#' Bayes Factors (BF) #' #' This function compte the Bayes factors (BFs) that are appropriate to the #' input. For vectors or single models, it will compute [`BFs for single #' parameters()`][bayesfactor_parameters], or is `hypothesis` is specified, #' [`BFs for restricted models()`][bayesfactor_restricted]. For multiple models, #' it will return the BF corresponding to [`comparison between #' models()`][bayesfactor_models] and if a model comparison is passed, it will #' compute the [`inclusion BF()`][bayesfactor_inclusion]. #' \cr\cr #' For a complete overview of these functions, read the [Bayes factor vignette](https://easystats.github.io/bayestestR/articles/bayes_factors.html). #' #' @param ... A numeric vector, model object(s), or the output from #' `bayesfactor_models`. #' @inheritParams bayesfactor_parameters #' @inheritParams bayesfactor_restricted #' @inheritParams bayesfactor_models #' @inheritParams bayesfactor_inclusion #' #' @return Some type of Bayes factor, depending on the input. See [bayesfactor_parameters()], [bayesfactor_models()] or [bayesfactor_inclusion()] #' #' @note There is also a [`plot()`-method](https://easystats.github.io/see/articles/bayestestR.html) implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}. #' #' @examples #' library(bayestestR) #' #' if (require("logspline")) { #' prior <- distribution_normal(1000, mean = 0, sd = 1) #' posterior <- distribution_normal(1000, mean = .5, sd = .3) #' #' bayesfactor(posterior, prior = prior) #' } #' \dontrun{ #' # rstanarm models #' # --------------- #' if (require("rstanarm")) { #' model <- stan_lmer(extra ~ group + (1 | ID), data = sleep) #' bayesfactor(model) #' } #' } #' #' if (require("logspline")) { #' # Frequentist models #' # --------------- #' m0 <- lm(extra ~ 1, data = sleep) #' m1 <- lm(extra ~ group, data = sleep) #' m2 <- lm(extra ~ group + ID, data = sleep) #' #' comparison <- bayesfactor(m0, m1, m2) #' comparison #' #' bayesfactor(comparison) #' } #' @export bayesfactor <- function(..., prior = NULL, direction = "two-sided", null = 0, hypothesis = NULL, effects = c("fixed", "random", "all"), verbose = TRUE, denominator = 1, match_models = FALSE, prior_odds = NULL) { mods <- list(...) effects <- match.arg(effects) if (length(mods) > 1) { bayesfactor_models(..., denominator = denominator) } else if (inherits(mods[[1]], "bayesfactor_models")) { bayesfactor_inclusion(..., match_models = match_models, prior_odds = prior_odds) } else if (inherits(mods[[1]], "BFBayesFactor")) { if (class(mods[[1]]@numerator[[1]]) == "BFlinearModel") { bayesfactor_inclusion(..., match_models = match_models, prior_odds = prior_odds) } else { bayesfactor_models(...) } } else if (!is.null(hypothesis)) { bayesfactor_restricted(..., prior = prior, verbose = verbose, effects = effects ) } else { bayesfactor_parameters( ..., prior = prior, direction = direction, null = null, effects = effects, verbose = verbose ) } }