Having a random effect with only 3 levels is not recommended, it usually
gives problems fitting. There are also some philosophical questions about
its use as a random effect.
A random effect for field is reasonable, but you may be fitting too many
parameters. With only 57 observations it is easy to overfit the models, and
a standard linear model may be all that is necessary.
On 14 May 2015 at 07:15, Joaqu?n Aldabe <joaquin.aldabe at gmail.com> wrote:
Hello, this is Joaqu?n Aldabe from Uruguay. I?m trying to model shorebird
counts (Buff breasted Sandpiper, BBSA) with glmm (using lme4 package),
using continuous variables (grass height, field area, forest cover) and
one
factor variable (presence/absence of other shorebird species: American
Golden Plover, AMGP). I sampled 19 fields during three years in December.
I?m interested in identifying predictors correlated with BBSA counts. I
used Year as a random effect as I?m not interested in Year as a fix effect
and because fields were counted three times (pseudoreplication).
The model doesn?t converge, and the output showed that the factorial
variable has not a significant effect. This is weird as in every field I
observed the Buff breasted Sandpiper I also observed the other
species. When I take AMGP out, the model runs ok.
This is the model I?m trying to run:
mysub3.3<-glmer(BBSA~Grass_height+Field_area+Field_enclosure_700m+Grass_height*Field_enclosure_700m+fAMGP+(1|fYear),family="poisson",
data=mysub3.2)
continuous variables were scaled.
I can send de data frame if somebody is interested.
Thanks in advanced for helping me on my master thesis.
Cheers,
Joaqu?n.
--
*Joaqu?n Aldabe*
*Grupo Biodiversidad, Ambiente y Sociedad*
Centro Universitario de la Regi?n Este, Universidad de la Rep?blica
Ruta 15 (y Ruta 9), Km 28.500, Departamento de Rocha
*Departamento de Conservaci?n*
Aves Uruguay
BirdLife International
Canelones 1164, Montevideo
https://sites.google.com/site/joaquin.aldabe
<https://sites.google.com/site/perfilprofesionaljoaquinaldabe>
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