Hello all,

I am working with mortality data - the number of three animals per experimental unit that had died by week 1, week2, ... during an experiment.  So, I am using a binomial GLMM approach to model proportions with a random effect to deal with the repeated measurements from the same exp. unit.  (Specifically, I use the experimental unit or "Tank" as the random effect, since multiple observations from the same unit must be related... and there were temporal/spatial blocks, so each tank is nested within a block.  I include sample "Day" as a fixed effect in the model)

sm7<-glmer(predc.survtbl~predator*Day +(1|Block/Tank), family="binomial", data=predc2)

All is well, except the model suggests there are no treatment or time effects, while graphical inspection suggests there may be both.

My question is whether having a very small upper limit (the denominator in the proportion) means that I will have an inherently difficult time identifying treatment effects - basically because the response variable can only take one of four options (0, .333, .666, 1.0), and thus will be rather noisy?  I have 12 replicate observations for each day*predator combination...

Any advice or reprimands are welcome.

Jeremy


Jeremy M. Wojdak
Associate Professor
Department of Biology
Radford University
PO BOX 6931 (mail)
236 Curie Hall (office)
Radford, VA 24142
540-831-5395 (phone)
jmwojdak@radford.edu


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