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clusterMix=function(zdraw,cutoff=.9,SILENT=FALSE,nprint=BayesmConstant.nprint){
#
#
# revision history:
# written by p. rossi 9/05
#
# purpose: cluster observations based on draws of indicators of
# normal mixture components
#
# arguments:
# zdraw is a R x nobs matrix of draws of indicators (typically output from rnmixGibbs)
# the rth row of zdraw contains rth draw of indicators for each observations
# each element of zdraw takes on up to p values for up to p groups. The maximum
# number of groups is nobs. Typically, however, the number of groups will be small
# and equal to the number of components used in the normal mixture fit.
#
# cutoff is a cutoff used in determining one clustering scheme it must be
# a number between .5 and 1.
#
# nprint - print every nprint'th draw
#
# output:
# two clustering schemes each with a vector of length nobs which gives the assignment
# of each observation to a cluster
#
# clustera (finds zdraw with similarity matrix closest to posterior mean of similarity)
# clusterb (finds clustering scheme by assigning ones if posterior mean of similarity matrix
# > cutoff and computing associated z )
#
# define needed functions
#
# ------------------------------------------------------------------------------------------
#
# check arguments
#
if(missing(zdraw)) {pandterm("Requires zdraw argument -- R x n matrix of indicator draws")}
if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')}
#
# check validity of zdraw rows -- must be integers in the range 1:nobs
#
nobs=ncol(zdraw)
R=nrow(zdraw)
if(sum(zdraw %in% (1:nobs)) < ncol(zdraw)*nrow(zdraw))
{pandterm("Bad zdraw argument -- all elements must be integers in 1:nobs")}
cat("Table of zdraw values pooled over all rows",fill=TRUE)
print(table(zdraw))
#
# check validity of cuttoff
if(cutoff > 1 || cutoff < .5) {pandterm(paste("cutoff invalid, = ",cutoff))}
###################################################################
# Keunwoo Kim
# 10/06/2014
###################################################################
out=clusterMix_rcpp_loop(zdraw, cutoff, SILENT, nprint)
###################################################################
return(list(clustera=as.vector(out$clustera),clusterb=as.vector(out$clusterb)))
}
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