[R] unexpected GAM result - at least for me!

[Monica Pisica]

Hi


I am afraid i am not understanding something  very fundamental.... and does not matter how much i am looking into the book "Generalized Additive Models"  of S. Wood i still don't understand my result.

I am trying to model presence / absence (presence = 1, absence = 0) of a species using some lidar metrics (i have 4 of these). I am using different models and such .... and when i used gam i got this very weird (for me) result which i thought it is not possible - or i have no idea how to interpret it.

> can3.gam <- gam(can>0~s(be)+s(crr)+s(ch)+s(home), family = 'binomial')
> summary(can3.gam)
Family: binomial
Link function: logit
Formula:
can> 0 ~ s(be) + s(crr) + s(ch) + s(home)
Parametric coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)    85.39     162.88   0.524      0.6
Approximate significance of smooth terms:
          edf Est.rank Chi.sq p-value
s(be)   1.000        1  0.100   0.751
s(crr)  3.929        8  0.380   1.000
s(ch)   6.820        9  0.396   1.000
s(home) 1.000        1  0.314   0.575
R-sq.(adj) =      1   Deviance explained =  100%
UBRE score = -0.81413  Scale est. = 1         n = 148

Is this a perfect fit with no statistical significance, an over-estimating or what???? It seems that the significance of the smooths terms is "null". Of course with such a model i predict perfectly presence / absence of species.

Again, i hope you don't mind i'm asking you this. Any explanation will be very much appreciated.

Thanks,

Monica

PS. I've contacted the author of the book who is the package maintainer as well but until now i didn't get a reply.

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esh_realtime_042008