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- SAS/STAT Procedures A-Z
The GAM Procedure
- fits generalized additive models as those models are defined by Hastie and
Tibshirani (1990). The generalized additive models fit by the GAM procedure combine the following:
- an additivity assumption (Stone 1985) that enables relatively many nonparametric relationships to be explored simultaneously
- the distributional flexibility of generalized linear models (Nelder and Wedderburn 1972)
- provides nonparametric estimates for additive models
- supports the use of multidimensional data
- fits both generalized semiparametric additive models and generalized additive models
- enables you to choose a particular model by specifying the model degrees of freedom or smoothing parameter
- permits the following smoothing effects:
- smoothing spline (SPLINE)
- local regression (LOESS)
- bivariate thin-plate smoothing spline (SPLINE2)
- supports the following distributions families for the response variables
- gaussian (continuous response variables)
- binomial (binary response variables)
- Poisson (nonnegative discrete response variables)
- gamma (positive continuous response variables)
- inverse gaussian (positive continuous response variables)
- obtain separate analyses on observations in groups
- scores new data sets
- creates an output data set containing diagnostic measures
- use ODS create a SAS data set corresponding to any table
- supports ODS Graphics
For further details see the SAS/STAT User's Guide:
The GAM Procedure
( PDF | HTML )
Examples
- Generalized Additive Model with Binary Data
- Poisson Regression Analysis of Component Reliability
- Comparing PROC GAM with PROC LOESS
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