mgcv-package {mgcv} | R Documentation |
mgcv
provides functions for generalized additive modelling and
generalized additive mixed modelling. Particular features of the package are
facilities for automatic smoothness selection, and the provision of a variety
of smooths of more than one variable. A Bayesian approach to confidence/credible
interval calculation is provided. Lower level routines for generalized
ridge regression and penalized linearly constrained least squares are also provided.
mgcv
provides generalized additive modelling functions gam
,
predict.gam
and plot.gam
, which are very similar
in use to the S functions of the same name designed by Trevor Hastie.
However the underlying representation and estimation of the models is based on a
penalized regression spline approach, with automatic smoothness selection. A
number of other functions such as summary.gam
and anova.gam
are also provided, for
extracting information from a fitted gamObject
.
Use of gam
is much like use of glm
, except that
within a gam
model formula, isotropic smooths of any number of predictors can be specified using
s
terms, while scale invariant smooths of any number of
predictors can be specified using te
terms. Estimation is by
penalized likelihood or quasi-likelihood maximization, with smoothness
selection by GCV or gAIC/ UBRE. See gam
, gam.models
and
gam.selection
for some discussion of model specification and
selection. For detailed control of fitting see gam.convergence
,
gam.method
and gam.control
. For checking and
visualization see gam.check
and vis.gam
and plot.gam
.
A Bayesian approach to smooth modelling is used to derive standard errors on
predictions, and hence credible intervals. The Bayesian covariance matrix for
the model coefficients is returned in Vp
of the
gamObject
. See predict.gam
for examples of how
this can be used to obtain credible regions for any quantity derived from the
fitted model, either directly, or by direct simulation from the posterior
distribution of the model coefficients. Frequentist approximations can be used
for hypothesis testing: see anova.gam
and
summary.gam
, but note that the underlying approximations are not
always good in this case.
The package also provides a generalized additive mixed modelling function,
gamm
, based on glmmPQL
from the MASS
library and
lme
from the nlme
library.
Some of the underlying GAM fitting methods are available as low level fitting
functions: see magic
and mgcv
. Penalized weighted
least squares with linear equality and inequality constraints is provided by pcls
.
For a complete list of functions type library(help=mgcv)
.
Simon Wood <simon.wood@r-project.org>
Maintainer: Simon Wood <simon.wood@r-project.org>
Wood, S.N. (2006) Generalized Additive Models: an introduction with R, CRC
## see examples for gam and gamm