R: ~ Class: "ParKml" ~

ParKml-class {kml} | R Documentation

## ~ Class: "ParKml" ~

### Description

`ParKml` is an object containing some parameter used by `kml`.

### Slots

`saveFreq`

  `[numeric]`: Long computations can take several days. So it is possible to save the object `ClusterLongData` on which works `kml` once in a while. `saveFreq` defines the frequency of the saving process. The `ClusterLongData` is saved every `saveFreq` clustering calculations. The object is saved in the file `objectName.Rdata` in the curent folder. If `saveFreq` is set to `Inf`, the object is never saved.

`maxIt`:

  `[numeric]`: Set a limit to the number of iteration if convergence is not reached.

`imputationMethod`:

  `[character]`: the calculation of quality criterion can not be done if some value are missing. `imputationMethod` define the method use to impute the missing value. See `imputation` for detail.

`distanceName`:

  `[character]`: name of the `distance` used by k-means. If the `distanceName` is one of "manhattan", "euclidean", "minkowski", "maximum", "canberra" or "binary", a compiled optimized version specificaly design for trajectories version is used. Otherwise, the function define in the slot `distance` is used.

`power`:

  `[numeric]`: If `distanceName="minkowski"`, this define the power that will be used.

`distance`:

  `[numeric <- function(trajA,trajB)]`: function that computes the distance between two trajectories. This field is used only if 'distanceName' is not one of the classical function.

`centerMethod`:

  `[numeric <- function(vector(numeric))]`: k-means algorithm computes the centers of each cluster. It is possible to personalize the definition of "center" by defining a function "centerMethod". This function should take a vector of numeric as argument and return a single numeric -the center of the vector-.

`startingCond`:

  `[character]`: specifies the starting condition. Should be one of "randomAll", "randomK", "maxDist", "kmeans++", "kmeans+", "kmeans-" or "kmeans–" (see `initializePartition` for details). It also could take two specifics values: "all" stands for c("maxDist","kmeans-") then an alternance of "kmeans–" and "randomK" while "nearlyAll" stands for "kmeans-" then an alternance of "kmeans–" and "randomK".

`nbCriterion`

  `[numeric]`: set the maximum number of quality criterion that are display on the graph (since displaying a high criterion number an slow down the overall process). The default value is 100.

scale

  `[logical]`: if TRUE, then the data will be automaticaly scaled (using the function `scale` with default values) before the execution of k-means on joint trajectories. Then the data will be restore (using the function `restoreRealData`) just before the end of the function `kml3d`. This option has no effect on `kml`.

### Methods

`object['xxx']`

  Get the value of the field `xxx`.

### Examples

    ### Building data
    myCld <- gald()

    ### Standard kml
    kml(myCld,,3,toPlot="both")

    ### Using median instead of mean
    parWithMedian <- parALGO(centerMethod=function(x){median(x,na.rm=TRUE)})
    kml(myCld,,3,toPlot="both",parAlgo=parWithMedian)

    ### Using distance max
    parWithMax <- parALGO(distanceName="maximum")
    kml(myCld,,3,toPlot="both",parAlgo=parWithMax)

[Package kml version 2.4.1 Index]