kmedrange: Compute 'wcKMedoids' clustering for different number of... In WeightedCluster: Clustering of Weighted Data

Description

Compute `wcKMedoids` clustering for different number of clusters.

Usage

 `1` ```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 `as.clustrange`.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```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) ```

Example output

```Loading required package: TraMineR

TraMineR stable version 2.0-7 (Built: "Sat,)
Website: http://traminer.unige.ch
Please type 'citation("TraMineR")' for citation information.

This is WeightedCluster stable version 1.2-1 (Built: 2017-09-21)

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
[>] 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: 711.57 - min/max: 0.13/33.43
[>] 490 sequences in the data set
[>] min/max sequence length: 70/70
[>] 490 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
[>] 490 distinct sequences
[>] min/max sequence length: 70/70
[>] computing distances using the HAM metric
[>] elapsed time: 0.1 secs
```

WeightedCluster documentation built on May 2, 2019, 6:35 a.m.