# lmBF: Function to compute Bayes factors for specific linear models In BayesFactor: Computation of Bayes Factors for Common Designs

 lmBF 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)

`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)
``````

BayesFactor documentation built on Sept. 22, 2023, 1:06 a.m.