clusterMix {bayesm} R Documentation

Cluster Observations Based on Indicator MCMC Draws

Description

clusterMix uses MCMC draws of indicator variables from a normal component mixture model to cluster observations based on a similarity matrix.

Usage

clusterMix(zdraw, cutoff=0.9, SILENT=FALSE, nprint=BayesmConstant.nprint)

Arguments

 zdraw R x nobs array of draws of indicators cutoff cutoff probability for similarity (def: 0.9) SILENT logical flag for silent operation (def: FALSE) nprint print every nprint'th draw (def: 100)

Details

Define a similarity matrix, Sim with Sim[i,j]=1 if observations i and j are in same component. Compute the posterior mean of Sim over indicator draws.

Clustering is achieved by two means:

Method A: Find the indicator draw whose similarity matrix minimizes loss(E[Sim]-Sim(z)), where loss is absolute deviation.

Method B: Define a Similarity matrix by setting any element of E[Sim] = 1 if E[Sim] > cutoff. Compute the clustering scheme associated with this "windsorized" Similarity matrix.

Value

A list containing:

 clustera: indicator function for clustering based on method A above clusterb: indicator function for clustering based on method B above

Warning

This routine is a utility routine that does not check the input arguments for proper dimensions and type.

Author(s)

Peter Rossi, Anderson School, UCLA, perossichi@gmail.com.

References

For further discussion, see Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch Chapter 3.

See Also

rnmixGibbs

Examples

if(nchar(Sys.getenv("LONG_TEST")) != 0) {

## simulate data from mixture of normals
n = 500
pvec = c(.5,.5)
mu1 = c(2,2)
mu2 = c(-2,-2)
Sigma1 = matrix(c(1,0.5,0.5,1), ncol=2)
Sigma2 = matrix(c(1,0.5,0.5,1), ncol=2)
comps = NULL
comps[[1]] = list(mu1, backsolve(chol(Sigma1),diag(2)))
comps[[2]] = list(mu2, backsolve(chol(Sigma2),diag(2)))
dm = rmixture(n, pvec, comps)

## run MCMC on normal mixture
Data = list(y=dm$x) ncomp = 2 Prior = list(ncomp=ncomp, a=c(rep(100,ncomp))) R = 2000 Mcmc = list(R=R, keep=1) out = rnmixGibbs(Data=Data, Prior=Prior, Mcmc=Mcmc) ## find clusters begin = 500 end = R outclusterMix = clusterMix(out$nmix$zdraw[begin:end,]) ## check on clustering versus "truth" ## note: there could be switched labels table(outclusterMix$clustera, dm$z) table(outclusterMix$clusterb, dm\$z)
}


[Package bayesm version 3.1-6 Index]