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kml3d (version 2.3)

K-Means for Joint Longitudinal Data

Description

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.

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Version

Install

install.packages('kml3d')

Monthly Downloads

703

Version

2.3

License

GPL (>= 2)

Maintainer

Christophe Genolini

Last Published

May 21st, 2015

Functions in kml3d (2.3)

kml3d

~ Algorithm kml3d: K-means for Joint Longitidinal data ~
kml3d-package

~ Overview: KmL3D, K-means for joint Longitudinal data ~
plot3dPdf

~ Function: plot3dPdf for ClusterLongData3d ~
pregnandiol

~ Pregnandiol measure (from QUIDEL database, Ren� �cochard) ~
choice

~ Function: choice ~
clusterLongData3d

~ Function: clusterLongData3d (or cld3d) ~
generateArtificialLongData3d

~ Function: generateArtificialLongData3d (or gald3d) ~
ClusterLongData3d-class

~ Class: ClusterLongData3d ~
plot,ClusterLongData3d

~ Function: plot for ClusterLongData3d ~
affectIndiv3d

~ Function: affectIndiv3d ~
plot3d,ClusterLongData3d

~ Function: plot3d for ClusterLongData3d ~
dist3d

~ Function: dist3d ~
calculTrajMean3d

~ Function: calculTrajMean3d ~
parKml3d

~ Function: parKml3d ~