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glm-model evaluation

Rafael Maia 
I just joined the list today, so sorry if this has already been suggested!

Package dRedging (developed by Kamil Barto?
<http://www.zbs.bialowieza.pl/staff/kbarton>) also produces AIC/AICc
tables, but unlike selMod will compute for all possible model
combinations (but model.avg lets you use only a priori defined models).
It also allows for multi-model inference and model averaging.

it isn't available through CRAN, but you can get it at
http://www.zbs.bialowieza.pl/users/kamil/r/

I have two issues with it, though. First, I'm not sure it handles
adequately as.factor variables, specially in model averaging, since it
gives the estimates and average weights for each factor level when there
are more than 2 (again, maybe its supposed to do so; I'm not all that
versed on model selection). Also, I can't get it to bypass the default
subset (AICc <=4) of the get.model function, without just changing it
for a very large number.

I just compared the results of selMod (from pgirmess package) and
model.avg and dredge (both from dRedging package) with the negative
binomial example (below) and with a lme model of mine, and the results
seem quite consistent. I do recall running the cement example from B&A,
though, with results differing slightly - something to do with how
LogLik is computed, I've been told...

hope it helps!

Best,
Rafael Maia
Kingsford Jones wrote:

Thread (17 messages)

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