[R-sig-ME] [R] glmm: random term, overdispersion and comparisons

[Ellen Andresen]

Dear Thierry,
Thank you for your advice. So, using (1|site) should suffice, even
though it was always the same sites sampled? I really worry about not
specifying the random part correctly.
Finally, do you know of a good tutorial on how to speciffy contrast
coefficients to do the comparisons I am interested in?
Thanks again.
Ellen

2015-10-02 3:03 GMT-05:00 Thierry Onkelinx <thierry.onkelinx at inbo.be>:
> Dear Ellen,
>
> You're using the Poisson distribution. There is no error (noise) term in a
> glmm with Poisson distribution.
>
> 1) The random part seems to be quite complicated given the sample size.
> (1|site) is probably sufficient. Note that your design is not nested but
> crossed.
> 2) Overdispersion is likely in bird abundance. You could use a negative
> binomial distribution instead of a Poisson distribution. Then the
> overdispersion is modeled. Use the glmer.nb() function.
> 3) Have a look at the glht() function in the multcomp package. That allows
> you to test specific contrasts of your model parameters.
>
> Note that the r-sig-mixedmodels list is more appropriate for follow-up
> questions.
>
> Best regards,
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
> Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
>
> To call in the statistician after the experiment is done may be no more than
> asking him to perform a post-mortem examination: he may be able to say what
> the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
> 2015-10-01 16:59 GMT+02:00 Ellen Andresen <eandresens at gmail.com>:
>>
>> Hello,
>> I studied the effect of a hurricane in Cozumel on understory birds. I
>> have bird abundances (i.e. counts) registered always on the SAME six
>> sites (i.e. blocks). I have data for: before the hurricane, first year
>> after the hurricane, second year after the hurricane. I each of these
>> time periods, I also have data for summer season and for winter
>> season. I do not have a balanced design, in one of the time periods I
>> only have data for 5 of the six sites, and for another period I only
>> have data for 3 of the six sites.
>> I am defining Poisson error distrubution for the response variable.
>> I am using 'glmer' with two fixed factors, and I am interested in
>> their interaction:
>> - factor hurricane (three levels: before, after 1 y, after 2 y)
>> - factor season (two levels: summer, winter)
>> I am also specifying a random factor (sites), and I am specifying the
>> nested structure of the design. However, I don't know if I am
>> specifying the random part of the model in the correct way; this is
>> what I am doing:
>>        abundance ~ hurricane*season + (1|site/hurricane/season)
>>
>> I have three questions:
>> 1. Is the random part specified correctly?
>> 2. How do I check for overdispersion, and how can I correct for it?
>> (for each site I only have one observation; sites are my replicates)
>> 3. How do I make the following comparisons: I am interested in testing
>> for each season separately, after 1 y vs. before the hurricane, and
>> after 2 years vs. before the hurricane.
>>
>> Thank you so much!
>> Ellen Andresen
>> UNAM-Mexico
>>
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>