forestplot.escalc {bayesmeta} | R Documentation |
escalc
object
(based on the forestplot
package's plotting functions).
Generates a forest plot, showing estimates along with their 95 percent confidence intervals.
## S3 method for class 'escalc' forestplot(x, labeltext, exponentiate=FALSE, digits=2, plot=TRUE, fn.ci_norm, fn.ci_sum, col, boxsize, ...)
x |
an |
labeltext |
an (alternative) “ |
exponentiate |
a logical flag indicating whether to exponentiate numbers (effect sizes) in table and plot. |
digits |
The number of significant digits to be shown. This is interpreted relative to the standard errors of all estimates. |
plot |
a logical flag indicating whether to actually generate a plot. |
fn.ci_norm, fn.ci_sum, col, boxsize, ... |
other arguments passed on to the
forestplot package's |
Generates a forest plot illustrating the data returned by the
“escalc()
” function (showing the
estimates potentially to be meta-analyzed, but without a combined
summary estimate).
This function is based on the forestplot package's
“forestplot()
” function.
Christian Roever christian.roever@med.uni-goettingen.de
C. Roever. Bayesian random-effects meta-analysis using the bayesmeta R package. Journal of Statistical Software, 93(6):1-51, 2020. doi: 10.18637/jss.v093.i06.
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.
escalc
,
forestplot
,
forestplot.bayesmeta
,
forestplot.bmr
.
# load data: data("CrinsEtAl2014") # compute effect sizes (log odds ratios) from count data # (using "metafor" package's "escalc()" function): crins.es <- escalc(measure="OR", ai=exp.AR.events, n1i=exp.total, ci=cont.AR.events, n2i=cont.total, slab=publication, data=CrinsEtAl2014) print(crins.es) ######################## # generate forest plots; # with default settings: forestplot(crins.es) # exponentiate values (shown in table and plot), show vertical line at OR=1: forestplot(crins.es, expo=TRUE, zero=1) # logarithmic x-axis: forestplot(crins.es, expo=TRUE, xlog=TRUE) # show more decimal places: forestplot(crins.es, digits=3) # change table values: # (here: add columns for event counts) fp <- forestplot(crins.es, expo=TRUE, plot=FALSE) labtext <- fp$labeltext labtext <- cbind(labtext[,1], c("treatment", paste0(CrinsEtAl2014[,"exp.AR.events"], "/", CrinsEtAl2014[,"exp.total"])), c("control", paste0(CrinsEtAl2014[,"cont.AR.events"], "/", CrinsEtAl2014[,"cont.total"])), labtext[,2:3]) labtext[1,4] <- "OR" print(fp$labeltext) # before print(labtext) # after forestplot(crins.es, labeltext=labtext, expo=TRUE, xlog=TRUE) # see also the "forestplot" help for more arguments that you may change, # e.g. the "clip", "xticks", "xlab" and "title" arguments, # or the "txt_gp" argument for label sizes etc.: forestplot(crins.es, clip=c(-4,1), xticks=(-3):0, xlab="log-OR", title="pediatric transplantation example", txt_gp = fpTxtGp(ticks = gpar(cex=1), xlab = gpar(cex=1))) ########################################################### # In case effects and standard errors are computed already # (and normally one wouldn't need to call "escalc()") # you can still use "escalc()" to assemble the plot, e.g.: data("HinksEtAl2010") print(HinksEtAl2010) hinks.es <- escalc(yi=log.or, sei=log.or.se, slab=study, measure="OR", data=HinksEtAl2010) forestplot(hinks.es)