R: Function to compute Bayes factors for specific linear models

lmBF {BayesFactor} | R Documentation

## Function to compute Bayes factors for specific linear models

### Description

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

### Usage

    lmBF(formula, data, whichRandom = NULL, rscaleFixed = "medium",
      rscaleRandom = "nuisance", rscaleCont = "medium", rscaleEffects = NULL,
      posterior = FALSE, progress = getOption("BFprogress", interactive()), ...)

### Arguments

`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 `TRUE`, return samples from the posterior distribution instead of the Bayes factor
`progress` | if `TRUE`, show progress with a text progress bar
`...` | further arguments to be passed to or from methods.

### Details

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.

### Value

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.

### Author(s)

Richard D. Morey (richarddmorey@gmail.com)

### See Also

`regressionBF` and `anovaBF` for testing many regression or ANOVA models simultaneously.

### Examples

    ## 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)

[Package BayesFactor version 0.9.12-4.2 Index]