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.
http://www.perossi.org/home/bsm-1

`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[] = list(mu1, backsolve(chol(Sigma1),diag(2)))
comps[] = 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 3.1-4 Index]