Just want to say first that I am a big fan of brms.
Currently, I want to compare variance components (group level estimates) and repeatability between two treatment groups. At the moment, I have been subsetting my data, running the same models using each subset data to estimate the variance components I wish to compare. However, I feel like this method reduces my statistical power. I have implemented this form of estimation in MCMCglmm previously using syntax such as: “random = ~us(sex):ID”. Is there a way to specify this in brms to estimate variance components for each treatment group and avoid subsetting my data.
My model structure looks like this
cc_mod.z3.1.obj <- brm(lnmr ~ treatment*z_temp + z_lnmass + z_age + (1 | id) + (1 | samp_session) + (1 | series_temp), data = data, chains = 4, cores = 1, iter = 4000, warmup = 1500, thin = 5, control = list(adapt_delta = 0.99))
- Operating System: Mac OSX High Sierra 10.13.6
- brms Version: 2.14
Apologies for such an old brms version, I am writing up my thesis right now and am too scared to update anything!