Rstan Sampling Options — rstan_sampling

Deprecated; use stan_sampling_opts() instead.

Usage

rstan_sampling_opts(
  cores = getOption("mc.cores", 1L),
  warmup = 250,
  samples = 2000,
  chains = 4,
  control = list(),
  save_warmup = FALSE,
  seed = as.integer(runif(1, 1, 1e+08)),
  future = FALSE,
  max_execution_time = Inf,
  ...
)

Arguments

cores: Number of cores to use when executing the chains in parallel, which defaults to 1 but it is recommended to set the mc.cores option to be as many processors as the hardware and RAM allow (up to the number of chains).
warmup: Numeric, defaults to 250. Number of warmup samples per chain.
samples: Numeric, default 2000. Overall number of posterior samples. When using multiple chains iterations per chain is samples / chains.
chains: Numeric, defaults to 4. Number of MCMC chains to use.
control: List, defaults to empty. control parameters to pass to underlying rstan function. By default adapt_delta = 0.9 and max_treedepth = 12 though these settings can be overwritten.
save_warmup: Logical, defaults to FALSE. Should warmup progress be saved.
seed: Numeric, defaults uniform random number between 1 and 1e8. Seed of sampling process.
future: Logical, defaults to FALSE. Should stan chains be run in parallel using future. This allows users to have chains fail gracefully (i.e when combined with max_execution_time). Should be combined with a call to future::plan().
max_execution_time: Numeric, defaults to Inf (seconds). If set wil kill off processing of each chain if not finished within the specified timeout. When more than 2 chains finish successfully estimates will still be returned. If less than 2 chains return within the allowed time then estimation will fail with an informative error.
...: Additional parameters to pass to rstan::sampling().

Value

A list of arguments to pass to rstan::sampling().