The GAM Procedure
- Overview
- Getting Started
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Syntax
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DetailsMissing ValuesNonparametric RegressionAdditive Models and Generalized Additive ModelsForms of Additive ModelsEstimates from PROC GAMBackfitting and Local Scoring AlgorithmsSmoothersSelection of Smoothing ParametersConfidence Intervals for SmoothersDistribution Family and Canonical LinkDispersion ParameterComputational ResourcesODS Table NamesODS 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:
![\[ \eta (x_1,x_2) = \beta _0 + \beta _1 x_1 + \beta _2 x_2 + s_1(x_1) + s_2(x_2) \]](images/statug_gam0031.png){kind=small} |
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.