##### https://github.com/cran/bayestestR

Tip revision:

**aee422d7cd4098dad89e31ecc6dfd9e539d2bda4**authored by**Dominique Makowski**on**06 August 2019, 10:20 UTC****version 0.2.5** Tip revision:

**aee422d** bayesfactor_restricted.Rd

```
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bayesfactor_restricted.R
\name{bayesfactor_restricted}
\alias{bayesfactor_restricted}
\alias{bayesfactor_restricted.stanreg}
\alias{bayesfactor_restricted.brmsfit}
\alias{bayesfactor_restricted.emmGrid}
\title{Bayes Factors (BF) for Order Restricted Models}
\usage{
bayesfactor_restricted(posterior, hypothesis, prior = NULL,
verbose = TRUE, ...)
\method{bayesfactor_restricted}{stanreg}(posterior, hypothesis,
prior = NULL, verbose = TRUE, effects = c("fixed", "random",
"all"), ...)
\method{bayesfactor_restricted}{brmsfit}(posterior, hypothesis,
prior = NULL, verbose = TRUE, effects = c("fixed", "random",
"all"), ...)
\method{bayesfactor_restricted}{emmGrid}(posterior, hypothesis,
prior = NULL, verbose = TRUE, ...)
}
\arguments{
\item{posterior}{A \code{stanreg} / \code{brmsfit} object, \code{emmGrid} or a data frame - representing a posterior distribution(s) from (see Details).}
\item{hypothesis}{A character vector specifying the restrictions as logical conditions (see examples below).}
\item{prior}{An object representing a prior distribution (see Details).}
\item{verbose}{Toggle off warnings.}
\item{...}{Currently not used.}
\item{effects}{Should results for fixed effects, random effects or both be returned?
Only applies to mixed models. May be abbreviated.}
}
\value{
A data frame containing the Bayes factor representing evidence \emph{against} the un-restricted model.
}
\description{
This method computes Bayes factors for comparing a model with an order restrictions on its parameters
with the fully unrestricted model. \emph{Note that this method should only be used for confirmatory analyses}.
\cr \cr
For more info, see \href{https://easystats.github.io/bayestestR/articles/bayes_factors.html}{the Bayes factors vignette}.
}
\details{
This method is used to compute Bayes factors for order-restricted models vs un-restricted
models by setting an order restriction on the prior and posterior distributions
(\cite{Morey & Wagenmakers, 2013}).
(Though it is possible to use \code{bayesfactor_restricted} to test interval restrictions,
it is more suitable for testing order restrictions (see examples)).
When \code{posterior} is a model (\code{stanreg}, \code{brmsfit}), posterior and prior samples are
extracted for each parameter, and Savage-Dickey Bayes factors are computed for each parameter.
\strong{NOTE:} For \code{brmsfit} models, the model must have been fitted with \emph{custom (non-default)} priors. See example below.
\subsection{Setting the correct \code{prior}}{
It is important to provide the correct \code{prior} for meaningful results.
\itemize{
\item When \code{posterior} is a \code{data.frame}, \code{prior} should also be a \code{data.frame}, with matching column order.
\item When \code{posterior} is a \code{stanreg} or \code{brmsfit} model: \itemize{
\item \code{prior} can be set to \code{NULL}, in which case prior samples are drawn internally.
\item \code{prior} can also be a model equvilant to \code{posterior} but with samples from the priors \emph{only}.
}
\item When \code{posterior} is an \code{emmGrid} object: \itemize{
\item \code{prior} should be the \code{stanreg} or \code{brmsfit} model used to create the \code{emmGrid} objects.
\item \code{prior} can also be an \code{emmGrid} object equvilant to \code{posterior} but created with a model of priors samples \emph{only}.
}
}}
\subsection{Interpreting Bayes Factors}{
A Bayes factor greater than 1 can be interpereted 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-hypothesis)
(\cite{Wetzels et al. 2011}).
}
}
\examples{
library(bayestestR)
prior <- data.frame(
X = rnorm(100),
X1 = rnorm(100),
X3 = rnorm(100)
)
posterior <- data.frame(
X = rnorm(100, .4),
X1 = rnorm(100, -.2),
X3 = rnorm(100)
)
hyps <- c(
"X > X1 & X1 > X3",
"X > X1"
)
bayesfactor_restricted(posterior, hypothesis = hyps, prior = prior)
\dontrun{
# rstanarm models
# ---------------
library(rstanarm)
fit_stan <- stan_glm(mpg ~ wt + cyl + am,
data = mtcars
)
hyps <- c(
"am > 0 & cyl < 0",
"cyl < 0",
"wt - cyl > 0"
)
bayesfactor_restricted(fit_stan, hypothesis = hyps)
# emmGrid objects
# ---------------
library(emmeans)
options(contrasts = c("contr.bayes", "contr.bayes")) # see `bfrms` package
# replicating http://bayesfactor.blogspot.com/2015/01/multiple-comparisons-with-bayesfactor-2.html
disgust_data <- read.table(url("http://www.learnbayes.org/disgust_example.txt"), header = TRUE)
fit_model <- stan_glm(score ~ condition, data = disgust_data, family = gaussian())
em_condition <- emmeans(fit_model, ~condition)
hyps <- c("lemon < control & control < sulfur")
bayesfactor_restricted(em_condition, prior = fit_model, hypothesis = hyps)
# > # Bayes Factor (Order-Restriction)
# >
# > Hypothesis P(Prior) P(Posterior) Bayes Factor
# > lemon < control & control < sulfur 0.17 0.75 4.49
# > ---
# > Bayes factors for the restricted movel vs. the un-restricted model.
}
}
\references{
\itemize{
\item Morey, R. D., & Wagenmakers, E. J. (2014). Simple relation between Bayesian order-restricted and point-null hypothesis tests. Statistics & Probability Letters, 92, 121-124.
\item Morey, R. D., & Rouder, J. N. (2011). Bayes factor approaches for testing interval null hypotheses. Psychological methods, 16(4), 406.
\item Morey, R. D. (Jan, 2015). Multiple Comparisons with BayesFactor, Part 2 – order restrictions. Retrived from https://richarddmorey.org/category/order-restrictions/.
}
}
```