plotAll,ClusterizLongData {kml} | R Documentation |
plot
the trajectories, the Calinski's criterions and the
subgroups of an object ClusterizLongData
on a
single graph.
plotAll(x, y, ...)
x |
[ClusterizLongData] Object containing the trajectories to plot. |
y |
[numeric] or [vector(numeric)] Give the Clusterization
to represent. If y is missing, the Clusterization with
the highest Calinski creterion is selected. If y is a number,
the first Clusterization of the sublist c y is
selected. If y is a couple of numeric, the y[2] th
Clusterization of the sublist c y[1] is selected. |
... |
Graphical
parameters to be passed to methods (see plot ). For
ClusterizLongData object specificly :
|
For every clusterizLongData
object, there is three possible
graphical representation: calinski criterion, all the trajectories at once
and trajectories clusters by clusters. This function combine these three
graphical output (or only two, or one, accorind to the three arguments printCal
, printTraj
and
printSub
). Thus every option (like colorMean,...) are
duplicate : one is for the main graph (colorMean), the second is for
the sub-graphs (colorMeanSub).
Christophe Genolini
PSIGIAM: Paris Sud Innovation Group in Adolescent Mental Health
INSERM U669 / Maison de Solenn / Paris
Contact author : <genolini@u-paris10.fr>
Raphaël Ricaud
Laboratoire "Sport & Culture" / "Sports & Culture" Laboratory
University of Paris 10 / Nanterre
Article submited
Web site: http://christophe.genolini.free.fr/kml
Overview: kml-package
Classes : ClusterizLongData
, ArtificialLongData
Plot : plot: overview
, plot(ClusterizLongData)
,
plot(Calinski)
, plotSubGroups(ClusterizLongData)
ld <- gald() kml(ld,2:5,5) kml(ld,16,5) par(ask=TRUE) ### Default ploting plotAll(ld) ### Only the calinski criterion (same effect than plotCalinski(ld)) plotAll(ld,printCal=TRUE,printTraj=FALSE,printSub=FALSE) ### Groups and sub-groups plotAll(ld,printCal=FALSE,printTraj=TRUE,printSub=TRUE) plotAll(ld,printCal=FALSE,printTraj=TRUE,printSub=TRUE,colorTraj="black",colorMean="no",colorMeanSub="black") ### All at once plotAll(ld,printCal=TRUE,printTraj=TRUE,printSub=TRUE) ### To see the clusterization with only 2 clusters plotAll(ld,2,printCal=FALSE,printTraj=TRUE,printSub=TRUE) ### To see the third clusterization with 16 clusters plotAll(ld,c(16,3),printCal=FALSE,printTraj=TRUE,printSub=TRUE) par(ask=FALSE)