/*-----------------------------------------------------------------
            S A S   S A M P L E   L I B R A R Y                   
                                                                  
      NAME: GENMGS2                                               
     TITLE: Getting Started Example 2 for PROC GENMOD             
   PRODUCT: STAT                                                  
    SYSTEM: ALL                                                   
      KEYS: generalized linear models, Bayesian analysis          
     PROCS: GENMOD                                                
      DATA:                                                       
                                                                  
   SUPPORT: sasgjj                                                
       REF: PROC GENMOD, INTRODUCTORY EXAMPLE 2.                  
      MISC:                                                       
-----------------------------------------------------------------*/

data Surg;
   input x1 x2 x3 x4 y logy;
   label x1 = 'Blood Clotting Score';
   label x2 = 'Prognostic Index';
   label x3 = 'Enzyme Function Test Score';
   label x4 = 'Liver Function Test Score';
   label y = 'Survival Time';
   Logx1 = log(x1);
   datalines;
6.7  62   81  2.59  200  2.3010
5.1  59   66  1.70  101  2.0043
7.4  57   83  2.16  204  2.3096
6.5  73   41  2.01  101  2.0043
7.8  65  115  4.30  509  2.7067
5.8  38   72  1.42   80  1.9031
5.7  46   63  1.91   80  1.9031
3.7  68   81  2.57  127  2.1038
6.0  67   93  2.50  202  2.3054
3.7  76   94  2.40  203  2.3075
6.3  84   83  4.13  329  2.5172
6.7  51   43  1.86   65  1.8129
5.8  96  114  3.95  830  2.9191
5.8  83   88  3.95  330  2.5185
7.7  62   67  3.40  168  2.2253
7.4  74   68  2.40  217  2.3365
6.0  85   28  2.98   87  1.9395
3.7  51   41  1.55   34  1.5315
7.3  68   74  3.56  215  2.3324
5.6  57   87  3.02  172  2.2355
5.2  52   76  2.85  109  2.0374
3.4  83   53  1.12  136  2.1335
6.7  26   68  2.10   70  1.8451
5.8  67   86  3.40  220  2.3424
6.3  59  100  2.95  276  2.4409
5.8  61   73  3.50  144  2.1584
5.2  52   86  2.45  181  2.2577
11.2 76   90  5.59  574  2.7589
5.2  54   56  2.71   72  1.8573
5.8  76   59  2.58  178  2.2504
3.2  64   65  0.74   71  1.8513
8.7  45   23  2.52   58  1.7634
5.0  59   73  3.50  116  2.0645
5.8  72   93  3.30  295  2.4698
5.4  58   70  2.64  115  2.0607
5.3  51   99  2.60  184  2.2648
2.6  74   86  2.05  118  2.0719
4.3   8  119  2.85  120  2.0792
4.8  61   76  2.45  151  2.1790
5.4  52   88  1.81  148  2.1703
5.2  49   72  1.84   95  1.9777
3.6  28   99  1.30   75  1.8751
8.8  86   88  6.40  483  2.6840
6.5  56   77  2.85  153  2.1847
3.4  77   93  1.48  191  2.2810
6.5  40   84  3.00  123  2.0899
4.5  73  106  3.05  311  2.4928
4.8  86  101  4.10  398  2.5999
5.1  67   77  2.86  158  2.1987
3.9  82  103  4.55  310  2.4914
6.6  77   46  1.95  124  2.0934
6.4  85   40  1.21  125  2.0969
6.4  59   85  2.33  198  2.2967
8.8  78   72  3.20  313  2.4955
;
proc print data=Surg (obs=20);
run;

proc genmod data=Surg;
   model y = Logx1 X2 X3 X4 / dist=normal;
   bayes seed=1 OutPost=PostSurg;
run;

data Prob;
   set PostSurg;
   Indicator = (logX1 > 0);
   label Indicator= 'log(Blood Clotting Score) > 0';
run;

proc Means data = Prob(keep=Indicator) n mean;
run;