The MCMC Procedure

 
Caterpillar Plot

The caterpillar plot is a side-by-side bar plot of 95% intervals for multiple parameters. Typically, it is used to visualize and compare random-effects parameters, which can come in large numbers in certain models. You can use the %CATER autocall macro to create a caterpillar plot. The %CATER macro requires you specify an input data set and a list of variables that you want to plot.

A random-effects model that has 21 random-effects parameters is used here for illustrational purpose. For an explanation of the random-effects model and the data involved, see Logistic Regression Random-Effects Model. The following statements generate a SAS data set and fit the model:

title 'Create a Caterpillar Plot';

data seeds;
   input r n seed extract @@;
   ind = _N_;
   datalines;
10  39  0  0    23  62  0  0    23  81  0  0    26  51  0  0
17  39  0  0     5   6  0  1    53  74  0  1    55  72  0  1
32  51  0  1    46  79  0  1    10  13  0  1     8  16  1  0
10  30  1  0     8  28  1  0    23  45  1  0     0   4  1  0
 3  12  1  1    22  41  1  1    15  30  1  1    32  51  1  1
 3   7  1  1
;
ods select none;
proc mcmc data=seeds outpost=postout seed=332786 nmc=20000;
   parms beta0 0 beta1 0 beta2 0 beta3 0 s2 1;
   prior s2 ~ igamma(0.01, s=0.01);
   prior beta: ~ general(0);
   w = beta0 + beta1*seed + beta2*extract + beta3*seed*extract;
   random delta ~ normal(w, var=s2) subject=ind;
   pi = logistic(delta);
   model r ~ binomial(n = n, p = pi);
run;
ods select all;

The output data set Postout contains posterior draws for all 21 random-effects parameters, delta_1 delta_21. You can use the following statements to generate a caterpillar plot for the 21 parameters:

ods graphics on;
%CATER(data=postout, var=delta:);
ods graphics off;

Figure 54.16 is a caterpillar plot of the random-effects parameters delta_1delta_21.

Figure 54.16 Caterpillar Plot of the Random-Effects Parameters
Caterpillar Plot of the Random-Effects Parameters

If you want to change the display of the caterpillar plot, such as using a different line pattern, color, or size of the markers, you need to first modify the Stat.MCMC.Graphics.Caterpillar template and then call the %CATER macro again.

You can use the following statements to view the source of the Stat.MCMC.Graphics.Caterpillar template:

proc template;
   path sashelp.tmplmst;
   source Stat.MCMC.Graphics.Caterpillar;
run;

Figure 54.17 lists the source statements of the template that is used to generate the template for the caterpillar plot.

Figure 54.17 Source Statements for Stat.MCMC.Graphics.Caterpillar Template
define statgraph Stat.MCMC.Graphics.Caterpillar;                                
   dynamic _OverallMean _VarName _VarMean _XLower _XUpper;                      
   begingraph;                                                                  
      entrytitle "Caterpillar Plot";                                            
      layout overlay / yaxisopts=(offsetmin=0.05 offsetmax=0.05 display=(line   
         ticks tickvalues)) xaxisopts=(display=(line ticks tickvalues));        
         referenceline x=_OVERALLMEAN / lineattrs=(color=                       
            GraphReference:ContrastColor);                                      
         HighLowPlot y=_VARNAME high=_XUPPER low=_XLOWER / lineattrs=           
            GRAPHCONFIDENCE;                                                    
         scatterplot y=_VARNAME x=_VARMEAN / markerattrs=(size=5 symbol=        
            circlefilled);                                                      
      endlayout;                                                                
   endgraph;                                                                    
end;                                                                            

You can use the TEMPLATE procedure (see Chapter 21, Statistical Graphics Using ODS ) to run any modified SAS/GRAPH graph template definition and then call the %CATER macro again. The %CATER macro picks up the change you made to the Caterpillar template and displays the new graph accordingly.