PReMiuM (version 3.2.13)

calcOptimalClustering: Calculation of the optimal clustering

Description

Calculates the optimal clustering.

Usage

calcOptimalClustering(disSimObj, maxNClusters=NULL, useLS=F)

Value

the output is a list with the following elements. This is an object of type clusObj.

clusObjRunInfoObj

Details on this run. An object of type runInfoObj.

clusterSizes

Cluster sizes.

clusteringPred

The predicted cluster memberships for the predicted scenarios.

clusObjDisSimMat

Dissimilarity matrix.

clustering

Cluster memberships.

nClusters

Optimal number of clusters.

avgSilhouetteWidth

Average silhouette width when using medoids method for clustering.

Arguments

disSimObj

A dissimilarity matrix (in vector format, as the output of the function calcDissimilarityMatrix(), and as described in ?calcDissimilarityMatrix) or a list of dissimilarity matrix, to combine the output of several runs of the MCMC.

maxNClusters

Set the maximum number of clusters allowed. This is set to the maximum number explored.

useLS

This is set to FALSE by default. If it is set to TRUE then the least-squares method is used for the calculation of the optimal clustering, as described in Molitor et al (2010). Note that this is set to TRUE by default if disSimObj$onlyLS is set to TRUE.

Authors

David Hastie, Department of Epidemiology and Biostatistics, Imperial College London, UK

Silvia Liverani, Department of Epidemiology and Biostatistics, Imperial College London and MRC Biostatistics Unit, Cambridge, UK

Maintainer: Silvia Liverani <[e-mail]>

References

Silvia Liverani, David I. Hastie, Lamiae Azizi, Michail Papathomas, Sylvia Richardson (2015). PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes. Journal of Statistical Software, 64(7), 1-30. tools:::Rd_expr_doi("10.18637/jss.v064.i07").

Examples

if (FALSE) {
generateDataList <- clusSummaryBernoulliDiscrete()
inputs <- generateSampleDataFile(generateDataList)
runInfoObj<-profRegr(yModel=inputs$yModel, xModel=inputs$xModel, 
    nSweeps=10, nBurn=20, data=inputs$inputData, output="output", 
    covNames=inputs$covNames, nClusInit=15)

dissimObj<-calcDissimilarityMatrix(runInfoObj)
clusObj<-calcOptimalClustering(dissimObj)
}