
> Message: 2
> Date: Fri, 31 Aug 2012 13:03:45 -0500
> From: Douglas Bates <bates@stat.wisc.edu>
> To: Lynne Clay <lynne.clay@xtra.co.nz>
> Cc: R-mixed models mailing list <r-sig-mixed-models@r-project.org>
> Subject: Re: [R-sig-ME] lme4 with Poisson
> Message-ID:
> 	<CAO7JsnQcWatj0ji2v5RPfXqYv97UPka2rfuz3V0P=gjA9ppuTg@mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> On Thu, Aug 30, 2012 at 5:25 PM, Lynne Clay <lynne.clay@xtra.co.nz> wrote:
>> Dear Prof Bates,
>> I'm a doctoral candidate in NZ trying to analyse survey data with random
>> effects with my outcome being a count.  I discovered your lme4 package and
>> have been using this with success, however, I need to check for
>> overdispersion and it is at this point I am having problems.  The formula I
>> have used before has been (1/df)*deviance and if I use this my model is
>> highly overdispersed.  I read on one of the discussion boards that adding an
>> extra random effect (1|id#) addresses the overdispersion problem which I
>> have included but overdispersion continues.
>>
>> Can overdispersion be calculated in this manner?


Dear Lynne,

See:

Elston DA, Moss R, Boulinier T, Arrowsmith C, Lambin X (2001) Analysis 
of aggregation, a worked example: numbers of ticks on red grouse chicks. 
Parasitology 122: 563--569.




Microsoft Word - ReferencesBookB.doc Having said that.....I think I 
prefer a negative binomial GLMM approach, compared to the observation 
level random intercept approach. Quite often you get a perfect fit with 
the observation level random intercept approach...too perfect.

Alain




> I'm sorry but I know nothing about overdispersion.  To me it is
> completely artificial because there is no probability distribution on
> which to base a statistical model with these properties.
>
>> Do you have any suggestions of how to deal with this?
> Sorry but I don't.  I have taken the liberty of sending a copy of this
> reply to the R-SIG-Mixed-Models mailing list in the hope that readers
> of that list can help you.
>>
>> Lynne
>>
>>
>> Lynne Clay
>> PhD Candidate
>> School of Physiotherapy
>> University of Otago
>> PO Box 56
>> Dunedin 9054
>> New Zealand
>
>





-- 

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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