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Documentation Example 18 for PROC MCMC

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/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: MCMCEX18                                            */
/*   TITLE: Documentation Example 18 for PROC MCMC              */
/*          Constrained Analysis                                */
/* PRODUCT: STAT                                                */
/*  SYSTEM: ALL                                                 */
/*    KEYS:                                                     */
/*   PROCS: MCMC                                                */
/*    DATA:                                                     */
/*                                                              */
/* SUPPORT: Fang Chen                                           */
/*     REF: PROC MCMC, EXAMPLE 18                               */
/*    MISC:                                                     */
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options validvarname=any;
proc format;
   value sizef  1 = '< 300cc' 2 = '300-550cc' 3 = '> 551cc';
   value pricef 1 = '< $5000' 2 = '$5000 - $6000'
                3 = '$6001 - $7000' 4 = '> $7000';
   value startf 1 = 'Electric Start' 2 = 'Kick Start';
   value balf   1 = 'Counter Balanced' 2 = 'Unbalanced';
   value orif   1 = 'Japanese' 2 = 'European';
run;
data bikes;
   input Size Price Start Balance Origin Rank @@;
   format size sizef. price pricef. start startf.
          balance balf. origin orif.;
   datalines;
2 1 2 1 2  3  1 4 2 2 2  7  1 2 1 1 2  6
3 3 1 1 2  1  1 3 2 1 1  5  3 4 2 2 2 12
2 3 2 2 1  9  1 1 1 2 1  8  2 2 1 2 2 10
2 4 1 1 1  4  3 1 1 2 1 11  3 2 2 1 1  2
;
title 'Ordinary Conjoint Analysis by PROC TRANSREG';
proc transreg data=bikes utilities cprefix=0 lprefix=0;
   ods select Utilities;
   model identity(rank / reflect) =
         class(size price start balance origin / zero=sum);
   output out=coded(drop=intercept) replace;
run;
title 'Constrained Conjoint Analysis by PROC TRANSREG';
proc transreg data=bikes utilities cprefix=0 lprefix=0;
   ods select ConservUtilities;
   model identity(rank / reflect) =
         monotone(price / tstandard=center)
         class(size start balance origin / zero=sum);
run;
title 'Bayesian Constrained Conjoint Analysis by PROC MCMC';
proc mcmc data=coded outpost=bikesout ntu=3000 nmc=50000
   propcov=quanew seed=448 diag=none;
   ods select PostSummaries;
   array pw[4] pw5000 pw5000_6000 pw6001_7000 pwCounterBalanced;
   array sigma[4,4];
   array mu[4];

   begincnst;
      call identity(sigma);
      call mult(sigma, 100, sigma);
      call zeromatrix(mu);
   endcnst;

   parms intercept pw300cc pw300_550cc pwElectricStart pwJapanese tau 1;
   parms pw5000 0.3 pw5000_6000 0.2 pw6001_7000 0.1 pwCounterBalanced 1;

   beginnodata;
   prior intercept pw300: pwE: pwJ: ~ normal(0, var=100);
   if (pw5000      >= pw5000_6000 & pw5000_6000 >= pw6001_7000 &
       pw6001_7000 >= 0           & pwCounterBalanced > 0) then
       lp = lpdfmvn(pw, mu, sigma);
   else
       lp = .;
   prior pw5000 pw5000_6000 pw6001_7000 pwC: ~ general(lp);
   prior tau  ~ gamma(0.01, iscale=0.01);
   endnodata;

   mean = intercept +
          pw300cc           * '< 300cc'n          +
          pw300_550cc       * '300-550cc'n        +
          pw5000            * '< $5000'n          +
          pw5000_6000       * '$5000 - $6000'n    +
          pw6001_7000       * '$6001 - $7000'n    +
          pwElectricStart   * 'Electric Start'n   +
          pwCounterBalanced * 'Counter Balanced'n +
          pwJapanese        * Japanese;
   model rank ~ normal(mean, prec=tau);
run;