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 exclude all;
%gmcmc;
ods exclude none;
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);