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

## Description

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

## Usage

 ```1 2 3``` ```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

 ``` 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) ```

### Example output

```Loading required package: coda
************

Type BFManual() to open the manual.
************
Bayes factor analysis
--------------
 shape + color + ID : 2.771108 <U+00B1>2.84%

Against denominator:
RT ~ shape + color + shape:color + ID
---
Bayes factor type: BFlinearModel, JZS
```

BayesFactor documentation built on May 2, 2019, 7 a.m.