kml (version 2.1.2)

clusterLongData: ~ Function: clusterLongData (or cld) ~

Description

clusterLongData (or cld in short) is the constructor for ClusterLongData object.

Usage

clusterLongData(traj, idAll, time, timeInData, varNames, maxNA)
cld(traj, idAll, time, timeInData, varNames, maxNA)

Arguments

traj

[matrix(numeric)] or [data.frame]: structure containning the trajectories. Each line is the trajectory of an individual. The columns refer to the time during which measures were made.

idAll

[vector(character)]: single identifier for each trajectory (ie each individual). Note that the identifiers are of type character (that allow to deal identifiers like XUK32-612, identifiers that our fav

time

[vector(numeric)]: time at which measures were made.

timeInData

[vector(numeric)]: precise the column containing the trajectories.

varNames

[character]: name of the variable being measured.

maxNA

[numeric]: maximum number of NA that are tolerates on a trajectory. If a trajectory has more missing than maxNA, then it is remove from the analysis.

Value

• An object of class ClusterLongData.

Author

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

Details

clusterLongData construct a object of class ClusterLongData. Two cases can be distinguised: [object Object],[object Object]

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

Overview: kml-package Classes : ClusterLongData Methods : choice, kml Plot : plot(ClusterLongData)

Examples

#####################
### From matrix

### Small data
mat <- matrix(c(1,NA,3,2,3,6,1,8,10),3,3,dimnames=list(c(101,102,104),c("T2","T4","T8")))
clusterLongData(mat)
(ld1 <- clusterLongData(traj=mat,idAll=as.character(c(101,102,104)),time=c(2,4,8),varNames="V"))
plot(ld1)

### Big data
mat <- matrix(runif(1051*325),1051,325)
(ld2 <- clusterLongData(traj=mat,idAll=paste("I-",1:1051,sep=""),time=(1:325)+0.5,varNames="Random"))

####################
### From data.frame

dn <- data.frame(id=1:3,v1=c(NA,2,1),v2=c(NA,1,0),v3=c(3,2,2),v4=c(4,2,NA))

### Basic
clusterLongData(dn)

### Selecting some times
(ld3 <- clusterLongData(dn,timeInData=c(1,2,4),varNames=c("Hyp")))

### Excluding trajectories with more than 1 NA
(ld3 <- clusterLongData(dn,maxNA=1))