wcKMedRange {WeightedCluster} | R Documentation |
wcKMedoids
clustering for different number of clusters.
Compute wcKMedoids
clustering for different number of clusters.
wcKMedRange(diss, kvals, weights=NULL, R=1, samplesize=NULL, ...)
diss |
A dissimilarity matrix or a dist object (see |
kvals |
A numeric vector containing the number of cluster to compute. |
weights |
Numeric. Optional numerical vector containing case weights. |
R |
Optional number of bootstrap that can be used to build confidence intervals. |
samplesize |
Size of bootstrap sample. Default to sum of weights. |
... |
Additionnal parameters passed to |
Compute a clustrange
object using the wcKMedoids
method. clustrange
objects contains a list of clustering solution with associated statistics and can be used to find the optimal clustering solution.
See as.clustrange
for more details.
See as.clustrange
.
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$aggWeights) ## Compute distance using Hamming distance diss <- seqdist(mvad.seq, method="HAM") ## Pam clustering pamRange <- wcKMedRange(diss, 2:15) ## Plot all statistics (standardized) plot(pamRange, stat="all", norm="zscoremed", lwd=3) ## Plotting sequences in 3 groups seqdplot(mvad.seq, group=pamRange$clustering$cluster3)