From: Yves Magliulo <ym_at_climpact.com>

Date: Fri 23 Sep 2005 - 19:33:33 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Fri Sep 23 19:36:28 2005

Date: Fri 23 Sep 2005 - 19:33:33 EST

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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R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Fri Sep 23 19:36:28 2005

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