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unexpected GAM result - at least for me!

4 messages · Monica Pisica, Duncan Murdoch

Monica Pisica
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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.
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
Duncan Murdoch
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On 3/31/2008 8:34 AM, Monica Pisica wrote:
Look at the data.  You can get a perfect fit to a logistic regression 
model fairly easily, and it looks as though you've got one.  (In fact, 
the huge intercept suggests that all predictions will be 1.  Do you 
actually have any variation in the data?)

Duncan Murdoch
Duncan Murdoch
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On 3/31/2008 9:01 AM, Monica Pisica wrote:
I repeat:  look at the data. Compare the observed and predicted. That's 
the only way to know whether this is reasonable or not.

If you're getting reasonable predictions, then it's a valid fit.  (The 
tests and approximations used in the reported p-values may not be at all 
valid.  I don't know what the requirements are for those in a GAM, but 
if you're getting a perfect fit, then they probably aren't being met.)

Duncan Murdoch