########## R function: summariseMCMC ##########

# For displaying a Monte Carlo Markov Chain 
#(MCMC) produced by a BRugsFit().

# Last changed: 18 APR 2007

summariseMCMC <- function(parName,label=parName)
{
   library(KernSmooth)
   samp <- samplesSample(parName)
   par(mfrow=c(2,2))

   # Trace plot
 
   plot(samp,type="l",xlab="iteration",ylab="",
        main=label,col="red")

   # ACF plot 

   acf(samp,main="estimated ACF",col="orange",lwd=2)

   # Density plot

   estPostDest <- bkde(samp,bandwidth=dpik(samp))
   plot(estPostDest,type="l",xlab=label,ylab="density",
        main="estimated posterior density",lwd=3,col="green3")

   # Summarise

   plot(0,0,type="n",xlim=c(0,1),ylim=c(0,1),xaxt="n",yaxt="n",
        xlab="",ylab="",main=label)
   postMean <- signif(mean(samp),4)
   CIlower <- signif(quantile(samp,0.025),4)
   CIupper <- signif(quantile(samp,0.975),4)
   text(0.5,0.75,paste("posterior mean:",postMean),col="purple")
   text(0.5,0.50,"95% credible interval:",col="DeepPink")
   text(0.5,0.25,paste("(",CIlower,",",CIupper,")"),col="DeepPink")

   cat("Hit enter to continue.\n")
   ans <- readline()
}

########## End of summariseMCMC ##########