% 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,
  effectsize_type = NULL,
  include_proportions = FALSE,
  verbose = TRUE,
  cohens_d = NULL,
  cramers_v = NULL,
  ...
)
}
\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"} 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).}

\item{ci}{Value or vector of probability of the CI (between 0 and 1)
to be estimated. Default to \code{.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{"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 list of two
values (e.g., \code{c(-0.1, 0.1)}) or \code{"default"}. 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{effectsize_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 warnings and messages.}

\item{cohens_d, cramers_v}{Deprecated. Please use \code{effectsize_type}.}

\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{
\donttest{
if (require("BayesFactor")) {
  # Bayesian t-test
  model <- ttestBF(x = rnorm(100, 1, 1))
  model_parameters(model)
  model_parameters(model, cohens_d = TRUE, ci = .9)

  # Bayesian contingency table analysis
  data(raceDolls)
  bf <- contingencyTableBF(raceDolls, sampleType = "indepMulti", fixedMargin = "cols")
  model_parameters(bf,
    centrality = "mean",
    dispersion = TRUE,
    verbose = FALSE,
    effectsize_type = "cramers_v"
  )
}
}
}
