Dear Joao,
Carsten is right.
However if your data are not left skewed you do not have any reason of 
using neg bin or quasi Poisson.

Poisson (= log-linear) models are very adapted to count data (more than 
Gaussian in any case) because they capture very well the fact that, as 
is often the case with count data, the variance tends to increase with 
the mean.
Another advantage of using the log link is that with count data the 
effects of environmental predictors are often multiplicative rather than 
additive. If the effet is in fact proportional to the count, working in 
the log scale leads to a much simpler model.

Hope it helps,
Wilfried



Carsten Dormann a écrit :
> Dear Joao,
>
> I propose you do the following (and wait for the outcry-responses to 
> this email to see if it is a reasonable proposal):
>
> Fit your model with different types of distributions and compare their 
> logLik-values:
> logLik(glm(y ~ x1+x2+x3+I(x1^2) + x1:x3, family=gaussian))
> logLik(glm(y ~ x1+x2+x3+I(x1^2) + x1:x3, family=poisson))
> logLik(glm(y ~ x1+x2+x3+I(x1^2) + x1:x3, family=quasipoisson))
> logLik(glm.nb(y ~ x1+x2+x3+I(x1^2) + x1:x3)) # require(MASS)
>
> The model with the highest log-Likelihood is the distribution of 
> choice and you can defend it against reviewer.
>
> A few notes:
> 1. You obviously cannot do this when one of the models uses 
> transformed responses (e.g. log(y)), because the LL will then be 
> completely different.
> 2. When you use a more complex model (say a GLMM), you can approximate 
> the neg.bin through a two-step procedure: 1. fit a (wrongly 
> structured) glm.nb and extract the theta value from the summary of the 
> model, say theta=4.5 (that is the second parameter of the neg.bin 
> distribution). Then fit the GLMM again, giving as family the argument: 
> negative.binomial(theta=4.5) (again from package MASS). The same holds 
> for GAMs and other models requiring a specification of family.
> 3. You may want to dig around for books recommending the above 
> procedure. I think I got this as advice from someone else, but haven't 
> bothered yet to look it up (obviously MASS would be a good starting 
> place, in their description of the neg.bin). I saw a paper that does 
> this (using the minimum AIC but otherwise this approach), but it is 
> not a statistical, but rather an ecological paper (although the 
> analyst in the author group is a biometrician whom I full trust): 
> Weigelt, A., Schumacher, J., Walther, T. Bartelheimer, M., Steinlein, 
> T., Beyschlag, W. (2006) Identifying mechanisms of competition in 
> multispecies communities. Journal of Ecology 95:53-64
>
> HTH,
>
> Carsten
>
>
> Canning-Clode, Joao wrote:
>> Hi all,
>>
>> 1st time user here!
>> I am an ecologist working with marine fouling assemblages. I just got 
>> a paper back for revision. I am working with count data (species 
>> richness). I have used a linear model but the reviewers are 
>> recommending the use of negative binomial or Poisson. As far as I 
>> could understand from the literature these complex models should be 
>> used and the distribution is skewed left (lots of zeros). Well, my 
>> data is perfectly normal distributed. My main question is: can I 
>> still use negative binomial or poisson even if my data is normal? 
>> Does that make sense?
>>
>> Thanks in advance
>>
>> João Canning Clode, PhD
>> Postdoctoral Fellow
>> Marine Invasions Research Lab
>> Smithsonian Environmental Research Center
>> 647 Contees Wharf Road
>> Edgewater, MD 21037
>>
>> Email: canning-clodej@si.edu
>> Web: www.canning-clode.com
>> Tel: 443-482-2354
>>
>> _______________________________________________
>> R-sig-ecology mailing list
>> R-sig-ecology@r-project.org
>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>>
>>   
>

-- 
--
Wilfried Thuiller
Laboratoire d'Ecologie Alpine, UMR-CNRS 5553
Université J. Fourier
BP 53, 38041 Grenoble Cedex 9, France
Tel: +33 (0)4 76 63 54 53
Fax: +33 (0)4 76 51 42 79

Email: wilfried.thuiller@ujf-grenoble.fr
Home page: http://www.will.chez-alice.fr
Website: http://www-leca.ujf-grenoble.fr/equipes/tde.htm

FP6 European MACIS project: http://www.macis-project.net
FP6 European EcoChange project: http://www.ecochange-project.eu


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