Sample posterior of TMB models in SAMtool

A convenient wrapper function (posterior) to sample the posterior using MCMC in rstan and returns a stanfit object for diagnostics. Use RCMstan to update the RCM and the enclosed operating model with MCMC samples..

Usage

posterior(x, ...)

# S4 method for RCModel
posterior(
  x,
  priors_only = FALSE,
  laplace = FALSE,
  chains = 2,
  iter = 2000,
  warmup = floor(iter/2),
  thin = 5,
  seed = 34,
  init = "last.par.best",
  cores = chains,
  ...
)

# S4 method for Assessment
posterior(x, priors_only = FALSE, ...)

RCMstan(RCModel, stanfit, sim, cores = 1, silent = FALSE)

Arguments

x: An object of class Assessment or RCModel.
...: Additional arguments to pass to rstan::sampling via tmbstan::tmbstan.
priors_only: Logical, whether to set the likelihood to zero and sample the priors only.
laplace: Logical, whether to do the Laplace approximation for random parameters.
chains: The numer of MCMC chains.
iter: The number of iterations for each chain, including warmup.
warmup: The number of burnin iterations
thin: The frequency at which iterations are kept (e.g., 5 saves every fifth iteration)
seed: Seed for random number generator during the MCMC.
init: The initial values of parameters for starting the MCMC chain. See tmbstan::tmbstan.
cores: The number of cores for running in parallel, e.g., one core per MCMC chain. Used in RCMstan for reconstructing the population.
RCModel: An object of class RCModel
stanfit: An object of class stanfit returned by posterior.
sim: A matrix of RCModel@OM@nsim rows and 2 columns that specifies the samples used to update the operating model. The first column specifies the chain and the second columns specifies the MCMC iteration.
silent: Logical to indicate if progress messages should be printed to console.

Value

posterior returns an object of class stanfit. See class?stanfit.

RCMstan returns an updated RCModel.

Online Documentation

A vignette on the steps to run the MCMC is available on the openMSE website.

Author

Q. Huynh