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Documentation Example 21 for PROC MCMC

/****************************************************************/
/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: MCMCEX21                                            */
/*   TITLE: Documentation Example 21 for PROC MCMC              */
/*          Gelman-Rubin Diagnostics                            */
/* PRODUCT: STAT                                                */
/*  SYSTEM: ALL                                                 */
/*    KEYS:                                                     */
/*   PROCS: MCMC                                                */
/*    DATA:                                                     */
/*                                                              */
/* SUPPORT: Fang Chen                                           */
/*     REF: PROC MCMC, EXAMPLE 21                               */
/*    MISC:                                                     */
/****************************************************************/
title 'Simple Linear Regression, Gelman-Rubin Diagnostics';

data Class;
   input Name $ Height Weight @@;
   datalines;
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
;
data init;
   input Chain beta0 beta1 sigma2;
   datalines;
   1   10  -5   1
   2  -15  10  20
   3    0   0  50
;
/* define constants */
%let nchain = 3;
%let nparm = 3;
%let nsim = 50000;
%let var = beta0 beta1 sigma2;

%macro gmcmc;
   %do i=1 %to &nchain;
      data _null_;
         set init;
         if Chain=&i;
         %do j = 1 %to &nparm;
            call symputx("init&j", %scan(&var, &j));
         %end;
         stop;
      run;

      proc mcmc data=class outpost=out&i init=reinit nbi=0 nmc=&nsim
                stats=none seed=7;
         parms beta0 &init1 beta1 &init2;
         parms sigma2 &init3 / n;
         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);
      run;
   %end;
%mend;

ods listing close;
%gmcmc;
ods listing;
data all;
   set out1(in=in1) out2(in=in2) out3(in=in3);
   if in1 then Chain=1;
   if in2 then Chain=2;
   if in3 then Chain=3;
run;

%gelman(all, &nparm, &var, &nsim);

data GelmanRubin(label='Gelman-Rubin Diagnostics');
   merge _Gelman_Parms _Gelman_Ests;
run;

proc print data=GelmanRubin;
run;

/* plot the trace plots of three Markov chains. */
%macro trace;
   %do i = 1 %to &nparm;
      proc sgplot data=all cycleattrs;
         series x=Iteration y=%scan(&var, &i) / group=Chain;
      run;
   %end;
%mend;
%trace;
/* define sliding window size */
%let nwin = 200;
data PSRF;
run;

%macro PSRF(nsim);
   %do k = 1 %to %sysevalf(&nsim/&nwin, floor);
      %gelman(all, &nparm, &var, nsim=%sysevalf(&k*&nwin));
      data GelmanRubin;
         merge _Gelman_Parms _Gelman_Ests;
      run;

      data PSRF;
         set PSRF GelmanRubin;
      run;
   %end;
%mend PSRF;

options nonotes;
%PSRF(&nsim);
options notes;

data PSRF;
   set PSRF;
   if _n_ = 1 then delete;
run;

proc sort data=PSRF;
   by Parameter;
run;

%macro sepPSRF(nparm=, var=, nsim=);
   %do k = 1 %to &nparm;
      data save&k; set PSRF;
         if _n_ > %sysevalf(&k*&nsim/&nwin, floor) then delete;
         if _n_ < %sysevalf((&k-1)*&nsim/&nwin + 1, floor) then delete;
         Iteration + &nwin;
      run;

      proc sgplot data=save&k(firstobs=10) cycleattrs;
         series x=Iteration y=Estimate;
         series x=Iteration y=upperbound;
         yaxis label="%scan(&var, &k)";
      run;
   %end;
%mend sepPSRF;

%sepPSRF(nparm=&nparm, var=&var, nsim=&nsim);