Running on brm with 41600 observations have taken 31 hours, which only finished 200 interations. The run code are:
lfm2.2 <- bf(relv~scal.abd+scal.abdsuper+logrich+scal.abd:logrich+scal.abdsuper:logrich+(scal.abd+scal.abdsuper|plotcode), sigma~scal.abd+scal.abdsuper+logrich+(scal.abd+scal.abdsuper|plotcode))
lprior2.2 <- c(prior(normal(0,100),class="b"), prior(normal(0,100),class="Intercept"), prior(normal(0,100),class="sd"))
bayes_t.relv2.2 <- brm(formula=lfm2.2,data=adat2.2,prior=lprior2.2,control = list(adapt_delta = 0.99,max_treedepth=15),cores=no_cores)
If the family change from normal to student, the fitting was much quicker to finish (only about 12 hours). However, the result is not good with Rhats ranging from 1.31 to 2.22.
What should I do to improve the fitting speed? Thanks a lot!