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kml3d

K-Means for Joint Longitudinal Data

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.

Functions (15)

ClusterLongData3d-class

~ Class: ClusterLongData3d ~

affectIndiv3d

~ Function: affectIndiv3d ~

calculTrajMean3d

~ Function: calculTrajMean3d ~

clusterLongData3d

~ Function: clusterLongData3d (or cld3d) ~

dist3d

~ Function: dist3d ~

generateArtificialLongData3d

~ Function: generateArtificialLongData3d (or gald3d) ~

kml3d

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

kml3d-package

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

parKml3d

~ Function: parKml3d ~

plot

~ Function: plot for ClusterLongData3d ~

plot3d

~ Function: plot3d for ClusterLongData3d ~

plot3dPdf

~ Function: plot3dPdf for ClusterLongData3d ~

plotMean3d

~ Function: plotMeans3d for ClusterLongData3d ~

plotTraj3d

~ Function: plotTraj3d for ClusterLongData3d ~

pregnandiol

~ Pregnandiol measure (from QUIDEL database, René Écochard) ~

kml3d

K-Means for Joint Longitudinal Data

v2.4.2
GPL (>= 2)
Authors
Christophe Genolini [cre, aut], Bruno Falissard [ctb], Jean-Baptiste Pingault [ctb]
Initial release
2017-08-01

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