# load 2 chains each one consisting of a
# small mcmc sample of 100 iterations
# with p=6 variables and q=2 factors.
data(small_posterior_2chains)
Nchains <- length(small_posterior_2chains)
reorderedPosterior <- vector('list',length=Nchains)
# post-process the 2 chains
for(i in 1:Nchains){
reorderedPosterior[[i]] <- rsp_exact( lambda_mcmc = small_posterior_2chains[[i]],
maxIter = 100,
threshold = 1e-6,
verbose=TRUE )
}
# plot posterior summary for chain 1:
plot(reorderedPosterior[[1]])
# plot posterior summary for chain 2:
plot(reorderedPosterior[[2]])
# make them comparable
makeThemSimilar <- compareMultipleChains(rspObjectList=reorderedPosterior)
# plot the traces of both chains
oldpar <- par(no.readonly =TRUE)
par(mfcol=c(2,6),mar=c(4,4,2,1))
plot(makeThemSimilar,auto.layout=FALSE,density=FALSE,
ylim=c(-1.1,1.1),smooth=FALSE,col=c('red','blue'))
legend('topright',c('post-processed chain 1',
'post-processed chain 2'),lty=1:2,col=c('red','blue'))
par(oldpar)
# you can also use the summary of mcmc.list
summary(makeThemSimilar)
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