> i'm using gam() function from package mgcv with default option (edf > estimated by GCV). > > >G=gam(y ~ s(x0, k = 5) + s(x1) + s(x2, k = 3)) > >SG=summary(G) > Formula: > y ~ +s(x0, k = 5) + s(x1) + s(x2, k = 3) > > Parametric coefficients: > Estimate std. err. t ratio Pr(>|t|) > (Intercept) 3.462e+07 1.965e+05 176.2 < 2.22e-16 > > Approximate significance of smooth terms: > edf chi.sq p-value > s(x0) 2.858 70.629 1.3129e-07 > s(x1) 8.922 390.39 2.6545e-13 > s(x2) 1.571 141.6 1.8150e-11 > > R-sq.(adj) = 0.955 Deviance explained = 97% > GCV score = 2.4081e+12 Scale est. = 1.5441e+12 n = 40 > -------------------------------------- > > I know i can estimate the significance of smooth terms with chi.sq & > p.value. > > With GCV, p-value are obtained by comparing the statistic to an F > distribution,isn't it? > help(summary.gam) says "use at your own risk!".Does it mean i should > only estimated signifiance of smooth terms by chi.sq?.Is there a way to > link both information (p.value and chi.sq)? No, using F as the reference distribution is always more conservative: using chi.sq will be even worse. The p values are *very approximate* since they are based on pretending that a penalized fit is equivalent to an unpenalized fit with the same effective degrees of freedom, and neglect the uncertainty associated with smoothing parameter estimation... they provide a reasonable `rough guide' to significance, but are by no means exact.
> Last question, using GAM with default, should i look at R-sq rather than > Deviance explain, or both? In this case devaince explained is just the unadjusted r^2... I'd look at the r^2, which is adjusted (to take into account the degrees of freedom `used up' when estimating the model). best, Simon ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
