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Documentation Example 3 for PROC GLIMMIX

/****************************************************************/
/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: gmxex03                                             */
/*   TITLE: Documentation Example 3 for PROC GLIMMIX            */
/*          Smoothing Disease Rates;                            */
/*          Standardized Mortality Ratios                       */
/* PRODUCT: STAT                                                */
/*  SYSTEM: ALL                                                 */
/*    KEYS: Generalized linear mixed models                     */
/*          Poisson model with offset                           */
/*          Relative risk                                       */
/*          Studentized residuals                               */
/*   PROCS: GLIMMIX, TEMPLATE, SGRENDER                         */
/*    DATA: Scottish lip cancer data                            */
/*                                                              */
/* SUPPORT: Oliver Schabenberger                                */
/*     REF: Clayton, D. and Kaldor, J. (1987)                   */
/*          Empirical Bayes Estimates of Age-standardized       */
/*          Relative Risks for Use in Disease Mapping           */
/*          Biometrics, 43, 671-681                             */
/*    MISC:                                                     */
/****************************************************************/

data lipcancer;
   input county observed expected employment SMR;
   if (observed > 0) then expCount = 100*observed/SMR;
   else expCount = expected;
   datalines;
 1  9  1.4 16 652.2
 2 39  8.7 16 450.3
 3 11  3.0 10 361.8
 4  9  2.5 24 355.7
 5 15  4.3 10 352.1
 6  8  2.4 24 333.3
 7 26  8.1 10 320.6
 8  7  2.3  7 304.3
 9  6  2.0  7 303.0
10 20  6.6 16 301.7
11 13  4.4  7 295.5
12  5  1.8 16 279.3
13  3  1.1 10 277.8
14  8  3.3 24 241.7
15 17  7.8  7 216.8
16  9  4.6 16 197.8
17  2  1.1 10 186.9
18  7  4.2  7 167.5
19  9  5.5  7 162.7
20  7  4.4 10 157.7
21 16 10.5  7 153.0
22 31 22.7 16 136.7
23 11  8.8 10 125.4
24  7  5.6  7 124.6
25 19 15.5  1 122.8
26 15 12.5  1 120.1
27  7  6.0  7 115.9
28 10  9.0  7 111.6
29 16 14.4 10 111.3
30 11 10.2 10 107.8
31  5  4.8  7 105.3
32  3  2.9 24 104.2
33  7  7.0 10  99.6
34  8  8.5  7  93.8
35 11 12.3  7  89.3
36  9 10.1  0  89.1
37 11 12.7 10  86.8
38  8  9.4  1  85.6
39  6  7.2 16  83.3
40  4  5.3  0  75.9
41 10 18.8  1  53.3
42  8 15.8 16  50.7
43  2  4.3 16  46.3
44  6 14.6  0  41.0
45 19 50.7  1  37.5
46  3  8.2  7  36.6
47  2  5.6  1  35.8
48  3  9.3  1  32.1
49 28 88.7  0  31.6
50  6 19.6  1  30.6
51  1  3.4  1  29.1
52  1  3.6  0  27.6
53  1  5.7  1  17.4
54  1  7.0  1  14.2
55  0  4.2 16   0.0
56  0  1.8 10   0.0
;
proc glimmix data=lipcancer;
   class county;
   x    = employment / 10;
   logn = log(expCount);
   model observed = x / dist=poisson offset=logn
                        solution ddfm=none;
   random county;
   SMR_pred = 100*exp(_zgamma_ + _xbeta_);
   id employment SMR SMR_pred;
   output out=glimmixout;
run;
ods graphics on;
ods select StudentPanel;
proc glimmix data=lipcancer plots=studentpanel;
   class county;
   x    = employment / 10;
   logn = log(expCount);
   model observed = x / dist=poisson offset=logn s ddfm=none;
   random county;
run;
ods graphics off;
proc template;
   define statgraph scatter;
   BeginGraph;
     layout overlayequated / yaxisopts=(label='Predicted SMR')
                             xaxisopts=(label='Observed SMR')
                             equatetype=square;
        lineparm y=0 slope=1 x=0 /
               lineattrs = GraphFit(pattern=dash)
               extend    = true;
        scatterplot y=SMR_pred x=SMR /
                markercharacter = employment;
     endlayout;
   EndGraph;
   end;
run;
proc sgrender data=glimmixout template=scatter;
run;
proc glimmix data=lipcancer;
   x    = employment / 10;
   logn = log(expCount);
   model observed = x / dist=poisson offset=logn
                        solution ddfm=none;
   SMR_pred = 100*exp(_zgamma_ + _xbeta_);
   id employment SMR SMR_pred;
   output out=glimmixout;
run;