##### https://github.com/cran/bayestestR
Tip revision: 68a979e
bayesfactor_inclusion.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bayesfactor_inclusion.R
\name{bayesfactor_inclusion}
\alias{bayesfactor_inclusion}
\alias{bf_inclusion}
\title{Inclusion Bayes Factors for testing predictors across Bayesian models}
\usage{
bayesfactor_inclusion(models, match_models = FALSE, prior_odds = NULL, ...)

bf_inclusion(models, match_models = FALSE, prior_odds = NULL, ...)
}
\arguments{
\item{models}{An object of class \code{\link[=bayesfactor_models]{bayesfactor_models()}} or \code{BFBayesFactor}.}

\item{match_models}{See details.}

\item{prior_odds}{Optional vector of prior odds for the models. See
\verb{BayesFactor::priorOdds<-}.}

\item{...}{Arguments passed to or from other methods.}
}
\value{
a data frame containing the prior and posterior probabilities, and
log(BF) for each effect.
}
\description{
The \verb{bf_*} function is an alias of the main function.
\cr \cr
}
\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}?

\subsection{Match Models}{
If \code{match_models=FALSE} (default), Inclusion BFs are computed by comparing
all models with a term against all models without that term. If \code{TRUE},
comparison is restricted to models that (1) do not include any interactions
with the term of interest; (2) for interaction terms, averaging is done only
across models that containe the main effect terms from which the interaction
term is comprised.
}
}
\note{
Random effects in the \code{lmer} style are converted to interaction terms:
i.e., \code{(X|G)} will become the terms \code{1:G} and \code{X:G}.
}
\section{Interpreting Bayes Factors}{

A Bayes factor greater than 1 can be interpreted as evidence against the
null, at which one convention is that a Bayes factor greater than 3 can be
considered as "substantial" evidence against the null (and vice versa, a
Bayes factor smaller than 1/3 indicates substantial evidence in favor of the
null-model) (\cite{Wetzels et al. 2011}).
}

\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 \href{https://www.cogsci.nl/blog/interpreting-bayesian-repeated-measures-in-jasp}{Blog post}.
}
}
\seealso{