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
```

*BayesFactor*version 0.9.12-4.7 Index]