ClusterLongData-class: ~ Class: ClusterLongData ~
In kml: K-Means for Longitudinal Data

ClusterLongData-class

R Documentation

~ Class: ClusterLongData ~

Description

ClusterLongData is an object containing trajectories and associated Partition

Objects from the Class

kml is an algorithm that builds a set of Partition from longitudinal data. ClusterLongData is the object containing the original longitudinal data and all the Partition that kml finds.

When created, an ClusterLongData object simply contains initial data (the trajectories). After the execution of kml, it contains the original data and the Partition which has just been calculated by kml.

Note that if kml is executed several times, every new Partition is added to the original ones, no pre-existing Partition is erased.

Slots

idAll: [vector(character)]: Single identifier for each of the trajectory (each individual). Usefull for exporting clusters.
idFewNA: [vector(character)]: Restriction of idAll to the trajectories that does not have 'too many' missing value. See maxNA for details.
time: [numeric]: Time at which measures are made.
varNames: [character]: Name of the variable measured.
traj: [matrix(numeric)]: Contains the longitudianl data. Each lines is the trajectories of an individual. Each column is the time at which measures are made.
dimTraj: [vector2(numeric)]: size of the matrix traj (ie dimTraj=c(length(idFewNA),length(time))).
maxNA: [numeric] or [vector(numeric)]: Individual whose trajectories contain 'too many' missing value are exclude from traj and will no be use in the analysis. Their identifier is preserved in idAll but not in idFewNA. 'too many' is define by maxNA: a trajectory with more missing than maxNA is exclude.
reverse: [matrix(numeric)]: if the trajectories are scale using the function scale, the 'scaling parameters' (probably mean and standard deviation) are saved in reverse. This is usefull to restaure the original data after a scaling operation.
criterionActif: [character]: Store the criterion name that will be used by functions that need a single criterion (like plotCriterion or ordered).
initializationMethod: [vector(chararcter)]: list all the initialization method that has already been used to find some Partition (usefull to not run several time a deterministic method).
sorted: [logical]: are the Partition curently hold in the object sorted in decreasing order ?
c1: [list(Partition)]: list of Partition with 1 clusters.
c2: [list(Partition)]: list of Partition with 2 clusters.
c3: [list(Partition)]: list of Partition with 3 clusters.
...
c26: [list(Partition)]: list of Partition with 26 clusters.

Extends

Class LongData, directly. Class ListPartition, directly.

Construction

Class ClusterizLongData objects can be constructed via function clusterLongData that turn a data.frame or a matrix into a ClusterLongData. Note that some artificial data can be generated using gald.

Methods

object['xxx']: Get the value of the field xxx. Inherit from LongData and ListPartition.
object['xxx']<-value: Set the field xxx to value. xxx. Inherit from ListPartition.
plot: Display the ClusterLongData according to a Partition.

Special thanks

Special thanks to Boris Hejblum for debugging the '[' and '[<-' operators (the previous version was not compatible with the matrix package, which is used by lme4).

Examples

### Move to tempdir
wd <- getwd()
setwd(tempdir()); getwd()

################
### Creation of some trajectories

traj <- matrix(c(1,2,3,1,4, 3,6,1,8,10, 1,2,1,3,2, 4,2,5,6,3, 4,3,4,4,4, 7,6,5,5,4),6)

myCld <- clusterLongData(
    traj=traj,
    idAll=as.character(c(100,102,103,109,115,123)),
    time=c(1,2,4,8,15),
    varNames="P",
    maxNA=3
)

################
### get and set
myCld["idAll"]
myCld["varNames"]
myCld["traj"]

################
### Creation of a Partition
part2 <- partition(clusters=rep(1:2,3),myCld)
part3 <- partition(clusters=rep(1:3,2),myCld)


################
### Adding a clusterization to a clusterizLongData
myCld["add"] <- part2
myCld["add"] <- part3
myCld

### Go back to current dir
setwd(wd)

kml documentation built on Feb. 16, 2023, 8:35 p.m.