This function computes Bayes factors, or samples from the posterior, of specific linear models (either ANOVA or regression).

1 2 3 |

`formula` |
a formula containing all factors to include in the analysis (see Examples) |

`data` |
a data frame containing data for all factors in the formula |

`whichRandom` |
a character vector specifying which factors are random |

`rscaleFixed` |
prior scale for standardized, reduced fixed effects. A number of preset values can be given as strings; see Details. |

`rscaleRandom` |
prior scale for standardized random effects |

`rscaleCont` |
prior scale for standardized slopes. A number of preset values can be given as strings; see Details. |

`rscaleEffects` |
A named vector of prior settings for individual factors, overriding rscaleFixed and rscaleRandom. Values are scales, names are factor names. |

`posterior` |
if |

`progress` |
if |

`...` |
further arguments to be passed to or from methods. |

This function provides an interface for computing Bayes factors for
specific linear models against the intercept-only null; other tests may be
obtained by computing two models and dividing their Bayes factors. Specifics
about the priors for regression models – and possible settings for
`rscaleCont`

– can be found in the help for `regressionBF`

;
likewise, details for ANOVA models – and settings for `rscaleFixed`

and `rscaleRandom`

– can be found in the help for `anovaBF`

.

Currently, the function does not allow for general linear models, containing both continuous and categorical predcitors, but this support will be added in the future.

If `posterior`

is `FALSE`

, an object of class
`BFBayesFactor`

, containing the computed model comparisons is
returned. Otherwise, an object of class `BFmcmc`

, containing MCMC
samples from the posterior is returned.

Richard D. Morey (richarddmorey@gmail.com)

`regressionBF`

and `anovaBF`

for
testing many regression or ANOVA models simultaneously.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## Puzzles data; see ?puzzles for details
data(puzzles)
## Bayes factor of full model against null
bfFull = lmBF(RT ~ shape + color + shape:color + ID, data = puzzles, whichRandom = "ID")
## Bayes factor of main effects only against null
bfMain = lmBF(RT ~ shape + color + ID, data = puzzles, whichRandom = "ID")
## Compare the main-effects only model to the full model
bfMain / bfFull
## sample from the posterior of the full model
samples = lmBF(RT ~ shape + color + shape:color + ID,
data = puzzles, whichRandom = "ID", posterior = TRUE,
iterations = 1000)
## Aother way to sample from the posterior of the full model
samples2 = posterior(bfFull, iterations = 1000)
``` |

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