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Last data update: 2014.03.03

R: MCAk

MCAk	R Documentation

MCAk

Description

Implements Cluster MCA (Hwang, Dillon and Takane, 2006) which combines Multiple Correspondence Analysis for dimension reduction with k-means for clustering.

Usage

MCAk(data,nclus,ndim,nstart=100,smartStart=F,seed=1234)

Arguments

`data`	categorical dataset
`nclus`	number of clusters
`ndim`	dimensionality of the solution
`nstart`	number of random starts
`smartStart`	If TRUE then starting values are obtained with k-means
`seed`	seed is used to set the random number seed when smartStart = FALSE

Value

`obscoord`	object scores
`attcoord`	attribute scores
`centroid`	cluster centroids
`cluID`	cluster membership
`criterion`	optimal value of the objective function

Author(s)

Markos, A. amarkos@gmail.com, Iodice D'Enza , A. iodicede@gmail.com and Van de Velden, M. vandevelden@ese.eur.nl

References

Hwang, H., Dillon, W. R. and Takane, Y. (2006). An extension of multiple correspondence analysis for identifying heterogenous subgroups of respondents, Psychometrika, 71, 161-171.

Examples

     data(underwear)
     attlab = c(c(1:15),"by","tr","vm","jd","ml","bn","bg","ck","a1","a2","a3")
     outMCAk <- MCAk(underwear,nclus=3,ndim=2,nstart=1,smartStart=TRUE,seed=1234)
     plotrd(outMCAk,attlabel=attlab)