stan_optimize — stan_optimize • StanEstimators

Estimate parameters using Stan's optimization algorithms

Usage

stan_optimize(
  fn,
  par_inits = NULL,
  n_pars = NULL,
  additional_args = list(),
  algorithm = "lbfgs",
  grad_fun = NULL,
  lower = -Inf,
  upper = Inf,
  eval_standalone = FALSE,
  globals = TRUE,
  packages = NULL,
  seed = NULL,
  refresh = NULL,
  quiet = FALSE,
  output_dir = NULL,
  output_basename = NULL,
  sig_figs = NULL,
  save_iterations = NULL,
  jacobian = NULL,
  init_alpha = NULL,
  iter = NULL,
  tol_obj = NULL,
  tol_rel_obj = NULL,
  tol_grad = NULL,
  tol_rel_grad = NULL,
  tol_param = NULL,
  history_size = NULL
)

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 optimization algorithm. One of "lbfgs", "bfgs", or "newton".
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 FALSE.
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
save_iterations: Save optimization iterations to output file
jacobian: (logical) Whether or not to use the Jacobian adjustment for constrained variables. For historical reasons, the default is FALSE, meaning optimization yields the (regularized) maximum likelihood estimate. Setting it to TRUE yields the maximum a posteriori estimate.
init_alpha: (positive real) The initial step size parameter.
iter: (positive integer) The maximum number of iterations.
tol_obj: (positive real) Convergence tolerance on changes in objective function value.
tol_rel_obj: (positive real) Convergence tolerance on relative changes in objective function value.
tol_grad: (positive real) Convergence tolerance on the norm of the gradient.
tol_rel_grad: (positive real) Convergence tolerance on the relative norm of the gradient.
tol_param: (positive real) Convergence tolerance on changes in parameter value.
history_size: (positive integer) The size of the history used when approximating the Hessian. Only available for L-BFGS.

Value

StanOptimize object