forestplot.escalc: Generate a forest plot for an 'escalc' object (based on the...
In bayesmeta: Bayesian Random-Effects Meta-Analysis and Meta-Regression

forestplot.escalc

R Documentation

Generate a forest plot for an `escalc` object (based on the `forestplot` package's plotting functions).

Description

Generates a forest plot, showing estimates along with their 95 percent confidence intervals.

Usage

  ## S3 method for class 'escalc'
forestplot(x, labeltext, exponentiate=FALSE,
           digits=2, plot=TRUE,
           fn.ci_norm, fn.ci_sum, col, boxsize, ...)

Arguments

`x`	an `escalc` object.
`labeltext`	an (alternative) “`labeltext`” argument which is then handed on to the `forestplot()` function (see the help there). You can use this to change contents or add columns to the displayed table; see the example below.
`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 `forestplot` function (see also the help there).

Details

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).

Note

This function is based on the forestplot package's “forestplot()” function.

Author(s)

Christian Roever christian.roever@med.uni-goettingen.de

References

C. Roever. Bayesian random-effects meta-analysis using the bayesmeta R package. Journal of Statistical Software, 93(6):1-51, 2020. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1136/bmj.d549")}.

Examples

# 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)

bayesmeta documentation built on July 9, 2023, 5:12 p.m.