metacor(cor, n, studlab,
data=NULL, subset=NULL,
sm=.settings$smcor,
level=.settings$level, level.comb=.settings$level.comb,
comb.fixed=.settings$comb.fixed, comb.random=.settings$comb.random,
hakn=.settings$hakn,
method.tau=.settings$method.tau, tau.preset=NULL, TE.tau=NULL,
tau.common=.settings$tau.common,
prediction=.settings$prediction, level.predict=.settings$level.predict,
method.bias=.settings$method.bias,
backtransf=.settings$backtransf,
title=.settings$title, complab=.settings$complab, outclab="",
byvar, bylab, print.byvar=.settings$print.byvar,
keepdata=.settings$keepdata
)"ZCOR" or "COR") is to be used for pooling of
studies."DL", "PM", "REML", "ML", "HS",
"SJ", "HE", o"rank", "linreg", or "mm", can
be abbreviated. See function metabiassm="ZCOR") should be back
transformed in printouts and plots. If TRUE (default), results
will be presented as correlations; otherwise Fisher's z
traevent.e).c("metacor", "meta") with corresponding
print, summary, plot function. The object is a
list containing the following components:sm="ZCOR") or correlations (sm="COR") for individual
studies."Inverse"hakn=TRUE).keepdata=TRUE).keepdata=TRUE).sm="ZCOR") or direct combination of correlations
(sm="COR") (see Cooper et al., p264-5 and p273-4). By
default, the DerSimonian-Laird estimate (1986) is used in the random
effects model (method.tau="DL"). Only few statisticians would advocate the use of untransformed
correlations unless sample sizes are very large (see Cooper et al.,
p265). The artificial example given below shows that the smallest
study gets the largest weight if correlations are combined directly
because the correlation is closest to 1.
For several arguments defaults settings are utilised (assignments
with .settings$). These defaults can be changed using the
settings.meta function.
Internally, both fixed effect and random effects models are
calculated regardless of values choosen for arguments
comb.fixed and comb.random. Accordingly, the estimate
for the random effects model can be extracted from component
TE.random of an object of class "meta" even if
argument comb.random=FALSE. However, all functions in R
package meta will adequately consider the values for
comb.fixed and comb.random. E.g. function
print.meta will not print results for the random
effects model if comb.random=FALSE.
A prediction interval for treatment effect of a new study is
calculated (Higgins et al., 2009) if arguments prediction and
comb.random are TRUE.
R function update.meta can be used to redo the
meta-analysis of an existing metacor object by only specifying
arguments which should be changed.
For the random effects, the method by Hartung and Knapp (2003) is
used to adjust test statistics and confidence intervals if argument
hakn=TRUE.
The iterative Paule-Mandel method (1982) to estimate the
between-study variance is used if argument
method.tau="PM". Internally, R function paulemandel is
called which is based on R function mpaule.default from R package
metRology from S.L.R. Ellison
If R package metafor (Viechtbauer 2010) is installed, the following
methods to estimate the between-study variance $\tau^2$
(argument method.tau) are also available:
method.tau="REML")method.tau="ML")method.tau="HS")method.tau="SJ")method.tau="HE")method.tau="EB").rma.uni of R package metafor
is called internally. See help page of R function rma.uni for
more details on these methods to estimate between-study variance.Paule RC & Mandel J (1982), Consensus values and weighting factors. Journal of Research of the National Bureau of Standards, 87, 377--385.
Viechtbauer W (2010), Conducting Meta-Analyses in R with the Metafor Package. Journal of Statistical Software, 36, 1--48.
update.meta, metacont, metagen, print.metam1 <- metacor(c(0.85, 0.7, 0.95), c(20, 40, 10))
#
# Print correlations (back transformed from Fisher's z transformation)
#
m1
#
# Print Fisher's z transformed correlations
#
print(m1, backtransf=FALSE)
#
# Forest plot with back transformed correlations
#
forest(m1)
#
# Forest plot with Fisher's z transformed correlations
#
forest(m1, backtransf=FALSE)
m2 <- update(m1, sm="cor")
m2
# Identical forest plots (as back transformation is the identity transformation)
# forest(m2)
# forest(m2, backtransf=FALSE)Run the code above in your browser using DataLab