# Beta regression with "random effect" of source plot in two seasons

I am attempting to model the effect of several continuous and categorical predictors on a continuous proportion response variable. My experiment had 3 treatments, which were replicated in each of 9 plots, in each of two seasons. My response variable was "percentage of total weight that is body fat." Weight is variable between organisms, so I need to use proportions. One of my predictors is "total body weight." I was previously modeling these data incorrectly with a GLMM, using the cbind() function to create a matrix for the components of my proportions. It seems that a beta regression is what I should actually be using. My problem is that I would like to include "source plot" as a random effect. Should I instead just make "source plot" another categorical predictor in the beta regression? If source plot has an effect, how can I remove it see the relative effects between each of the three treatments within every source plot? Should source plot be nested within season?

• you could use glmmADMB or glmmTMB (on Github) to fit a Beta model with a random effect of plot