R: Recompute a Bayes factor computation or MCMC object.

recompute {BayesFactor} | R Documentation

## 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 = getOption("BFprogress", interactive()),
      multicore = FALSE, callback = function(...) as.integer(0), ...)

    ## S4 method for signature 'BFBayesFactor'
    recompute(x, progress = getOption("BFprogress",
      interactive()), multicore = FALSE, callback = function(...) as.integer(0),
      ...)

    ## S4 method for signature 'BFBayesFactorTop'
    recompute(x, progress = getOption("BFprogress",
      interactive()), multicore = FALSE, callback = function(...) as.integer(0),
      ...)

    ## S4 method for signature 'BFmcmc'
    recompute(x, progress = getOption("BFprogress",
      interactive()), multicore = FALSE, callback = function(...) as.integer(0),
      ...)

    ## S4 method for signature 'BFodds'
    recompute(x, progress = getOption("BFprogress",
      interactive()), 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)

[Package BayesFactor version 0.9.12-4.2 Index]