedsCompilation: |
no |
| In views: |
Cluster |
| CRAN checks: |
kml3d results |
Downloads: