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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods_BayesFactor.R
\name{model_parameters.BFBayesFactor}
\alias{model_parameters.BFBayesFactor}
\title{Parameters from BayesFactor objects}
\usage{
\method{model_parameters}{BFBayesFactor}(
model,
centrality = "median",
dispersion = FALSE,
ci = 0.95,
ci_method = "eti",
test = "pd",
rope_range = "default",
rope_ci = 0.95,
priors = TRUE,
es_type = NULL,
include_proportions = FALSE,
verbose = TRUE,
...
)
}
\arguments{
\item{model}{Object of class \code{BFBayesFactor}.}
\item{centrality}{The point-estimates (centrality indices) to compute. Character
(vector) or list with one or more of these options: \code{"median"}, \code{"mean"}, \code{"MAP"}
(see \code{\link[bayestestR:map_estimate]{map_estimate()}}), \code{"trimmed"} (which is just \code{mean(x, trim = threshold)}),
\code{"mode"} or \code{"all"}.}
\item{dispersion}{Logical, if \code{TRUE}, computes indices of dispersion related
to the estimate(s) (\code{SD} and \code{MAD} for \code{mean} and \code{median}, respectively).
Dispersion is not available for \code{"MAP"} or \code{"mode"} centrality indices.}
\item{ci}{Value or vector of probability of the CI (between 0 and 1)
to be estimated. Default to \code{0.95} (\verb{95\%}).}
\item{ci_method}{The type of index used for Credible Interval. Can be \code{"ETI"}
(default, see \code{\link[bayestestR:eti]{eti()}}), \code{"HDI"} (see \code{\link[bayestestR:hdi]{hdi()}}), \code{"BCI"} (see \code{\link[bayestestR:bci]{bci()}}),
\code{"SPI"} (see \code{\link[bayestestR:spi]{spi()}}), or \code{"SI"} (see \code{\link[bayestestR:si]{si()}}).}
\item{test}{The indices of effect existence to compute. Character (vector) or
list with one or more of these options: \code{"p_direction"} (or \code{"pd"}),
\code{"rope"}, \code{"p_map"}, \code{"p_significance"} (or \code{"ps"}), \code{"p_rope"},
\code{"equivalence_test"} (or \code{"equitest"}), \code{"bayesfactor"} (or \code{"bf"}) or
\code{"all"} to compute all tests. For each "test", the corresponding
\pkg{bayestestR} function is called (e.g. \code{\link[bayestestR:rope]{rope()}} or \code{\link[bayestestR:p_direction]{p_direction()}})
and its results included in the summary output.}
\item{rope_range}{ROPE's lower and higher bounds. Should be a vector of two
values (e.g., \code{c(-0.1, 0.1)}), \code{"default"} or a list of numeric vectors of
the same length as numbers of parameters. If \code{"default"}, the bounds are
set to \code{x +- 0.1*SD(response)}.}
\item{rope_ci}{The Credible Interval (CI) probability, corresponding to the
proportion of HDI, to use for the percentage in ROPE.}
\item{priors}{Add the prior used for each parameter.}
\item{es_type}{The effect size of interest. Not that possibly not all
effect sizes are applicable to the model object. See 'Details'. For Anova
models, can also be a character vector with multiple effect size names.}
\item{include_proportions}{Logical that decides whether to include posterior
cell proportions/counts for Bayesian contingency table analysis (from
\code{BayesFactor::contingencyTableBF()}). Defaults to \code{FALSE}, as this
information is often redundant.}
\item{verbose}{Toggle off warnings.}
\item{...}{Additional arguments to be passed to or from methods.}
}
\value{
A data frame of indices related to the model's parameters.
}
\description{
Parameters from \code{BFBayesFactor} objects from \code{{BayesFactor}} package.
}
\details{
The meaning of the extracted parameters:
\itemize{
\item For \code{\link[BayesFactor:ttestBF]{BayesFactor::ttestBF()}}: \code{Difference} is the raw difference between
the means.
\item For \code{\link[BayesFactor:correlationBF]{BayesFactor::correlationBF()}}: \code{rho} is the linear correlation
estimate (equivalent to Pearson's \emph{r}).
\item For \code{\link[BayesFactor:lmBF]{BayesFactor::lmBF()}} / \code{\link[BayesFactor:generalTestBF]{BayesFactor::generalTestBF()}}
/ \code{\link[BayesFactor:regressionBF]{BayesFactor::regressionBF()}} / \code{\link[BayesFactor:anovaBF]{BayesFactor::anovaBF()}}: in addition to
parameters of the fixed and random effects, there are: \code{mu} is the
(mean-centered) intercept; \code{sig2} is the model's sigma; \code{g} / \verb{g_*} are
the \emph{g} parameters; See the \emph{Bayes Factors for ANOVAs} paper
(\doi{10.1016/j.jmp.2012.08.001}).
}
}
\examples{
\dontshow{if (require("BayesFactor")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
\donttest{
# Bayesian t-test
model <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
model_parameters(model)
model_parameters(model, es_type = "cohens_d", ci = 0.9)
# Bayesian contingency table analysis
data(raceDolls)
bf <- BayesFactor::contingencyTableBF(
raceDolls,
sampleType = "indepMulti",
fixedMargin = "cols"
)
model_parameters(bf,
centrality = "mean",
dispersion = TRUE,
verbose = FALSE,
es_type = "cramers_v"
)
}
\dontshow{\}) # examplesIf}
}
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