kmedrange: Compute 'wcKMedoids' clustering for different number of...

In WeightedCluster: Clustering of Weighted Data

Compute wcKMedoids clustering for different number of clusters.

Description

Compute wcKMedoids clustering for different number of clusters.

Usage

wcKMedRange(diss, kvals, weights=NULL, R=1,  samplesize=NULL, ...)

Arguments

diss	A dissimilarity matrix or a dist object (see dist).
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 wcKMedoids.

Details

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 Also

See as.clustrange.

Examples

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)

WeightedCluster documentation built on July 9, 2023, 5:34 p.m.