Nothing

```
##' This function computes Bayes factors for all main-effects and interaction
##' contrasts in an ANOVA design.
##'
##' Models, priors, and methods of computation are provided in Rouder et al.
##' (2012).
##'
##' The ANOVA model for a vector of observations \eqn{y} is \deqn{ y = \mu + X_1
##' \theta_1 + \ldots + X_p\theta_p +\epsilon,} where
##' \eqn{\theta_1,\ldots,\theta_p} are vectors of main-effect and interaction
##' effects, \eqn{X_1,\ldots,X_p} are corresponding design matrices, and
##' \eqn{\epsilon} is a vector of zero-centered noise terms with variance
##' \eqn{\sigma^2}. Zellner and Siow (1980) inspired g-priors are placed on
##' effects, but with a separate g-prior parameter for each covariate:
##' \deqn{\theta_1~N(0,g_1\sigma^2), \ldots, \theta_p~N(0,g_p \sigma^2).} A
##' Jeffries prior is placed on \eqn{\mu} and \eqn{\sigma^2}. Independent
##' scaled inverse-chi-square priors with one degree of freedom are placed on
##' \eqn{g_1,\ldots,g_p}. The square-root of the scale for g's corresponding to
##' fixed and random effects is given by \code{rscaleFixed} and
##' \code{rscaleRandom}, respectively.
##'
##' When a factor is treated as random, there are as many main effect terms in
##' the vector \eqn{\theta} as levels. When a factor is treated as fixed, the
##' sums-to-zero linear constraint is enforced by centering the corresponding
##' design matrix, and there is one fewer main effect terms as levels. The
##' Cornfield-Tukey model of interactions is assumed. Details are provided in
##' Rouder et al. (2012)
##'
##' Bayes factors are computed by integrating the likelihood with respect to the
##' priors on parameters. The integration of all parameters except
##' \eqn{g_1,\ldots,g_p} may be expressed in closed-form; the integration of
##' \eqn{g_1,\ldots,g_p} is performed through Monte Carlo sampling, and
##' \code{iterations} is the number of iterations used to estimate the Bayes
##' factor.
##'
##' \code{anovaBF} computes Bayes factors for either all submodels or select
##' submodels missing a single main effect or covariate, depending on the
##' argument \code{whichModels}. If no random factors are specified, the null
##' model assumed by \code{anovaBF} is the grand-mean only model. If random
##' factors are specified, the null model is the model with an additive model on
##' all random factors, plus a grand mean. Thus, \code{anovaBF} does not
##' currently test random factors. Testing random factors is possible with
##' \code{\link{lmBF}}.
##'
##' The argument \code{whichModels} controls which models are tested. Possible
##' values are 'all', 'withmain', 'top', and 'bottom'. Setting
##' \code{whichModels} to 'all' will test all models that can be created by
##' including or not including a main effect or interaction. 'top' will test all
##' models that can be created by removing or leaving in a main effect or
##' interaction term from the full model. 'bottom' creates models by adding
##' single factors or interactions to the null model. 'withmain' will test all
##' models, with the constraint that if an interaction is included, the
##' corresponding main effects are also included.
##'
##' For the \code{rscaleFixed} and \code{rscaleRandom} arguments, several named
##' values are recognized: "medium", "wide", and "ultrawide", corresponding to
##' \eqn{r} scale values of 1/2, \eqn{\sqrt{2}/2}{sqrt(2)/2}, and 1,
##' respectively. In addition, \code{rscaleRandom} can be set to the "nuisance",
##' which sets \eqn{r=1} (and is thus equivalent to "ultrawide"). The "nuisance"
##' setting is for medium-to-large-sized effects assumed to be in the data but
##' typically not of interest, such as variance due to participants.
##' @title Function to compute Bayes factors for ANOVA designs
##' @param formula a formula containing all factors to include in the analysis
##' (see Examples)
##' @param data a data frame containing data for all factors in the formula
##' @param whichRandom a character vector specifying which factors are random
##' @param whichModels which set of models to compare; see Details
##' @param iterations How many Monte Carlo simulations to generate, if relevant
##' @param progress if \code{TRUE}, show progress with a text progress bar
##' @param rscaleFixed prior scale for standardized, reduced fixed effects. A
##' number of preset values can be given as strings; see Details.
##' @param rscaleRandom prior scale for standardized random effects
##' @param rscaleEffects A named vector of prior settings for individual factors,
##' overriding rscaleFixed and rscaleRandom. Values are scales, names are factor names.
##' @param multicore if \code{TRUE} use multiple cores through the \code{doMC}
##' package. Unavailable on Windows.
##' @param method approximation method, if needed. See \code{\link{nWayAOV}} for
##' details.
##' @param noSample if \code{TRUE}, do not sample, instead returning NA.
##' @return An object of class \code{BFBayesFactor}, containing the computed
##' model comparisons. Bayes factors can be extracted using extractBF(), as.vector()
##' or as.data.frame().
##' @param callback callback function for third-party interfaces
##' @author Richard D. Morey (\email{richarddmorey@@gmail.com})
##' @export
##' @references Gelman, A. (2005) Analysis of Variance---why it is more
##' important than ever. Annals of Statistics, 33, pp. 1-53.
##'
##' 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.
##'
##' Zellner, A. and Siow, A., (1980) Posterior Odds Ratios for Selected
##' Regression Hypotheses. In Bayesian Statistics: Proceedings of the First
##' Interanational Meeting held in Valencia (Spain). Bernardo, J. M.,
##' Lindley, D. V., and Smith A. F. M. (eds), pp. 585-603. University of
##' Valencia.
##'
##' @note The function \code{anovaBF} will compute Bayes factors for all
##' possible combinations of fixed factors and interactions, against the null
##' hypothesis that \emph{all} effects are 0. The total number of tests
##' computed will be \eqn{2^{2^K - 1}}{2^(2^K - 1)} for \eqn{K} fixed factors.
##' This number increases very quickly with the number of factors. For
##' instance, for a five-way ANOVA, the total number of tests exceeds two
##' billion. Even though each test takes a fraction of a second, the time
##' taken for all tests could exceed your lifetime. An option is included to
##' prevent this: \code{options('BFMaxModels')}, which defaults to 50,000, is
##' the maximum number of models that `anovaBF` will analyze at once. This can
##' be increased by increasing the option value.
##'
##' 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 \code{whichModels} argument. Setting
##' this argument to 'top' reduces the number of tests to \eqn{2^K-1}, which
##' is more manageable. The Bayes factor for each restriction against the most
##' complex model can be interpreted as a test of the removed
##' factor/interaction. Setting \code{whichModels} to 'withmain' will not
##' reduce the number of tests as much as 'top' but the results may be more
##' interpretable, since an interaction is only allowed when all interacting
##' effects (main or interaction) are also included in the model.
##'
##' @examples
##' ## Classical example, taken from t.test() example
##' ## Student's sleep data
##' data(sleep)
##' plot(extra ~ group, data = sleep)
##'
##' ## traditional ANOVA gives a p value of 0.00283
##' summary(aov(extra ~ group + Error(ID/group), data = sleep))
##'
##' ## Gives a Bayes factor of about 11.6
##' ## in favor of the alternative hypothesis
##' anovaBF(extra ~ group + ID, data = sleep, whichRandom = "ID",
##' progress=FALSE)
##'
##' ## Demonstrate top-down testing
##' data(puzzles)
##' result = anovaBF(RT ~ shape*color + ID, data = puzzles, whichRandom = "ID",
##' whichModels = 'top', progress=FALSE)
##' result
##'
##' ## In orthogonal designs, the top down Bayes factor can be
##' ## interpreted as a test of the omitted effect
##' @keywords htest
##' @seealso \code{\link{lmBF}}, for testing specific models, and
##' \code{\link{regressionBF}} for the function similar to \code{anovaBF} for
##' linear regression models.
anovaBF <-
function(formula, data, whichRandom = NULL,
whichModels = "withmain", iterations = 10000, progress = getOption('BFprogress', interactive()),
rscaleFixed = "medium", rscaleRandom = "nuisance", rscaleEffects = NULL, multicore = FALSE, method="auto", noSample=FALSE, callback=function(...) as.integer(0))
{
data <- marshallTibble(data)
checkFormula(formula, data, analysis = "anova")
# pare whichRandom down to terms that appear in the formula
whichRandom <- whichRandom[whichRandom %in% fmlaFactors(formula, data)[-1]]
if(all(fmlaFactors(formula, data)[-1] %in% whichRandom)){
# No fixed factors!
bf = lmBF(formula, data, whichRandom, rscaleFixed, rscaleRandom, rscaleEffects = rscaleEffects, progress = progress, method = method, noSample = noSample)
return(bf)
}
dataTypes <- createDataTypes(formula, whichRandom, data, analysis = "anova")
fmla <- createFixedAnovaModel(dataTypes, formula)
models <- enumerateAnovaModels(fmla, whichModels, data)
if(length(models)>getOption('BFMaxModels', 50000)) stop("Maximum number of models exceeded (",
length(models), " > ",getOption('BFMaxModels', 50000) ,"). ",
"The maximum can be increased by changing ",
"options('BFMaxModels').")
if(length(whichRandom) > 0 ){
models <- lapply(models, addRandomModelPart, dataTypes = dataTypes)
models <- c(models, addRandomModelPart(fmla, dataTypes, null=TRUE))
}
if(multicore){
message("Note: Progress bars and callbacks are suppressed when running multicore.")
if(!requireNamespace("doMC", quietly = TRUE)){
stop("Required package (doMC) missing for multicore functionality.")
}
doMC::registerDoMC()
if(foreach::getDoParWorkers()==1){
warning("Multicore specified, but only using 1 core. Set options(cores) to something >1.")
}
bfs <- foreach::"%dopar%"(
foreach::foreach(gIndex=models, .options.multicore=mcoptions),
lmBF(gIndex,data = data, whichRandom = whichRandom,
rscaleFixed = rscaleFixed, rscaleRandom = rscaleRandom,
rscaleEffects = rscaleEffects, iterations = iterations,
method=method, progress=FALSE, noSample = noSample)
)
}else{ # Single core
checkCallback(callback,as.integer(0))
bfs = NULL
myCallback <- function(prgs){
frac <- (i - 1 + prgs/1000)/length(models)
ret <- callback(frac*1000)
return(as.integer(ret))
}
if(progress){
pb = txtProgressBar(min = 0, max = length(models), style = 3)
}else{
pb = NULL
}
for(i in 1:length(models)){
oneModel <- lmBF(models[[i]],data = data, whichRandom = whichRandom,
rscaleFixed = rscaleFixed, rscaleRandom = rscaleRandom,
rscaleEffects = rscaleEffects, iterations = iterations,
progress = FALSE, method = method,
noSample=noSample,callback=myCallback)
if(inherits(pb,"txtProgressBar")) setTxtProgressBar(pb, i)
bfs = c(bfs,oneModel)
}
if(inherits(pb,"txtProgressBar")) close(pb)
checkCallback(callback,as.integer(1000))
}
# combine all the Bayes factors into one BFBayesFactor object
bfObj = do.call("c", bfs)
# If we have random effects, make those the denominator
if(length(whichRandom) > 0) bfObj = bfObj[-length(bfObj)] / bfObj[length(bfObj)]
if(whichModels=="top") bfObj = BFBayesFactorTop(bfObj)
return(bfObj)
}
```

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