[R] Where is gam?
snw at mcs.st-and.ac.uk
Tue Oct 3 17:54:51 CEST 2000
> I noticed that there is no generalised additive model functions in R
> (1.1.1) ... is there a package that implements them?
I've produced a preliminary version of a package that fits gams
implemented using 1-d penalized regression splines: it includes a version
of gam(). Smoothing parameter selection is done on all terms
simultaneously using GCV. It's available from:
... the plan is to add multidimensional terms fairly soon, and to make it
more widely available after a bit more testing. It's only advantages over
gss is speed on large datasets and the fact that it provides a version of
I don't know what other people think, but my concerns about gams
relate to the oddness of their structure. Often, modelling amounts to
trying to find some unknown function linking your covariates to your
response i.e. you want to find the f in:
E(y) = f(x_1, x_2, x_3....)
unless there's good prior reason to do so why would you start by letting:
f(.) = f_1(x_1) + f_2 (x_2) + f_3 (x_3) +.... ?
The point is maybe clearest by considering polynomial models. If you don't
know f(.) it seems reasonable to use some taylor expansion of f(.) as a
f(.) = a + b x_1 + c x_2 + d x_3 + e x_1 x_2 + g x_1 x_3 + h x_2 x_3 +
p x_1^2 + q x_2^2 + r x_3^2 + ...
Using gams is rather like deleting all mixed terms in this expansion
(i.e. e x_1 x_2, g x_1 x_3 etc). This seems like a very odd thing to do
and gets wierder the more degrees of freedom you allow the model.
> Simon Wood snw at st-and.ac.uk http://www.ruwpa.st-and.ac.uk/simon.html
> The Mathematical Institute, North Haugh, St. Andrews, Fife KY16 9SS UK
> Direct telephone: (0)1334 463799 Indirect fax: (0)1334 463748
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