Build a clustrange object to compare different clustering solutions.

Description

Build a clustrange object to compare different clustering solutions.

Usage

as.clustrange(object, diss, weights=NULL, R=1,  samplesize=NULL, ...)
## S3 method for class 'twins'
as.clustrange(object, diss, weights=NULL, R=1, samplesize=NULL, 
		ncluster=20, ...) 
## S3 method for class 'hclust'
as.clustrange(object, diss, weights=NULL, R=1, samplesize=NULL, 
		ncluster=20, ...) 
## S3 method for class 'dtclust'
as.clustrange(object, diss, weights=NULL, R=1, samplesize=NULL, 
		ncluster=20, labels = TRUE, ...)
## S3 method for class 'clustrange'
plot(x, stat="noCH", legendpos="bottomright", 
    norm="none", withlegend=TRUE, lwd=1, col=NULL, ylab="Indicators", 
	xlab="N clusters", conf.int=0.9, ci.method="none", ci.alpha=.3, line="t0", ...)

Arguments

Details

as.clustrange convert objects to clustrange objects. clustrange objects contains a list of clustering solution with associated statistics and can be used to find the optimal clustering solution.

If object is a data.frame or a matrix, each column should be a clustering solution to be evaluated.

If object is an hclust or twins objects (i.e. hierarchical clustering output, see hclust, diana or agnes), the function compute all clustering solution ranging from two to ncluster and compute the associated statistics.

Value

An object of class clustrange with the following elements:

clustering:

A data.frame of all clustering solutions.

stats:

A matrix containing the clustering statistics of each cluster solution.

See Also

See also clustassoc (other cluster quality measures), wcKMedRange, wcClusterQuality.

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")

## Ward clustering
wardCluster <- hclust(as.dist(diss), method="ward", members=aggMvad$aggWeights)

## Computing clustrange from Ward clustering
wardRange <- as.clustrange(wardCluster, diss=diss, 
		weights=aggMvad$aggWeights, ncluster=15)

## Plot all statistics (standardized)
plot(wardRange, stat="all", norm="zscoremed", lwd=3)

## Plot HC, RHC and ASW
plot(wardRange, stat=c("HC", "RHC", "ASWw"), norm="zscore", lwd=3)

Convert a hierarchical clustering object to a seqtree object.

Description

Convert a hierarchical clustering object to a seqtree object which can then be displayed using seqtreedisplay.

Usage

as.seqtree(object, seqdata, diss, weighted=TRUE, ...)
## S3 method for class 'twins'
as.seqtree(object, seqdata, diss, weighted=TRUE, ncluster, ...)
## S3 method for class 'hclust'
as.seqtree(object, seqdata, diss, weighted=TRUE, ncluster, ...)

Arguments

Details

By default as.seqtree try to convert the object to a data.frame assuming that it contains a list of nested clustering solutions. Be aware that seqtree and as.seqtree only support binary splits.

If object is an hclust or twins objects (i.e. hierarchical clustering output, see hclust, diana or agnes), the function returns a seqtree object reproducing the agglomerative schedulde.

Value

A seqtree object.

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")

## Ward clustering
wardCluster <- hclust(as.dist(diss), method="ward", members=aggMvad$weight)

st <- as.seqtree(wardCluster, seqdata=mvad.seq, diss=diss, weighted=TRUE, ncluster=10)

print(st)

## You typically want to run (You need to install GraphViz before)
## seqtreedisplay(st, type="d", border=NA)

Cluster Quality Indices estimation by subsampling

Description

bootclustrange estimates the quality of the clustering based on subsamples of the data to avoid computational overload.

Usage

bootclustrange(object, seqdata, seqdist.args = list(method = "LCS"),
               R = 100, sample.size = 1000, parallel = FALSE,
               progressbar = FALSE, sampling = "clustering",
               strata = NULL)
## S3 method for class 'bootclustrange'
plot(x, stat = "noCH", legendpos = "bottomright",
                              norm = "none", withlegend = TRUE, lwd = 1,
                              col = NULL, ylab = "Indicators", 
                              xlab = "N clusters", conf.int = 0.95, 
                              ci.method = "perc", ci.alpha = 0.3, 
                              line = "median", ...)
## S3 method for class 'bootclustrange'
print(x, digits = 2, bootstat = c("mean"), ...)

Arguments

Details

bootclustrange estimates the quality of the clustering based on subsamples of the data to avoid computational overload. It randomly samples R times sample.size sequences from seqdata using the sampling procedure defined by the sampling arguments. In each subsample, a distance matrix is computed using the selected sequences and the seqdist.args arguments and the cluster quality indices are then estimated using as.clustrange.

The clustering can be specified either as a seqclararange object or a data.frame.

Value

A clustrange object, see as.clustrange with the bootrapped values.

References

Studer, M., R. Sadeghi and L. Tochon (2024). Sequence Analysis for Large Databases. LIVES Working Papers 104 doi:10.12682/lives.2296-1658.2024.104

See Also

See Also as.clustrange for the list of cluster quality indices that are computed, and seqclararange for example of use

Share of an association between an object (described by a dissimilarity matrix) and a covariate that is reproduced by a clustering solution.

Description

The clustassoc measures to which extent a clustering solution can account for the relationship between a covariate and the objects of interest, i.e. the sequences or any other object described by a dissimilarity matrix. It can be used to guide the choice of the number of groups ensuring that the clustering captures the relevant information to account for a statistical relationship of interest. This is useful when the clustering is used in subsequent analyses, such as regressions. In this case, the within-cluster variation is ignored, as objects clustered together are described by a single value. Ensuring that the association is accounted for by the clustering can avoid drawing wrong conclusions (see Unterlerchner et al. 2023).

Usage

clustassoc(clustrange, diss, covar, weights = NULL)
## S3 method for class 'clustassoc'
plot(x, stat=c("Unaccounted", "Remaining", "BIC"), type="b", ...)

Arguments

Details

The clustassoc measures to which extent a clustering solution can account for the relationship between a covariate and the objects of interest. It can be used to guide the choice of the number of groups of the clustering to ensure that it captures the relevant information to account for a statistical relationship of interest.

The method works as follows. The relationship between trajectories (or any objects described by a distance matrix) and covariates can be studied directly using discrepancy analysis (see Studer et al. 2011). It measures the strength of the relationship with a Pseudo-R2, measuring the share of the variation of the object explained by a covariate. The method works without prior clustering, and therefore, without data simplification. The method is provided by the dissmfacw function from the TraMineR package.

Multifactor discrepancy analysis allows measuring a relationship while controlling for other covariates. the clustassoc function measures the remaining association between the objects and the covariate while controlling for the clustering. If the covariate Pseudo-R2 remains high (or at the same level), it means that the clustering does not capture the relationship between covariates and the objects. In other words, the clustering has simplified the relevant information to capture this relationship. Conversely, if the Pseudo-R2 is much lower, it means that the clustering reproduces the key information to understand the relationship. Using this strategy, the clustassoc measure the share of the original Pseudo-R2 that is taken into account by our clustering.

The function also compute the BIC of a regression predicting the covariate using the clustering solution as proposed by Han et al. 2017. A lower BIC is to be preferred. The method is, however, less reliable than the previous one.

Value

A clustassoc object containing the following information for each clustering:

Author(s)

Matthias Studer

References

Unterlerchner, L., M. Studer and A. Gomensoro (2023). Back to the Features. Investigating the Relationship Between Educational Pathways and Income Using Sequence Analysis and Feature Extraction and Selection Approach. Swiss Journal of Sociology.

Studer, M. 2013. WeightedCluster Library Manual: A Practical Guide to Creating Typologies of Trajectories in the Social Sciences with R.LIVES Working Papers 2013(24): 1-32.

Studer, M., G. Ritschard, A. Gabadinho and N. S. Mueller (2011). Discrepancy analysis of state sequences, Sociological Methods and Research, Vol. 40(3), 471-510, doi:10.1177/0049124111415372.

Han, Y., A. C. Liefbroer and C. H. Elzinga. 2017. Comparing Methods of Classifying Life Courses: Sequence Analysis and Latent Class Analysis. Longitudinal and Life Course Studies 8(4): 319-41.

See Also

See Also as.clustrange for cluster quality indexes, and the dissmfacw function from the TraMineR package.

Examples

data(mvad)

## Small subsample to reduce computations
mvad <- mvad[1:50,]

## Sequence object
mvad.seq <- seqdef(mvad[, 17:86])

## Compute distance using Hamming distance
diss <- seqdist(mvad.seq, method="HAM")

## Ward clustering
wardCluster <- hclust(as.dist(diss), method="ward.D")

## Computing clustrange from Ward clustering up to 5 groups
wardRange <- as.clustrange(wardCluster, diss=diss, ncluster=5)

## Compute clustassoc
## How many groups are required to account for the relationship 
## between trajectories and the gcse5eq covariate 
assoc <- clustassoc(wardRange, covar=mvad$gcse5eq, diss=diss)

## Plot unaccounted share of the association 
## A value close to zero means that the relationship is accounted for.
## Here at least 2-4 groups are required
plot(assoc)

## Plot BIC
## A low value means that an association between trajectories and the covariate is identified.
## 2-3 groups show best results.
plot(assoc, stat="BIC")


## Plot remaining share of the variability of the sequences not explained by clustering
## A value close to zero means that there is no association left (similar)
## Here at least 2-4 groups are required
plot(assoc, stat="Remaining")

Plot sequences according to a fuzzy clustering.

Description

This funciton propose a graphical representation of a fuzzy clustering results where sequences are weighted according to their cluster membership strength.

Usage

fuzzyseqplot(seqdata, group = NULL, membership.threashold = 0, type = "i", 
			members.weighted = TRUE, memb.exp = 1, ...)

Arguments

Details

The dataset is augmented by repeating the sequence s_i of individual i k times (i.e., once per cluster). We therefore have k sequences for individual i, denoted as s_{i1}... s_{ik}. These sequences are therefore weighted according to their membership degree u_{i1}... u_{ik}. Hence, even if the same sequence were repeated k times, its total weight sum to 1. An additional categorical covariate is created in this augmented dataset that specifies the cluster (ranging from 1 to k) of the associated membership degree. This weighting strategy allows us to use any tools available for weighted sequence data (see seqplot).

For index plots, we additionally suggest ordering the sequences according to membership degree by setting sortv="membership" (see example). The most typical sequence lies at the top of the subfigures, with a high membership degree; meanwhile, the bottom shows less-characteristic patterns. Restricting to sequences with the highest membership degree can be achieved with the membership.treashold argument.

References

Studer, M. (2018). Divisive property-based and fuzzy clustering for sequence analysis. In G. Ritschard and M. Studer (Eds.), Sequence Analysis and Related Approaches: Innovative Methods and Applications, Life Course Research and Social Policies.

See Also

See also fanny for fuzzy clustering.

Examples

	data(mvad)
	mvad.seq <- seqdef(mvad[1:100, 17:86])

	## COmpute distance using Hamming distance
	diss <- seqdist(mvad.seq, method="HAM")
	library(cluster)
	fclust <- fanny(diss, k=2, diss=TRUE)
	
	fuzzyseqplot(mvad.seq, group=fclust, type="d")
	fuzzyseqplot(mvad.seq, group=fclust, type="I", sortv="membership")
	fuzzyseqplot(mvad.seq, group=fclust, type="f")

Plot of cluster quality of CLARA algorithm.

Description

Plot of the cluster quality of a seqclararange object.

Usage

## S3 method for class 'seqclararange'
plot(x, stat = "CQI", type = "o", main = NULL, 
  xlab = "Number of clusters", ylab = stat, col = "blue", 
	legend.pos = "topright", pch = 19, norm = "none", ...)

Arguments

References

Studer, M., R. Sadeghi and L. Tochon (2024). Sequence Analysis for Large Databases. LIVES Working Papers 104 doi:10.12682/lives.2296-1658.2024.104

See Also

See seqclararange to produce a clustering objects.

CLARA Clustering for Sequence Analysis

Description

Cluster large databases of sequences for a different number of groups using the CLARA algorithm based on subsampling to reduce computational burden. Crisp, fuzzy and representativeness clustering are available. The function further computes several cluster quality measures.

Usage

seqclararange(seqdata, R = 100, sample.size = 40 + 2 * max(kvals), 
  kvals = 2:10,  seqdist.args = list(method = "LCS"),
  method=c("crisp", "fuzzy", "representativeness", "noise"), 
  m = 1.5, criteria = c("distance"), stability = FALSE, dnoise=NULL,
  parallel = FALSE, progressbar = FALSE, keep.diss = FALSE, 
  max.dist = NULL)

Arguments

Details

seqclararange relies on the CLARA algorithm to cluster large database. The algorithm works as follows.

1. Randomly take a subsample of the data of size sample.size.

2. Cluster the subsample using the PAM algorithm initialized using Ward to speed up the computations (see wcKmedoids).

3. Use the identified medoids to assign cluster membership in the whole dataset.

4. Evaluate the resulting clustering using a criteria (see argument), the average distances to the medoids by default.

These steps are repeated R times and the best solution according to the given criterion is kept.

To minimize the computation, the operation is repeated for different number of groups, which then allows to choose the best number of groups according to different cluster quality indices. The following indices are computed automatically: "Avg dist" (Average distance to cluster medoids), "PBM"(PBM Index), "DB" (Davies-Bouldin Index), "XB" (Xie-Beni Index), "AMS" (Average medoid silhouette width), "ARI>0.8" (Number of iteration similar to the current best, only if stability=TRUE, "JC>0.8" (Number of iteration similar to the current best, only if stability=TRUE.

Value

A seqclararange object with the following components:

References

Studer, M., R. Sadeghi and L. Tochon (2024). Sequence Analysis for Large Databases. LIVES Working Papers 104 doi:10.12682/lives.2296-1658.2024.104

See Also

See also as plot.seqclararange to plot the results.

Examples

data(biofam) #load illustrative data
## Defining the new state labels 
statelab <- c("Parent", "Left", "Married", "Left/Married",  "Child", 
            "Left/Child", "Left/Married/Child", "Divorced")
## Creating the state sequence object,
biofam.seq <- seqdef(biofam[1:100, 10:25], alphabet=0:7, states=statelab)



## Clara clustering
bfclara <- seqclararange(biofam.seq, R = 3, sample.size = 10, kvals = 2:3, 
  seqdist.args = list(method = "HAM"), parallel=FALSE, 
  stability=TRUE)


#Show the cluster quality measures.
bfclara
#Plot and normalize the values for easier identification of minimum and maximum values.
plot(bfclara, norm="range")
## Stability values.
plot(bfclara, stat="stabmean")
plot(bfclara, stat="stability")

seqdplot(biofam.seq, group=bfclara$clustering$cluster3)

## Cluster quality indices estimation using boostrap

bCQI <- bootclustrange(bfclara, biofam.seq, seqdist.args = list(method = "HAM"), 
  R = 3, sample.size = 10,  parallel=FALSE)

bCQI
plot(bCQI, norm="zscore")

## Not run: 
## Fuzzy clustering
bfclaraf <- seqclararange(biofam.seq, R = 3, sample.size = 20, kvals = 2:3, 
  method="fuzzy", seqdist.args = list(method = "HAM"), 
	parallel=FALSE)


bfclaraf
plot(bfclaraf, norm="zscore")


fuzzyseqplot(biofam.seq, group=bfclaraf$clustering$cluster3, type="I", 
  sortv="membership", membership.threashold=0.2)

## Noise clustering
bfclaran <- seqclararange(biofam.seq, R = 3, sample.size = 20, kvals = 2:3, 
  method="noise", seqdist.args = list(method = "HAM"), dnoise=6,
	parallel=FALSE)

fuzzyseqplot(biofam.seq, group=bfclaran$clustering$cluster3, type="I", 
  sortv="membership", membership.threashold=0.2)


## End(Not run)

Automatic labeling of cluster using sequence medoids

Description

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

Usage

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

Arguments

Value

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

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)
## 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)

Generate nonclustered sequence data according to different null models.

Description

This function generates sequence data that is similar to the original sequence data, but nonclusterd on specific aspects related to the sequencing, timing or time spend in the different states. The function is typically used by only specifying a model among "combined", "duration", "sequencing", "stateindep" or "Markov". The "userpos" model allows to fully specify a sequence generating model using a starting distribution and a transition rate matrix.

Usage

seqnull(seqdata, model = c("combined", "duration", "sequencing", 
        "stateindep", "Markov", "userpos"), imp.trans = NULL, 
		imp.trans.limit = -1, trate = "trate", begin = "freq", 
		time.varying = TRUE, weighted = TRUE)

Arguments

Details

This function generates sequence data that is similar to the original sequence data, but nonclusterd on specific aspects related to the sequencing, timing or time spend in the different states. The function is typically used by only specifying a model among "combined", "duration", "sequencing", "stateindep" or "Markov". The models are shortly described below. More information about their usefulness can be found in Studer (2021) (see below).

The "combined", "duration" and "sequencing" models generate sequence in spell format, by generating a vector of state and their attached durations. The "combined" model generate random sequencing and duration. The "duration" model only randomizes duration, while keeping the original sequencing of the states found in the data. Finally, the "sequencing" only randomizes the sequencing of the states and keep the time spent in a state as found in the data.

The "stateindep" model generate sequence by randomly selecting a state at each time point without taking into account the previous one. It can generate highly unlikely sequence because it doesn't account for coherence of trajectories over time.

The "Markov" model use a time-invariant (homogeneouns) transition rate matrix to generate the sequences. It can reveals difference in the timing of transitions.

Value

A state sequence object of class stslist.

References

Studer, M. (2021). Validating Sequence Analysis Typologies Using Parametric Bootstrap. Sociological Methodology. doi:10.1177/00811750211014232

See Also

See Also seqnullcqi.

Examples

data(biofam)

bf.seq <- seqdef(biofam[1:200,10:25])

##Plot the sequences generated by different null models.
seqdplot(seqnull(bf.seq, model="combined"))

seqdplot(seqnull(bf.seq, model="duration"))

seqdplot(seqnull(bf.seq, model="sequencing"))

seqdplot(seqnull(bf.seq, model="stateindep"))

seqdplot(seqnull(bf.seq, model="Markov"))

Sequence Analysis Typologies Validation Using Parametric Bootstrap

Description

seqnullcqi implements the methodology proposed by Studer (2021) for the validation of sequence analysis typologies using parametric bootstraps. The method works by comparing the cluster quality of an observed typology with the quality obtained by clustering similar but nonclustered data. Several models to test the different structuring aspects of the sequences important in life-course research, namely, sequencing, timing, and duration (see function seqnull). This strategy allows identifying the key structural aspects captured by the observed typology. Plot and print methods of the seqnullcqi results are also provide.

Usage

seqnullcqi(seqdata, clustrange, R, model=c("combined", "duration", "sequencing", 
                    "stateindep", "Markov", "userpos"), seqdist.args=list(), 
					kmedoid = FALSE, hclust.method="ward.D", 
					parallel=FALSE, progressbar=FALSE, ...)
		   
## S3 method for class 'seqnullcqi'
plot(x, stat, type = c("line", "density", "boxplot", "seqdplot"),
                          quant = 0.95, norm = TRUE, legendpos = "topright",
                          alpha = 0.2, ...)

## S3 method for class 'seqnullcqi'
print(x, norm=TRUE, quant=0.95, digits=2, ...) 

Arguments

Details

The seqnullcqi function provides a validation method for sequence analysis typologies using parametric bootstraps as proposed in Studer (2021). This method works by comparing the value of the cluster quality of an observed typology with the cluster quality obtained by clustering similar but nonclustered data. More precisely it works as follows.

1. Cluster the observed sequence data and compute the associated cluster quality indices.

2. Repeat R times:

   1. Generate similar but nonclustered data using a null model (see seqnull for available null models).

   2. Cluster the generated data using the same distance measure and clustering algorithm as in step 1.

   3. Record the quality indices values of this null clustering.

3. Compare the quality of the observed typology with the one obtained in the R bootstraps with the null sequence data using plot and print methods.

4. If the cluster quality measure of the observed typology is constantly higher than the ones obtained with null data, a “good” typology has been found.

Several null models are provided to test the different structuring aspects of the sequences important in life-course research, namely, sequencing, timing, and duration (see function seqnull and Studer, 2021). This strategy allows identifying the key structural aspects captured by the observed typology.

Value

seqnullcqi returns a "seqnullcqi" object with the following components:

References

Studer, M. (2021). Validating Sequence Analysis Typologies Using Parametric Bootstrap. Sociological Methodology. doi:10.1177/00811750211014232

A brief introduction to the R code needed to use parametric bootstraps for typology validation in sequence analysis is provided here https://sequenceanalysis.org/2023/10/19/validating-sequence-analysis-typologies-using-parametric-bootstrap/

See Also

See Also seqnull for description of the null models.

Examples

data(biofam)

## Create the sequence object
bf.seq <- seqdef(biofam[sample.int(nrow(biofam), 100),10:25])

## Library fastcluster greatly improve computation time when using hclust
# library(fastcluster)
## Computing distances
diss <- seqdist(bf.seq, method="HAM")
## Hierarchical clustering
hc <- hclust(as.dist(diss), method="ward.D")
# Computing cluster quality measures.
clustqual <- as.clustrange(hc, diss=diss, ncluster=7)

# Compute cluster quality measure for the null model "combined"
# seqdist.args should be the same as for seqdist above except the sequence data.
# Clustering methods should be the same as above.
bcq <- seqnullcqi(bf.seq, clustqual, R=5, model=c("combined"), 
				seqdist.args=list(method="HAM"),
				hclust.method="ward.D")

# Print the results
bcq

## Different kind of plots

plot(bcq, stat="ASW", type="line")
plot(bcq, stat="ASW", type="density")
plot(bcq, stat="ASW", type="boxplot")

Monothetic clustering of state sequences

Description

Monothetic divisive clustering of the data using object properties. For state sequences object different set of properties are automoatically extracted.

Usage

seqpropclust(seqdata, diss, properties = c("state", "duration", "spell.age", 
		"spell.dur", "transition", "pattern", "AFtransition", "AFpattern", 
		"Complexity"), other.prop = NULL, prop.only = FALSE, pmin.support = 0.05, 
		max.k = -1, with.missing = TRUE, R = 1, weight.permutation = "diss", 
		min.size = 0.01, max.depth = 5, maxcluster = NULL, ...)
		
wcPropertyClustering(diss, properties, maxcluster = NULL, ...)
dtcut(st, k, labels = TRUE)

Arguments

Details

The method implement the DIVCLUS-T algorithm.

Value

Return a seqpropclust object, which is (in fact) a distree object. See disstree.

References

Studer, M. (2018). Divisive property-based and fuzzy clustering for sequence analysis. In G. Ritschard and M. Studer (Eds.), Sequence Analysis and Related Approaches: Innovative Methods and Applications, Life Course Research and Social Policies. Springer.

Piccarreta R, Billari FC (2007). Clustering work and family trajectories by using a divisive algorithm. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(4), 1061-1078.

Chavent M, Lechevallier Y, Briant O (2007). DIVCLUS-T: A monothetic divisive hierarchical clustering method. Computational Statistics & Data Analysis, 52(2), 687-701.

See Also

as.clustrange, seqtreedisplay, disstree.

Examples

data(mvad)
mvad.seq <- seqdef(mvad[1:100, 17:86])

## COmpute distance using Hamming distance
diss <- seqdist(mvad.seq, method="HAM")

pclust <- seqpropclust(mvad.seq , diss=diss, maxcluster=5, properties=c("state", "duration")) 

## Run it to visualize the results
##seqtreedisplay(pclust, type="d", border=NA, showdepth=TRUE)

pclustqual <- as.clustrange(pclust, diss=diss, ncluster=5)

Aggregate identical cases.

Description

Function to aggregate identical cases.

Usage

wcAggregateCases(x, weights = NULL, ...)
## S3 method for class 'data.frame'
wcAggregateCases(x, weights=NULL, ...) 
## S3 method for class 'matrix'
wcAggregateCases(x, weights=NULL, ...) 
## S3 method for class 'stslist'
wcAggregateCases(x, weights=NULL, weighted=TRUE, ...)
## S3 method for class 'wcAggregateCases'
print(x,  ...) 

Arguments

Value

A wcAggregateCases object with the following components:

aggIndex

Index of the unique cases in the original object data.

aggWeights

Aggregated case weights

disaggIndex

Index of the original object data in the unique cases.

disaggWeights

Original weights used.

Examples

data(mvad)
## Taking only the father unemployment and 
## success at the end of compulsory schooling.
myData <- mvad[ , c("funemp", "gcse5eq")]
## Computing aggregated cases informations
ac <- wcAggregateCases(myData, weights=mvad$weight)
print(ac)
## Retrieving unique cases in the original data set
uniqueData <- myData[ac$aggIndex, ]
## Table from original data
table.orig <- xtabs(mvad$weight~funemp+gcse5eq, data=myData)

## Table from aggregated data
table.agg <- xtabs(ac$aggWeights~funemp+gcse5eq, data=uniqueData)

## Both table are equal, no information is lost 
## (only the call command is different)
all(table.orig == table.agg)

Cluster quality statistics

Description

Compute several quality statistics of a given clustering solution.

Usage

wcClusterQuality(diss, clustering, weights = NULL)

Arguments

Details

Compute several quality statistics of a given clustering solution. See value for details.

Value

A list with two elements stats and ASW:

stats

with the following statistics:

PBC

Point Biserial Correlation. Correlation between the given distance matrice and a distance which equal to zero for individuals in the same cluster and one otherwise.

HG

Hubert's Gamma. Same as previous but using Kendall's Gamma coefficient.

HGSD

Hubert's Gamma (Somers'D). Same as previous but using Somers' D coefficient.

ASW

Average Silhouette width (observation).

ASWw

Average Silhouette width (weighted).

CH

Calinski-Harabasz index (Pseudo F statistics computed from distances).

R2

Share of the discrepancy explained by the clustering solution.

CHsq

Calinski-Harabasz index (Pseudo F statistics computed from squared distances).

R2sq

Share of the discrepancy explained by the clustering solution (computed using squared distances).

HC

Hubert's C coefficient.

ASW:

The Average Silhouette Width of each cluster, one column for each ASW measure.

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)
## 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, cluster.only=TRUE)

## Compute the silhouette of each observation
qual <- wcClusterQuality(diss, clust5, weights=aggMvad$aggWeights)

print(qual)

Automatic comparison of clustering methods.

Description

Automatically compute different clustering solutions and associated quality measures to help identifying the best one.

Usage

wcCmpCluster(diss, weights = NULL, maxcluster, method = "all", pam.combine = TRUE)
## S3 method for class 'clustrangefamily'
print(x, max.rank=1, ...)
## S3 method for class 'clustrangefamily'
summary(object, max.rank=1, ...)
## S3 method for class 'clustrangefamily'
plot(x, group="stat", method="all", pam.combine=FALSE, 
    stat="noCH", norm="none", withlegend=TRUE, lwd=1, col=NULL, legend.prop=NA, 
	rows=NA, cols=NA, main=NULL, xlab="", ylab="", ...)

Arguments

Value

An object of class clustrangefamily with the following elements:

Method name:

the results of as.clustrange objects under each method name (see argument method for a list of possible values)

allstats:

A matrix containing the clustering statistics for each cluster solution and method.

param:

The parameters set when the function was called.

See Also

See Also as.clustrange

Examples

data(mvad)

#Creating state sequence object
mvad.seq <- seqdef(mvad[, 17:86])

# COmpute distance using Hamming distance
diss <- seqdist(mvad.seq, method="HAM")

#Ward clustering
allClust <- wcCmpCluster(diss, maxcluster=15, method=c("average", "pam", "beta.flexible"), 
                         pam.combine=FALSE)

summary(allClust, max.rank=3)

##Plot PBC, RHC and ASW
plot(allClust, stat=c("PBC", "RHC", "ASW"), norm="zscore", lwd=2)


##Plot PBC, RHC and ASW grouped by cluster method
plot(allClust, group="method", stat=c("PBC", "RHC", "ASW"), norm="zscore", lwd=2)

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

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)

K-Medoids or PAM clustering of weighted data.

Description

K-Medoids or PAM clustering of weighted data.

Usage

	wcKMedoids(diss, k, weights=NULL, npass = 1, initialclust=NULL, 
		method="PAMonce", cluster.only = FALSE, debuglevel=0)

Arguments

Details

K-Medoids algorithms aim at finding the best partition of the data in a k predefined number of groups. Based on a dissimilarity matrix, those algorithms seeks to minimize the (weighted) sum of distance to the medoid of each group. The medoid is defined as the observation that minimize the sum of distance to the other observations of this group. The function wcKMedoids support three differents algorithms specified using the method argument:

"KMedoids"

Start with a random solution and then iteratively adapt the medoids using an algorithm similar to kmeans. Part of the code is inspired (but completely rewritten) by the C clustering library (see de Hoon et al. 2010). If you use this solution, you should set npass>1 to try several solution.

"PAM"

See pam in the cluster library. This code is based on the one available in the cluster library (Maechler et al. 2011). The advantage over the previous method is that it try to minimize a global criteria instead of a local one.

"PAMonce"

Same as previous but with two optimizations. First, the optimization presented by Reynolds et al. 2006. Second, only evaluate possible swap if the dissimilarity is greater than zero. This algorithm is used by default.

wcKMedoids works differently according to the diss argument. It may be faster using a matrix but require more memory (since all distances are stored twice). All combination between method and diss argument are possible, except for the "PAM" algorithm were only distance matrix may be used (use the "PAMonce" algorithm instead).

Value

An integer vector with the index of the medoids associated with each observation.

References

Maechler, M., P. Rousseeuw, A. Struyf, M. Hubert and K. Hornik (2011). cluster: Cluster Analysis Basics and Extensions. R package version 1.14.1 — For new features, see the 'Changelog' file (in the package source).

Hoon, M. d.; Imoto, S. & Miyano, S. (2010). The C Clustering Library. Manual

See Also

pam in the cluster library, wcClusterQuality, wcKMedRange.

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)
## Computing Hamming distance between sequence
diss <- seqdist(mvad.seq, method="HAM")

## K-Medoids
clust5 <- wcKMedoids(diss, k=5, weights=aggMvad$aggWeights)

## clust5$clustering contains index number of each medoids
## Those medoids are
unique(clust5$clustering)

## Print the medoids sequences
print(mvad.seq[unique(clust5$clustering), ], informat="SPS")

## Some info about the clustering
print(clust5)

## Plot sequences according to clustering solution.
seqdplot(mvad.seq, group=clust5$clustering)

Compute the silhouette of each object using weighted data.

Description

Compute the silhouette of each object using weighted data.

Usage

wcSilhouetteObs(diss, clustering, weights = NULL, measure="ASW")

Arguments

Details

See the silhouette function in the cluster package for a detailed explanation of the silhouette.

Value

A numeric vector containing the silhouette of each observation.

References

Maechler, M., P. Rousseeuw, A. Struyf, M. Hubert and K. Hornik (2011). cluster: Cluster Analysis Basics and Extensions. R package version 1.14.1 — For new features, see the 'Changelog' file (in the package source).

See Also

See also silhouette.

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)
## 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, cluster.only=TRUE)

## Compute the silhouette of each observation
sil <- wcSilhouetteObs(diss, clust5, weights=aggMvad$aggWeights, measure="ASWw")

## If you want to compute the average silhouette width, 
## you should take weights into account
weighted.mean(sil, w=aggMvad$aggWeights)

## Plotting sequences ordred by silhouette width, 
## best classified are draw on the top.
seqIplot(mvad.seq, group=clust5, sortv=sil)