metacor(cor, n, studlab,
data=NULL, subset=NULL,
sm="ZCOR",
level=0.95, level.comb=level,
comb.fixed=TRUE, comb.random=TRUE,
hakn=FALSE,
method.tau="DL", tau.preset=NULL, TE.tau=NULL,
tau.common=FALSE,
prediction=FALSE, level.predict=level,
method.bias="linreg",
title="", complab="", outclab="",
byvar, bylab, print.byvar=TRUE,
keepdata=TRUE
)"ZCOR" or "COR") is to be used for pooling of
studies."DL", "REML", "ML", "HS", "SJ",
"HE", or "EB""rank", "linreg", or "mm", can
be abbreviated.event.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).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.
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 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.
If R package metafor (Viechtbauer 2010) is installed, the following statistical methods are also available.
For the random effects model (argument comb.random=TRUE), the
method by Hartung and Knapp (Knapp, Hartung 2003) is used to adjust
test statistics and confidence intervals if argument
hakn=TRUE (internally R function rma.uni of R package
metafor is called).
Several methods are available to estimate the between-study variance
$\tau^2$ (argument method.tau):
method.tau="DL") (default)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 the various
methods to estimate between-study variance $\tau^2$. 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.
Viechtbauer W (2010), Conducting Meta-Analyses in R with the Metafor Package. Journal of Statistical Software, 36, 1--48.
update.meta, metacont, metagen, print.metametacor(c(0.85, 0.7, 0.95), c(20, 40, 10))
forest(metacor(c(0.85, 0.7, 0.95), c(20, 40, 10)))
metacor(c(0.85, 0.7, 0.95), c(20, 40, 10), sm="cor")Run the code above in your browser using DataLab