

>> This is something I always battle with given the plethora of great model
>> fitting methods available for other models.
>>
>> I always use a variant of Hugh's suggestion and look at the % of correct
>> predictions between models as a quick model fitting statistic.
>>
>> And for overdispersion I believe one way is to fit individual level random
>> effects and see if this is a substantively better model. There is more on
>> this in the wiki http://glmm.wikidot.com/faq
>    Yes, but this is unidentifiable for Bernoulli responses (as also
> explained there).


The statement on  'unidentifiable for Bernoulli 
responses'....well...apparently this is not that trivial.
See:  http://www.highstat.com/BGGLM.htm

Follow the link to: the Discussion Board....
Go to: Chapter 1 Introduction to generalized linear models
And see the topic: Can binary logistic models be overdispersed?

Alain



>    It's not as systematic, but where possible I like to compare
> parametric fits to a less-parametric fit, either a (marginal)
> GAM fit or binning the data and computing (marginal) mean proportions (and
> possibly binomial CIs) within bins (the latter is essentially
> the basis of the Hosmer-Lemeshow test).  The effects of other
> variables might lead to either a false positive or a false
> negative when comparing non-parametric marginal to parametric
> conditional predictions, but it's a start.
>
>    Ben Bolker
>
>



> ------------------------------
>
> Message: 2
> Date: Fri, 28 Jun 2013 04:12:50 +0000 (UTC)
> From: Ken Knonlauch <ken.knoblauch@inserm.fr>
> To: r-sig-mixed-models@r-project.org
> Subject: Re: [R-sig-ME] Model validation for Presence / Absence
> 	(binomial)	GLMs
> Message-ID: <loom.20130628T060912-404@post.gmane.org>
> Content-Type: text/plain; charset=us-ascii
>
> Ben Bolker <bbolker@...> writes:
>
>> Chris Howden <chris <at> ...> writes:
>>
>>> This is something I always battle with given the plethora of great model
>>> fitting methods available for other models.
>>>
>>> I always use a variant of Hugh's suggestion and look at the % of correct
>>> predictions between models as a quick model fitting statistic.
>>>
>>> And for overdispersion I believe one way is to fit individual level random
>>> effects and see if this is a substantively better model. There is more on
>>> this in the wiki http://glmm.wikidot.com/faq
> --- snip ---
>
>
>
> Also see the binomTools package on CRAN for some diagnostic tests
> for binomial models.
>
> Ken
>
>
>
> ------------------------------
>
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> R-sig-mixed-models mailing list
> R-sig-mixed-models@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>
> End of R-sig-mixed-models Digest, Vol 78, Issue 34
> **************************************************
>


-- 

Dr. Alain F. Zuur
First author of:

1. Analysing Ecological Data (2007).
Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p.
URL: www.springer.com/0-387-45967-7


2. Mixed effects models and extensions in ecology with R. (2009).
Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer.
http://www.springer.com/life+sci/ecology/book/978-0-387-87457-9


3. A Beginner's Guide to R (2009).
Zuur, AF, Ieno, EN, Meesters, EHWG. Springer
http://www.springer.com/statistics/computational/book/978-0-387-93836-3


4. Zero Inflated Models and Generalized Linear Mixed Models with R. (2012) Zuur, Saveliev, Ieno.
http://www.highstat.com/book4.htm

Other books: http://www.highstat.com/books.htm


Statistical consultancy, courses, data analysis and software
Highland Statistics Ltd.
6 Laverock road
UK - AB41 6FN Newburgh
Tel: 0044 1358 788177
Email: highstat@highstat.com
URL: www.highstat.com
URL: www.brodgar.com


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