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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