[Deprecated] Draw MCMC samples from a model posterior using a Random Walk Metropolis (RWM) sampler.

Source: R/sample_tmb_deprecated.R

[Deprecated] Draw MCMC samples from a model posterior using a Random Walk Metropolis (RWM) sampler.

sample_tmb_rwm(
  iter,
  fn,
  init,
  alpha = 1,
  chain = 1,
  warmup = floor(iter/2),
  thin = 1,
  seed = NULL,
  control = NULL
)

Arguments

iter

The number of samples to draw.
fn

A function that returns the log of the posterior density.
init

A list of lists containing the initial parameter vectors, one for each chain or a function. It is strongly recommended to initialize multiple chains from dispersed points. A of NULL signifies to use the starting values present in the model (i.e., obj$par) for all chains.
alpha

The amount to scale the proposal, i.e, Xnew=Xcur+alpha*Xproposed where Xproposed is generated from a mean-zero multivariate normal. Varying alpha varies the acceptance rate.
chain

The chain number, for printing only.
warmup

The number of warmup iterations.
thin

The thinning rate to apply to samples. Typically not used with NUTS.
seed

The random seed to use.
control

A list to control the sampler. See details for further use.

Value

A list containing samples and other metadata.

Details

This algorithm does not yet contain adaptation of alpha so some trial and error may be required for efficient sampling.

References

Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E., 1953. Equation of state calculations by fast computing machines. J Chem Phys. 21:1087-1092.

See also

sample_tmb