[R-sig-ME] GLMM for repeated measures in space and time series

[Bárbara Baraibar Padró]

Hello,

I'm trying to choose the correct model to analyze my data and I need 
some help. I'm measuring seed predation (I leave 1 gram of seeds for 48 
in the field and after 48 hours I take what is left and weigh it again). 
I do this in the same 50 petri dishes (stations), 25 of which have one 
weed species and the other 25 have another and repeat the same in 3 
different fields during 3 months. So, I have a nested design with:

Fixed effects: Weed_species, Date (time)

Random effects: Station, Field

My results are a bit weird in the sense that I have a lot of dishes with 
100% seeds predated and some with 0% predated and few in the middle.

My boss says that my response variable follows a binomial distribution 
because each seed can be either predated or not, so I have constructed a 
response variable with a success column 
(seeds_predated/initial_seedweight) and a failure column 
(initial_seedweight-seeds_predated)/initial_seedweight

I have tried a GLMM like the one below and I would like to know if the 
model is ok for this kind of data (repeated measures in space and in 
different times) and how I can validate the model. I have done a Binned 
residuals plot and almost all my residuals fit within the intervals, do 
I need to do something else?

Thank you very much!!!

success<- seeds_predated/initial_seedweight

failure <- (initial_seedweight-seeds_predated)/initial_seedweight

resposta<- cbind (success, failure)

GLMM1<-glmer(resposta ~ Weed_species + Data + (1|Station/Field), 
family=binomial)

Warning message:In eval(expr, envir, enclos) : non-integer counts in a 
binomial glm!

Generalized linear mixed model fit by maximum likelihood 
['glmerMod']Family: binomial ( logit )Formula: Depredacio ~ Especie + 
Data + (1 | Station/Camp)
AICBIClogLikdeviance
479.6651501.8878 -233.8326467.6651

Random effects:
GroupsNameVarianceStd.Dev.
Camp:Station (Intercept) 5.036e-10 2.244e-05
Station(Intercept) 0.000e+00 0.000e+00
Number of obs: 300, groups: Camp:Station, 100; Station, 50

Fixed effects:
      Estimate Std. Error z value Pr(>|z|)
(Intercept)-0.41330.1787-2.3130.02072 *
EspecieLolium0.50080.21002.3850.01709 *
Data2-0.84620.2681-3.1560.00160 **
Data30.74700.25412.9400.00328 **
---Signif. codes:0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' '1

Correlation of Fixed Effects:
(Intr) EspcLl Data2
EspecieLolm -0.600
Data2-0.404 -0.037
Data3-0.4730.0380.299

-- 
Barbara Baraibar Padro
ETSEA- Universitat de Lleida
Dep. Hortofruticultura, Botanica i Jardineria
Av. Rovira Roure 191
25198 Lleida (Spain)
Telf: +34 973 702912