rstan_options(auto_write = TRUE) options(mc.cores = parallel::detectCores()) pooledDso <- stan_model(file = "Pooled_Model_for_Loss_Reserving.stan", model_name = "GrowthCurve") fit <- sampling(pooledDso, iter=5500, warmup=500, thin=2, cores=2, chains=4, seed=1037025002, data = list(N = nrow(dat), cum=dat$cum, dev=dat$dev, new_N=nrow(newdat), new_dev=newdat$dev)) print(fit, pars=c("ult", "omega", "theta", "sigma"), probs=c(0.5, 0.75, 0.95)) # Inference for Stan model: GrowthCurve. # 4 chains, each with iter=5500; warmup=500; thin=2; # post-warmup draws per chain=2500, total post-warmup draws=10000. # # mean se_mean sd 50% 75% 95% n_eff Rhat # ult 4756.83 3.65 244.35 4732.59 4900.01 5206.47 4481 1 # omega 1.35 0.00 0.07 1.35 1.40 1.47 5201 1 # theta 41.71 0.05 3.56 41.30 43.78 48.13 4208 1 # sigma 6.81 0.01 0.70 6.75 7.25 8.03 6251 1 # # Samples were drawn using NUTS(diag_e) at Tue Nov 3 06:41:45 2015. # For each parameter, n_eff is a crude measure of effective sample size, # and Rhat is the potential scale reduction factor on split chains (at # convergence, Rhat=1). rstan::traceplot(fit, pars=c("ult", "omega", "theta", "sigma")) stan_dens(fit, pars=c("ult", "omega", "theta", "sigma"), fill="skyblue")