hi,
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)?
I have read an article where chi.sq was interpreted like residual
deviance (reduction in deviance by each smooth). Can i do something like
that in my case?
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 smooth?
Last question, using GAM with default, should i look at R-sq rather than
Deviance explain, or both?
I hope it's ~ clear
thanks.
Yves
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