Hi,
I have some queries on S-plus GAM.
If you have many covariates for GAM, eg: X1, X2, X3 etc, is it advisable to use
step.gam to model the individual covariates and then put them together in the
final model or incorporate more terms into the existing GAM model as you
progress on?
For example, in a mortality analysis, if you model Seasonality first, then you
go on to model Temperature. When you are trying to find a suitable span for
lo(Temperature),
1. When you are trying to find a suitable span for lo(Temperature), do you
include the term that you have already found-- lo(Seasonality, span a) into
your step.gam function? Then if you model for a third term, you include the lo
terms of Seasonality and Temperature?
2. Or do you find the spans required for each of the lo terms(X1, X2, X3,...)
separately and then put them together in the final gam model?
Is there any difference between these 2 ways?
Prof Ripley had pointed it to me earlier, "AIC is not even defined for a real
GAM [ for AIC presumes maximum-likelihood fitting (amongst other things)], and
gam() uses an approximation to a penalized fit." Will someone tell me how
useful/efficient is the 'AIC' produced in step.gam(), to determine the most
suitable span based on AIC? How do we calculate this 'AIC'?
Thanks.
Rgds,
Siewhua
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