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.wood@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
> <dwinsemius@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
>

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