seqclustname: Automatic labeling of cluster using sequence medoids

Description Usage Arguments Value Examples

View source: R/seqclustname.R

Description

This function automatically name the cluster using the sequence medoid of each cluster.

Usage

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seqclustname(seqdata, group, diss, weighted = TRUE, perc = FALSE)

Arguments

seqdata

State sequence object (see seqdef).

group

A vector of clustering membership.

diss

a dissimilarity matrix or a dist object.

weighted

Logical. If TRUE, weights of the seqdata object are taken to find the medoids.

perc

Logical. If TRUE, the percentage of sequences in each cluster is added to the label of each group.

Value

A factor of clustering membership. The labels are defined using sequences medoids and optionnaly percentage of case in each cluster.

Examples

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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)

clust5.labels <- seqclustname(mvad.seq, clust5$clustering, diss=diss, perc=TRUE)
seqdplot(mvad.seq, group=clust5.labels)

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.

Loading required package: cluster
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: 0.097 secs

WeightedCluster documentation built on June 20, 2017, 9:04 a.m.