On Tue, 20 Sep 2005, Yves Magliulo wrote: > hi, > > i'm using gam() function from package mgcv. > > if G is my gam object, then > >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 > -------------------------------------- > > => > But how can i estimate numericaly the contribution of each smooth > against the others. In others words, is there a way to quantify this > significance like a percentage of how the model is improved by each of > my predictors?
- The easiest thing to do is probably to refit the model without each predictor, and look at how much the r^2 drops. You might want to fix the smoothing parameters when you do this: G$sp gives the original smoothing parameter estimates for the model with all terms, so you can pick out the appropriate smoothing parameters to send to `gam' via the `sp' argument, for the 2 term fits. 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
