run_emc: Fine-Tuned Model Estimation

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Fine-Tuned Model Estimation

Description

Although typically users will rely on fit, this function can be used for more fine-tuned specification of estimation needs. The function will throw an error if a stage is skipped, the stages have to be run in order ("preburn", "burn", "adapt", "sample"). More details can be found in the fit help files (?fit).

Usage

run_emc(
  emc,
  stage,
  stop_criteria,
  search_width = 1,
  step_size = 100,
  verbose = FALSE,
  verboseProgress = FALSE,
  fileName = NULL,
  particle_factor = 50,
  cores_per_chain = 1,
  cores_for_chains = length(emc),
  max_tries = 20,
  n_blocks = 1,
  thin = FALSE,
  trim = TRUE
)

Arguments

emc	An emc object
stage	A string. Indicates which stage is to be run, either preburn, burn, adapt or sample
stop_criteria	A list. Defines the stopping criteria and for which types of parameters these should hold. See ?fit.
search_width	A double. Tunes target acceptance probability of the MCMC process. This fine-tunes the width of the search space to obtain the desired acceptance probability. 1 is the default width, increases lead to broader search.
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.
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.
thin	A boolean. If TRUE will automatically thin the MCMC samples, closely matched to the ESS. Can also be set to a double, in which case 1/thin of the chain will be removed (does not have to be an integer).
trim	A boolean. If TRUE will automatically remove redundant samples (i.e. from preburn, burn, adapt).

Value

An emc object

Examples

# First define a design
design_in <- design(data = forstmann,model=DDM,
                           formula =list(v~0+S,a~E, t0~1, s~1, Z~1),
                           constants=c(s=log(1)))
# Then make the emc, we've omitted a prior here for brevity so default priors will be used.
emc <- make_emc(forstmann, design_in)

# Now for example we can specify that we only want to run the "preburn" phase
# for MCMC 10 iterations
emc <- run_emc(emc, stage = "preburn", stop_criteria = list(iter = 10), cores_for_chains = 1)

EMC2 documentation built on April 11, 2025, 5:50 p.m.