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

[Chris Howden]

I 'quick and dirty' method I like to us is to calculate the mean predicted
value by actual value. If your model is working well the predicted score
for actual 0's should be quite low and the predicted score for actual 1's
quite high.



Chris Howden
Founding Partner
Tricky Solutions
Tricky Solutions 4 Tricky Problems
Evidence Based Strategic Development, IP Commercialisation and Innovation,
Data Analysis, Modelling, and Training
(mobile) 0410 689 945
(fax / office) (+618) 8952 7878
chris at trickysolutions.com.au

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Arnaud
Mosnier
Sent: Saturday, 4 December 2010 12:51 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Evaluating mixed logistic model efficiency

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

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