The PROBIT Procedure
- Overview
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Getting Started
-
Syntax
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Details
Missing Values Response Level Ordering Computational Method Distributions INEST= SAS-data-set Model Specification Lack-of-Fit Tests Rescaling the Covariance Matrix Tolerance Distribution Inverse Confidence Limits OUTEST= SAS-data-set XDATA= SAS-data-set Traditional High-Resolution Graphics Displayed Output ODS Table Names ODS Graphics
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Examples
- References
| Distributions |
The distributions, 
, allowed in the PROBIT procedure are specified with the DISTRIBUTION= option in the MODEL statement. The cumulative distribution functions for the available distributions are
Cumulative Distribution Function |
Distribution |
|---|---|
|
Normal |
|
Logistic |
|
Extreme value or Gompertz |
The variances of these three distributions are not all equal to 1, and their means are not all equal to zero. Their means and variances are shown in the following table, where 
is the Euler constant.
Distribution |
Mean |
Variance |
||
|---|---|---|---|---|
Normal |
0 |
1 |
||
Logistic |
0 |
|
||
Extreme value or Gompertz |
|
|
When comparing parameter estimates by using different distributions, you need to take into account the different scalings and, for the extreme value (or Gompertz) distribution, a possible shift in location. For example, if the fitted probabilities are in the neighborhood of 0.1 to 0.9, then the parameter estimates from the logistic model should be about 
larger than the estimates from the probit model.