Determines the options (number of steps, save interval, etc.) for running MCMC based on the estimated mode (maximum likelihood estimate) and parameter variance-covariance matrix
mcmc.control(mcmc = 1000, mcmc2 = 0, mcsave, mcnoscale = FALSE,
mcgrope = FALSE, mcmult = 1, mcmcpars = NULL)Returns a list of options suitable for passing as the
mcmc.opts argument to do_admb
Total number of MCMC steps
MCMC2 steps (see ADMB-RE manual)
Thinning interval for values saved in the PSV file. Default is
pmax(1,floor(mcmc/1000)), i.e. aim to save 1000 steps
don't rescale step size for mcmc depending on acceptance rate
(double) Use a candidate distribution that is a mixture of a
multivariate normal and a fatter-tailed distribution with a proportion
mcmcgrope of the fatter-tailed distribution; the ADMB manual suggests
values of mcgrope between 0.05 and 0.1
Multiplier for the MCMC candidate distribution
(character) vector of parameters to track in MCMC run.
At least one must be specified. ADMB produces two kinds of output for
MCMC. For any sdreport parameters it will produce a hst file
that contains a summary histogram; mcmcpars constructs appropriate
sdreport parameters in the auto-generated TPL file. Step-by-step
output for all parameters (regulated by mcsave) is saved in the
PSV file.
Ben Bolker
See the AD Model Builder reference manual. The mcrb option (reduce
correlation of the Hessian when constructing the candidate distribution) and
the mcseed options (seed for random number generator) are not yet
implemented; mcnoscale above may not work properly