GAMPL Procedure
The GAMPL procedure is a highperformance procedure that fits
generalized additive models that are based on lowrank regression splines.
This procedure provides powerful tools for
nonparametric regression and smoothing.
Generalized additive models are extensions of generalized linear
models. They relax the linearity assumption in generalized linear
models by allowing spline terms in order to characterize nonlinear
dependency structures. Each spline term is constructed by the
thinplate regression spline technique.
A roughness penalty is applied to each spline term by a smoothing
parameter that controls the balance between goodness of fit and
the roughness of the spline curve.
PROC GAMPL fits models for standard distributions in the
exponential family, such as normal, Poisson, and gamma distributions.
PROC GAMPL runs in either singlemachine mode or distributed mode.
 estimates the regression parameters of a generalized additive
model that has fixed smoothing parameters by using penalized
likelihood estimation
 estimates the smoothing parameters of a generalized additive
model by using either the performance iteration method or the
outer iteration method
 estimates the regression parameters of a generalized linear
model by using maximum likelihood techniques
 tests the total contribution of each spline term based on the Wald statistic
 provides modelbuilding syntax in the CLASS
statement and effectbased parametric effects in the
MODEL statement, which are used in other
SAS/STAT analytic procedures (in particular, the GLM, LOGISTIC,
GLIMMIX, and MIXED procedures)
 provides responsevariable options
 enables you to construct a spline term by using multiple variables
 provides control options for constructing a spline term, such as
fixed degrees of freedom, initial smoothing parameter, fixed
smoothing parameter, smoothing parameter search range, usersupplied
knot values, and so on

 provides multiple link functions for any distribution
 provides a WEIGHT statement for weighted analysis
 provides a FREQ statement for grouped analysis
 provides an OUTPUT statement to produce a data set
that has predicted values and other observationwise statistics
 produces graphs by using ODS Graphics
 enables you to run in distributed mode on a cluster of machines that
distribute the data and the computations
 enables you to run in singlemachine mode on the server where SAS is installed
 exploits all the available cores and concurrent threads, regardless of execution mode

For further details see the GAMPL Procedure
Examples