clusterMix {bayesm}  R Documentation 
clusterMix
uses MCMC draws of indicator variables from a normal component mixture model to cluster observations based on a similarity matrix.
clusterMix(zdraw, cutoff=0.9, SILENT=FALSE, nprint=BayesmConstant.nprint)
zdraw 

cutoff 
cutoff probability for similarity (def: 
SILENT 
logical flag for silent operation (def: 
nprint 
print every nprint'th draw (def: 
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.
A list containing:
clustera: 
indicator function for clustering based on method A above 
clusterb: 
indicator function for clustering based on method B above 
This routine is a utility routine that does not check the input arguments for proper dimensions and type.
Peter Rossi, Anderson School, UCLA, perossichi@gmail.com.
For further discussion, see Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch Chapter 3.
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)
}