kml (version 0.9.0)

choice: ~ function: choice ~

Description

choice lets the user choose the Clusterization he wants to export.

Usage

choice(x)

Arguments

x

[ClusterizLongData]: Contains all the clusterization found by KmL.

Value

• Non applicable

1. Choice of the <code>Clusterization</code>

choice opens two graphic windows. In the left one, the Calinski criteria of all Clusterization found by kml are represented. They are filed according to the number of clusters, from the biggest to the smallest. This windows let the user to select a Clusterization. The selected Clusterization is pointed with a dark spot. In the right window, the Clusterization selected by the user is represented. When choice is called, the Clusterization having the larger Calinski criterion is selected. It is possible to visualize the other Clusterization by using the arrows on the keyboard. When the choice of a Clusterization has been made and needs to be exported, the use can go on to the next step by pressing "Return".

2. Exporting the <code>Clusterization</code>

When a Clusterization is being chosen, there are three possibilities :

1. "Return" enables the visualizationClusterizationon screen (but nothing else).
2. Entering the name of a filenomDeFichierenables the user to export theClusterization. Clusters are exported in the filenomDeFichier-cluster.csv. Criteria are exported innomDeFichier-criteres.csv. Distances and posterior probabilities are innomDeFichier-distance.csv(not implemented yet).
3. Entering a name preceded by the symbol->(like->variablesNames) enables the user to save theClusterization. Clusters are stored in the variablenomVariable_cluster.csvas adata.frame. Criteria are saved innomDeVariable_criters.csvas lists. Distances and posterior probabilities are savednomDeVariable_distance.csv(not implemented yet).

3. Selection of a graphic representation

Finally, it is possible to export a graphic representation of the Clusterization. With the keyboard, the user can modify the aspect of the graph.

• Keyse,randtdeal with individual trajectories.
• Keysd,fandgdeal with the average trajectories of clusters.
• Keysc,vandbdeal with subgroups.
• Keysh,j,k,landodeal with symbols on the graph.

More precisely:

• e

{Suppresses the display of individual trajectories.} r{Displays individual trajectories in black and white.} t{Displays individual trajectories in color.} d{Suppresses the display of average trajectories of clusters.} f{Displays the average trajectories of clusters in black and white.} g{Displays the average trajectories of clusters in color.} c{Suppresses the display of subgroups.} v{Displays the subgroups in black and white.} b{Displays subgroups in color.} h{Suppresses the display of points of average trajectories} j{Displays the points of average trajectories as letters} k{Displays the points of average trajectories as symbols} o{Enlarges fonts} l{Makes fonts smaller}

Author(s)

Christophe Genolini PSIGIAM: Paris Sud Innovation Group in Adolescent Mental Health INSERM U669 / Maison de Solenn / Paris Responsable :

English translation

Rapha�l Ricaud Laboratoire "Sport & Culture" / "Sports & Culture" Laboratory University of Paris 10 / Nanterre

Details

choice run in three steps:

1. Choice of theClusterization
2. Exporting theClusterization
3. Selection of a graphic representation.

See Also

kml-package

Examples

cld1 <- as.cld(gald())
kml(cld1,2:3,3)
#choice(cld1)