Chapter Contents Previous Next
 The TRANSREG Procedure

## Missing Values, UNTIE, and Hypothesis Tests

The TRANSREG procedure has the ability to estimate missing data and monotonically transform variables while untying tied values. Estimates of ordinary missing values (.) may all be different. Analyses with UNTIE transformations, the UNTIE= a-option, and ordinary missing data estimation are all prone to degeneracy problems. Consider the following example. A perfect fit is found by collapsing all observations except the one with two missing values into a single value in Y and X1.

```   data x;
input y x1 x2 @@;
datalines;
1 3 7    8 3 9    1 8 6    . . 9    3 3 9
8 5 1    6 7 3    2 7 2    1 8 2    . 9 1
;

proc transreg dummy;
model linear(y) = linear(x1 x2);
output;
run;

proc print;
run;
```

 Obs _TYPE_ _NAME_ y Ty Intercept x1 x2 TIntercept Tx1 Tx2 1 SCORE ROW1 1 2.7680 1 3 7 1 5.1233 7 2 SCORE ROW2 8 2.7680 1 3 9 1 5.1233 9 3 SCORE ROW3 1 2.7680 1 8 6 1 5.1233 6 4 SCORE ROW4 . 12.5878 1 . 9 1 12.7791 9 5 SCORE ROW5 3 2.7680 1 3 9 1 5.1233 9 6 SCORE ROW6 8 2.7680 1 5 1 1 5.1233 1 7 SCORE ROW7 6 2.7680 1 7 3 1 5.1233 3 8 SCORE ROW8 2 2.7680 1 7 2 1 5.1233 2 9 SCORE ROW9 1 2.7680 1 8 2 1 5.1233 2 10 SCORE ROW10 . 2.7680 1 9 1 1 5.1233 1

Figure 65.7: Missing Values Example

Generally, the use of ordinary missing data estimation, the UNTIE transformation, and the UNTIE= a-option should be avoided, particularly with hypothesis tests. With these options, parameters are estimated based on only a single observation, and they can exert tremendous influence over the results. Each of these parameters has one model degree of freedom associated with it, so small or zero error degrees of freedom can also be a problem.

 Chapter Contents Previous Next Top