partition: ~ Function: partition ~
In longitudinalData: Longitudinal Data

Description Usage Arguments Details Value Author References See Also Examples

partition is the constructor of the class Partition. It can be build either alone or relatively to a object LongData.

1	partition(clusters, traj, details=character())

`clusters`	`[vector(factor)]`: cluters to which each individual belongs. Each clusters is represented by an upper letters.
`traj`	`[matrix]` or `[array]`: if an object `LongData` is provide, it will be used to compute the quality criterion of the clustering. `array` are simply turn into matrix by "sticking" all the variables one behind the other.
`details`	`[vector(character)]`: the slot `details` is used to store various informations. If the `Partition` has been find using an algorithm, it can store the name of the algorithm, the time before convergence, the number of iteration and any other informations. The syntaxe is `details=c(algoritm="kmeans",convergenceTime="6",otherInfo="WhateverIWant")`.

partition construct a object of class Partition. It does not provide any default values. yLongData and details are optional.

An object of class Partition.

Christophe Genolini
1. UMR U1027, INSERM, Universit<e9> Paul Sabatier / Toulouse III / France
2. CeRSM, EA 2931, UFR STAPS, Universit<e9> de Paris Ouest-Nanterre-La D<e9>fense / Nanterre / France

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

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

Partition,ordered

### Empty partition
partition()

### Small partition
partition(clusters=c("A","B","A","C","C"))

### Random partition
partition(clusters=LETTERS[floor(runif(100,1,5))])

### Partition that clusters correctly some data
###   Quality criterion are high
data(artificialLongData)
traj <- as.matrix(artificialLongData[,-1])
partition(clusters=rep(1:4,each=50),traj)

### Partition that does not cluster correctly the data
###   Quality criterion are low
partition(clusters=rep(1:4,50),traj)