library(rstan); library(tidyverse); library(rlist); library(ggmcmc);library(bayesplot) library(magrittr); library(pipeR); library(Hmisc); library(readxl); library(caret); #rstan_options(auto_write=TRUE) #options(mc.cores=parallel::detectCores()) d <- read.csv(file='d.csv', header = TRUE) year <- 2 data <- list(N=nrow(d), T=year, T_pred=3, HA=d) stanmodel <- stan_model(file='test.stan') fit <- sampling( stanmodel, data = data, seed =10, chains=4, iter=6000, warmup=1000, thin=5, init=function(){ list(cutpoints=c(-6,-5,-4,-3,-2,-1,0,1,2,3), mu_raw=matrix(rnorm(nrow(d)*year, mean = 0, sd = 1), nrow = nrow(d), ncol = year), phi_raw=matrix(rnorm(nrow(d)*year, mean = 0, sd = 1), nrow = nrow(d), ncol = year), mu_ori=0,s_mu=0.1,s_phi=1,s_ori=2) } ) print(fit, pars=c("s_mu", "s_phi", "s_ori")) pairs(fit, pars=c("s_mu", "s_phi", "s_ori")) mcmc_combo(fit, pars=c("s_mu", "s_phi", "s_ori")) monitor(rstan::extract(fit, permuted = FALSE, inc_warmup = FALSE, pars=c("s_mu", "s_phi", "s_ori")))