% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrappers.R
\name{pooled_effect.brma}
\alias{pooled_effect.brma}
\title{Pooled Effect Size for brma Objects}
\usage{
\method{pooled_effect}{brma}(
  object,
  bias_adjusted = TRUE,
  output_measure = NULL,
  transform = NULL,
  probs = c(0.025, 0.975),
  conditional = FALSE,
  ...
)
}
\arguments{
\item{object}{a fitted brma object}

\item{bias_adjusted}{whether to adjust for publication bias. Defaults to
\code{TRUE}. For PET/PEESE models this removes the regression bias term from
the pooled location effect. Selection-model weighting affects response
predictions, not this \code{type = "terms"} wrapper.}

\item{output_measure}{effect-size measure for location/effect predictions.
Defaults to the fitted measure. Supported conversions are among \code{"SMD"},
\code{"COR"}, \code{"ZCOR"}, and \code{"OR"}; \code{"RR"}, \code{"HR"},
\code{"IRR"}, \code{"RD"}, and \code{"GEN"} can only be returned on their
fitted measure. Use \code{transform = "EXP"} for ratio-scale output from
log-scale measures.}

\item{transform}{optional display transformation. Currently \code{"EXP"}
exponentiates log-scale measures \code{"OR"}, \code{"RR"}, \code{"HR"},
and \code{"IRR"}.}

\item{probs}{quantiles of the posterior distribution to be displayed.
Defaults to \code{c(.025, .975)} for 95\% credible intervals.}

\item{conditional}{whether to return the pooled effect conditional on the
effect component for RoBMA product-space objects. Defaults to \code{FALSE}.}

\item{...}{additional arguments passed to \code{\link{predict.brma}}; wrapper
arguments such as \code{newdata}, \code{type}, and \code{quiet} are fixed.}
}
\value{
A \code{brma_samples} object containing posterior samples. When printed,
displays a summary table. Use \code{summary()} to obtain the summary table directly.
The samples can be converted to \pkg{posterior} draws formats using \code{as_draws()}.
}
\description{
Computes the pooled (aggregated) effect size estimate
from a fitted brma object by averaging across the moderator model matrix.
}
\details{
This function is a convenience wrapper around \code{predict.brma(...,
type = "terms", newdata = TRUE, bias_adjusted = TRUE, quiet = TRUE)}.

For meta-regression models, the pooled effect averages the effect size
estimate across moderator levels proportionately to the levels observed
in the data. This provides an estimate representative of the sample of
studies.

For models without moderators, this returns the single mu parameter.
}
\examples{
\dontrun{
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")
  fit <- brma(
    yi      = yi,
    vi      = vi,
    data    = dat.lehmann2018,
    measure = "SMD",
    seed    = 1,
    silent  = TRUE
  )

  pooled_effect(fit)
}
}

}
\seealso{
\code{\link[=predict.brma]{predict.brma()}}, \code{\link[=pooled_heterogeneity]{pooled_heterogeneity()}}, \code{\link[=blup]{blup()}}
}
