Dear s-plus users,
I run a glm( , family=poisson). The response variable is number of eggs
in a bird's nest (1-7). The explanatory variables in the full model are
two continous variables, and two factors with two levels each.
In the summary.glm of the models I see that the residual deviance is far
from equal to the residual degree of freedom (residual dev. 12.70, 151
df). If I use the 'common way' to discover underdispersion, this seems
to be a serious case.
From the help and MASS I understood that I might specify the dispersion
in 'summary.glm(object, dispersion=' and it is refered to equation (7.8)
on p. 187. in MASS.
So now I wonder:
Lets assume that I really have an underdispersion of a factor around
0.1. How serious is it? Does this mean that I have broken all possible
assumption of glm(...family=poisson) and should specify models in a
completely different way?
Can I correct for it by calculating the scale parameter obtained from
equation 7.8 and put this value as the dispersion parameter in
summary.glm? Are there any 'short cuts' in s plus to obtain the scale directly or do I need to calculat it from eg. 7.8?
If, so I am not entirely sure of from where I
extract the necessary terms in the equation 7.8 so I better ask for all
of them...
The residuals: is this the residuals$glm.object?
V(ûi): is this the same as var(ûi)?
Ai: 'is a /known/ prior weight' but where do I find it?
Sorry for my ignorance on glm...
Thanks a lot in advance for any kind of help!
Sincerely,
Henrik
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Henrik Pärn
Department of Biology
NTNU
7491 Trondheim
Norway
+47 735 96282 (office)
+47 909 89 255 (mobile)
+47 735 96100 (fax)
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