Hello, I am fitting GAMs using mgcv. My models have both linear and functional effects, for instance: b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3) + x4 + as.factor(x5),data=dat) I would like to extract the proportion of deviance explained by a single term in the model, for instance, s(x1). Having read through some R help posts I realize that if all terms in the model are functional (i.e. ?smooth?) that this is achievable by calculating the difference in deviance explained between models with s(x1), and without it, provided that the model parameters in both models are held fixed (i.e. are identical). For instance, if all model covariates are functional this would be achieved (according to a post by Simon Wood; http://r.789695.n4.nabble.com/Re-variance-explained-by-each-predictor-in-GAM-td896222.html#a896223) with: 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 This works great, and the ?sp=? option in the above code holds the smoothing parameters fixed. I?m looking to extend this so as to also hold any non-smooth parameters fixed. Does anyone know how/if this would be possible? Thanks so much. -- View this message in context: http://r.789695.n4.nabble.com/Deviance-explained-by-individual-terms-in-GAM-tp4670756.html Sent from the R help mailing list archive at Nabble.com.