I have results from an experiment where seeds from 3 source populations (fixed factor 'Source') were transplanted into 4 sites (fixed factor 'site') in 3 different years (factor with levels 2011,2012,2013). I measured the proportion of seeds to emerge per plot (a binomial response variable: propEm).
The problem is that one site was only planted in 2011, so site and year are not fully crossed, so I cannot run a single fixed effect model:
glm(propEm ~ site*Source*year, family=binomial)
I have been analysing the data in 2 fixed-effect models, one for 2011-2013 for the sites that were planted each year, and one for 2011 with all four sites. But since I don't care about the effect of year per se (just that the effects of site and Source varied among years), I'm wondering whether it would be appropriate to use a single mixed model using glmer:
glmer(propEm ~ site*Source + (1+site|year) + (1+Source|year), family=binomial)
My questions as someone brought up in the fixed effect world:
In the mixed model above, is it problematic the the factors site and year are not fully crossed?
3 years is not really a random sample of years, and yet it is in that I didn't choose these years specifically (that's just when my dissertation was done) and don't really care about their means. Does year in this case seem like an appropriate random effect?