kml3d (version 2.3)

choice: ~ Function: choice ~

Description

choice lets the user choose some Partition he wants to export.

Usage

choice(object, typeGraph = "bmp")

Arguments

object

[ClusterLongData3d]: Object containing the trajectories and all the Partition found by kml3d.

typeGraph

[character] for every selected Partition, choice export some graphs. typeGraph set the format that will be used. Possible formats are the ones available for sa

Value

• For each selected Partition, four files are saved, plus two global files.

Details

choice is a function that lets the user see the Partition found by kml3d. At first, choice opens a graphics window (for Linux user, the windows should be explicitly open using x11(type = "Xlib")). On the left side, all the Partition contain in Object are ploted by a number (the number of cluster of the Partition). The level of the number is proportionnal to a quality criteria (like Calinski & Harabatz). One Partition is 'active', it is the one marked by a black dot. On the right side, the trajectories of Object are drawn (one graph for each variable), according to the active Partition. From there, choice offers numerous options : [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object] When 'return' is pressed (or 'm' using Linux), the selected Partition are exported. Exporting is done in a specific named objectName-Cx-y where x is the number of cluster and y is the order in the sublist. Four files are created : [object Object],[object Object],[object Object],[object Object] This four file are created for each selected Partition. In addition, two 'global' graphes are created : [object Object],[object Object]

See Also

Overview: kml3d-package Classes : ClusterLongData3d, Partition Methods : kml3d Plot : plot(ClusterLongData)

Examples

### Creation of articficial data
cld1 <- gald3d(20)

### Clusterisation (real analysis needs at least 20 redrawings, not 2)
kml3d(cld1,nbRedrawing=2,toPlot='both')

### Selection of the clustering we want
#     (note that "try" is for compatibility with CRAN only,
#     you probably can use "choice(cld1)")
try(choice(cld1))