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Second Getting Started Example for PROC FMM

/****************************************************************/
/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: fmmgs2                                              */
/*   TITLE: Second Getting Started Example for PROC FMM         */
/*          Zero-inflated Poisson regression                    */
/* PRODUCT: STAT                                                */
/*  SYSTEM: ALL                                                 */
/*    KEYS: Excess zeros                                        */
/*          Count data                                          */
/*          Bayesian analysis                                   */
/*   PROCS: FMM                                                 */
/*    DATA:                                                     */
/*                                                              */
/* SUPPORT: Dave Kessler                                        */
/*     REF:                                                     */
/*    MISC:                                                     */
/****************************************************************/

data catch;
   input gender $ age count @@;
   datalines;
  F   54  18   M   37   0   F   48  12   M   27   0
  M   55   0   M   32   0   F   49  12   F   45  11
  M   39   0   F   34   1   F   50   0   M   52   4
  M   33   0   M   32   0   F   23   1   F   17   0
  F   44   5   M   44   0   F   26   0   F   30   0
  F   38   0   F   38   0   F   52  18   M   23   1
  F   23   0   M   32   0   F   33   3   M   26   0
  F   46   8   M   45   5   M   51  10   F   48   5
  F   31   2   F   25   1   M   22   0   M   41   0
  M   19   0   M   23   0   M   31   1   M   17   0
  F   21   0   F   44   7   M   28   0   M   47   3
  M   23   0   F   29   3   F   24   0   M   34   1
  F   19   0   F   35   2   M   39   0   M   43   6
;

proc fmm data=catch;
   class gender;
   model count = gender*age / dist=Poisson;
run;

proc fmm data=catch;
   class gender;
   model count = gender*age / dist=Poisson ;
   model       +            / dist=Constant;
run;

proc fmm data=catch seed=12345;
   class gender;
   model count = gender*age / dist=Poisson;
   model       +            / dist=constant;
   performance cpucount=2;
   bayes;
run;

ods graphics on;
ods select TADPanel;
proc fmm data=catch seed=12345;
   class gender;
   model count = gender*age / dist=Poisson;
   model       +            / dist=constant;
   performance cpucount=2;
   bayes;
run;
ods graphics off;