Deprecated version of wrapper function. Use sample_nuts or sample_rwm instead.

Usage

sample_admb(
  model,
  path = getwd(),
  iter = 2000,
  init = NULL,
  chains = 3,
  warmup = NULL,
  seeds = NULL,
  thin = 1,
  mceval = FALSE,
  duration = NULL,
  parallel = FALSE,
  cores = NULL,
  control = NULL,
  skip_optimization = TRUE,
  algorithm = "NUTS",
  skip_monitor = FALSE,
  skip_unbounded = TRUE,
  admb_args = NULL
)

Arguments

model: Name of model (i.e., 'model' for model.tpl). For non-Windows systems this will automatically be converted to './model' internally. For Windows, long file names are sometimes shortened from e.g., 'long_model_filename' to 'LONG_~1'. This should work, but will throw warnings. Please shorten the model name. See https://en.wikipedia.org/wiki/8.3_filename.
path: Path to model executable. Defaults to working directory. Often best to have model files in a separate subdirectory, particularly for parallel.
iter: The number of samples to draw.
init: Can be either a list containing a vector for each chain, a function which returns a vector of parameters, or NULL which specifies to use the MLE as stored in the admodel.hes file. It is generally recommended to use dispersed initial values to improve diagnostic checks (starting from the same point makes it less likely to find multiple modes).
chains: The number of chains to run.
warmup: The number of warmup iterations.
seeds: A vector of seeds, one for each chain.
thin: The thinning rate to apply to samples. Typically not used with NUTS.
mceval: Whether to run the model with -mceval on samples from merged chains.
duration: The number of minutes after which the model will quit running. It is recommended to set the warmup carefully and iter higher than expected so it runs through duration. This usually results in chains with different lengths, so the minimum is taken across them all.
parallel: A deprecated argument, use cores=1 for serial execution or cores>1 for parallel (default is to parallel with cores equal to the available-1)
cores: The number of cores to use for parallel execution. Default is number available in the system minus 1. If cores=1, serial execution occurs (even if chains>1), otherwise parallel execution via package snowfall is used. For slow analyses it is recommended to set chains<=cores so each core needs to run only a single chain.
control: A list to control the sampler. See details for further use.
skip_optimization: Whether to run the optimizer before running MCMC. This is rarely need as it is better to run it once before to get the covariance matrix, or the estimates are not needed with adaptive NUTS.
algorithm: The algorithm to use, one of "NUTS" or "RWM"
skip_monitor: Whether to skip calculating diagnostics (effective sample size, Rhat) via the rstan::monitor function. This can be slow for models with high dimension or many iterations. The result is used in plots and summaries so it is recommended to turn on. If model run with skip_monitor=FALSE you can recreate it post-hoc by setting fit$monitor=rstan::monitor(fit$samples, fit$warmup, print=FALSE).
skip_unbounded: Whether to skip returning the unbounded version of the posterior samples in addition to the bounded ones. It may be advisable to set to FALSE for very large models to save space.
admb_args: A character string which gets passed to the command line, allowing finer control

Warning

This is deprecated and will cease to exist in future releases