[R-sig-eco] Corrected AIC for binary response variables?

[Mark_Wotawa at nps.gov]

Good question,
Burnham and Anderson 2002 (p332) mention but don't elaborate on the issue.
Intuitively it seems  it would -not- be 2811 as you'd have something along
the lines of pseudo-replication.  B&A also mention in a capture-recapture
context that 334 could be n for survival and 2811 could be n for recapture.
So, applied to your situation, you're recapture rate is 1,  which leads to
my (again intuitive) guess that 334 would be appropriate.  This gives you
only about 8 parameters before you should be using the AICc according to
the   n/K < 40 rule, so I'd use AICc regardless.   If you come up with
something concrete on this, it would be great to know more.
Regards,
Mark


***************************************************************
Mark A. Wotawa
Quantitative Ecologist
National Park Service
Biological Resources Management Division
1201 Oak Ridge Drive, Suite 200
Fort Collins, CO  80525-5589
Office: 970-225-3567
FAX: 970-225-3585
Email: mark_wotawa at nps.gov
***************************************************************




Greetings all,

I'm using logistic regression to investigate mortality of trees.  I'm using
AIC to compare models, and I'm wondering if I should use AICc instead of
AIC.  Burnham and Anderson [1] recommend using AICc when n/K < 40.  But
what do I consider for n?  The logistic regression is based on 2811
observations (334 trees observed annually for <= 10 yr), but I've only
observed 32 deaths.  Harrell [2] would consider 32 to be the "limiting
sample size" for determining the feasible number of predictor variables.
Is AIC the same?  Should I use 2811, 334, or 32 to figure out AICc?

Thanks for any help.

Sincerely,

Matt

[1] Burnham K. and D. Anderson.  2002.  Model selection and multi-model
inference:  a practical information-theoretic approach.  Springer.
[2] Harrell, F. 2001.  Regression modeling strategies.  Springer.

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