Usage
autojags(data, inits, parameters.to.save, model.file,
n.chains, n.adapt=100, iter.increment=1000, n.burnin=0, n.thin=1,
save.all.iter=FALSE, modules=c('glm'), parallel=FALSE, DIC=TRUE,
store.data=FALSE, codaOnly=FALSE,seed=floor(runif(1,1,10000)),
bugs.format=FALSE, Rhat.limit=1.1, max.iter=100000, verbose=TRUE)Arguments
data
A named list of the data objects required by the model, or a character vector containing the names of the data objects required by the model.
inits
A list with n.chains elements; each element of the
list is itself a list of starting values for the BUGS model,
or a function creating (possibly random) initial values. If inits is
NULL,
parameters.to.save
Character vector of the names of the
parameters in the model which should be monitored.
model.file
Path to file containing the model written in BUGS code
n.chains
Number of Markov chains to run.
n.adapt
Number of iterations to run in the JAGS adaptive phase. Sometimes JAGS chooses not to run these iterations; therefore they are separated from the burn-in in this package.
iter.increment
Number of iterations per model auto-update. Set to larger values when you suspect the model will take a long time to converge.
n.burnin
Number of iterations at the beginning of the chain to discard (i.e., the burn-in). Does not include the adaptive phase iterations.
n.thin
Thinning rate. Must be a positive integer.
save.all.iter
Option to combine MCMC samples from all iterative updates into final posterior (by default only the final iteration is included in the posterior).
modules
List of JAGS modules to load before analysis. By default only module 'glm' is loaded (in addition to 'basemod' and 'bugs'). To force no additional modules to load, set modules=NULL.
parallel
If TRUE, run MCMC chains in parallel on multiple CPU cores. Each chain is assigned to a different core, so n.chains must be
DIC
Option to report DIC and the estimated number of parameters (pD). Defaults to TRUE.
store.data
Option to store the input dataset and initial values in the output object for future use. Defaults to FALSE.
codaOnly
Optional character vector of parameter names for which you do NOT want to calculate detailed statistics. This may be helpful when you have many output parameters (e.g., predicted values) and you want to save time. For these parameters, only the mean value
seed
Set a custom seed for the R random number generator and JAGS. The current state of the random number generator is saved in the output object.
bugs.format
Option to print JAGS output in classic R2WinBUGS format. Default is FALSE.
Rhat.limit
Set the desired cutoff point for convergence; when all Rhat values are less than this value the model assumes convergence has been reached and will stop auto-updating.
max.iter
Maximum number of total iterations allowed via auto-update (including burn-in).
verbose
If set to FALSE, all text output in the console will be suppressed as the function runs (including most warnings).