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" <[email protected]> > Subject: gam --- a new contributed package > Date: August 6, 2004 10:35:36 AM PDT > To: <[email protected]> > > 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 [email protected] 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 [email protected] https://stat.ethz.ch/mailman/listinfo/r-packages ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
