inst/doc/Running_bhmbasket_on_HPC.R

In bhmbasket: Bayesian Hierarchical Models for Basket Trials

## ----knitr_options, include = FALSE-------------------------------------------
knitr::opts_chunk$set(
  eval     = FALSE,  # en- / disables R code evaluation globally
  cache    = FALSE,  # en- / disables R code caching globally
  collapse = TRUE,
  comment  = "#>"
)

## ----setup--------------------------------------------------------------------
#  library(bhmbasket)
#  library(doFuture)
#  library(future.batchtools)
#  
#  rng_seed <- 5440
#  set.seed(rng_seed)

## ----SLURM_Setup--------------------------------------------------------------
#  ## Adapt the SLURM template to requirements
#  job_time  <- 1   # time for job in hours
#  n_workers <- 24  # number of worker nodes
#  n_cpus    <- 16  # number of cpus per worker node
#  gb_memory <- 2   # memory [GB] per cpu
#  
#  slurm <- tweak(batchtools_slurm,
#             template  = system.file('templates/slurm-simple.tmpl',
#                                     package = 'batchtools'),
#             workers   = n_workers,
#             resources = list(
#               walltime  = 60 * 60 * job_time,
#               ncpus     = n_cpus,
#               memory    = 1000 * gb_memory))
#  
#  ## Register the parallel backend
#  registerDoFuture()
#  
#  ## Specify how the futures should be resolved
#  plan(list(slurm, multisession))

## -----------------------------------------------------------------------------
#  scenarios_list <- simulateScenarios(
#      n_subjects_list     = list(c(10, 20, 30)),
#      response_rates_list = list(c(0.1, 0.2, 3)),
#      n_trials            = 10)
#  
#    analyses_list <- performAnalyses(
#      scenario_list       = scenarios_list,
#      target_rates        = c(0.1, 0.1, 0.1),
#      calc_differences    = matrix(c(3, 2, 2, 1), ncol = 2),
#      n_mcmc_iterations   = 100)
#  
#    getEstimates(analyses_list)