library(dplyr) library(cmdstanr) options(mc.cores = parallel::detectCores()) #Data data <- read.csv("sample_data.csv",stringsAsFactors = FALSE) #Making data ready for stan matdata <- data %>% select(-y,-customer_no) model_data <- list(N=nrow(matdata),K=ncol(matdata),x=matdata,y=data$y,L=length(unique(data$customer_no)),ll=data$customer_no) str(model_data) #Creating model - compilation mod <- cmdstan_model("reduce_sum_logit_int.stan", cpp_options = list(stan_threads = TRUE))#cpp_options = list(stan_threads = TRUE) -> is to enable multithreading #Sampling fit <- mod$sample( data = model_data, chains = 1, parallel_chains = 3, threads_per_chain = 2, refresh = 100, iter_warmup = 1000, iter_sampling = 1000, ) #saving fit object saveRDS(stanfit,"logit_reduce_sum_fit.rds") #diagnosing results fit$cmdstan_summary() fit$cmdstan_diagnose()