## 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.byvar is not missing. The plot.meta function produces basic forest plots. For nicer
looking forest plots the forest function can be used.
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, metabin, metacont, metagendata(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)Run the code above in your browser using DataLab