PROC ENTROPY: Use of the PDATA= Option :: SAS/ETS(R) 9.22 User's Guide

The ENTROPY Procedure

Example 12.4 Use of the PDATA= Option

It is sometimes useful to specify priors and supports by using the PDATA= option. This example illustrates how to create a PDATA= data set which contains the priors and support points for use in a subsequent PROC ENTROPY step. In order to have a model to estimate in PROC ENTROPY, you must first have data to analyze. The following DATA step generates the data used in this analysis:

title "Using a PDATA= data set";
data a;
   array x[4];
   do t = 1 to 100;
      ys = -5;
      do k = 1 to 4;
         x[k] = rannor( 55372 )  ;
         ys = ys + x[k] * k;
      end;
      ys = ys + rannor( 55372 );
      output;
    end;
run;

Next you fit this data with some arbitrary parameter support points and priors by using the following PROC ENTROPY statements:

proc entropy data = a gme primal;
   priors          x1  -10(2) 30(1)
                   x2  -20(3) 30(2)
                   x3  -15(4) 30(4)
                   x4  -25(3) 30(2)
            intercept  -13(4) 30(2) ;
   model ys = x1 x2 x3 x4 / esupports=(-25 0 25);
run;

These statements produce the output shown in Output 12.4.1.

Output 12.4.1 Output From PROC ENTROPY

Using a PDATA= data set

The ENTROPY Procedure

GME Variable Estimates
Variable	Estimate	Approx Std Err	t Value	Approx Pr > \|t\|
x1	1.195688	0.1078	11.09	<.0001
x2	1.844903	0.1018	18.12	<.0001
x3	3.268396	0.1136	28.77	<.0001
x4	3.908194	0.0934	41.83	<.0001
intercept	-4.94319	0.1005	-49.21	<.0001

You can estimate the same model by first creating a PDATA= data set, which includes the same information as the PRIORS statement in the preceding PROC ENTROPY step.

A data set that defines the supports and priors for the model parameters is shown in the following statements:

data test;
   length Variable $ 12 Equation $ 12;
   input Variable $ Equation $ Nsupport Support Prior ;
datalines;
     Intercept   .  2 -13 0.66667
     Intercept   .  2  30 0.33333
            x1   .  2 -10 0.66667
            x1   .  2  30 0.33333
            x2   .  2 -20 0.60000
            x2   .  2  30 0.40000
            x3   .  2 -15 0.50000
            x3   .  2  30 0.50000
            x4   .  2 -25 0.60000
            x4   .  2  30 0.40000
;

The following statements reestimate the model by using these support points.

proc entropy data=a gme primal pdata=test;
   model ys = x1 x2 x3 x4 / esupports=(-25 0 25);
run;

These statements produce the output shown in Output 12.4.2.

Output 12.4.2 Output From PROC ENTROPY with PDATA= option

Using a PDATA= data set

The ENTROPY Procedure

GME Variable Estimates
Variable	Estimate	Approx Std Err	t Value	Approx Pr > \|t\|
x1	1.195686	0.1078	11.09	<.0001
x2	1.844902	0.1018	18.12	<.0001
x3	3.268395	0.1136	28.77	<.0001
x4	3.908194	0.0934	41.83	<.0001
Intercept	-4.94319	0.1005	-49.21	<.0001

These results are identical to the ones produced by the previous PROC ENTROPY step.

Note: This procedure is experimental.

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