I was trying to work out this on my own but I find myself overwhelmed.
I am looking at the amount of moisture present in forest fuels (FMC) following forest thinning. Forest fuels are classified by their size into five distinct size classes (FuelType). I monitored changes in FMC over the course of one dry season by monthly sampling (SamplingMonth) of each FuelType in 13 different forest stands (UnitID). Forest stands include 10 thinned forest, and 3 unthinned forests (Treatment). Forest stands vary based on their age, and time since thinning took place.
I would like to determine if there are differences in FMC between thinned and unthinned forests. Due to the fact that FuelType respond to seasonal changes differently, it is necessary to consider them separately. Using lme4 package I built the following models:
model.one = lmer(log(FMC) ~ Treatment + FuelType + (SamplingMonth | UnitID) , data=moisture) model.two = lmer(log(FMC) ~ Treatment + FuelType + (1+SamplingMonth | UnitID) , data=moisture)
I was able to run them both, which is a success in itself. But I am not convinced that these models are really telling me what I wanted to know. For some reason I anticipated a random effect
(SamplingMonth | FuelType) in there somewhere but the model will not budge.
Upon request I am attaching a link to my data:
And the R code to retrieve it:
moisture <- read.csv("http://radekonline.com/download/ForAnalysis/MoistureDataBasic.dat")
Any help and opinion would be greatly appreciated!