forest(x, ...)## S3 method for class 'meta':
forest(x, sortvar, studlab=TRUE,
comb.fixed=x$comb.fixed, comb.random=x$comb.random,
overall=TRUE,
text.fixed=if (x$level!=x$level.comb) paste("Fixed effect model (",
round(x$level.comb*100), "%-CI)", sep="")
else "Fixed effect model",
text.random=if (x$level!=x$level.comb) paste("Random effects model (",
round(x$level.comb*100), "%-CI)", sep="")
else "Random effects model",
lty.fixed=2, lty.random=3,
prediction=x$prediction,
text.predict=if (!(length(x$level.predict)==0) &&
x$level!=x$level.predict)
paste("Prediction interval (",
round(x$level.predict*100), "%)",
sep="") else "Prediction interval",
bylab=x$bylab, print.byvar=x$print.byvar,
text.fixed.w=text.fixed, text.random.w=text.random,
bysort=FALSE,
pooled.totals=comb.fixed|comb.random, pooled.events=FALSE,
xlab="", xlab.pos=ref,
smlab=NULL, smlab.pos=ref, xlim="symmetric",
allstudies=TRUE,
weight,
pscale=1,
ref=ifelse(backtransf & is.relative.effect(x$sm), 1, 0),
layout="meta",
leftcols=NULL, rightcols=NULL,
leftlabs=NULL, rightlabs=NULL,
lab.e=x$label.e, lab.c=x$label.c,
lab.e.attach.to.col=NULL, lab.c.attach.to.col=NULL,
label.right=x$label.right, label.left=x$label.left,
lab.NA=".",
lab.NA.effect="",
lwd=1,
at=NULL, label=TRUE,
col.i="black", col.i.inside.square="white",
col.square="gray", col.square.lines=col.square,
col.diamond="gray",
col.diamond.fixed=col.diamond, col.diamond.random=col.diamond,
col.diamond.lines="black",
col.diamond.fixed.lines=col.diamond.lines,
col.diamond.random.lines=col.diamond.lines,
col.predict="red", col.predict.lines="black",
col.by="darkgray",
print.I2=comb.fixed|comb.random, print.I2.ci=FALSE,
print.tau2=comb.fixed|comb.random,
print.Q=FALSE, print.pval.Q=comb.fixed|comb.random,
hetstat=print.I2|print.tau2|print.Q|print.pval.Q,
overall.hetstat=overall&hetstat,
hetlab="Heterogeneity: ",
text.I2 = "I-squared",
text.tau2 = "tau-squared",
test.overall=.settings$test.overall,
test.overall.fixed=comb.fixed&overall&test.overall,
test.overall.random=comb.random&overall&test.overall,
label.test.overall.fixed=paste("Test for overall effect",
if (comb.fixed & comb.random) " (fixed effect)", ": ", sep=""),
label.test.overall.random=paste("Test for overall effect",
if (comb.fixed & comb.random) " (random effects)", ": ", sep=""),
test.subgroup=.settings$test.subgroup,
test.subgroup.fixed=if (missing(test.subgroup)) FALSE else test.subgroup,
test.subgroup.random=if (missing(test.subgroup)) !is.null(x$byvar)&
comb.random&test.subgroup else test.subgroup,
print.Q.subgroup=print.Q,
label.test.subgroup.fixed="Test for subgroup differences (fixed effect): ",
label.test.subgroup.random=paste("Test for subgroup differences",
if (test.subgroup.fixed | comb.fixed) " (random effects)", ": ", sep=""),
fontsize=12,
fs.heading=fontsize,
fs.fixed=fontsize, fs.random=fs.fixed, fs.predict=fs.fixed, fs.study=fontsize,
fs.fixed.labels=fs.fixed, fs.random.labels=fs.random, fs.predict.labels=fs.predict,
fs.study.labels=fs.study, fs.hetstat=fontsize-2,
fs.test.overall=fs.hetstat, fs.test.subgroup=fs.hetstat, fs.axis=fontsize,
fs.smlab=fontsize, fs.xlab=fontsize, fs.lr=fontsize,
ff.heading="bold",
ff.fixed="bold", ff.random=ff.fixed, ff.predict=ff.fixed, ff.study="plain",
ff.fixed.labels=ff.fixed, ff.random.labels=ff.random, ff.predict.labels=ff.predict,
ff.study.labels=ff.study, ff.hetstat="bold.italic",
ff.test.overall=ff.hetstat, ff.test.subgroup=ff.hetstat, ff.axis="plain",
ff.smlab="bold", ff.xlab="plain", ff.lr="plain",
#
squaresize=0.8,
#
plotwidth=grid::unit(6, "cm"),
colgap=grid::unit(2, "mm"),
colgap.left=colgap, colgap.right=colgap,
colgap.forest=colgap,
colgap.forest.left=colgap.forest, colgap.forest.right=colgap.forest,
#
just="right", just.studlab="left", just.addcols="center",
#
addspace=TRUE,
#
new=TRUE,
#
backtransf=x$backtransf,
digits = .settings$digits.forest, digits.se = .settings$digits.se,
digits.pval = .settings$digits.pval, digits.pval.Q = .settings$digits.pval.Q,
digits.Q = .settings$digits.Q, digits.tau2 = .settings$digits.tau2,
digits.I2 = .settings$digits.I2, digits.weight = .settings$digits.weight,
...)
meta.x$TE).x$TE then).byvar if summaries should only be plotted on group
level."same", "fixed", or
"random", can be abbreviated. Plot symbols metaprop.ref is equal to NA.lab.e should be attached to in table heading.lab.c should be attached to in table heading.par.grid.xaxis.grid.xaxis.byvar) if heterogeneity statistics
shgpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.gpar.backtransf=TRUE,
results for sm="OR" are presented as odds ratios rather
than log odds ratios and results for sm="ZCOR" print.default.print.default.print.default.print.default.print.default.print.default.print.default.print.default.byvar is not missing.
The forest function is based on the grid graphics
system. In order to print the forest plot, (i) resize the graphics
window, (ii) either use dev.copy2eps or
dev.copy2pdf. Result of a test for overall effect or subgroup differences are
printed if argument test.overall.fixed,
test.overall.random, test.subgroup.fixed, or
test.subgroup.random is TRUE; by default, these
arguments are FALSE. R function settings.meta
can be used to change this default for the entire R session by
setting argument test.overall=TRUE or
test.subgroup=TRUE.
Information from object x is utilised if argument weight
is missing. Weights from the fixed effect model are used
(weight="fixed") if argument x$comb.fixed is
TRUE; weights from the random effects model are used
(weight="random") if argument x$comb.random
is TRUE and x$comb.fixed is FALSE.
The arguments leftcols and rightcols can be used to
specify columns which are plotted on the left and right side of the
forest plot, respectively. If argument rightcols is
FALSE, no columns will be plotted on the right side. By
default, i.e. if arguments leftcols and rightcols are
NULL and layout="meta", the following default columns
will be plotted.
rightcols
--- c("effect", "ci")
comb.fixed=TRUE & comb.random=FALSE c("effect", "ci", "w.fixed")
comb.fixed=FALSE & comb.random=TRUE c("effect", "ci", "w.random")
comb.fixed=TRUE & comb.random=TRUE c("effect", "ci", "w.fixed", "w.random")
}
By default, estimated treatment effect and corresponding confidence
interval will be printed. Depending on arguments comb.fixed
and comb.random, weights of the fixed effect and/or random
effects model will be given too. For an object of class
metacum or metainf only the estimated treatment effect
with confidence interval are plotted.
Depending on the class of the meta-analysis object a different set
of columns is printed on the left side of a forest plot.
leftcols
metabin c("studlab", "event.e", "n.e", "event.c", "n.c")
metacont c("studlab", "n.e", "mean.e",
"sd.e", "n.c", "mean.c", "sd.c")
metacor c("studlab", "n")
metagen c("studlab", "TE", "seTE")
metainc c("studlab", "event.e",
"time.e", "event.c", "time.c")
metaprop c("studlab", "event", "n")
metacum c("studlab")
metainf c("studlab")
}
The arguments leftlabs and rightlabs can be used to
specify column headings which are plotted on left and right side of
the forest plot, respectively. For certain columns predefined labels
exist. If the arguments leftlabs and rightlabs are
NULL, the following default labels will be used:
studlab TE seTE n.e n.c n
Label "Study" "TE" "seTE" "Total" "Total" "Total"
Column event.e event.c event mean.e mean.c
Label "Events" "Events" "Events" "Mean" "Mean"
Column sd.e sd.c time.e time.c
Label "SD" "SD" "Time" "Time"
Column effect ci w.fixed w.random
Label x$sm x$level-CI "W(fixed)" "W(random)" }
For additional columns the column name will be used
as label. It is possible to only provide labels for new columns (see
Examples). Otherwise the length of leftlabs and
rightlabs must be the same as the number of printed
columns. The value NA can be used to specify columns using
the default labels (see Example).
If argument layout="revman5" (and arguments leftcols
and rightcols are NULL) all columns mentioned above
will be printed on the left side of the forest plot.
If arguments lab.e and lab.c are NULL,
"Experimental" and "Control" are used as labels for experimental and
control group, respectively.
The arguments pscale can be used to rescale proportions
for objects of class metaprop, e.g. pscale=100 means
that proportions are expressed per 100 observations. This is useful in
situations with (very) low proportions. For pscale=100, column
heading and x-axis label are changed to "Prop (in %)" and
"Proportion (in %)", respectively.
A prediction interval for treatment effect of a new study (Higgins
et al., 2009) is given in the forest plot if arguments
prediction and comb.random are TRUE. For
graphical presentation of prediction intervals the approach by
Guddat et al. (2012) is used.
Note, in R package meta, version 3.0-0 the following arguments have
been removed from R function forest.meta: byvar, level, level.comb,
level.predict. This functionality is now provided by R function
update.meta (or directly in R functions
metabin, metacont,
metagen, metacor, and
metaprop).
Higgins JPT, Thompson SG, Spiegelhalter DJ (2009), A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society: Series A, 172, 137-159.
metabin, metacont, metagendata(Olkin95)
meta1 <- metabin(event.e, n.e, event.c, n.c,
data=Olkin95, subset=c(41,47,51,59),
sm="RR", method="I",
studlab=paste(author, year))
#
# Do (symmetric) forest plot
#
forest(meta1)
#
# Forest plot specifying argument xlim
#
forest(meta1, xlim=c(0.01, 10))
#
# Add prediction interval to forest plot
#
forest(meta1, prediction=TRUE)
#
# Print results of test for overall effect
#
forest(meta1, test.overall.fixed=TRUE, test.overall.random=TRUE)
#
# Forest plot with 'classic' layout used in
# R package meta, version < 1.6-0
#
forest(meta1, col.square="black", hetstat=FALSE)
#
# Layout of forest plot similar to RevMan 5
#
forest(meta1, layout="revman5", comb.random=FALSE)
#
# Change set of columns printed on left side
# of forest plot
#
forest(meta1, comb.random=FALSE,
leftcols="studlab")
#
# Do not print columns on right side of forest plot
#
forest(meta1, rightcols=FALSE)
#
# Change study label to "Author"
#
forest(meta1, comb.random=FALSE,
leftlabs=c("Author", NA, NA, NA, NA))
#
# Just give effect estimate and 95% confidence interval
# on right side of forest plot
#
forest(meta1, rightcols=c("effect", "ci"))
#
# 1. Change order of columns on left side
# 2. Attach labels to columns 'event.e' and 'event.c'
# instead of columns 'n.e' and 'n.c'
#
forest(meta1,
leftcols=c("studlab", "n.e", "event.e", "n.c", "event.c"),
lab.e.attach.to.col="event.e",
lab.c.attach.to.col="event.c")
#
# Specify column labels only for newly created variables
# 'year' and 'author' (which are part of dataset Olkin95)
#
forest(meta1,
leftcols=c("studlab", "event.e", "n.e", "event.c", "n.c",
"author", "year"),
leftlabs=c("Author", "Year of Publ"))
#
# Center text in all columns
#
forest(meta1,
leftcols=c("studlab", "event.e", "n.e", "event.c", "n.c",
"author", "year"),
leftlabs=c("Author", "Year of Publ"), hetstat=FALSE,
just="center", just.addcols="center", just.studlab="center")
#
# Same result
#
forest(meta1,
leftcols=c("studlab", "event.e", "n.e", "event.c", "n.c",
"author", "year"),
leftlabs=c("Author", "Year of Publ"), hetstat=FALSE,
just="c", just.addcols="c", just.studlab="c")
#
# Change some fontsizes and fontfaces
#
forest(meta1,
fs.study=10, ff.study="italic",
fs.study.label=11, ff.study.label="bold",
fs.axis=5, ff.axis="italic",
ff.smlab="bold.italic",
ff.fixed="plain", ff.hetstat="plain")
#
# Change some colours
#
forest(meta1,
col.diamond="green", col.diamond.lines="red",
col.i=c("green", "blue", "red", "orange"),
col.square="pink", col.square.lines="black")
#
# Sort by weight in fixed effect model
#
forest(meta1, sortvar=1/w.fixed, comb.random=FALSE)
#
# Sort by decreasing weight in fixed effect model
#
forest(meta1, sortvar=-1/w.fixed, comb.random=FALSE)
#
# Sort by size of treatment effect
#
forest(meta1, sortvar=TE, comb.random=FALSE)
#
# Sort by size of treatment effect
#
forest(meta1, sortvar=-TE, comb.random=FALSE)
#
# Sort by decreasing year of publication
#
forest(meta1, sortvar=-year, comb.random=FALSE)
#
# Sort studies by decreasing treatment effect within year subgroups
#
meta2 <- update(meta1, byvar=ifelse(year < 1987, "Before 1987", "1987 and later"),
print.byvar=FALSE)
forest(meta2,
sortvar=-TE, comb.random=FALSE)
#
# Print results of test for subgroup differences (random effects model)
#
forest(meta2,
sortvar=-TE, comb.fixed=FALSE,
test.subgroup.random=TRUE)Run the code above in your browser using DataLab