R help - Oct 2007 - How to avoid overfitting in gam(mgcv)

Dear listers,

I'm using gam(from mgcv) for semi-parametric regression on small and
noisy datasets(10 to 200
observations), and facing a problem of overfitting.

According to the book(Simon N. Wood / Generalized Additive Models: An
Introduction with R), it is
suggested to avoid overfitting by inflating the effective degrees of
freedom in GCV evaluation with
increased "gamma" value(e.g. 1.4). But in my case, it didn't make
a
significant change in the
results.

The only way I've found to suppress overfitting is to set the basis
dimension "k" at very low values
(3 to 5). However, I don't think this is reasonable because knots
selection will then be an
important issue.

Is there any other means to avoid overfitting when alalyzing small datasets?

Thank you for your help in advance,
Ariyo Kanno

--
Ariyo Kanno
1st-year doctor's degree student at
Institute of Environmental Studies,
The University of Tokyo

What sort of model structure are you using? In particular what is the response 
distribution? For poisson and binomial then overfitting can be a sign of 
overdispersion and quasipoisson or quasibinomial may be better. Also I would 
not expect to get useful smoothing parameter estimates from 10 data!

best,
Simon

On Wednesday 03 October 2007 06:55, ???? wrote:
> Dear listers,
>
> I'm using gam(from mgcv) for semi-parametric regression on small and
> noisy datasets(10 to 200
> observations), and facing a problem of overfitting.
>
> According to the book(Simon N. Wood / Generalized Additive Models: An
> Introduction with R), it is
> suggested to avoid overfitting by inflating the effective degrees of
> freedom in GCV evaluation with
> increased "gamma" value(e.g. 1.4). But in my case, it didn't
make a
> significant change in the
> results.
>
> The only way I've found to suppress overfitting is to set the basis
> dimension "k" at very low values
> (3 to 5). However, I don't think this is reasonable because knots
> selection will then be an
> important issue.
>
> Is there any other means to avoid overfitting when alalyzing small
> datasets?
>
> Thank you for your help in advance,
> Ariyo Kanno
>
> --
> Ariyo Kanno
> 1st-year doctor's degree student at
> Institute of Environmental Studies,
> The University of Tokyo
>
> ______________________________________________
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> PLEASE do read the posting guide
> R-project.org/posting-guide.html and provide commented, minimal,
> self-contained, reproducible code.
-- 
> Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK
> +44 1225 386603  maths.bath.ac.uk/~sw283