R: ~ Function: clusterLongData (or cld) ~

clusterLongData {kml} | R Documentation

## ~ 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 favorite epidemiologists are so good at providing). If `idAll` are `numeric`, they are converted into `characters`.
`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.

### Details

`clusterLongData` construct a object of class `ClusterLongData`. Two cases can be distinguised:

`traj` is an `array`:

  lines are individual. Column are time of measurment.

  If `idAll` is missing, the individuals are labelled `i1`, `i2`, `i3`,...

  If `timeInData` is missing, all the column are used (`timeInData=1:ncol(traj)`).

If `traj` is a `data.frame`:

  lines are individual. Column are time of measurement.

  If `idAll` is missing, then the first column of the `data.frame` is used for `idAll`

  If `timeInData` is missing and `idAll` is missing, then all the columns but the first are used for `timeInData` (the first is omited since it is already used for `idAll`): `idAll=traj[,1],timeInData=2:ncol(traj)`.

  If `timeInData` is missing but `idAll` is not missing, then all the column including the first are used for `timeInData`: `timeInData=1:ncol(traj)`.

### Value

An object of class `ClusterLongData`.

### 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

### 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="R"))

    ####################
    ### 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))

[Package kml version 2.4.1 Index]