








Philip and Alain,
 
Thank you for your assistence,
So, that mean that the  fuction offset its only possible if there is a relationship between the damaged number of embryos and the total number of embryos per amphipod as you explained?
The relationship between the two variables in my case looks like more as a gaussian dist, so I assume that is not possible for this case. The data that i use is from a monitoring program where the stations are in "not pollutet" areas, that make the frecuency of malformation be very low. Im using a hurdle model  to analys the malformations due that the data are zero-inflated and overdispers, in the first part of the model I look the presence and absence of females with malformations that model the zero count. The second part is  truncated at zero count where I look  the frequency of malformations in only those amphipods that have damaged embryos, here I applay a negative binomial due the overdispersion. But since the poisson models its only for count data I dont now how to continuo.  Is there any other solucion for analys ratio data overdisperded with this characteristics? Im proving now to apply a boxcox transformation to the frequency of malformations variable and then use a glm with gaussian family and link=log, but its not a solucion for the overdispersion.  Any help will be welcome!
Thanks,Matias
> From: r-sig-ecology-request@r-project.org
> Subject: R-sig-ecology Digest, Vol 63, Issue 13
> To: r-sig-ecology@r-project.org
> Date: Mon, 17 Jun 2013 12:00:02 +0200
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>    1. Re: Offsets in Poisson or Neg. Bin regression
>       (Highland Statistics Ltd)
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> ----------------------------------------------------------------------
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> Message: 1
> Date: Mon, 17 Jun 2013 07:35:48 +0100
> From: Highland Statistics Ltd <highstat@highstat.com>
> To: r-sig-ecology@r-project.org
> Subject: Re: [R-sig-eco] Offsets in Poisson or Neg. Bin regression
> Message-ID: <51BEAE44.6040401@highstat.com>
> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
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> 
> 
> Matias,
> The only problem with the offset is that you implicitly assume:
> 
> double the number of embryos per individual (FECUND)  ==> double the 
> expected value of damaged embryos per individual
> 
> This simply follows from the equation that Philip wrote down. For some 
> scenarios this makes sense, but not for other scenarios.
> 
> Alain
> 
> > Matias,
> >
> > The situation when you most want to use an offset is when FECUND differs across individuals.  If FECUND is a constant for all observations, you could ignore it if you chose to.  If you did that, its constant effect gets rolled into the intercept.
> >
> > When it's not constant, the logic is:
> >
> > log(mu_i / F_i) = your model   <==> Log(mu_i) = your model + log(F_i)
> > where mu_i is the mean count and F_i is the fecundity, both for individual i.
> >
> > When F_i is a constant = F, the logic is:
> > log(mu_i / F) = your model   <==> Log(mu_i) = B0 + rest of your model + log(F)  <==> B0 + log(F)  + rest of your model
> > where B0 + log(F) is the new "intercept"
> >
> > Best wishes,
> > Philip Dixon
> >
> >
> >
> > ------------------------------
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> > End of R-sig-ecology Digest, Vol 63, Issue 12
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> 
> 
> -- 
> 
> 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
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