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Documentation Example 1 for PROC GLIMMIX

/****************************************************************/
/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: gmxex01                                             */
/*   TITLE: Documentation Example 1 for PROC GLIMMIX            */
/*          Binomial Counts in Randomized Blocks                */
/* PRODUCT: STAT                                                */
/*  SYSTEM: ALL                                                 */
/*    KEYS: Generalized linear mixed models                     */
/*          Correlated binomial data                            */
/*   PROCS: GLIMMIX                                             */
/*    DATA:                                                     */
/*                                                              */
/* SUPPORT: Oliver Schabenberger                                */
/*     REF: Gotway, C.A. and Stroup, W.W. (1997),               */
/*          A Generalized Linear Model Approach to Spatial      */
/*          Data and Prediction                                 */
/*          Journal of Agricultural, Biological, and            */
/*          Environmental Statistics, 2, 157--187               */
/*    MISC:                                                     */
/****************************************************************/

data HessianFly;
  label Y = 'No. of damaged plants'
        n = 'No. of plants';
  input block entry lat lng n Y @@;
  datalines;
  1 14 1 1  8 2    1 16 1 2  9 1
  1  7 1 3 13 9    1  6 1 4  9 9
  1 13 2 1  9 2    1 15 2 2 14 7
  1  8 2 3  8 6    1  5 2 4 11 8
  1 11 3 1 12 7    1 12 3 2 11 8
  1  2 3 3 10 8    1  3 3 4 12 5
  1 10 4 1  9 7    1  9 4 2 15 8
  1  4 4 3 19 6    1  1 4 4  8 7
  2 15 5 1 15 6    2  3 5 2 11 9
  2 10 5 3 12 5    2  2 5 4  9 9
  2 11 6 1 20 10   2  7 6 2 10 8
  2 14 6 3 12 4    2  6 6 4 10 7
  2  5 7 1  8 8    2 13 7 2  6 0
  2 12 7 3  9 2    2 16 7 4  9 0
  2  9 8 1 14 9    2  1 8 2 13 12
  2  8 8 3 12 3    2  4 8 4 14 7
  3  7 1 5  7 7    3 13 1 6  7 0
  3  8 1 7 13 3    3 14 1 8  9 0
  3  4 2 5 15 11   3 10 2 6  9 7
  3  3 2 7 15 11   3  9 2 8 13 5
  3  6 3 5 16 9    3  1 3 6  8 8
  3 15 3 7  7 0    3 12 3 8 12 8
  3 11 4 5  8 1    3 16 4 6 15 1
  3  5 4 7 12 7    3  2 4 8 16 12
  4  9 5 5 15 8    4  4 5 6 10 6
  4 12 5 7 13 5    4  1 5 8 15 9
  4 15 6 5 17 6    4  6 6 6  8 2
  4 14 6 7 12 5    4  7 6 8 15 8
  4 13 7 5 13 2    4  8 7 6 13 9
  4  3 7 7  9 9    4 10 7 8  6 6
  4  2 8 5 12 8    4 11 8 6  9 7
  4  5 8 7 11 10   4 16 8 8 15 7
;

proc glimmix data=HessianFly;
   class block entry;
   model y/n = block entry / solution;
run;

proc glimmix data=HessianFly;
   class block entry;
   model y/n = entry / solution;
   random block;
run;

proc glimmix data=HessianFly;
   class entry;
   model y/n = entry / solution ddfm=contain;
   random _residual_ / subject=intercept type=sp(exp)(lng lat);
run;