[R] dreaded p-val for d^2 of a glm / gam

[Monica Pisica]

OK,

I really dread to ask that .... much more that I know some discussion about p-values and if they are relevant for regressions were already on the list. I know to get p-val of regression coefficients - this is not a problem. But unfortunately one editor of a journal where i would like to publish some results insists in giving p-values for the squared deviance i get out from different glm and gam models. I came up with this solution, but sincerely i would like to get yours'all opinion on the matter.

p1.glm <- glm(count ~be+ch+crr+home,   family = 'poisson')

# count - is count of species (vegetation)
# be, ch, crr, home - different lidar metrics

# calculating d^2
d2.p1 <- round((p1.glm[[12]]-p1.glm[[10]])/p1.glm[[12]],4)
d2.p1
0.6705

# calculating f statistics with N = 148 and n=4; f = (N-n-1)/(N-1)(1-d^2)
f <-  (148-4-1)/(147*(1-0.6705))
f
[1] 2.952319

#calculating p-value
pval.glm <- 1-pf(f, 147,143)
pval.glm
[1] 1.135693e-10

So, what do you think? Is this acceptable if i really have to give a p-value for the deviance squared? If it is i think i will transform everything in a fuction ....

Thanks,

Monica
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