library(brms)
library(methods)
library(rstan)
library(survival)
library(magrittr)
library(plyr)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
#Read arguments listed on command line
args = (commandArgs(TRUE))
i = as.integer(args[1])
print(i)
#source gen_stan_dat function
source("R/misc.R")
#Load data --------------
mc_samp <- readRDS("data-raw/mc_samp.RDS")
#Bayesian clinical model ----------
surv_form <- c( "age_std", "npi_std", "her2pos", "erpos")
form <- as.formula( paste0(c( "status~1+offset(log_dtime)+s(time)+", paste(surv_form, collapse = "+")), collapse = ""))
# loop object through splits
x <- mc_samp$splits[[i]] #choose splits
x <- as.data.frame(x$data)[x$in_id,]
long_x <- gen_stan_dat(x)
bayes_test <- brm(bf(form),
data = long_x, family = poisson(), cores = 4, seed = 17,
iter = 14000, thin = 10, refresh = 0,
control = list(adapt_delta = 0.99) )
saveRDS(bayes_test, paste0("bayes_model_brms_clinical_full_", i, ".RDS") )
rm(list=ls())
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