~ Overview: KmL3D, K-means for joint Longitudinal data ~
KmL3D is a new implementation of k-means for longitudinal data (or trajectories).
Here is an overview of the package.
| Package: | KmL3D |
| Type: | Package |
| Version: | 2.4.2 |
| Date: | 2017-08-01 |
| License: | GPL (>= 2) |
| LazyData: | yes |
| Depends: | methods,graphics,rgl,misc3d,longitudinalData(>=2.2),KmL(>=2.2) |
| URL: | http://www.r-project.org |
| URL: | http://christophe.genolini.free.fr/kml |
To cluster data, KmL3D go through three steps, each of which
is associated to some functions:
Data preparation
Building "optimal" clusterization.
Exporting results
Visualizing and exporting 3D object
kml3d works on object of class ClusterLongData3d.
Data preparation therefore simply consists in transforming data into an object ClusterLongData3d.
This can be done via function
clusterLongData3d
(cld3d in short) that
converts a data.frame or an array into a ClusterLongData3d.
Working on several variables mesured on different scales can give to
much weight to one of the dimension. So the function scale normalizes data.
Instead of working on real data, one can also work on artificial
data. Such data can be created with generateArtificialLongData3d
(gald3d in short).
Once an object of class ClusterLongData3d has been created, the algorithm
kml3d can be run.
Starting with a ClusterLongData3d, kml3d built several Partition.
A object of class Partition is a partition of trajectories
into subgroups. It also contains some information like the
percentage of trajectories contained in each group or some quality critetion (like the Calinski &
Harabasz).
k-means is a "hill-climbing" algorithm. The specificity of this
kind of algorithm is that it always converges towards a maximum, but
one cannot know whether it is a local or a global maximum. It offers
no guarantee of optimality.
To maximize one's chances of getting a quality Partition,
it is better to execute the hill climbing algorithm several times,
then to choose the best solution. By default, kml3d executes the hill climbing algorithm 20 times.
To date, it is not possible to know the optimum number of clusters
even if the calculatous of some qualities criterion can gives some
clues. kml3d computes various of them.
In the end, kml3d tests by default 2, 3, 4, 5 et 6 clusters, 20 times each.
When some Partition has been selected (the user can select
more than 1), it is possible to
save them. The clusters are therefore exported towards the file
name-cluster.csv. Criteria are exported towards
name-criteres.csv. The graphs are exported according to their
extension.
KmL3D also propose tools to visualize the trajectories in
3D. plot3d using the library rgl to plot two
variables according to time (either the all set of joint-trajectories, or
just the mean joint-trajectories). Then the user can make the
graphical representation turn using the mouse. plot3dPdf build an
Triangles object. These kind of
object can be include in a pdf file using
saveTrianglesAsASY and the software
asymptote. Once again, it is possible to make the image in the
pdf file move using the mouse -so the reader gets real 3D-.
For those who are not familiar with S4 programming: In S4 programming, each function can be adapted for some specific arguments.
To get help on a function (for example plot), use:
?(plot).
To get help on a function adapted to its argument (for example plot
on argument ClusterLongData), used: ?"plot,ClusterLongData".
### 1. Data Preparation data(pregnandiol) names(pregnandiol) cld3dPregTemp <- cld3d(pregnandiol,timeInData=list(temp=1:30*2,preg=1:30*2+1)) ### 2. Building "optimal" clusteration (with only 2 redrawings) ### Real analysis needs at least 20 redrawings kml3d(cld3dPregTemp,3:5,nbRedrawing=2,toPlot="both") ### 3. Exporting results try(choice(cld3dPregTemp)) ### 4. Visualizing in 3D plotMeans3d(cld3dPregTemp,4)
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