Hello, I am a newbie to R and GLMM and having a difficult time understanding the model design that best captures my test scenario. I am interested in the following question: 1. whether average values of a variable explain a certain response lesser than individual values. 1.1. For this, I have a single response, say y. 1.2. I have a bunch of fixed predictors, say x1, x2, x3 and I can derive my models for those. 1.3 I have two kinds of random effects - a site (r1) and a species (r2), within the site. My average values of some of the fixed predictors is based on the species (r2). I am not especially interested in looking at site level variations, but I did build it into the model, all the same. So, I was able to develop a set of models with the individual values like so: y ~ x1+ x2 + x3 + (1|r1/r2) I was able to get some output in terms of significance for certain parameter estimates. So far, its ok. Now, I wanted to test whether the average values of x1 and x2 based on r2 will predict y with less powerful estimates. My doubt is whether in that case, r2 should be removed from the random variable since I now actually have average values for all x1 and x2 for a certain value of r2. Basically is the below model with average values logically wrong? y ~ x1avg + x2avg + x3 + (1|r1/r2) my averages for x1 and x2 are over each value of r2. Should r2 move to a fixed effect or be removed totally from the model? Any inputs would be appreciated. Thanks and Regards, Sharada