Use the stan MCMC sampler — sampler_stan

This option will use stan's NUTS sampler via cmdstanr for Markov Chain Monte Carlo (MCMC) sampling of the EpiSewer model.

Usage

sampler_stan_mcmc(
  chains = 4,
  iter_warmup = 500,
  iter_sampling = 500,
  adapt_delta = 0.99,
  max_treedepth = 15,
  step_size = 0.01,
  parallel_chains = NULL,
  threads_per_chain = 1,
  seed = 0,
  refresh = 200,
  show_messages = TRUE,
  show_exceptions = FALSE,
  ...
)

Arguments

chains: (positive integer) The number of Markov chains to run. The default is 4.
iter_warmup: (positive integer) The number of warmup iterations to run per chain. Note: in the CmdStan User's Guide this is referred to as num_warmup.
iter_sampling: (positive integer) The number of post-warmup iterations to run per chain. Note: in the CmdStan User's Guide this is referred to as num_samples.
adapt_delta: (real in (0,1)) The adaptation target acceptance statistic.
max_treedepth: (positive integer) The maximum allowed tree depth for the NUTS engine. See the Tree Depth section of the CmdStan User's Guide for more details.
step_size: (positive real) The initial step size for the discrete approximation to continuous Hamiltonian dynamics. This is further tuned during warmup.
parallel_chains: (positive integer) The maximum number of MCMC chains to run in parallel. If parallel_chains is not specified then the default is to look for the option "mc.cores", which can be set for an entire R session by options(mc.cores=value). If the "mc.cores" option has not been set then the default is 1.
threads_per_chain: (positive integer) If the model was compiled with threading support, the number of threads to use in parallelized sections within an MCMC chain (e.g., when using the Stan functions reduce_sum() or map_rect()). This is in contrast with parallel_chains, which specifies the number of chains to run in parallel. The actual number of CPU cores used is parallel_chains*threads_per_chain. For an example of using threading see the Stan case study Reduce Sum: A Minimal Example.
seed: (positive integer(s)) A seed for the (P)RNG to pass to CmdStan. In the case of multi-chain sampling the single seed will automatically be augmented by the the run (chain) ID so that each chain uses a different seed. The exception is the transformed data block, which defaults to using same seed for all chains so that the same data is generated for all chains if RNG functions are used. The only time seed should be specified as a vector (one element per chain) is if RNG functions are used in transformed data and the goal is to generate different data for each chain.
refresh: (non-negative integer) The number of iterations between printed screen updates. If refresh = 0, only error messages will be printed.
show_messages: (logical) When TRUE (the default), prints all output during the execution process, such as iteration numbers and elapsed times. If the output is silenced then the $output() method of the resulting fit object can be used to display the silenced messages.
show_exceptions: (logical) When TRUE (the default), prints all informational messages, for example rejection of the current proposal. Disable if you wish to silence these messages, but this is not usually recommended unless you are very confident that the model is correct up to numerical error. If the messages are silenced then the $output() method of the resulting fit object can be used to display the silenced messages.
...: Further arguments to pass to cmdstanr.

Value

A list with settings for the MCMC sampler.