I have posted an update to the GAM package. Note that this package
implements gam() as described
in the "White" S book (Statistical models in S). In particular, you
can
fit models with lo() terms (local regression)
and/or s() terms (smoothing splines), mixed in, of course, with any
terms appropriate for glms.
A number of bugs in version 0.92 have been fixed; notably
1) some problems with predict and newdata
2) plot.gam now works with any model for which predict( ...,
type="terms") is appropriate
(well, at least several). Examples are lm, glm, gam and coxph models.
So for example, if you have fit a Cox model
cox1 <- coxph( Surv(Survival, death) ~ Grade + ns(Age,4) + ns(Size,4))
Then plot.gam(cox1, se=T) will produce three plots, one for each term
in the model, with standard error bands.
3) I have implemented the fast versions of backfitting for models
consisting of all local regression
terms (lo.wam) or all smoothing spline terms (s.wam).
Please let me know of any problems with the gam package
Trevor Hastie
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Trevor Hastie hastie@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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