stan_sample — stan_sample • StanEstimators

Estimate parameters using Stan's sampling algorithms

Usage

stan_sample(
  fn,
  par_inits = NULL,
  n_pars = NULL,
  additional_args = list(),
  algorithm = "hmc",
  engine = "nuts",
  grad_fun = NULL,
  lower = -Inf,
  upper = Inf,
  eval_standalone = (parallel_chains > 1),
  globals = TRUE,
  packages = NULL,
  seed = NULL,
  refresh = NULL,
  quiet = FALSE,
  output_dir = NULL,
  output_basename = NULL,
  sig_figs = NULL,
  num_chains = 4,
  parallel_chains = 1,
  num_samples = 1000,
  num_warmup = 1000,
  save_warmup = NULL,
  thin = NULL,
  adapt_engaged = NULL,
  adapt_gamma = NULL,
  adapt_delta = NULL,
  adapt_kappa = NULL,
  adapt_t0 = NULL,
  adapt_init_buffer = NULL,
  adapt_term_buffer = NULL,
  adapt_window = NULL,
  int_time = NULL,
  max_treedepth = NULL,
  metric = NULL,
  metric_file = NULL,
  stepsize = NULL,
  stepsize_jitter = NULL,
  check_diagnostics = TRUE
)

Arguments

fn: Function to estimate parameters for
par_inits: Initial values for parameters. This can either be a numeric vector of initial values (which will be used for all chains), a list of numeric vectors (of length equal to the number of chains), a function taking a single argument (the chain ID) and returning a numeric vector of initial values, or NULL (in which case Stan will generate initial values automatically). (must be specified if n_pars is NULL)
n_pars: Number of parameters to estimate (must be specified if par_inits is NULL)
additional_args: List of additional arguments to pass to the function
algorithm: (string) The sampling algorithm. One of "hmc" or "fixed_param".
engine: (string) The HMC engine to use, one of "nuts" or "static"
grad_fun: Function calculating gradients w.r.t. each parameter
lower: Lower bound constraint(s) for parameters
upper: Upper bound constraint(s) for parameters
eval_standalone: (logical) Whether to evaluate the function in a separate R session. Defaults to (parallel_chains > 1). Must be TRUE if parallel_chains > 1.
globals: (optional) a logical, a character vector, or a named list to control how globals are handled when evaluating functions in a separate R session. Ignored if eval_standalone = FALSE. For details, see section 'Globals used by future expressions' in the help for future::future().
packages: (optional) a character vector specifying packages to be attached in the R environment evaluating the function. Ignored if eval_standalone = FALSE.
seed: Random seed
refresh: Number of iterations for printing
quiet: (logical) Whether to suppress Stan's output
output_dir: Directory to store outputs
output_basename: Basename to use for output files
sig_figs: Number of significant digits to use for printing
num_chains: (positive integer) The number of Markov chains to run. The default is 4.
parallel_chains: (positive integer) The number of chains to run in parallel, the default is 1.
num_samples: (positive integer) The number of post-warmup iterations to run per chain.
num_warmup: (positive integer) The number of warmup iterations to run per chain.
save_warmup: (logical) Should warmup iterations be saved? The default is FALSE.
thin: (positive integer) The period between saved samples. This should typically be left at its default (no thinning) unless memory is a problem.
adapt_engaged: (logical) Do warmup adaptation? The default is TRUE.
adapt_gamma: (positive real) Adaptation regularization scale.
adapt_delta: (real in (0,1)) The adaptation target acceptance statistic.
adapt_kappa: (positive real) Adaptation relaxation exponent.
adapt_t0: (positive real) Adaptation iteration offset.
adapt_init_buffer: (nonnegative integer) Width of initial fast timestep adaptation interval during warmup.
adapt_term_buffer: (nonnegative integer) Width of final fast timestep adaptation interval during warmup.
adapt_window: (nonnegative integer) Initial width of slow timestep/metric adaptation interval.
int_time: (positive real) Total integration time
max_treedepth: (positive integer) The maximum allowed tree depth for the NUTS engine.
metric: (string) One of "diag_e", "dense_e", or "unit_e", specifying the geometry of the base manifold.
metric_file: (character vector) The paths to JSON or Rdump files (one per chain) compatible with CmdStan that contain precomputed inverse metrics.
stepsize: (positive real) The initial step size for the discrete approximation to continuous Hamiltonian dynamics.
stepsize_jitter: (real in (0,1)) Allows step size to be “jittered” randomly during sampling to avoid any poor interactions with a fixed step size and regions of high curvature.
check_diagnostics: (logical) Whether to check for common problems with HMC sampling (divergent transitions, max tree depth hits, and low Bayesian fraction of missing information). Default is TRUE.

Value

StanMCMC object