% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bayesfactor_inclusion.R \name{bayesfactor_inclusion} \alias{bayesfactor_inclusion} \title{Inclusion Bayes Factors for testing predictors across Bayesian models} \usage{ bayesfactor_inclusion(models, match_models = FALSE, prior_odds = NULL, ...) } \arguments{ \item{models}{An object of class \code{\link{bayesfactor_models}} or \code{BFBayesFactor}.} \item{match_models}{See details.} \item{prior_odds}{Optional vector of prior odds for the models. See \code{BayesFactor::priorOdds<-}.} \item{...}{Arguments passed to or from other methods.} } \value{ a data frame containing the prior and posterior probabilities, and BF for each effect. } \description{ Inclusion Bayes Factors for testing predictors across Bayesian models } \details{ Inclusion Bayes factors answer the question: Are the observed data more probable under models with a particular effect, than they are under models without that particular effect? In other words, on average - are models with effect \eqn{X} more likely to have produced the observed data than models without effect \eqn{X}? \cr \cr For more info, see \href{https://easystats.github.io/bayestestR/articles/bayes_factors.html}{the Bayes factors vignette}. \subsection{Match Models}{ If \code{match_models=FALSE} (default), Inclusion BFs are computed by comparing all models with a predictor against all models without that predictor. If \code{TRUE}, comparison is restricted to models that (1) do not include any interactions with the predictor of interest; (2) for interaction predictors, averaging is done only across models that containe the main effect from which the interaction predictor is comprised. } } \note{ Random effects in the \code{lme} style will be displayed as interactions: i.e., \code{(X|G)} will become \code{1:G} and \code{X:G}. } \examples{ library(bayestestR) # Using bayesfactor_models: # ------------------------------ mo0 <- lm(Sepal.Length ~ 1, data = iris) mo1 <- lm(Sepal.Length ~ Species, data = iris) mo2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris) mo3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris) BFmodels <- bayesfactor_models(mo1, mo2, mo3, denominator = mo0) bayesfactor_inclusion(BFmodels) \dontrun{ # BayesFactor # ------------------------------- library(BayesFactor) BF <- generalTestBF(len ~ supp * dose, ToothGrowth, progress = FALSE) bayesfactor_inclusion(BF) # compare only matched models: bayesfactor_inclusion(BF, match_models = TRUE) } } \references{ \itemize{ \item Hinne, M., Gronau, Q. F., van den Bergh, D., and Wagenmakers, E. (2019, March 25). A conceptual introduction to Bayesian Model Averaging. \doi{10.31234/osf.io/wgb64} \item Clyde, M. A., Ghosh, J., & Littman, M. L. (2011). Bayesian adaptive sampling for variable selection and model averaging. Journal of Computational and Graphical Statistics, 20(1), 80-101. \item Mathot. S. (2017). Bayes like a Baws: Interpreting Bayesian Repeated Measures in JASP [Blog post]. Retrieved from https://www.cogsci.nl/blog/interpreting-bayesian-repeated-measures-in-jasp } } \author{ Mattan S. Ben-Shachar }