ClusterLongData-class {kml} | 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 ofidAll
to the trajectories that does not have 'too many' missing value. SeemaxNA
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 matrixtraj
(iedimTraj=c(length(idFewNA),length(time))
).maxNA
[numeric]
or[vector(numeric)]
: Individual whose trajectories contain 'too many' missing value are exclude fromtraj
and will no be use in the analysis. Their identifier is preserved inidAll
but not inidFewNA
. 'too many' is define bymaxNA
: a trajectory with more missing thanmaxNA
is exclude.reverse
[matrix(numeric)]
: if the trajectories are scale using the functionscale
, the 'scaling parameters' (probably mean and standard deviation) are saved inreverse
. 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 thePartition
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 fromLongData
andListPartition
.object['xxx']<-value
Set the field
xxx
tovalue
.xxx
. Inherit fromListPartition
.plot
Display the
ClusterLongData
according to aPartition
.
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).
See Also
Overview: kml-package
Classes : Partition
, LongData
, ListPartition
Methods : clusterLongData
, kml
, choice
Plot : plot(ClusterLongData)
,
plotCriterion
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)