[R-sig-ME] Evaluating mixed logistic model efficiency

Drew Tyre atyre2 at unl.edu
Fri Dec 3 16:50:46 CET 2010


If you have binary 0/1 data one widely used method for looking at the
quality of the fit uses the Area under the Receiver Operating
Characteristic Curve - variously abbreviated ROC or AUC. It gives the
probability your model will correctly rank any two observations. There
is a nice package PresenceAbsence which does the calculations and
makes pretty plots.

If you have binomial data you can compare the residual deviance with a
Chisquare distribution with n-p df (n is sample size and p is the
number of parameters), but I'm not sure how well that works in the
mixed model case. I look forward to other suggestions.

On Fri, Dec 3, 2010 at 9:20 AM, Arnaud Mosnier <a.mosnier at gmail.com> wrote:
> Dear mixed modelers,
> In my actual knowledge status, the best available measures to evaluate
> goodness-of-fit of a mixed logistic model are Deviance, LogLikelihood, AIC,
> AICc if necessary, BIC (I may forgot some of them but they are all linked).
> However, these measures only permit to classify models from the best to the
> worse without knowing if the best model is really efficient in explaining
> the data.
> It exists pseudo-R2 calculation based on differences between the likelihood
> of  the intersect only model and the likelihood of the model that you wanted
> to evaluate. But use of those methods are generally discouraged.
> I would like your opinion about that !
> If you have any suggestion I will be happy to learn from you !
> Thanks for your help.
> Arnaud
>        [[alternative HTML version deleted]]
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Drew Tyre

School of Natural Resources
University of Nebraska-Lincoln
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Lincoln, NE 68583-0974

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email: atyre2 at unl.edu

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