SAS/STAT 9.1 Users Guide Volume 2 only

Overview

The GAM procedure fits generalized additive models as those models are defined by Hastie and Tibshirani (1990). This procedure provides an array of powerful tools for data analysis, based on nonparametric regression and smoothing techniques.

Nonparametric regression relaxes the usual assumption of linearity and enables you to uncover structure in the relationship between the independent variables and the dependent variable that might otherwise be missed. The SAS System provides many procedures for nonparametric regression, such as the LOESS procedure for local regression and the TPSPLINE procedure for thin-plate smoothing splines. The generalized additive models fit by the GAM procedure combine

Thus, you can use the GAM procedure when you have multiple independent variables whose effect you want to model nonparametrically, or when the dependent variable is not normally distributed. See the Nonparametric Regression section on page 1569 for more details on the form of generalized additive models.

The GAM procedure

Experimental graphics are now available with the GAM procedure. For more information, see the ODS Graphics section on page 1581.

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