generalTestBF: Function to compute Bayes factors for general designs
In BayesFactor: Computation of Bayes factors for common designs

Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function computes Bayes factors corresponding to restrictions on a full model.

  generalTestBF(formula, data, whichRandom = NULL,
    whichModels = "withmain", neverExclude = NULL,
    iterations = 10000, progress = options()$BFprogress,
    rscaleFixed = "medium", rscaleRandom = "nuisance",
    rscaleCont = "medium", multicore = FALSE,
    method = "auto", noSample = FALSE)

`formula`	a formula containing the full model for 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
`whichModels`	which set of models to compare; see Details
`neverExclude`	a character vector containing a regular expression (see help for regex for details) that indicates which terms to always keep in the analysis
`iterations`	How many Monte Carlo simulations to generate, if relevant
`progress`	if `TRUE`, show progress with a text progress bar
`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
`multicore`	if `TRUE` use multiple cores through the `doMC` package. Unavailable on Windows.
`method`	approximation method, if needed. See `nWayAOV` for details.
`noSample`	if `TRUE`, do not sample, instead returning NA.

See the help for anovaBF and anovaBF or details.

Models, priors, and methods of computation are provided in Rouder et al. (2012) and Liang et al (2008).

An object of class BFBayesFactor, containing the computed model comparisons

The function generalTestBF can compute Bayes factors for all restrictions of a full model against the null hypothesis that all effects are 0. The total number of tests computed – if all tests are requested – will be 2^K - 1 for K factors or covariates. This number increases very quickly with the number of tested predictors. An option is included to prevent testing too many models: options('BFMaxModels'), which defaults to 50,000, is the maximum number of models that will be analyzed at once. This can be increased by increased using options.

It is possible to reduce the number of models tested by only testing the most complex model and every restriction that can be formed by removing one factor or interaction using the whichModels argument. See the help for anovaBF for details.

Richard D. Morey (richarddmorey@gmail.com)

Rouder, J. N., Morey, R. D., Speckman, P. L., Province, J. M., (2012) Default Bayes Factors for ANOVA Designs. Journal of Mathematical Psychology. 56. p. 356-374.

Liang, F. and Paulo, R. and Molina, G. and Clyde, M. A. and Berger, J. O. (2008). Mixtures of g-priors for Bayesian Variable Selection. Journal of the American Statistical Association, 103, pp. 410-423

lmBF, for testing specific models, and regressionBF and anovaBF for other functions for testing multiple models simultaneously.

## Puzzles example: see ?puzzles and ?anovaBF
data(puzzles)
## neverExclude argument makes sure that participant factor ID
## is in all models
result = generalTestBF(RT ~ shape*color + ID, data = puzzles, whichRandom = "ID",
neverExclude="ID", progress=FALSE)
result

Loading required package: coda
Loading required package: Matrix
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Welcome to BayesFactor 0.9.12-2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).

Type BFManual() to open the manual.
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Bayes factor analysis
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[1] shape + ID                       : 313165.3 <U+00B1>1.07%
[2] color + ID                       : 313268.2 <U+00B1>0.9%
[3] shape + color + ID               : 1329350  <U+00B1>1.61%
[4] shape + color + shape:color + ID : 471112.9 <U+00B1>2.15%
[5] ID                               : 111516.6 <U+00B1>0%

Against denominator:
  Intercept only 
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Bayes factor type: BFlinearModel, JZS