I am in the midst of analyzing data that I thought were rather simplistic, but I find myself in need of guidance. The function
lmer() has been suggested, but I'm unclear about groups to be included when identifying random effects despite consulting every resource I could get my hands on. I think I know just enough about R to get myself into trouble, so please accept my apologies in advance if this should be obvious!
The design: Three blocks [to which experimental units & treatments were randomly assigned], each containing every combination of 3 species (
spp) and 4 treatments (
trt). Response (
PN) was measured weekly for 10 weeks (
Week). I expect the species will differ, so - to simplify - I am running a separate model for each species. Within each of the
trt "tubs", I have three units which allowed a mean response (
meanPN) and variance (
varPN), so I'm using
meanPN as my response and will weight the model by
meanPN ~ Trt + Block + Time (+ interactions) + error
maybe the statement (excluding interactions for now as I try to wrap my mind around this) would be:
lmer(meanPN ~ Trt + (Trt|Week) + (1|Block) , data=SumExpt, weights=1/varPN)
Can you help me to understand why I would use
(Trt|Week) - or perhaps, when would each case be appropriate?
Also, does one need to identify
Week as the repeated variable, or is R interpreting that based on the pipe character?
Finally, if I understand what I've read correctly, the interactions are handled in the denominators calculating the F-statistic. Is that proper understanding?