[R] question about logistic models (AIC)

Kyle. ambertk at ohsu.edu
Fri Aug 3 18:42:33 CEST 2007


That's a good question.  AIC, as i'm sure you know, is usually  
calculated as 2k-2ln(L), where k is the number of free parameters in  
the model, and L is the log-likelihood of the model.  The goal of  
AIC--and the reason one normally tries to select a model with minimal  
AIC--is to minimize what's referred to as the Kullback-Leibler  
distance between the distribution of your data's density from the  
theoretical "true" theoretical density as defined by the model.  More  
concisely, the AIC is an index of the amount of information regarding  
your data that is lost when your model is used to describe it.  To  
get back to your question, I can't say without a little more  
information why the AIC's your referring to are negative (but perhaps  
it's an issue of using the log-likelihood instead of the negative log- 
likelihood), but my first instinct is that it doesn't matter.  I  
would go with the AIC that is closest to zero.  I hope that helps.

Kyle H. Ambert
Graduate Student, Dept. Behavioral Neuroscience
Oregon Health & Science University
ambertk at ohsu.edu

On Aug 3, 2007, at 8:41 AM, Tom Willems wrote:

>  Dear fellow R-ussers,
> I have a question about the Akaike Information Criterion  in the  R
> output.
> Normally you would want it to be as small as possible, yet when i  
> check up
> the information in my study books, the AIC is usually displayed as a
> negative number. In the exercises it is given as " - AIC ".
> R displays it as a positive number, does this mean that a large "AIC"
> gives a small " - AIC ", so the bigger the better?
> Kind regards,
> Tom.
> Tom Willems
> Department of Virology
> Epizootic Diseases Section
> Groeselenberg 99
> B-1180 Ukkel
> Tel.: +32 2 3790522
> Fax: +32 2 3790666
> E-mail: towil at var.fgov.be
> www.var.fgov.be
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