parSetFactory: Advanced control over JAGS on parallel workers

In datacloning/dclone: Data Cloning and MCMC Tools for Maximum Likelihood Methods

Advanced control over JAGS on parallel workers

Description

JAGS modules contain factory objects for samplers, monitors, and random number generators for a JAGS model. These functions allow fine-grained control over which factories are active on parallel workers.

Usage

parListFactories(cl, type)
parSetFactory(cl, name, type, state)

Arguments

cl	a cluster object created by the parallel package.
name	name of the factory to set
type	type of factory to query or set. Possible values are "sampler", "monitor", or "rng"
state	a logical. If TRUE then the factory will be active, otherwise the factory will become inactive.

Value

parListFactories returns a a list of data frame with two columns per each worker, the first column shows the names of the factory objects in the currently loaded modules, and the second column is a logical vector indicating whether the corresponding factory is active or not.

sparStFactory is called to change the future behaviour of factory objects. If a factory is set to inactive then it will be skipped.

Note

When a module is loaded, all of its factory objects are active. This is also true if a module is unloaded and then reloaded.

Author(s)

Peter Solymos, [e-mail]

See Also

list.modules, set.factory

Examples

## Not run: 
if (require(rjags)) {
cl <- makePSOCKcluster(3)
parListFactories(cl, "sampler")
parListFactories(cl, "monitor")
parListFactories(cl, "rng")
parSetFactory(cl, "base::Slice", "sampler", FALSE)
parListFactories(cl, "sampler")
parSetFactory(cl, "base::Slice", "sampler", TRUE)
stopCluster(cl)
}

## End(Not run)

datacloning/dclone documentation built on Jan. 7, 2023, 2:38 p.m.