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
| Estimates from PROC GAM |
PROC GAM provides the capability to fit both nonparametric and semiparametric models. So that you can better understand the underlying trend of any given factor, PROC GAM separates the linear trend from any general nonparametric trend during the fitting as well as in the final report. This makes it easy to determine whether the significance of a smoothing variable is associated with a simple linear trend or a more complicated pattern.
For example, suppose you want to fit a semiparametric model as
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The GAM estimate for this model is
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where 
and 
are linear-adjusted nonparametric estimates of the 
and 
effects. The p-values for 
are reported in the parameter estimates table. 
and 
are the estimates labeled Linear(x1) and Linear(x2) in the table. The p-values for and are reported in the analysis of deviance table.
Only , , and 
are output to the output data set, with the corresponding variable names P_x1, P_x2, and P_y. For Gaussian data, the complete marginal prediction for variable x1 is:
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If the additive component plots are requested by the ADDITIVE suboption, the additive component for variable x2 is computed as:
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where 
is the mean for variable x2.