Thanks, the sequence of x0 values was clearly too short. However, is there a way to overlay the (marginal) curve from plot.gam() over a plot of (x,y) values?
Best wishes Christoph Am 16/07/2013 11:04, schrieb Simon Wood: > Probably you didn't want to set x0=0:1? Here is some code, to do what you > want. > (The CI shape is not identical to the plot(b) version as the uncertainty > includes > the uncertainty in the other smooths and the intercept now.) > > library(mgcv) > set.seed(2) > dat <- gamSim(1,n=400,dist="normal",scale=2) > b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat) > plot(b,select=1) > > plot(y~x0,dat) > mydata=data.frame(x0=0:200/200,x1=mean(dat$x1),x2=mean(dat$x2),x3=mean(dat$x3)) > pv <- predict(b,mydata,type="response",se=TRUE) > lines(mydata$x0,pv$fit) > lines(mydata$x0,pv$fit+2*pv$se.fit,lty=2) > lines(mydata$x0,pv$fit-2*pv$se.fit,lty=2) > > > > > On 16/07/13 09:52, Christoph Scherber wrote: >> Dear R users, >> >> I´ve stumbled over a problem that can be easily seen from the R code below: >> >> - When I use plot.gam() on a fitted model object, I get a nice and >> well-looking smooth curve for all >> terms in the model. >> >> - However, when I use predict(model) for a given predictor, with values of >> all other predictors set >> to their means, the resulting curve doesn´t fit well at all. >> >> Is there a way to "overlay" the curve produced by plot.gam() over a plot of >> the original data? >> >> Here´s some reproducible code with mgcv version 1.7-22 on R3.0.1 (Windows 7): >> >> ## >> >> library(mgcv) >> set.seed(2) >> dat <- gamSim(1,n=400,dist="normal",scale=2) >> b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat) >> plot(b,select=1) >> >> plot(y~x0,dat) >> mydata=data.frame(x0=0:1,x1=mean(dat$x1),x2=mean(dat$x2),x3=mean(dat$x3)) >> lines(0:1,predict(b,mydata,type="response")) >> >> ## >> >> Best wishes, >> Christoph >> >> > > > ______________________________________________ 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.
