forest.bayesmeta {bayesmeta} | R Documentation |
bayesmeta
object
(based on the metafor
package's plotting functions).
Generates a forest plot, showing individual estimates along with their 95 percent confidence intervals, resulting effect estimate and prediction interval.
## S3 method for class 'bayesmeta' forest(x, xlab="effect size", refline=0, cex=1,...)
x |
a |
xlab |
title for the x-axis. |
refline |
value at which a vertical ‘reference’ line should be drawn (default is 0). The line can be suppressed by setting this argument to ‘NA’. |
cex |
character and symbol expansion factor. |
... |
other arguments. |
Generates a simple forest plot illustrating the underlying data and resulting estimates (effect estimate and prediction interval).
This function requires the metafor package to be installed.
Christian Roever christian.roever@med.uni-goettingen.de
C. Lewis and M. Clarke. Forest plots: trying to see the wood and the trees. BMJ, 322:1479, 2001. doi: 10.1136/bmj.322.7300.1479.
R.D. Riley, J.P. Higgins and J.J. Deeks. Interpretation of random effects meta-analyses. BMJ, 342:d549, 2011. doi: 10.1136/bmj.d549.
bayesmeta
, forest.default
,
addpoly
, forestplot.bayesmeta
data("CrinsEtAl2014") ## Not run: # compute effect sizes (log odds ratios) from count data # (using "metafor" package's "escalc()" function): require("metafor") es.crins <- escalc(measure="OR", ai=exp.AR.events, n1i=exp.total, ci=cont.AR.events, n2i=cont.total, slab=publication, data=CrinsEtAl2014) # derive a prior distribution for the heterogeneity: tp.crins <- TurnerEtAlPrior("surgical", "pharma", "placebo / control") # perform meta-analysis: ma.crins <- bayesmeta(es.crins, tau.prior=tp.crins$dprior) ######## # plot: forest(ma.crins, xlab="log odds ratio") forest(ma.crins, trans=exp, refline=1, xlab="odds ratio") ## End(Not run)