Generalized Linear Models Predictors
The Predictors tab allows you to specify the factors and covariates used to build model effects and to specify an optional offset.
Factors. Factors are categorical predictors; they can be numeric or string.
Covariates. Covariates are scale predictors; they must be numeric.
Note: When the response is binomial with binary format, the procedure computes deviance and chi-square goodness-of-fit statistics by subpopulations that are based on the cross-classification of observed values of the selected factors and covariates. You should keep the same set of predictors across multiple runs of the procedure to ensure a consistent number of subpopulations.
Offset. The offset term is a "structural" predictor. Its coefficient is not estimated by the model but is assumed to have the value 1; thus, the values of the offset are simply added to the linear predictor of the target. This is especially useful in Poisson regression models, where each case may have different levels of exposure to the event of interest.
For example, when modeling accident rates for individual drivers, there is an important difference between a driver who has been at fault in one accident in three years of experience and a driver who has been at fault in one accident in 25 years! The number of accidents can be modeled as a Poisson or negative binomial response with a log link if the natural log of the experience of the driver is included as an offset term.
Other combinations of distribution and link types would require other transformations of the offset variable.
How To Select Predictors for Generalized Linear Models
This feature requires the Advanced Statistics option.
- From the menus choose:
- In the Generalized Linear Models dialog box, click Predictors.