The GAM Procedure
- Overview
- Getting Started
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Syntax
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Details
Missing Values Nonparametric Regression Additive Models and Generalized Additive Models Forms of Additive Models Estimates from PROC GAM Backfitting and Local Scoring Algorithms Smoothers Selection of Smoothing Parameters Confidence Intervals for Smoothers Distribution Family and Canonical Link Dispersion Parameter Computational Resources ODS Table Names ODS Graphics
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Examples
- References
| Forms of Additive Models |
Suppose that y is a continuous variable and x1 and x2 are two explanatory variables of interest. To fit an additive model, you can use a MODEL statement similar to that used in many regression procedures in the SAS System:
model y = spline(x1) spline(x2);
This model statement requires the procedure to fit the following model:
 |
where the 
terms denote nonparametric spline functions of the respective explanatory variables.
The GAM procedure can fit semiparametric models. The following MODEL statement assumes a linear relation with x1 and an unknown functional relation with x2:
model y = param(x1) spline(x2);
If you want to fit a model containing a functional two-way interaction between x1 and x2, you can use the following MODEL statement:
model y = spline2(x1,x2);
In this case, the GAM procedure fits a model equivalent to that of PROC TPSPLINE.