kml3d: calculTrajMean3d – R documentation

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calculTrajMean3d

~ Function: calculTrajMean3d ~

Description

Given some joint longitudinal data and a cluster affectation, calculTrajMean3d computes the mean joint-trajectories of each cluster.

Usage

calculTrajMean3d(traj, clust,centerMethod=function(x){mean(x,na.rm=TRUE)})

Arguments

`traj`	`[array(numeric)]`: joint longitudinal data. Each line is an individual, each column is a time measurement, the third dimension is for variables.
`clust`	`[vector(numeric)]`: affectation of each individual.
`centerMethod`	`[joint-trajectory <- function(array(numeric))]`: function used to compute the clusters' centers.

Details

Given a vector of affectation to a cluster, the function calculTrajMean3d compute the "central" trajectory of each clusters. The "center" can be define using the argument centerMethod.

affectIndiv3d used with calculTrajMean3d simulates one k-means step.

Value

An array of dimension (k,t,v) with k number of groups, t number of time mesurement and v number of variables.

Examples

#######################
### calculTrajMean3d

### Some LongitudinalData3d
traj <- gald3d()["traj"]

### A partition
part <- floor(runif(150,1,5))
plot(clusterLongData3d(traj),parTraj=parTRAJ(col=part+1))

### Clusters center
(center <- calculTrajMean3d(traj,part))


#################
### K-means simulation (4 steps)
plot(clusterLongData3d(traj),parTraj=parTRAJ(col=part+1))
for (i in 1:4){
    part <- affectIndiv3d(traj,center)
    center <- calculTrajMean3d(traj,part)
    plot(clusterLongData3d(traj),parTraj=parTRAJ(col=part+1))
}

kml3d

K-Means for Joint Longitudinal Data

v2.4.2

GPL (>= 2)

Authors

Christophe Genolini [cre, aut], Bruno Falissard [ctb], Jean-Baptiste Pingault [ctb]

Initial release

2017-08-01