So I have read many textbooks and so many R tutorials that I am going crazy here. How do you decide on which model to use? I really hope this comes with experience but with the amount of modern techniques coming out and evidence for and against transformations, etc., how is anyone supposed to actually create a model that produces the correct result?
All I want to know is if there is a significant difference between the number of points in a plot covered with wood between two treatments (Low and High elephant impact). I would also like to know if any of the effects are significant. Each site has 5 plots (1,2,3,4,5). The number of points covered with wood were counted in each plot in 2013 and then again in 2014 and 2015. Therefore I have repeated measures.
My response variable is
Number = number of points covered with wood
My fixed effects or predictor variable are
Year (2013,2014,2015) and
Site (High and Low)
To account for the repeated measure,
Site are also my random effects. Or should this actually be
The first option is to use a GLMM, as I have both random and fixed effects; because I have count data, I selected the Poisson family:
Treatment act as both fixed and random effects in the same model? I haven't included plot as I'm assuming the repeated measure is actually YearL is that correct?
Secondly, if my data is not normally distributed, should I log-transform it and then run the GLMM?
Or should I rather leave it untransformed and use a linear mixed effects model (LME) instead?
For the LME, should I stipulate a distribution? Or does it automatically use the Gaussian distribution (Normal distribution)?
Treatment be both fixed and random effects?
Could this actually be non-linear?