run_adapt: Runs adapt stage for emc.

Description

Special instance of run_emc, with default arguments specified for completing adaptation.

Usage

run_adapt(
  emc,
  stop_criteria = NULL,
  p_accept = 0.8,
  step_size = 100,
  verbose = FALSE,
  verboseProgress = FALSE,
  fileName = NULL,
  particles = NULL,
  particle_factor = 50,
  cores_per_chain = 1,
  cores_for_chains = length(emc),
  max_tries = 20,
  n_blocks = 1
)

Value

An emc object

Arguments

emc: An emc object
stop_criteria: A list. Defines the stopping criteria and for which types of parameters these should hold. See ?fit.
p_accept: A double. The target acceptance probability of the MCMC process. This fine-tunes the width of the search space to obtain the desired acceptance probability. Defaults to .8
step_size: An integer. After each step, the stopping requirements as specified by stop_criteria are checked and proposal distributions are updated. Defaults to 100.
verbose: Logical. Whether to print messages between each step with the current status regarding the stop_criteria.
verboseProgress: Logical. Whether to print a progress bar within each step or not. Will print one progress bar for each chain and only if cores_for_chains = 1.
fileName: A string. If specified will autosave emc at this location on every iteration.
particles: An integer. How many particles to use, default is NULL and particle_factor is used instead. If specified will override particle_factor.
particle_factor: An integer. particle_factor multiplied by the square root of the number of sampled parameters determines the number of particles used.
cores_per_chain: An integer. How many cores to use per chain. Parallelizes across participant calculations. Only available on Linux or Mac OS. For Windows, only parallelization across chains (cores_for_chains) is available.
cores_for_chains: An integer. How many cores to use across chains. Defaults to the number of chains. the total number of cores used is equal to cores_per_chain * cores_for_chains.
max_tries: An integer. How many times should it try to meet the finish conditions as specified by stop_criteria? Defaults to 20. max_tries is ignored if the required number of iterations has not been reached yet.
n_blocks: An integer. Number of blocks. Will block the parameter chains such that they are updated in blocks. This can be helpful in extremely tough models with a large number of parameters.