I am trying to run the model below. The dataset is very large (n = 139027) and so I cannot upload it here for a reproducible example. E143 and E035 are ordinal variables, coop and conf are continuous, and countryISO is a factor identifying countries.
bf1 <- bf(E143 ~ mo(E035) + (mo(E035) |p| countryISO)) + cumulative() bf2 <- bf(coop ~ conf + (conf |p| countryISO)) + gaussian() fit <- brm(bf1 + bf2 + set_rescor(FALSE), data = d_temp, prior = c(prior(normal(0, 0.5), class = Intercept, resp = E143), prior(normal(0, 0.5), class = b, resp = E143), prior(normal(0, 0.5), class = Intercept, resp = coop), prior(normal(0, 0.5), class = b, resp = coop)), iter = 4000, warmup = 2000, chains = 4, cores = 4, control = list(adapt_delta = 0.99, max_treedepth = 15), sample_prior = TRUE)
I am interested in the correlation between the random slopes across module formulae.
However, after all 4 chains have completed (which takes a few days), R does not save the model object. It just keeps the process going indefinitely. In the image below, I had been waiting two hours for the model object to save. I was then forced to quit R and lose all of my work.
I have never experienced this with brms before. Am I doing something wrong?
- Operating System: macOS Mojave Version 10.14.5
- brms Version: 2.10.0