Dear List, I've just tried to specify a GAM without an intercept -- I've got one of the (rare) cases where it is appropriate for E(y) -> 0 as X ->0. Naively running a GAM with the "-1" appended to the formula and the calling "predict.gam", I see that the model isn't behaving as expected.
I don't understand why this would be. Google turns up this old R help thread: http://r.789695.n4.nabble.com/GAM-without-intercept-td4645786.html Simon writes: *Smooth terms are constrained to sum to zero over the covariate values. ** **This is an identifiability constraint designed to avoid confounding with ** **the intercept (particularly important if you have more than one smooth). * If you remove the intercept from you model altogether (m2) then the smooth will still sum to zero over the covariate values, which in your case will mean that the smooth is quite a long way from the data. When you include the intercept (m1) then the intercept is effectively shifting the constrained curve up towards the data, and you get a nice fit. Why? I haven't read Simon's book in great detail, though I have read Ruppert et al.'s Semiparametric Regression. I don't see a reason why a penalized spline model shouldn't equal the intercept (or zero) when all of the regressors equals zero. Is anyone able to help with a bit of intuition? Or relevant passages from a good description of why this would be the case? Furthermore, why does the "-1" formula specification work if it doesn't work "as intended" by for example lm? Many thanks, Andrew [[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.
