mcmc.control {R2admb} | R Documentation |
Control options for MCMC after ADMB fitting
Description
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
Usage
mcmc.control(mcmc = 1000, mcmc2 = 0, mcsave, mcnoscale = FALSE,
mcgrope = FALSE, mcmult = 1, mcmcpars = NULL)
Arguments
mcmc |
Total number of MCMC steps |
mcmc2 |
MCMC2 steps (see ADMB-RE manual) |
mcsave |
Thinning interval for values saved in the PSV file. Default is
|
mcnoscale |
don't rescale step size for mcmc depending on acceptance rate |
mcgrope |
(double) Use a candidate distribution that is a mixture of a
multivariate normal and a fatter-tailed distribution with a proportion
|
mcmult |
Multiplier for the MCMC candidate distribution |
mcmcpars |
(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 |
Details
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
Value
Returns a list of options suitable for passing as the
mcmc.opts
argument to do_admb
Note
Some options (mcmc2
, etc.) that can be used in AD Model Builder
and ADMB-RE may not be available
Author(s)
Ben Bolker
Examples
mcmc.control(mcmc=2000)