unexpected GAM result - at least for me!
On 3/31/2008 8:34 AM, Monica Pisica wrote:
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.
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