LongData3d-class: ~ Class: LongData3d ~
In longitudinalData: Longitudinal Data

LongData3d-class

R Documentation

~ Class: LongData3d ~

Description

LongData3d is an objet containing joint longitudinal data and some associate value (like time, individual identifiant,...).

Objects from the Class

Object LongData3d can be created using the fonction longData3d on a data.frame or on an array.

Slots

idAll: [vector(character)]: Single identifier for each of the longData3d (each individual). Usefull to export clusters.
idFewNA: [vector(character)]: Restriction of idAll to the trajectories that does not have 'too many' missing value. See maxNA for 'too many' definition.
time: [numeric]: Time at which measures are made.
varNames: [vector(character)]: Names of the variable measured.
traj: [array(numeric)]: Contains the joint variable-trajectories. Each horizontal plan (first dimension) corresponds to the joint-trajectories of an individual. Vertical plans (second dimension) refer to the time at which measures are made. Transversal plans (the third dimension) are for variables.
dimTraj: [vector3(numeric)]: size of the array traj (ie dimTraj=c(length(idFewNA),length(time),length(varNames))).
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. When maxNA is a single number, it is recycled for all the variables.
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 restore the original data after a scaling operation.

Construction

LongData3d can be created by calling the fonction longData3d on a data.frame or on an array.

Get [

Object["idAll"]: [vecteur(character)]: Gets the full list of individual identifiant (the value of the slot idAll)
Object["idFewNA"]: [vecteur(character)]: Gets the list of individual identifiant with not too many missing values (the value of the slot idFewNA)
Object["varNames"]: [character]: Gets the name(s) of the variable (the value of the slot varNames)
Object["time"]: [vecteur(numeric)]: Gets the times (the value of the slot time)
Object["traj"]: [array(numeric)]: Gets all the joint trajectories (the value of the slot traj)
Object["dimTraj"]: [vector3(numeric)]: Gets the dimension of traj.
Object["nbIdFewNA"]: [numeric]: Gets the first dimension of traj (ie the number of individual include in the analysis).
Object["nbTime"]: [numeric]: Gets the second dimension of traj (ie the number of time measurement).
Object["nbVar"]: [numeric]: Gets the third dimension of traj (ie the number of variables).
Object["maxNA"]: [vecteur(numeric)]: Gets maxNA.
Object["reverse"]: [matrix(numeric)]: Gets the matrix of the scaling parameters.

Methods

scale: scale the trajectories. Usefull to normalize variable trajectories measured with different units.
restoreRealData: restore original data that have been modified after a scaling operation.
longDataFrom3d: Create a LongData by extracting a single variable trajectory form a dataset of joint variable-trajectories.
plotTrajMeans: plot all the variable of the LongData3d, optionnaly according to a Partition.
plotTrajMeans3d: plot two variables of the LongData3d in a 3 dimensions graph, optionnaly according to a Partition.
plot3dPdf: create 'Triangle objects' representing in 3D the cluster's center according to a Partition. 'Triangle object' can latter be include in a LaTeX file to get a dynamique (rotationg) pdf figure.
imputation: Impute the missing values of the trajectories.
qualityCriterion: Compute some quality criterion that can be use to compare the quality of differents Partition.

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

Examples

#################
### building joint trajectories

dn <- data.frame(id=1:3,v1=c(11,14,16),t1=c(1,5,7),v2=c(12,10,13),t2=c(2,5,0),t3=c(3,6,8))
(ld <- longData3d(dn,timeInData=list(Vir=c(2,4,NA),Tes=c(3,5,6))))

### Scaling
scale(ld)
(ld)

### Plotting
plotTrajMeans3d(ld)
restoreRealData(ld)

longitudinalData documentation built on May 29, 2024, 11:54 a.m.