The MCMC Procedure

Regenerating Diagnostics Plots

By default, PROC MCMC generates three plots: the trace plot, the autocorrelation plot, and the kernel density plot. Unless ODS Graphics is enabled before calling the procedure, it is hard to generate the same graph afterwards. Directly using the Stat.MCMC.Graphics.TraceAutocorrDensity template is not feasible. The easiest way to regenerate the same graph is with the %TADPlot autocall macro. The %TADPlot macro requires you to specify an input data set (which usually is the output data set from a previous PROC MCMC call) and a list of variables that you want to plot.

For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21: Statistical Graphics Using ODS.

A simple regression example, with three parameters, is used here for illustrational purposes. For an explanation of the regression model and the data involved, see the section Simple Linear Regression. The following statements generate a SAS data set and fit a regression model:

title 'Regenerating Diagnostics Plots';

data Class;
   input Name $ Height Weight @@;
Alfred  69.0 112.5   Alice  56.5  84.0   Barbara 65.3  98.0
Carol   62.8 102.5   Henry  63.5 102.5   James   57.3  83.0
Jane    59.8  84.5   Janet  62.5 112.5   Jeffrey 62.5  84.0
John    59.0  99.5   Joyce  51.3  50.5   Judy    64.3  90.0
Louise  56.3  77.0   Mary   66.5 112.0   Philip  72.0 150.0
Robert  64.8 128.0   Ronald 67.0 133.0   Thomas  57.5  85.0
William 66.5 112.0
ods select none;
proc mcmc data=class nmc=50000 thin=5 outpost=classout seed=246810;
   parms beta0 0 beta1 0;
   parms sigma2 1;
   prior beta0 beta1 ~ normal(0, var = 1e6);
   prior sigma2 ~ igamma(3/10, scale = 10/3);
   mu = beta0 + beta1*height;
   model weight ~ normal(mu, var = sigma2);
ods select all;

The output data set Classout contains posterior draws for beta0, beta1, and sigma2. It also stores the log of the prior density (LogPrior), log of the likelihood (LogLike), and the log of the posterior density (LogPost). If you want to examine the beta0 and LogPost variable, you can use the following statements to generate the graphs:

ods graphics on;
%tadplot(data=classout, var=beta0 logpost);
ods graphics off;

Figure 55.16 displays the regenerated diagnostics plots for variables beta0 and Logpost from the data set Classout.

Figure 55.16: Regenerated Diagnostics Plots for beta0 and Logpost

Regenerated Diagnostics Plots for beta0 and Logpost
External File:images/mcmcreng1.png