[R] compare GLM coefficients

I have a data set of repeated abundance counts over time. I am investigating whether count data reduced to presence-absence (presence) data will reveal similar population trends. I am using a negative binomial distribution for the glm (package MASS) because the count data contains many zeros and

Re: [R] variance explained by each predictor in GAM

1)+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 - som

Re: [R] variance explained by each predictor in GAM

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 wrote: > > On Jul 12, 2009, at 5:06 PM, Kayce An

Re: [R] variance explained by each predictor in GAM

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%.