Estimate predictor contribution in GAM models
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
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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