R Under development (unstable) (2024-10-01 r87205 ucrt) -- "Unsuffered Consequences"
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> library(WeightedCluster)
Loading required package: TraMineR

TraMineR stable version 2.2-10 (Built: 2024-10-02)
Website: http://traminer.unige.ch
Please type 'citation("TraMineR")' for citation information.

Loading required package: cluster
This is WeightedCluster stable version 1.8-0 (Built: 2024-10-02)

To get the manuals, please run:
   vignette("WeightedCluster") ## Complete manual in English
   vignette("WeightedClusterFR") ## Complete manual in French
   vignette("WeightedClusterPreview") ## Short preview in English

To cite WeightedCluster in publications please use:
Studer, Matthias (2013). WeightedCluster Library Manual: A practical
   guide to creating typologies of trajectories in the social sciences
   with R. LIVES Working Papers, 24. doi:
   10.12682/lives.2296-1658.2013.24
> data(mvad)
> library(RUnit)
> 
> ## Aggregating state sequence
> aggMvad <- wcAggregateCases(mvad[1:10, 17:86], weights=mvad$weight[1:10])
> 
> ## Creating state sequence object
> mvad.seq <- seqdef(mvad[aggMvad$aggIndex, 17:86], weights=aggMvad$aggWeights)
 [>] 6 distinct states appear in the data: 
     1 = FE
     2 = HE
     3 = employment
     4 = joblessness
     5 = school
     6 = training
 [>] state coding:
       [alphabet]  [label]     [long label] 
     1  FE          FE          FE
     2  HE          HE          HE
     3  employment  employment  employment
     4  joblessness joblessness joblessness
     5  school      school      school
     6  training    training    training
 [>] sum of weights: 15.16 - min/max: 0.33/3.6
 [>] 10 sequences in the data set
 [>] min/max sequence length: 70/70
> ## Computing Hamming distance between sequence
> diss <- seqdist(mvad.seq, method="HAM")
 [>] 10 sequences with 6 distinct states
 [>] creating a 'sm' with a single substitution cost of 1
 [>] creating 6x6 substitution-cost matrix using 1 as constant value
 [>] 10 distinct  sequences 
 [>] min/max sequence lengths: 70/70
 [>] computing distances using the HAM metric
 [>] elapsed time: 0.05 secs
> clust4 <- wcKMedoids(diss, k=4, weights=aggMvad$aggWeights, cluster.only=TRUE)
> dissd <- seqdist(mvad.seq, method="HAM", full.matrix=FALSE)
 [>] 10 sequences with 6 distinct states
 [>] creating a 'sm' with a single substitution cost of 1
 [>] creating 6x6 substitution-cost matrix using 1 as constant value
 [>] 10 distinct  sequences 
 [>] min/max sequence lengths: 70/70
 [>] computing distances using the HAM metric
 [>] elapsed time: 0.07 secs
> clust4d <- wcKMedoids(dissd, k=4, weights=aggMvad$aggWeights, cluster.only=TRUE)
> #clust4d <- wcKMedoids(diss, k=4, weights=aggMvad$aggWeights,debuglevel=11)
> checkEquals(clust4, clust4d)
[1] TRUE
> 
> 
> 
> 
> ## Compute the silhouette of each observation
> clust1 <- rep(1, nrow(mvad.seq))
> checkException(qual <- wcClusterQuality(diss, clust1, weights=aggMvad$aggWeights))
Error in wcClusterQualityInternal(diss = diss, clustering = clustering,  : 
  [!] The clustering should have at least two different values.
[1] TRUE
> 
> ## Compute the silhouette of each observation
> checkException(sil <- wcSilhouetteObs(diss, clust1, weights=aggMvad$aggWeights))
Error in wcSilhouetteObs(diss, clust1, weights = aggMvad$aggWeights) : 
  [!] The clustering should have at least two different values.
[1] TRUE
> 
> qual1 <- wcClusterQuality(diss, clust4, weights=aggMvad$aggWeights)
> qual2 <- wcClusterQuality(dissd, clust4, weights=aggMvad$aggWeights)
> checkEquals(qual1, qual2)
[1] TRUE
> 
> sil1 <- wcSilhouetteObs(diss, clust4, weights=aggMvad$aggWeights)
> sil2 <- wcSilhouetteObs(dissd, clust4, weights=aggMvad$aggWeights)
> checkEquals(sil1, sil2)
[1] TRUE
> 
> proc.time()
   user  system elapsed 
   1.90    0.40    2.26