clustrd (version 0.1.2)

MCAk: 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

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.

See Also

groupals, iFCB, fuzzyMCAk

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)