Methods for posterior inference of states and parameters.
# S3 method for ssm_sde
run_mcmc(
model,
iter,
nsim,
output_type = "full",
mcmc_type = "da",
L_c,
L_f,
burnin = floor(iter/2),
thin = 1,
gamma = 2/3,
target_acceptance = 0.234,
S,
end_adaptive_phase = TRUE,
n_threads = 1,
seed = sample(.Machine$integer.max, size = 1),
...
)Model model.
Number of MCMC iterations.
Number of state samples per MCMC iteration.
Either "full"
(default, returns posterior samples of states alpha and hyperparameters theta),
"theta" (for marginal posterior of theta),
or "summary" (return the mean and variance estimates of the states
and posterior samples of theta). If nsim = 0, this is argument ignored and set to "theta".
What MCMC algorithm to use? Possible choices are
"pm" for pseudo-marginal MCMC,
"da" for delayed acceptance version of PMCMC (default),
or one of the three importance sampling type weighting schemes:
"is3" for simple importance sampling (weight is computed for each MCMC iteration independently),
"is2" for jump chain importance sampling type weighting, or
"is1" for importance sampling type weighting where the number of particles used for
weight computations is proportional to the length of the jump chain block.
Integer values defining the discretization levels for first and second stages (defined as 2^L). For PM methods, maximum of these is used.
Length of the burn-in period which is disregarded from the
results. Defaults to iter / 2.
Thinning rate. Defaults to 1. Increase for large models in
order to save memory. For IS-corrected methods, larger
value can also be statistically more effective.
Note: With output_type = "summary", the thinning does not affect the computations
of the summary statistics in case of pseudo-marginal methods.
Tuning parameter for the adaptation of RAM algorithm. Must be between 0 and 1 (not checked).
Target acceptance ratio for RAM. Defaults to 0.234.
Initial value for the lower triangular matrix of RAM algorithm, so that the covariance matrix of the Gaussian proposal distribution is \(SS'\). Note that for some parameters (currently the standard deviation and dispersion parameters of bsm_ng models) the sampling is done for transformed parameters with internal_theta = log(theta).
If TRUE (default), $S$ is held fixed after the burnin period.
Number of threads for state simulation.
Seed for the random number generator.
Ignored.