longitudinalData (version 2.4.7)

plotTrajMeans3d,LongData: ~ Function: plotTrajMeans3d for LongData3d ~

Description

Plot two variables of a LongData3d object in 3D, optionnaly relatively to a Partition.

Usage

plotTrajMeans3d(x,y,varY=1,varZ=2,
   parTraj=parTRAJ(),parMean=parMEAN(type="n"),...)

Arguments

x

[LongData3d]: Object containing the trajectories to plot.

y

[Partition]: Partition that will be use to plot the object. If y is missing, a Partition with a single clusters is considered.

varY

[numeric] or [character]: either the number or the name of the first variable to display. 1 by default.

varZ

[numeric] or [character]: either the number or the name of the second variable to display. 2 by default.

parTraj

[parLongData]: Set the graphical parameters used to plot the trajectories of the LongData3d. See ParLongData and examples for details.

parMean

[parLongData]: Set the graphical parameters used to plot the mean trajectories of each clusters LongData3d (only when y is non missing). See ParLongData and examples for details.

...

Arguments to be passed to methods, such as graphical parameters.

Author

Christophe Genolini
1. UMR U1027, INSERM, Université Paul Sabatier / Toulouse III / France
2. CeRSME, EA 2931, UFR STAPS, Université de Paris Ouest-Nanterre-La Défense / Nanterre / France

Details

Plot two variables of a LongData3d object in 3D. It use the rgl library. The user can make the graphical representation turn using the mouse.

References

[1] C. Genolini and B. Falissard
"KmL: k-means for longitudinal data"
Computational Statistics, vol 25(2), pp 317-328, 2010

[2] C. Genolini and B. Falissard
"KmL: A package to cluster longitudinal data"
Computer Methods and Programs in Biomedicine, 104, pp e112-121, 2011

See Also

LongData3d

Examples

##################
### Construction of the data

# \donttest{
time=c(1,2,3,4,8,12,16,20)
id2=1:120
f <- function(id,t)((id-1)%%3-1) * t
g <- function(id,t)(id%%2+1)*t
h <- function(id,t)(id%%4-0.5)*(20-t)
ld <- longData3d(array(cbind(outer(id2,time,f),outer(id2,time,g),outer(id2,time,h))+
   rnorm(120*8*3,0,3),dim=c(120,8,3)))
part <- partition(rep(1:6,20))

### Basic plotting
plotTrajMeans3d(ld)
plotTrajMeans3d(ld,part)

### Variable 1 and 3, then 2 and 3
plotTrajMeans3d(ld,part)
plotTrajMeans3d(ld,part,varY=3,varZ=2)
plotTrajMeans3d(ld,part,varY=1,varZ=3)

##################
### Changing graphical parameters 'par'

### Color individual according to its clusters (col="clusters")
plotTrajMeans3d(ld,part,parTraj=parTRAJ(col="clusters"))
plotTrajMeans3d(ld,part,parTraj=parTRAJ(col="clusters"),varY=1,varZ=3)

### No mean trajectories (type="n"), only few trajectories
### Color individual according to its clusters (col="clusters")
plotTrajMeans3d(ld,part,parTraj=parTRAJ(col="clusters"),parMean=parMEAN(type="n"),nbSample=10)
# }