wcSilhouetteObs {WeightedCluster} | R Documentation |
Compute the silhouette of each object using weighted data.
wcSilhouetteObs(diss, clustering, weights = NULL, measure="ASW")
diss |
A dissimilarity matrix or a dist object (see |
clustering |
Factor. A vector of clustering membership. |
weights |
optional numerical vector containing weights. |
measure |
"ASW" or "ASWw", the measure of the silhouette. See the WeigthedCluster vignettes. |
See the silhouette
function in the cluster
package for a detailed explanation of the silhouette.
A numeric vector containing the silhouette of each observation.
Maechler, M., P. Rousseeuw, A. Struyf, M. Hubert and K. Hornik (2011). cluster: Cluster Analysis Basics and Extensions. R package version 1.14.1 — For new features, see the 'Changelog' file (in the package source).
See also silhouette
.
data(mvad) ## Aggregating state sequence aggMvad <- wcAggregateCases(mvad[, 17:86], weights=mvad$weight) ## Creating state sequence object mvad.seq <- seqdef(mvad[aggMvad$aggIndex, 17:86], weights=aggMvad$aggWeights) ## Computing Hamming distance between sequence diss <- seqdist(mvad.seq, method="HAM") ## KMedoids using PAMonce method (clustering only) clust5 <- wcKMedoids(diss, k=5, weights=aggMvad$aggWeights, cluster.only=TRUE) ## Compute the silhouette of each observation sil <- wcSilhouetteObs(diss, clust5, weights=aggMvad$aggWeights, measure="ASWw") ## If you want to compute the average silhouette width, ## you should take weights into account weighted.mean(sil, w=aggMvad$aggWeights) ## Plotting sequences ordred by silhouette width, ## best classified are draw on the top. seqIplot(mvad.seq, group=clust5, sortv=sil)