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Hi all,
I am trying to model activity data against weather
variables in S-PLUS 2000 using AIC as a model selection
criterion....
The range of the response variable is 0 - 850,000
and is poisson-like in distribution. I am using a poisson family GLM to do
this. The AIC values of these models are huge ( in the millions).
Therefore what tends to happen when you are ranking different models via the
Akaike weights is that the best model is ranked as one (of course), and because
the actual magnitude of the difference between the first and second best is in
the thousands it gets an Akaike weight of essentially 0 and therefore all other
candidate models become ranked as tied for second best. This seemed
odd..
I experimented with transforming the response
variable by dividing by 100,000. What I am finding is that the values of
the AIC are now in the order to 100 or so and the ranking the models via Akaike
weights appears to be ok.
HOWEVER, when I rank the models using both
approaches from lowest to highest AIC values they are not the same-- The AIC
selected best models were different, WHY??.
Can anyone provide insight into why
this occurs.
Thanks in advance
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