BFBayesFactor-class | R Documentation |

The `BFBayesFactor`

class is a general S4 class for representing models model comparison via Bayes factor.

## S4 method for signature 'numeric,BFBayesFactor' e1 / e2 ## S4 method for signature 'BFBayesFactor,BFBayesFactor' e1 / e2 ## S4 method for signature 'BFBayesFactor,index,missing,missing' x[i, j, ..., drop = TRUE] ## S4 method for signature 'BFBayesFactor' t(x) ## S4 method for signature 'BFBayesFactor' which.max(x) ## S4 method for signature 'BFBayesFactor' which.min(x) ## S4 method for signature 'BFBayesFactor' is.na(x) ## S4 method for signature 'BFBayesFactor,BFodds' e1 * e2 ## S4 method for signature 'BFBayesFactorTop,index,missing,missing' x[i, j, ..., drop = TRUE]

`e1` |
Numerator of the ratio |

`e2` |
Denominator of the ratio |

`x` |
BFBayesFactor object |

`i` |
indices indicating elements to extract |

`j` |
unused for BFBayesFactor objects |

`...` |
further arguments passed to related methods |

`drop` |
unused |

`BFBayesFactor`

objects can be inverted by taking the reciprocal and can
be divided by one another, provided both objects have the same denominator. In addition,
the `t`

(transpose) method can be used to invert Bayes factor objects.

The `BFBayesFactor`

class has the following slots defined:

- numerator
a list of models all inheriting

`BFmodel`

, each providing a single denominator- denominator
a single

`BFmodel`

object serving as the denominator for all model comparisons- bayesFactor
a data frame containing information about the comparison between each numerator and the denominator

- data
a data frame containing the data used for the comparison

- version
character string giving the version and revision number of the package that the model was created in

## Compute some Bayes factors to demonstrate division and indexing data(puzzles) bfs <- anovaBF(RT ~ shape*color + ID, data = puzzles, whichRandom = "ID", progress=FALSE) ## First and second models can be separated; they remain BFBayesFactor objects b1 = bfs[1] b2 = bfs[2] b1 ## We can invert them, or divide them to obtain new model comparisons 1/b1 b1 / b2 ## Use transpose to create a BFBayesFactorList t(bfs)

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