I am attempting to fit multiple imputed datasets with brm_multiple, however, when I provide the three datasets as the list for brm_multiple, they do not converge. When I fit them separately using brm they converge. Can you help me understand why this might be the case? For reference, I am also running the imputation within brm, using mi() but these models take over a week with my 62,000 observations and 5 step imputations, thus why I’m pursuing imputed prior to fitting.
Here is my general code for the brm_multiple (which doesn’t converge and has very small effective sample sizes) vs the brm model (which converges with many effective sample sizes).
output6_a <- brm(formula6, prior = set_prior("normal(0, 1)", class = "b"), data = imp_a, chains = 4, iter = 4000, warmup = 2000, seed = 02152019, cores = 4)
output6_b <- brm(formula6, prior = set_prior("normal(0, 1)", class = "b"), data = imp_b, chains = 4, iter = 4000, warmup = 2000, seed = 02152019, cores = 4)
output6_c <- brm(formula6, prior = set_prior("normal(0, 1)", class = "b"), data = imp_c, chains = 4, iter = 4000, warmup = 2000, seed = 02152019, cores = 4)
imps <- list(imp_a,imp_b,imp_c)
output <- brm_multiple(formula6, data = imps, prior = set_prior("normal(0, 1)"), chains = 4, iter = 4000, warmup = 2000, seed = 02152019, cores = 4)
Thank you for brms and all the hard work. It’s an amazing product. Any insights are greatly appreciated.
Please also provide the following information in addition to your question:
- brms Version: 3.8.0