BayesFactor (version 0.9.1)

recompute: Recompute a Bayes factor computation or MCMC object.

Description

Take an object and redo the computation (useful for sampling). In cases where sampling is used to compute the Bayes factor, the estimate of the precision of new samples will be added to the estimate precision of the old sample will be added to produce a new estimate of the precision.

Usage

recompute(x, progress = options()$BFprogress, multicore = FALSE,
  callback = function(...) as.integer(0), ...)
## S3 method for class 'BFBayesFactor':
recompute(x, progress = options()$BFprogress,
  multicore = FALSE, callback = function(...) as.integer(0), ...)
## S3 method for class 'BFBayesFactorTop':
recompute(x, progress = options()$BFprogress,
  multicore = FALSE, callback = function(...) as.integer(0), ...)
## S3 method for class 'BFmcmc':
recompute(x, progress = options()$BFprogress,
  multicore = FALSE, callback = function(...) as.integer(0), ...)
## S3 method for class 'BFodds':
recompute(x, progress = options()$BFprogress,
  multicore = FALSE, callback = function(...) as.integer(0), ...)

Arguments

x

object to recompute

progress

report progress of the computation?

multicore

Use multicore, if available

callback

callback function for third-party interfaces

...

arguments passed to and from related methods

Value

• Returns an object of the same type, after repeating the sampling (perhaps with more iterations)

Examples

## Sample from the posteriors for two models
data(puzzles)

## Main effects model; result is a BFmcmc object, inheriting
## mcmc from the coda package
bf = lmBF(RT ~ shape + color + ID, data = puzzles, whichRandom = "ID",
   progress = FALSE)

## recompute Bayes factor object
recompute(bf, iterations = 1000, progress = FALSE)

## Sample from posterior distribution of model above, and recompute:
chains = posterior(bf, iterations = 1000, progress = FALSE)
newChains = recompute(chains, iterations = 1000, progress=FALSE)