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, 神野有生 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 > > ______________________________________________ > R-help@r-project.org 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. -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603 www.maths.bath.ac.uk/~sw283 ______________________________________________ R-help@r-project.org 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.
