This example uses a specially created custom scoring data set and produces Output 88.3.1 and Output 88.3.2. The first scoring coefficient creates a variable that is Age
–Weight
; the second scoring coefficient evaluates the variable RunPulse
–RstPulse
; and the third scoring coefficient totals all six variables. Since the scoring coefficients data set (data set A
) does not contain any observations with _TYPE_
=’MEAN’ or _TYPE_
=’STD’, the data in the Fitness
data set (see Example 88.1) are not standardized before scoring.
The following statements produce Output 88.3.1 and Output 88.3.2:
data A; input _type_ $ _name_ $ Age Weight RunTime RunPulse RestPulse; datalines; SCORE AGE_WGT 1 -1 0 0 0 SCORE RUN_RST 0 0 0 1 -1 SCORE TOTAL 1 1 1 1 1 ;
proc print data=A; title 'Constructed Scoring Example'; title2 'Scoring Coefficients'; run;
proc score data=Fitness score=A out=B; var Age Weight RunTime RunPulse RestPulse; run;
proc print data=B; title2 'Scored Data'; run;
Output 88.3.2: Custom Scored Fitness Data: PROC PRINT
Constructed Scoring Example |
Scored Data |
Obs | Age | Weight | Oxygen | RunTime | RestPulse | RunPulse | AGE_WGT | RUN_RST | TOTAL |
---|---|---|---|---|---|---|---|---|---|
1 | 44 | 89.47 | 44.609 | 11.37 | 62 | 178 | -45.47 | 116 | 384.84 |
2 | 40 | 75.07 | 45.313 | 10.07 | 62 | 185 | -35.07 | 123 | 372.14 |
3 | 44 | 85.84 | 54.297 | 8.65 | 45 | 156 | -41.84 | 111 | 339.49 |
4 | 42 | 68.15 | 59.571 | 8.17 | 40 | 166 | -26.15 | 126 | 324.32 |
5 | 38 | 89.02 | 49.874 | 9.22 | 55 | 178 | -51.02 | 123 | 369.24 |
6 | 47 | 77.45 | 44.811 | 11.63 | 58 | 176 | -30.45 | 118 | 370.08 |
7 | 40 | 75.98 | 45.681 | 11.95 | 70 | 176 | -35.98 | 106 | 373.93 |
8 | 43 | 81.19 | 49.091 | 10.85 | 64 | 162 | -38.19 | 98 | 361.04 |
9 | 44 | 81.42 | 39.442 | 13.08 | 63 | 174 | -37.42 | 111 | 375.50 |
10 | 38 | 81.87 | 60.055 | 8.63 | 48 | 170 | -43.87 | 122 | 346.50 |
11 | 44 | 73.03 | 50.541 | 10.13 | 45 | 168 | -29.03 | 123 | 340.16 |
12 | 45 | 87.66 | 37.388 | 14.03 | 56 | 186 | -42.66 | 130 | 388.69 |