I think I am close to the error structure I want for random effects but not sure about some parts of it. I am carrying an experiment on wheat plots in a field to measure the increase in aphid numbers over time.
Experimental design and coding for predictors
I put 4 populations of aphids into a wheat plot and subjected them to four different cage treatments (
Cage_treatment). I measured the number of aphids before they were subjected to the treatments and after e.g. two time periods, before and after(
Time). I repeated this in many wheat plots within a field. Three wheat plots are part of different three types of crop rotation (
Rotation). These 3 plots x 3 crop rotations equal a block. There are four blocks in total. I am measuring aphid numbers per tiller (
The effects I am interested in e.g. fixed effects is how the
Total_per_tiller aphid numbers change over
Time, how this differs for each
Cage_treatment and how the
Rotation effects this.
Fixed and Random effects
- Plot ID,
This is the following code for my maximal model:
model <- lmer(Total_per_tiller~Cage_treatment*Rotation*Time + (Time|PlotID) + (1|Block/Rotation), REML=FALSE)
Does this look right?