https://github.com/cran/bayestestR
Tip revision: 092b63c552bdf3196413c25583520dc23033769b authored by Dominique Makowski on 30 October 2021, 13:00:02 UTC
version 0.11.5
version 0.11.5
Tip revision: 092b63c
bayesfactor.R
#' 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
)
}
}