Thank you for your responses, Brad, John, and Jose.
As for specifying (...,type='response'), my problem is
that the larger dataset to which I am applying the
regression equation is pragmatically too large to
import into S Plus. (I'm regressing tree percent
canopy cover from about 150 Mb of satellite remotely
sensed imagery.) Therefore, I have to find a way to
manually predict my sample-calibrated equation in an
image-processing software that can't create GLM's on
its own.
As for using the exp() transformation, here are some
results:
> coefficients(dougfir100.x.glm)
Value Std. Error t value
(Intercept) -4.991452 1.176776 -4.2416314
poly(fall.tc1, 1) -12.739892 15.805608 -0.8060362
poly(fall.ndvi, 1) 7.341085 6.594062 1.1132872
poly(sumr.tc1, 2)1 4.707552 20.419682 0.2305399
poly(sumr.tc1, 2)2 9.380219 10.596375 0.8852290
poly(sumr.tc2, 2)1 3.081442 20.380504 0.1511956
poly(sumr.tc2, 2)2 -6.603397 12.827755 -0.5147742
poly(aspen.30.r, 2)1 -2.507069 6.497554 -0.3858481
poly(aspen.30.r, 2)2 -5.316410 4.269693 -1.2451505
Applying the coefficients above to the training
vectors (in S Plus),
>0-4.991452-12.739892*(fall.tc1)+7.341085*(fall.ndvi)+4.707552*(sumr.tc1)+9.380219*(sumr.tc1^2)+3.081442*(sumr.tc2)-6.603397*(sumr.tc2^2)-2.507069*(aspen.30.r)-5.316410*(aspen.30.r^2)
yields these results:
1 2 3 4...
398728.1 350754.5 497222.4 317500.5...
But when the exp() function is applied to transform
the predictions--as Jose and I had thought
appropriate--to (...,family='poisson'), the values are
a bit off:
> exp(x)
1 2 3 4...
Inf Inf Inf Inf...
This is especially interesting when compared to the
values yielded by predict(...,type='response'),
recommended by Brad and John:
> predict.glm(dougfir1.x.glm,contrain,type='response'
1 2 3 4...
0.003677987 0.008057072 0.0102533 0.008796681,
which are perfectly appropriate.
So, the question remains, is S Plus using some
function besides exp() as the transformation? Anyone?
Thanks again,
Joe
=====
Landscape Ecology: Modeling and Analysis Center
& Department of Forest, Range, and Wildlife Science
Utah State University
"Go deeper."
-Zarathustra
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