## S3 method for class 'meta':
plot(x, byvar=x$byvar, bylab=x$bylab,
print.byvar=x$print.byvar,
sortvar, studlab=TRUE, level=x$level, level.comb=x$level.comb,
comb.fixed=x$comb.fixed, comb.random=x$comb.random, overall=TRUE,
text.fixed="Fixed effect model", text.random="Random effects model",
lty.fixed=2, lty.random=3, xlab=NULL, xlim, ylim, lwd=1, cex=1,
cex.comb=1.2 * cex, cex.axis=cex, cex.lab=cex,
log=ifelse(x$sm \%in\% c("RR", "OR", "HR"), "x", ""),
axes=TRUE, allstudies=TRUE,
weight=ifelse(comb.random, "random", "fixed"), scale.diamond=1,
scale.square= 1, col.i="black",
clim=xlim, arrow.length=0.1,
ref=ifelse(x$sm %in% c("RR", "OR", "HR"), 1, 0),
...)
meta
.x$TE
).x$TE
).x$TE
then).byvar
if summaries should only be plotted on group
level.cex
.cex
."x"
if the x axis
is to be logarithmic (other values for log
are not
reasonable)."same"
, "fixed"
, or "random"
, can be
abbreviated. Plot symbols have the same size for par
may also be
passed as arguments.arrows
.plot.meta
is no longer
maintained. Use of the function might result in (unexpected) error
messages or even in wrong results. Please use the function
forest.meta
instead. A forest plot, also called confidence interval plot, is drawn in the
active graphics window. Sub-group analyses are conducted and displayed
in the plot if byvar
is not missing.
Review Manager 5 (RevMan 5) is the current software used for
preparing and maintaining Cochrane Reviews
(read.rm5
. If a meta-analysis is then conducted using
function metacr
, information on subgroups is available in R
(components byvar
, bylab
, and print.byvar
,
byvar
in an object of class "meta"
). Accordingly, by using function
metacr
there is no need to define subgroups in order to redo
the statistical analysis conducted in the Cochrane review.
forest.meta
, metabin
, metacont
, metagen
data(Olkin95)
meta1 <- metabin(event.e, n.e, event.c, n.c,
data=Olkin95, subset=c(41,47,51,59),
sm="RR", meth="I")
oldpar <- par(mfrow=c(2, 2))
plot(meta1)
plot(meta1, byvar=c(1,2,1,2), bylab="label")
plot(meta1, byvar=1:4, xlim=c(0.02, 10))
par(oldpar)
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