\name{ranef}
\alias{ranef}
\alias{ranef.rma.uni}
\alias{ranef.rma.mv}
\title{Best Linear Unbiased Predictions for 'rma.uni' and 'rma.mv' Objects}
\description{
   The function calculates best linear unbiased predictions (BLUPs) of the random effects for objects of class \code{"rma.uni"} and \code{"rma.mv"}. Corresponding standard errors and prediction interval bounds are also provided.
}
\usage{
\method{ranef}{rma.uni}(object, level, digits, transf, targs, \dots)
\method{ranef}{rma.mv}(object, level, digits, transf, targs, verbose=FALSE, \dots)
}
\arguments{
   \item{object}{an object of class \code{"rma.uni"} or \code{"rma.mv"}.}
   \item{level}{numerical value between 0 and 100 specifying the prediction interval level (if unspecified, the default is to take the value from the object).}
   \item{digits}{integer specifying the number of decimal places to which the printed results should be rounded (if unspecified, the default is to take the value from the object).}
   \item{transf}{optional argument specifying the name of a function that should be used to transform the predicted values and interval bounds (e.g., \code{transf=exp}; see also \link{transf}). If unspecified, no transformation is used.}
   \item{targs}{optional arguments needed by the function specified under \code{transf}.}
   \item{verbose}{logical indicating whether output should be generated on the progress of the computations (the default is \code{FALSE}).}
   \item{\dots}{other arguments.}
}
\value{
   For objects of class \code{"rma.uni"}, an object of class \code{"list.rma"}. The object is a list containing the following components:
   \item{pred}{predicted values.}
   \item{se}{corresponding standard errors.}
   \item{pi.lb}{lower bound of the prediction intervals.}
   \item{pi.ub}{upper bound of the prediction intervals.}
   \item{\dots}{some additional elements/values.}

   The \code{"list.rma"} object is formatted and printed with \code{\link{print.list.rma}}.

   For objects of class \code{"rma.mv"}, a list of data frames with the same components as described above.
}
\note{
   For best linear unbiased predictions that combine the fitted values based on the fixed effects and the estimated contributions of the random effects, see \code{\link{blup}}.

   For predicted/fitted values that are based only on the fixed effects of the model, see \code{\link{fitted.rma}} and \code{\link{predict.rma}}.

   Fixed-effects models (with or without moderators) do not contain random study effects. The BLUPs for these models will therefore be 0.

   When using the \code{transf} argument, the transformation is applied to the predicted values and the corresponding interval bounds. The standard errors are then set equal to \code{NA} and are omitted from the printed output.

   The normal distribution is used to calculate the prediction intervals. When the model was fitted with the Knapp and Hartung (2003) method (i.e., \code{test="knha"} in the \code{\link{rma.uni}} function), then the t-distribution with \eqn{k-p} degrees of freedom is used.

   To be precise, it should be noted that the function actually calculates empirical BLUPs (eBLUPs), since the predicted values are a function of the estimated variance component(s).
}
\author{
   Wolfgang Viechtbauer \email{wvb@metafor-project.org} \url{http://www.metafor-project.org/}
}
\references{
   Kackar, R. N., & Harville, D. A. (1981). Unbiasedness of two-stage estimation and prediction procedures for mixed linear models. Communications in Statistics, Theory and Methods, \bold{10}, 1249--1261.

   Raudenbush, S. W., & Bryk, A. S. (1985). Empirical Bayes meta-analysis. \emph{Journal of Educational Statistics}, \bold{10}, 75--98.

   Robinson, G. K. (1991). That BLUP is a good thing: The estimation of random effects. \emph{Statistical Science}, \bold{6}, 15--32.

   Searle, S. R., Casella, G., & McCulloch, C. E. (1992). \emph{Variance components}. Hoboken, NJ: Wiley.

   Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. \emph{Journal of Statistical Software}, \bold{36}(3), 1--48. \url{https://www.jstatsoft.org/v036/i03}.
}
\seealso{
   \code{\link{rma.uni}}, \code{\link{predict.rma}}, \code{\link{fitted.rma}}, \code{\link{blup.rma.uni}}
}
\examples{
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### meta-analysis of the log risk ratios using a random-effects model
res <- rma(yi, vi, data=dat)

### BLUPs of the random effects
ranef(res)
}
\keyword{models}
