calcOptimalClustering function

Calculation of the optimal clustering

Calculates the optimal clustering.

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

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.

Returns

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.

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 liveranis@gmail.com

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

## Not run:

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)
## End(Not run)