| 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)
zdraw |
R x nobs array of draws of indicators |
cutoff |
cutoff probability for similarity (def=.9) |
SILENT |
logical flag for silent operation (def= FALSE) |
define a similarity matrix, Sim, 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.
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, Graduate School of Business, University of Chicago Peter.Rossi@ChicagoGsb.edu.
For further discussion, see Bayesian Statistics and Marketing
by Rossi, Allenby and McCulloch Chapter 3.
http://faculty.chicagogsb.edu/peter.rossi/research/bsm.html
##
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,.5,.5,1),ncol=2)
Sigma2=matrix(c(1,.5,.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
R=2000
Data=list(y=dm$x)
ncomp=2
Prior=list(ncomp=ncomp,a=c(rep(100,ncomp)))
Mcmc=list(R=R,keep=1)
out=rnmixGibbs(Data=Data,Prior=Prior,Mcmc=Mcmc)
begin=500
end=R
## find clusters
outclusterMix=clusterMix(out$zdraw[begin:end,])
##
## check on clustering versus "truth"
## note: there could be switched labels
##
table(outclusterMix$clustera,dm$z)
table(outclusterMix$clusterb,dm$z)
}
##