[R-sig-ME] mixed model?

[Evan Palmer-Young]

Joaquin,
It looks like you could use Year and Field as random effects, since there
might be variation in bird abundance across years, and similarly, variation
across fields.

So in this case your model is
Birdmodel<- glmer(Presence~ GrassHeight * ForestCover + (1|Year) +
(1|Field), data=BirdData, family =                  "binomial")

Alternatively you could use Year as a fixed effect, if you are interested
in particular years.
Another option is to include interaction terms as random effects, eg
(1|Field:GrassHeight), to allow the effect of GrassHeight to vary across
fields.


On Fri, Apr 28, 2017 at 9:32 AM, Joaquín Aldabe <joaquin.aldabe at gmail.com>
wrote:

> Dear all, I'm analysing bird presence/absence in 16 grassland fields over 4
> seasons (different years) and want to know the effect of grass height and
> forest cover on presence/absence of the species. Grass height varied among
> season but not forest cover in each field. So we have a spatial dimension
> and a time dimension. I tried a binomial glm but wonder if I should use
> generalized linear mixed models with field identity as the random as I have
> repeated measures (bird counts) in each field.
>
> I appreciate your opinion.
>
> Thanks in advanced,
>
> Joaquin Aldabe.
>
> --
> *Joaquín Aldabe*
>
> *Grupo Biodiversidad, Ambiente y Sociedad*
> Centro Universitario de la Región Este, Universidad de la República
> Ruta 15 (y Ruta 9), Km 28.500, Departamento de Rocha
>
> *Departamento de Conservación*
> Aves Uruguay
> BirdLife International
> Canelones 1164, Montevideo
>
> https://sites.google.com/site/joaquin.aldabe
> <https://sites.google.com/site/perfilprofesionaljoaquinaldabe>
>
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-- 
Evan Palmer-Young
PhD candidate
Department of Biology
221 Morrill Science Center
611 North Pleasant St
Amherst MA 01003
https://scholar.google.com/citations?user=VGvOypoAAAAJ&hl=en
https://sites.google.com/a/cornell.edu/evan-palmer-young/
epalmery at cns.umass.edu
ecp52 at cornell.edu

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