Documentation Example 8 for PROC MCMC
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: MCMCEX8 */
/* TITLE: Documentation Example 8 for PROC MCMC */
/* Nonlinear Poisson Multilevel Random-Effects Model */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: */
/* PROCS: MCMC */
/* DATA: */
/* */
/* SUPPORT: Fang Chen */
/* REF: PROC MCMC, EXAMPLE 8 */
/* MISC: */
/****************************************************************/
title 'Nonlinear Poisson Regression Random-Effects Model';
data pump;
input y t group @@;
pump = _n_;
logtstd = log(t) - 2.4564900;
datalines;
5 94.320 1 1 15.720 2 5 62.880 1
14 125.760 1 3 5.240 2 19 31.440 1
1 1.048 2 1 1.048 2 4 2.096 2
22 10.480 2
;
ods graphics on;
proc mcmc data=pump outpost=postout seed=248601 nmc=10000
plots=trace stats=none diag=none;
ods select tracepanel;
array u[2] alpha beta;
array mu[2] (0 0);
parms s2;
prior s2 ~ igamma(0.01, scale=0.01);
random u ~ MVNAR(mu, sd=1e6, rho=0) subject=group monitor=(u);
random e ~ normal(0, var=s2) subject=pump monitor=(random(1));
w = alpha + beta * logtstd;
lambda = exp(w+e);
model y ~ poisson(lambda);
run;
proc mcmc data=pump outpost=postout_c seed=248601 nmc=10000
plots=trace diag=none;
ods select tracepanel postsumint;
array u[2] alpha beta;
array mu[2] (0 0);
parms s2 1;
prior s2 ~ igamma(0.01, scale=0.01);
random u ~ MVNAR(mu, sd=1e6, rho=0) subject=group monitor=(u);
w = alpha + beta * logtstd;
random llambda ~ normal(w, var = s2) subject=pump monitor=(random(1));
lambda = exp(llambda);
model y ~ poisson(lambda);
run;
%CATER(data=postout_c, var=llambda_:);
ods graphics off;