R: ~ Class: ClusterLongData ~

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 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).

### See Also

Overview: `kml-package`
Classes : `Partition`, `LongData`, `ListPartition`
Methods : `clusterLongData`, `kml`, `choice`
Plot : `plot(ClusterLongData)`, `plotCriterion`

### Examples

    ################
    ### 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

[Package kml version 2.4.1 Index]