I have contributed a "gam" library to CRAN,
which implements "Generalized Additive Models".
This implementation follows closely the description in
the GAM chapter 7 of the "white" book "Statistical Models in
S"
(Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy
in "Generalized Additive Models" (Hastie & Tibshirani 1990,
Chapman and
Hall). Hence it behaves pretty much like the Splus version of GAM.
Note: this gam library and functions therein are different from the
gam function in package mgcv, and both libraries should not be used
simultaneously.
The gam library allows both local regression (loess) and smoothing
spline smoothers, and uses backfitting and local scoring to fit gams.
It also allows users to supply their own smoothing methods which can
then be included in gam fits.
The gam function in mgcv uses only smoothing spline smoothers, with a
focus on automatic parameter selection via gcv.
Some of the features of the gam library:
* full compatibility with the R functions glm and lm - a fitted gam
inherits from class "glm" and "lm"
* print, summary, anova, predict and plot methods are provided, as
well as the usual extractor methods like coefficients, residuals etc
* the method step.gam provides a flexible and customizable approach to
model selection.
Some differences with the Splus version of gam:
* predictions with new data are improved, without need for the
"safe.predict.gam" function. This was partly facilitated by
the improved prediction strategy used in R for GLMs and LMs
* Currently the only backfitting algorithm is all.wam. In the earlier
versions of gam, dedicated fortran routines fit models that had only
smoothing spline terms (s.wam) or all local regression terms
(lo.wam), which in fact made calls back to Splus to update the
working response and weights. These were designed for efficiency. It
seems now with much faster computers this efficiency is no longer
needed, and all.wam is modular and "visible"
This package is numbered 0.9 in anticipation of a few bug fixes and
glitches. I have tested many aspects of the functions, but there are
always a few that slip by. I will be happy to hear of any problems,
bugs and suggestions.
Plans for future versions:
* exact standard error calculations. gam employs approximations as
described in the white book. With a bit more computing (now
possible), we will have a function that computes exact standard
errors along the lines described in the GAM book page 127.
Trevor Hastie
--------------------------------------------------------------------
Trevor Hastie hastie at stanford.edu
Professor, Department of Statistics, Stanford University
Phone: (650) 725-2231 (Statistics) Fax: (650) 725-8977
(650) 498-5233 (Biostatistics) Fax: (650) 725-6951
URL: http://www-stat.stanford.edu/~hastie
address: room 104, Department of Statistics, Sequoia Hall
390 Serra Mall, Stanford University, CA 94305-4065
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I have posted a new version of the gam package: gam_1.09 to CRAN. Thus update improved the step.gam function considerably, and gives it a parallel option. I am posting this update announcement along with the original package announcement below, which may be of interest to those new to the list Trevor Hastie Begin forwarded message:> From: "Trevor Hastie" <hastie at stanford.edu> > Subject: gam --- a new contributed package > Date: August 6, 2004 10:35:36 AM PDT > To: <r-packages at stat.math.ethz.ch> > > I have contributed a "gam" library to CRAN, > which implements "Generalized Additive Models". > > This implementation follows closely the description in > the GAM chapter 7 of the "white" book "Statistical Models in S" > (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy > in "Generalized Additive Models" (Hastie & Tibshirani 1990, Chapman and > Hall). Hence it behaves pretty much like the Splus version of GAM. > > Note: this gam library and functions therein are different from the > gam function in package mgcv, and both libraries should not be used > simultaneously. > > The gam library allows both local regression (loess) and smoothing > spline smoothers, and uses backfitting and local scoring to fit gams. > It also allows users to supply their own smoothing methods which can > then be included in gam fits. > > The gam function in mgcv uses only smoothing spline smoothers, with a > focus on automatic parameter selection via gcv. > > Some of the features of the gam library: > > * full compatibility with the R functions glm and lm - a fitted gam > inherits from class "glm" and "lm" > > * print, summary, anova, predict and plot methods are provided, as > well as the usual extractor methods like coefficients, residuals etc > > * the method step.gam provides a flexible and customizable approach to > model selection. > > Some differences with the Splus version of gam: > > * predictions with new data are improved, without need for the > "safe.predict.gam" function. This was partly facilitated by > the improved prediction strategy used in R for GLMs and LMs > > * Currently the only backfitting algorithm is all.wam. In the earlier > versions of gam, dedicated fortran routines fit models that had only > smoothing spline terms (s.wam) or all local regression terms > (lo.wam), which in fact made calls back to Splus to update the > working response and weights. These were designed for efficiency. It > seems now with much faster computers this efficiency is no longer > needed, and all.wam is modular and "visible" >---------------------------------------------------------------------------------------- Trevor Hastie hastie at stanford.edu Professor, Department of Statistics, Stanford University Phone: (650) 725-2231 Fax: (650) 725-8977 URL: http://www.stanford.edu/~hastie address: room 104, Department of Statistics, Sequoia Hall 390 Serra Mall, Stanford University, CA 94305-4065 -------------------------------------------------------------------------------------- [[alternative HTML version deleted]] _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages
I have posted a new version of the gam package: gam_1.09 to CRAN. Thus update improved the step.gam function considerably, and gives it a parallel option. I am posting this update announcement along with the original package announcement below, which may be of interest to those new to the list Trevor Hastie Begin forwarded message:> From: "Trevor Hastie" <hastie@stanford.edu> > Subject: gam --- a new contributed package > Date: August 6, 2004 10:35:36 AM PDT > To: <r-packages@stat.math.ethz.ch> > > I have contributed a "gam" library to CRAN, > which implements "Generalized Additive Models". > > This implementation follows closely the description in > the GAM chapter 7 of the "white" book "Statistical Models in S" > (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy > in "Generalized Additive Models" (Hastie & Tibshirani 1990, Chapman and > Hall). Hence it behaves pretty much like the Splus version of GAM. > > Note: this gam library and functions therein are different from the > gam function in package mgcv, and both libraries should not be used > simultaneously. > > The gam library allows both local regression (loess) and smoothing > spline smoothers, and uses backfitting and local scoring to fit gams. > It also allows users to supply their own smoothing methods which can > then be included in gam fits. > > The gam function in mgcv uses only smoothing spline smoothers, with a > focus on automatic parameter selection via gcv. > > Some of the features of the gam library: > > * full compatibility with the R functions glm and lm - a fitted gam > inherits from class "glm" and "lm" > > * print, summary, anova, predict and plot methods are provided, as > well as the usual extractor methods like coefficients, residuals etc > > * the method step.gam provides a flexible and customizable approach to > model selection. > > Some differences with the Splus version of gam: > > * predictions with new data are improved, without need for the > "safe.predict.gam" function. This was partly facilitated by > the improved prediction strategy used in R for GLMs and LMs > > * Currently the only backfitting algorithm is all.wam. In the earlier > versions of gam, dedicated fortran routines fit models that had only > smoothing spline terms (s.wam) or all local regression terms > (lo.wam), which in fact made calls back to Splus to update the > working response and weights. These were designed for efficiency. It > seems now with much faster computers this efficiency is no longer > needed, and all.wam is modular and "visible" >---------------------------------------------------------------------------------------- Trevor Hastie hastie@stanford.edu Professor, Department of Statistics, Stanford University Phone: (650) 725-2231 Fax: (650) 725-8977 URL: http://www.stanford.edu/~hastie address: room 104, Department of Statistics, Sequoia Hall 390 Serra Mall, Stanford University, CA 94305-4065 -------------------------------------------------------------------------------------- [[alternative HTML version deleted]] _______________________________________________ R-packages mailing list R-packages@r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages