## This produces multiple acf plots on top of each other for each chain,
## not unlike the winbugs output.
## It assumes a one variable MCMC object.
overlayedacfplot <-
function(x, ...,main=paste("Autocorrelation for ",substitute(x))) {
  if (all(is.na(x))) {
    warn("Missing data, not calculating ",main)
  } else {
    for (ichain in 1:nchain(x)) {
      xacf <- acf(as.ts.mcmc(x[[ichain]]),plot=FALSE)
      if (ichain == 1) {
        plot(xacf$lag,xacf$acf,type="h",
             ylab="Autocorrelation", xlab="Lag", ylim=c(-1,1),
             main=main, ...)
      } else {
        points(xacf$lag,xacf$acf,type="h",col=ichain)
      }
    }
  }
}


traceAutoplot <-
function(x,smooth=TRUE, ...) {
  if (all(is.na(x))) {
    warn("Missing data, not traceplots for ",substitute(x))
  } else {
    oldpar <- par(mfrow = c(nvar(x),2))
    on.exit(par(oldpar))
    for (ivar in 1:nvar(x)) {
      avar <- x[, ivar, drop = FALSE]
      traceplot(avar, smooth = smooth, ...)
      overlayedacfplot(avar,...,main=paste("Autocorrelation for ",varnames(x)[ivar]))
    }
    invisible(x)
  }
}

## This is the usual collection of coda plots I like to apply to a model.
"grandPlots" <-
function(param, ...) {
  if (!all(is.na(param))) {
    plot(param, ...)
    try(gelman.plot(param, transform=TRUE, ...))
    try(autocorr.plot(param, ...))
  }
}