Simon,That produced exactly what I was looking for. Thanks so much for the humble help.
KC On Mon, Jul 13, 2009 at 9:10 AM, Simon Wood <s.w...@bath.ac.uk> wrote: > You can get some idea by doing something like the following, which compares > the r^2 for models b and b2, i.e. with and without s(x2). It keeps the > smoothing parameters fixed for the comparison. (s(x,fx=TRUE) removes > penalization altogether btw, which is not what was wanted). > > dat <- gamSim(1,n=400,dist="normal",scale=2) > b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat) > b2<-gam(y~s(x0)+s(x1)+s(x3),sp=b$sp[-3],data=dat) > summary(b2)$dev.expl > summary(b)$dev.expl > > > On Monday 13 July 2009 15:09, Kayce Anderson wrote: > > Many thanks for the advice David. I would really like to figure out, > > though, how to get the contribution of each factor to the Rsq - something > > like a Beta coefficient for GAM. Ideas? > > KC > > > > On Sun, Jul 12, 2009 at 5:41 PM, David Winsemius > <dwinsem...@comcast.net>wrote: > > > On Jul 12, 2009, at 5:06 PM, Kayce Anderson wrote: > > > > > > Hi, > > > > > >> I am using mgcv:gam and have developed a model with 5 smoothed > > >> predictors and one factor. > > >> > > >> gam1 <- gam(log.sp~ s(Spr.precip,bs="ts") + s(Win.precip,bs="ts") + > s( > > >> Spr.Tmin,bs="ts") + s(P.sum.Tmin,bs="ts") + s( Win.Tmax,bs="ts") > > >> +factor(site),data=dat3) > > >> > > >> > > >> The total deviance explained = 70.4%. > > >> > > >> > > >> I would like to extract the variance explained by each predictor. Is > > >> there > > >> a straightforward way to do this? I have tried dropping a term and > > >> recalculating the model, but the edf's change if there is any > > >> correlation among variables, thereby making all of the relationships > > >> different. I haven't yet figured out how to fix the smoothing terms- > I > > >> get syntax error messages. Among other variations, I tried, for > > >> example, > > >> log.sp~s(Spr.precip, sp=3.9, fx=TRUE) +... > > > > > > ?anova.gam > > > > > > Obviously I cannot test this with your dat3. You get an F-statistic for > > > each s() term by default and you are referred to saummary.gam for > further > > > explanation. > > > > > > David Winsemius, MD > > > Heritage Laboratories > > > West Hartford, CT > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > 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 > [[alternative HTML version deleted]] ______________________________________________ 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.
