Model Fitting: Generalized Linear Models |
The generalized linear model is a generalization of the traditional linear model. It differs from a linear model in that it assumes that the response distribution is related to the linear predictor through a function called the link function.
Specifically, a generalized linear model has a linear component
The explanatory variables in the Generalized Linear Models analysis can be interval variables or nominal variables (also known as classification variables). You can also specify more complex model terms such as interactions and nested effects.
As mentioned in Chapter 21, "Model Fitting: Linear Regression," the Linear Regression analysis in
Stat Studio does not support classification variables.
You can use the
Generalized Linear Models analysis to fit a linear regression with
classification variables by specifying that the
response variable is normally distributed and that the link
function is the identity function. The first example in this
chapter demonstrates this technique. The second example in this chapter fits a
Poisson regression model. The link function for this example is the
function.
You can run a Generalized Linear Models analysis by selecting
Analysis Model Fitting
Generalized Linear Models from the main menu.
The computation of
the regression function and related
statistics is implemented by calling the GENMOD procedure in
SAS/STAT. See the
documentation for the GENMOD procedure
in the SAS/STAT User's Guide for additional details.