R: Sample from the posterior distribution of one of several...

posterior {BayesFactor} | R Documentation

## Sample from the posterior distribution of one of several models.

### Description

This function samples from the posterior distribution of a `BFmodel`, which can be obtained from a `BFBayesFactor` object. If there is more than one numerator in the `BFBayesFactor` object, the `index` argument can be passed to select one numerator.

### Usage

    posterior(model, index, data, iterations, ...)

    ## S4 method for signature 'BFmodel,missing,data.frame,missing'
    posterior(model, index, data,
      iterations, ...)

    ## S4 method for signature 'BFBayesFactor,missing,missing,missing'
    posterior(model, index, data,
      iterations, ...)

    ## S4 method for signature 'BFBayesFactor,numeric,missing,numeric'
    posterior(model, index, data,
      iterations, ...)

    ## S4 method for signature 'BFBayesFactor,missing,missing,numeric'
    posterior(model,
      index = NULL, data, iterations, ...)

    ## S4 method for signature 'BFlinearModel,missing,data.frame,numeric'
    posterior(model,
      index = NULL, data, iterations, ...)

    ## S4 method for signature 'BFindepSample,missing,data.frame,numeric'
    posterior(model,
      index = NULL, data, iterations, ...)

    ## S4 method for signature 'BFcontingencyTable,missing,data.frame,numeric'
    posterior(model,
      index = NULL, data, iterations, ...)

    ## S4 method for signature 'BFoneSample,missing,data.frame,numeric'
    posterior(model,
      index = NULL, data, iterations, ...)

    ## S4 method for signature 'BFmetat,missing,data.frame,numeric'
    posterior(model, index = NULL,
      data, iterations, ...)

    ## S4 method for signature 'BFproportion,missing,data.frame,numeric'
    posterior(model,
      index = NULL, data, iterations, ...)

    ## S4 method for signature 'BFcorrelation,missing,data.frame,numeric'
    posterior(model,
      index = NULL, data, iterations, ...)

### Arguments

`model` | or set of models from which to sample
`index` | the index within the set of models giving the desired model
`data` | the data to be conditioned on
`iterations` | the number of iterations to sample
`...` | arguments passed to and from related methods

### Details

The data argument is used internally, and will y not be needed by end-users.

Note that if there are fixed effects in the model, the reduced parameterzation used internally (see help for `anovaBF`) is unreduced. For a factor with two levels, the chain will contain two effect estimates that sum to 0.

Two useful arguments that can be passed to related methods are `thin` and `columnFilter`, currently implemented for methods using `nWayAOV` (models with more than one categorical covariate, or a mix of categorical and continuous covariates). `thin`, an integer, will keep only every `thin` iterations. The default is `thin=1`, which keeps all iterations. Argument `columnFilter` is either `NULL` (for no filtering) or a character vector of extended regular expressions (see regex help for details). Any column from an effect that matches one of the filters will not be saved.

### Value

Returns an object containing samples from the posterior distribution of the specified model

### Examples

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

    bf = lmBF(extra ~ group + ID, data = sleep, whichRandom="ID", progress=FALSE)

    ## sample from the posterior of the numerator model
    ## data argument not needed - it is included in the Bayes factor object
    chains = posterior(bf, iterations = 1000, progress = FALSE)

    plot(chains)

    ## demonstrate column filtering by filtering out participant effects
    data(puzzles)
    bf = lmBF(RT ~ shape + color + shape:color + ID, data=puzzles)
    chains = posterior(bf, iterations = 1000, progress = FALSE, columnFilter="^ID$")
    colnames(chains) # Contains no participant effects

[Package BayesFactor version 0.9.12-4.2 Index]