kml-package: ~ Overview: K-means for Longitudinal data ~
In kml: K-Means for Longitudinal Data

kml-package

R Documentation

~ Overview: K-means for Longitudinal data ~

Description

This package is a implementation of k-means for longitudinal data (or trajectories). Here is an overview of the package. For the description of the algorithm, see kml.

Details

Package:	kml
Type:	Package
Version:	2.4.1
Date:	2016-02-02
License:	GPL (>= 2)
LazyData:	yes
Depends:	methods,clv,longitudinalData(>= 2.1.2)
URL:	http://www.r-project.org
URL:	http://christophe.genolini.free.fr/kml

Overview

To cluster data, KmL go through three steps, each of which is associated to some functions:

Data preparation
Building "optimal" partition
Exporting results

1. Data preparation

KmL works on object of class ClusterLongData. Data preparation therefore simply consists in transforming data into an object ClusterLongData. This can be done via function clusterLongData (cld in short). It converts a data.frame or a matrix into a ClusterLongData.

Instead of working on real data, one can also work on artificial data. Such data can be created with generateArtificialLongData (gald in short).

2. Building "optimal" partition

Once an object of class ClusterLongData has been created, the algorithm kml can be run.

Starting with a ClusterLongData, kml built a Partition, a class in package longitudinalData. An object of class Partition is a partition of trajectories into subgroups. It also contains some information like the percentage of trajectories contained in each group or some quality critetion.

kml is a "hill-climbing" algorithm. The specificity of this kind of algorithm is that it always converges towards a maximum, but one cannot know whether it is a local or a global maximum. It offers no guarantee of optimality. To maximize one's chances of getting a quality Partition, it is better to run the hill climbing algorithm several times, then to choose the best solution. By default, kml executes the hill climbing algorithm 20 times and chooses the Partition maximizing the determinant of the matrix between.

Likewise, it is not possible to know beforehand the optimum number of clusters. On the other hand, afterwards, it is possible to calculate clues that will enable us to choose. In the end, kml tests by default 2, 3, 4, 5 et 6 clusters, 20 times each.

3. Exporting results

When kml has constructed some Partition, the user can examine them one by one and choose to export some. This can be done via function choice. choice opens a graphic windows showing various information including the trajectories clutered by a specific Partition.

When some Partition has been selected (the user can select more than 1), it is possible to save them. The clusters are therefore exported towards the file name-cluster.csv. Criteria are exported towards name-criteres.csv. The graphs are exported according to their extension.

It is also possible to extract a partition from the object ClusterLongData using the function getClusters.

Examples


### Move to tempdir
wd <- getwd()
setwd(tempdir()); getwd()

### 1. Data Preparation
data(epipageShort)
names(epipageShort)
cldSDQ <- cld(epipageShort,timeInData=3:6,time=c(3,4,5,8))

### 2. Building "optimal" clusteration (with only 3 redrawings)
kml(cldSDQ,nbRedrawing=3,toPlot="both")

### 3. Exporting results
### To check the best's cluster numbers
plotAllCriterion(cldSDQ)


# To see the best partition
try(choice(cldSDQ))

### 4. Further analysis
epipageShort$clust <- getClusters(cldSDQ,4)
summary(glm(gender~clust,data=epipageShort,family="binomial"))

### Go back to current dir
setwd(wd)

kml documentation built on Oct. 30, 2024, 9:09 a.m.