forest.meta  R Documentation 
Draws a forest plot in the active graphics window (using grid graphics system).
## S3 method for class 'meta'
forest(
x,
sortvar,
studlab = TRUE,
layout = gs("layout"),
common = x$common,
random = x$random,
overall = x$overall,
text.common = x$text.common,
text.random = x$text.random,
lty.common = gs("lty.common"),
lty.random = gs("lty.random"),
col.common = gs("col.common"),
col.random = gs("col.random"),
text.w.common = x$text.w.common,
text.w.random = x$text.w.random,
prediction = x$prediction,
text.predict = x$text.predict,
subgroup = TRUE,
subgroup.hetstat = subgroup & (is.character(hetstat)  hetstat),
print.subgroup.labels = TRUE,
subgroup.name = x$subgroup.name,
print.subgroup.name = x$print.subgroup.name,
sep.subgroup = x$sep.subgroup,
text.common.w = text.common,
text.random.w = text.random,
text.predict.w = text.predict,
sort.subgroup = gs("sort.subgroup"),
pooled.totals = common  random,
pooled.events = gs("pooled.events"),
pooled.times = gs("pooled.times"),
study.results = gs("study.results"),
xlab = "",
xlab.pos,
smlab = NULL,
smlab.pos,
xlim = "symmetric",
allstudies = TRUE,
weight.study = NULL,
pscale = x$pscale,
irscale = x$irscale,
irunit = x$irunit,
ref,
lower.equi = gs("lower.equi"),
upper.equi = gs("upper.equi"),
lty.equi = gs("lty.equi"),
col.equi = gs("col.equi"),
fill.equi = gs("fill.equi"),
fill.lower.equi = fill.equi,
fill.upper.equi = rev(fill.equi),
leftcols = NULL,
rightcols = NULL,
leftlabs = NULL,
rightlabs = NULL,
label.e = x$label.e,
label.c = x$label.c,
label.e.attach = NULL,
label.c.attach = NULL,
label.right = x$label.right,
label.left = x$label.left,
bottom.lr = gs("bottom.lr"),
lab.NA = gs("lab.NA"),
lab.NA.effect = gs("lab.NA.effect"),
lab.NA.weight = gs("lab.NA.weight"),
lwd = gs("lwd"),
at = NULL,
label = TRUE,
type.study = gs("type.study"),
type.common = gs("type.common"),
type.random = type.common,
type.subgroup = ifelse(study.results, "diamond", "square"),
type.subgroup.common = type.subgroup,
type.subgroup.random = type.subgroup,
col.study = gs("col.study"),
col.square = gs("col.square"),
col.square.lines = col.square,
col.inside = gs("col.inside"),
col.inside.common = col.inside,
col.inside.random = col.inside,
col.diamond = gs("col.diamond"),
col.diamond.common = col.diamond,
col.diamond.random = col.diamond,
col.diamond.lines = gs("col.diamond.lines"),
col.diamond.lines.common = col.diamond.lines,
col.diamond.lines.random = col.diamond.lines,
col.predict = gs("col.predict"),
col.predict.lines = gs("col.predict.lines"),
col.subgroup = gs("col.subgroup"),
col.label.right = gs("col.label.right"),
col.label.left = gs("col.label.left"),
hetstat = common  random  overall.hetstat,
overall.hetstat = x$overall.hetstat,
hetlab = gs("hetlab"),
resid.hetstat = gs("resid.hetstat"),
resid.hetlab = gs("resid.hetlab"),
print.I2 = gs("forest.I2"),
print.I2.ci = gs("forest.I2.ci"),
print.tau2 = gs("forest.tau2"),
print.tau2.ci = gs("forest.tau2.ci"),
print.tau = gs("forest.tau"),
print.tau.ci = gs("forest.tau.ci"),
print.Q = gs("forest.Q"),
print.pval.Q = gs("forest.pval.Q"),
print.Rb = gs("forest.Rb"),
print.Rb.ci = gs("forest.Rb.ci"),
text.subgroup.nohet = gs("text.subgroup.nohet"),
LRT = gs("LRT"),
test.overall = gs("test.overall"),
test.overall.common = common & overall & test.overall,
test.overall.random = random & overall & test.overall,
label.test.overall.common,
label.test.overall.random,
print.stat = gs("forest.stat"),
test.subgroup = x$test.subgroup,
test.subgroup.common = test.subgroup & common,
test.subgroup.random = test.subgroup & random,
prediction.subgroup = x$prediction.subgroup,
print.Q.subgroup = gs("forest.Q.subgroup"),
label.test.subgroup.common,
label.test.subgroup.random,
test.effect.subgroup = gs("test.effect.subgroup"),
test.effect.subgroup.common,
test.effect.subgroup.random,
label.test.effect.subgroup.common,
label.test.effect.subgroup.random,
text.addline1,
text.addline2,
header.line,
fontsize = gs("fontsize"),
fontfamily = gs("fontfamily"),
fs.heading = fontsize,
fs.common = gs("fs.common"),
fs.random = gs("fs.random"),
fs.predict = gs("fs.predict"),
fs.common.labels = gs("fs.common.labels"),
fs.random.labels = gs("fs.random.labels"),
fs.predict.labels = gs("fs.predict.labels"),
fs.study = fontsize,
fs.study.labels = fs.study,
fs.hetstat = gs("fs.hetstat"),
fs.test.overall = gs("fs.test.overall"),
fs.test.subgroup = gs("fs.test.subgroup"),
fs.test.effect.subgroup = gs("fs.test.effect.subgroup"),
fs.addline = gs("fs.addline"),
fs.axis = fontsize,
fs.smlab = fontsize,
fs.xlab = fontsize,
fs.lr = fontsize,
ff.heading = "bold",
ff.common = gs("ff.common"),
ff.random = gs("ff.random"),
ff.predict = gs("ff.predict"),
ff.common.labels = gs("ff.common.labels"),
ff.random.labels = gs("ff.random.labels"),
ff.predict.labels = gs("ff.predict.labels"),
ff.study = "plain",
ff.study.labels = ff.study,
ff.hetstat = gs("ff.hetstat"),
ff.test.overall = gs("ff.test.overall"),
ff.test.subgroup = gs("ff.test.subgroup"),
ff.test.effect.subgroup = gs("ff.test.effect.subgroup"),
ff.addline = gs("ff.addline"),
ff.axis = gs("ff.axis"),
ff.smlab = gs("ff.smlab"),
ff.xlab = gs("ff.xlab"),
ff.lr = gs("ff.lr"),
squaresize = 0.8/spacing,
plotwidth = if (layout == "JAMA") "8cm" else "6cm",
colgap = gs("colgap"),
colgap.left = colgap,
colgap.right = colgap,
colgap.studlab = colgap.left,
colgap.forest = colgap,
colgap.forest.left = colgap.forest,
colgap.forest.right = colgap.forest,
calcwidth.pooled = (common  random) & (overall  !is.null(x$subgroup)),
calcwidth.common = calcwidth.pooled,
calcwidth.random = calcwidth.pooled,
calcwidth.predict = gs("calcwidth.predict"),
calcwidth.hetstat = gs("calcwidth.hetstat"),
calcwidth.tests = gs("calcwidth.tests"),
calcwidth.subgroup = gs("calcwidth.subgroup"),
calcwidth.addline = gs("calcwidth.addline"),
just = if (layout == "JAMA") "left" else "right",
just.studlab = gs("just.studlab"),
just.addcols = gs("just.addcols"),
just.addcols.left = just.addcols,
just.addcols.right = just.addcols,
spacing = gs("spacing"),
addrow = gs("addrow"),
addrow.overall = gs("addrow.overall"),
addrow.subgroups = gs("addrow.subgroups"),
addrows.below.overall = gs("addrows.below.overall"),
new = TRUE,
backtransf = x$backtransf,
digits = gs("digits.forest"),
digits.se = gs("digits.se"),
digits.stat = gs("digits.stat"),
digits.pval = max(gs("digits.pval")  2, 2),
digits.pval.Q = max(gs("digits.pval.Q")  2, 2),
digits.Q = gs("digits.Q"),
digits.tau2 = gs("digits.tau2"),
digits.tau = gs("digits.tau"),
digits.I2 = max(gs("digits.I2")  1, 0),
digits.weight = gs("digits.weight"),
digits.mean = digits,
digits.sd = digits.se,
digits.cor = digits,
digits.time = digits,
digits.TE = gs("digits.TE.forest"),
digits.addcols = digits,
digits.addcols.right = digits.addcols,
digits.addcols.left = digits.addcols,
scientific.pval = gs("scientific.pval"),
big.mark = gs("big.mark"),
zero.pval = if (layout == "JAMA") FALSE else gs("zero.pval"),
JAMA.pval = if (layout == "JAMA") TRUE else gs("JAMA.pval"),
warn.deprecated = gs("warn.deprecated"),
...
)
x 
An object of class 
sortvar 
An optional vector used to sort the individual
studies (must be of same length as 
studlab 
A logical indicating whether study labels should be
printed in the graph. A vector with study labels can also be
provided (must be of same length as 
layout 
A character string specifying the layout of the forest plot (see Details). 
common 
A logical indicating whether common effect estimate should be plotted. 
random 
A logical indicating whether random effects estimate should be plotted. 
overall 
A logical indicating whether overall summaries should be plotted. This argument is useful in a metaanalysis with subgroups if summaries should only be plotted on group level. 
text.common 
A character string used in the plot to label the pooled common effect estimate. 
text.random 
A character string used in the plot to label the pooled random effects estimate. 
lty.common 
Line type (pooled common effect estimate). 
lty.random 
Line type (pooled random effects estimate). 
col.common 
Line colour (pooled common effect estimate). 
col.random 
Line colour (pooled random effects estimate). 
text.w.common 
A character string used to label weights of common effect model. 
text.w.random 
A character string used to label weights of random effects model. 
prediction 
A logical indicating whether a prediction interval should be printed. 
text.predict 
A character string used in the plot to label the prediction interval. 
subgroup 
A single logical or logical vector indicating whether / which subgroup results should be shown in forest plot. This argument is useful in a metaanalysis with subgroups if summaries should not be plotted for (some) subgroups. 
subgroup.hetstat 
A single logical or logical vector indicating whether / which information on heterogeneity in subgroups should be shown in forest plot. This argument is useful in a metaanalysis with subgroups if heterogeneity statistics should not be printed for (some) subgroups. 
print.subgroup.labels 
A logical indicating whether subgroup label should be printed. 
subgroup.name 
A character string with a label for the grouping variable. 
print.subgroup.name 
A logical indicating whether the name of the grouping variable should be printed in front of the group labels. 
sep.subgroup 
A character string defining the separator between label and levels of grouping variable. 
text.common.w 
A character string to label the pooled common effect estimate within subgroups, or a character vector of same length as number of subgroups with corresponging labels. 
text.random.w 
A character string to label the pooled random effect estimate within subgroups, or a character vector of same length as number of subgroups with corresponging labels. 
text.predict.w 
A character string to label the prediction interval within subgroups, or a character vector of same length as number of subgroups with corresponging labels. 
sort.subgroup 
A logical indicating whether groups should be ordered alphabetically. 
pooled.totals 
A logical indicating whether total number of observations should be given in the figure. 
pooled.events 
A logical indicating whether total number of events should be given in the figure. 
pooled.times 
A logical indicating whether total person time at risk should be given in the figure. 
study.results 
A logical indicating whether results for individual studies should be shown in the figure (useful to only plot subgroup results). 
xlab 
A label for the xaxis. 
xlab.pos 
A numeric specifying the center of the label on the xaxis. 
smlab 
A label for the summary measure (printed at top of figure). 
smlab.pos 
A numeric specifying the center of the label for the summary measure. 
xlim 
The x limits (min,max) of the plot, or the character string "symmetric" to produce symmetric forest plots. 
allstudies 
A logical indicating whether studies with inestimable treatment effects should be included in the forest plot. 
weight.study 
A character string indicating weighting used to
determine size of squares or diamonds (argument

pscale 
A numeric giving scaling factor for printing of
single event probabilities or risk differences, i.e. if argument

irscale 
A numeric defining a scaling factor for printing of
single incidence rates or incidence rate differences, i.e. if
argument 
irunit 
A character specifying the time unit used to calculate rates, e.g., personyears. 
ref 
A numerical giving the reference value to be plotted as
a line in the forest plot. No reference line is plotted if
argument 
lower.equi 
A numerical giving the lower limit of equivalence
to be plotted as a line in the forest plot. Or a vector to
provide several limits, e.g., for large, moderate and small
effects. No line is plotted if argument 
upper.equi 
A numerical giving the upper limit of equivalence
to be plotted as a line in the forest plot. Or a vector to
provide several limits, e.g., for small, moderate and large
effects. No line is plotted if argument 
lty.equi 
Line type (limits of equivalence). 
col.equi 
Line colour (limits of equivalence). 
fill.equi 
Colour(s) for area between limits of equivalence or more general limits. 
fill.lower.equi 
Colour of area between lower limit(s) and reference value. Can be equal to the number of lower limits or the number of limits plus 1 (in this case the the region between minimum and smallest limit is also filled). 
fill.upper.equi 
Colour of area between reference value and upper limit(s). Can be equal to the number of upper limits or the number of limits plus 1 (in this case the region between largest limit and maximum is also filled). 
leftcols 
A character vector specifying (additional) columns to be printed on the left side of the forest plot or a logical value (see Details). 
rightcols 
A character vector specifying (additional) columns to be printed on the right side of the forest plot or a logical value (see Details). 
leftlabs 
A character vector specifying labels for (additional) columns on left side of the forest plot (see Details). 
rightlabs 
A character vector specifying labels for (additional) columns on right side of the forest plot (see Details). 
label.e 
Label to be used for experimental group in table heading. 
label.c 
Label to be used for control group in table heading. 
label.e.attach 
A character specifying the column name where
label 
label.c.attach 
A character specifying the column name where
label 
label.right 
Graph label on right side of forest plot. 
label.left 
Graph label on left side of forest plot. 
bottom.lr 
A logical indicating whether labels on right and left side should be printed at bottom or top of forest plot. 
lab.NA 
A character string to label missing values. 
lab.NA.effect 
A character string to label missing values in individual treatment estimates and confidence intervals. 
lab.NA.weight 
A character string to label missing weights. 
lwd 
The line width, see 
at 
The points at which tickmarks are to be drawn, see

label 
A logical value indicating whether to draw the labels
on the tick marks, or an expression or character vector which
specify the labels to use. See 
type.study 
A character string or vector specifying how to plot treatment effects and confidence intervals for individual studies (see Details). 
type.common 
A character string specifying how to plot treatment effect and confidence interval for common effect metaanalysis (see Details). 
type.random 
A character string specifying how to plot treatment effect and confidence interval for random effects metaanalysis (see Details). 
type.subgroup 
A character string specifying how to plot treatment effect and confidence interval for subgroup results (see Details). 
type.subgroup.common 
A character string specifying how to plot treatment effect and confidence interval for subgroup results (common effect model). 
type.subgroup.random 
A character string specifying how to plot treatment effect and confidence interval for subgroup results (random effects model). 
col.study 
The colour for individual study results and confidence limits. 
col.square 
The colour for squares reflecting study's weight in the metaanalysis. 
col.square.lines 
The colour for the outer lines of squares reflecting study's weight in the metaanalysis. 
col.inside 
The colour for individual study results and confidence limits if confidence limits are completely within squares. 
col.inside.common 
The colour for result of common effect metaanalysis if confidence limit lies completely within square. 
col.inside.random 
The colour for result of random effects metaanalysis if confidence limit lies completely within square. 
col.diamond 
The colour of diamonds representing the results for common effect and random effects models. 
col.diamond.common 
The colour of diamonds for common effect estimates. 
col.diamond.random 
The colour of diamonds for random effects estimates. 
col.diamond.lines 
The colour of the outer lines of diamonds representing the results for common effect and random effects models. 
col.diamond.lines.common 
The colour of the outer lines of diamond for common effect estimate. 
col.diamond.lines.random 
The colour of the outer lines of diamond for random effects estimate. 
col.predict 
Background colour of prediction interval. 
col.predict.lines 
Colour of outer lines of prediction interval. 
col.subgroup 
The colour to print information on subgroups. 
col.label.right 
The colour for label on right side of null effect. 
col.label.left 
The colour for label on left side of null effect. 
hetstat 
Either a logical value indicating whether to print results for heterogeneity measures at all or a character string (see Details). 
overall.hetstat 
A logical value indicating whether to print heterogeneity measures for overall treatment comparisons. This argument is useful in a metaanalysis with subgroups if heterogeneity statistics should only be printed on subgroup level. 
hetlab 
Label printed in front of results for heterogeneity measures. 
resid.hetstat 
A logical value indicating whether to print measures of residual heterogeneity in a metaanalysis with subgroups. 
resid.hetlab 
Label printed in front of results for residual heterogeneity measures. 
print.I2 
A logical value indicating whether to print the value of the Isquared statistic. 
print.I2.ci 
A logical value indicating whether to print the confidence interval of the Isquared statistic. 
print.tau2 
A logical value indicating whether to print the
value of the betweenstudy variance 
print.tau2.ci 
A logical value indicating whether to print
the confidence interval of 
print.tau 
A logical value indicating whether to print

print.tau.ci 
A logical value indicating whether to print the
confidence interval of 
print.Q 
A logical value indicating whether to print the value of the heterogeneity statistic Q. 
print.pval.Q 
A logical value indicating whether to print the pvalue of the heterogeneity statistic Q. 
print.Rb 
A logical value indicating whether to print the value of the Isquared statistic. 
print.Rb.ci 
A logical value indicating whether to print the confidence interval of the Isquared statistic. 
text.subgroup.nohet 
A logical value or character string which is printed to indicate subgroups with less than two studies contributing to metaanalysis (and thus without heterogeneity). If FALSE, heterogeneity statistics are printed (with NAs). 
LRT 
A logical value indicating whether to report LikelihoodRatio or Waldtype test of heterogeneity for generalized linear mixed models. 
test.overall 
A logical value indicating whether to print results of test for overall effect. 
test.overall.common 
A logical value indicating whether to print results of test for overall effect (common effect model). 
test.overall.random 
A logical value indicating whether to print results of test for overall effect (random effects model). 
label.test.overall.common 
Label printed in front of results of test for overall effect (common effect model). 
label.test.overall.random 
Label printed in front of results of test for overall effect (random effects model). 
print.stat 
A logical value indicating whether z or tvalue for test of treatment effect should be printed. 
test.subgroup 
A logical value indicating whether to print results of test for subgroup differences. 
test.subgroup.common 
A logical value indicating whether to print results of test for subgroup differences (common effect model). 
test.subgroup.random 
A logical value indicating whether to print results of test for subgroup differences (random effects model). 
prediction.subgroup 
A single logical or logical vector indicating whether / which prediction intervals should be printed for subgroups. 
print.Q.subgroup 
A logical value indicating whether to print the value of the heterogeneity statistic Q (test for subgroup differences). 
label.test.subgroup.common 
Label printed in front of results of test for subgroup differences (common effect model). 
label.test.subgroup.random 
Label printed in front of results of test for subgroup differences (random effects model). 
test.effect.subgroup 
A single logical or logical vector indicating whether / which tests for effect in subgroups should be printed. 
test.effect.subgroup.common 
A single logical or logical vector indicating whether / which tests for effect in subgroups should be printed (common effect model). 
test.effect.subgroup.random 
A single logical or logical vector indicating whether / which tests for effect in subgroups should be printed (random effects model). 
label.test.effect.subgroup.common 
Label printed in front of results of test for effect in subgroups (common effect model). 
label.test.effect.subgroup.random 
Label printed in front of results of test for effect in subgroups (random effects model). 
text.addline1 
Text for first additional line (below metaanalysis results). 
text.addline2 
Text for second additional line (below metaanalysis results). 
header.line 
A logical value indicating whether to print a header line or a character string ("both", "below", ""). 
fontsize 
The size of text (in points), see

fontfamily 
The font family, see 
fs.heading 
The size of text for column headings, see

fs.common 
The size of text for results of common effect
model, see 
fs.random 
The size of text for results of random effects
model, see 
fs.predict 
The size of text for results of prediction
interval, see 
fs.common.labels 
The size of text for label of common effect
model, see 
fs.random.labels 
The size of text for label of random
effects model, see 
fs.predict.labels 
The size of text for label of prediction
interval, see 
fs.study 
The size of text for results of individual studies,
see 
fs.study.labels 
The size of text for labels of individual
studies, see 
fs.hetstat 
The size of text for heterogeneity measures, see

fs.test.overall 
The size of text of test for overall effect,
see 
fs.test.subgroup 
The size of text of test of subgroup
differences, see 
fs.test.effect.subgroup 
The size of text of test of effect
in subgroups, see 
fs.addline 
The size of text for additional lines, see

fs.axis 
The size of text on xaxis, see 
fs.smlab 
The size of text of label for summary measure, see

fs.xlab 
The size of text of label on xaxis, see

fs.lr 
The size of text of label on left and right side of
forest plot, see 
ff.heading 
The fontface for column headings, see

ff.common 
The fontface of text for results of common effect
model, see 
ff.random 
The fontface of text for results of random effects
model, see 
ff.predict 
The fontface of text for results of prediction
interval, see 
ff.common.labels 
The fontface of text for label of common
effect model, see 
ff.random.labels 
The fontface of text for label of random
effects model, see 
ff.predict.labels 
The fontface of text for label of
prediction interval, see 
ff.study 
The fontface of text for results of individual
studies, see 
ff.study.labels 
The fontface of text for labels of
individual studies, see 
ff.hetstat 
The fontface of text for heterogeneity measures,
see 
ff.test.overall 
The fontface of text of test for overall
effect, see 
ff.test.subgroup 
The fontface of text for test of subgroup
differences, see 
ff.test.effect.subgroup 
The fontface of text for test of
effect in subgroups, see 
ff.addline 
The fontface of text for additional lines, see

ff.axis 
The fontface of text on xaxis, see

ff.smlab 
The fontface of text of label for summary measure,
see 
ff.xlab 
The fontface of text of label on xaxis, see

ff.lr 
The fontface of text of label on left and right side
of forest plot, see 
squaresize 
A numeric used to increase or decrease the size of squares in the forest plot. 
plotwidth 
Either a character string, e.g., "8cm", "60mm", or
"3inch", or a 
colgap 
Either a character string or a

colgap.left 
Either a character string or a

colgap.right 
Either a character string or a

colgap.studlab 
Either a character string or a

colgap.forest 
Either a character string or a

colgap.forest.left 
Either a character string or a

colgap.forest.right 
Either a character string or a

calcwidth.pooled 
A logical indicating whether text for common effect and random effects model should be considered to calculate width of the column with study labels. 
calcwidth.common 
A logical indicating whether text given in
arguments 
calcwidth.random 
A logical indicating whether text given in
arguments 
calcwidth.predict 
A logical indicating whether text given in
argument 
calcwidth.hetstat 
A logical indicating whether text for heterogeneity statistics should be considered to calculate width of the column with study labels. 
calcwidth.tests 
A logical indicating whether text for tests of overall effect or subgroup differences should be considered to calculate width of the column with study labels. 
calcwidth.subgroup 
A logical indicating whether text with subgroup labels should be considered to calculate width of the column with study labels. 
calcwidth.addline 
A logical indicating whether text for additional lines should be considered to calculate width of the column with study labels. 
just 
Justification of text in all columns but columns with study labels and additional variables (possible values: "left", "right", "center"). 
just.studlab 
Justification of text for study labels (possible values: "left", "right", "center"). 
just.addcols 
Justification of text for additional columns (possible values: "left", "right", "center"). 
just.addcols.left 
Justification of text for additional columns on left side of forest plot (possible values: "left", "right", "center"). Can be of same length as number of additional columns on left side of forest plot. 
just.addcols.right 
Justification of text for additional columns on right side of forest plot (possible values: "left", "right", "center"). Can be of same length as number of additional columns on right side of forest plot. 
spacing 
A numeric determining line spacing in a forest plot. 
addrow 
A logical value indicating whether an empty row is printed above study results. 
addrow.overall 
A logical value indicating whether an empty row is printed above overall metaanalysis results. 
addrow.subgroups 
A logical value indicating whether an empty row is printed between results for subgroups. 
addrows.below.overall 
A numeric value indicating how many empty rows are printed between metaanalysis results and heterogeneity statistics and test results. 
new 
A logical value indicating whether a new figure should be printed in an existing graphics window. 
backtransf 
A logical indicating whether results should be
back transformed in forest plots. If 
digits 
Minimal number of significant digits for treatment
effects, see 
digits.se 
Minimal number of significant digits for standard
errors, see 
digits.stat 
Minimal number of significant digits for z or
tstatistic for test of overall effect, see 
digits.pval 
Minimal number of significant digits for pvalue
of overall treatment effect, see 
digits.pval.Q 
Minimal number of significant digits for
pvalue of heterogeneity test, see 
digits.Q 
Minimal number of significant digits for
heterogeneity statistic Q, see 
digits.tau2 
Minimal number of significant digits for
betweenstudy variance, see 
digits.tau 
Minimal number of significant digits for square
root of betweenstudy variance, see 
digits.I2 
Minimal number of significant digits for Isquared
statistic, see 
digits.weight 
Minimal number of significant digits for
weights, see 
digits.mean 
Minimal number of significant digits for means;
only applies to 
digits.sd 
Minimal number of significant digits for standard
deviations; only applies to 
digits.cor 
Minimal number of significant digits for
correlations; only applies to 
digits.time 
Minimal number of significant digits for times;
only applies to 
digits.TE 
Minimal number of significant digits for list element 'TE'. 
digits.addcols 
A vector or scalar with minimal number of significant digits for additional columns. 
digits.addcols.right 
A vector or scalar with minimal number of significant digits for additional columns on right side of forest plot. 
digits.addcols.left 
A vector or scalar with minimal number of significant digits for additional columns on left side of forest plot. 
scientific.pval 
A logical specifying whether pvalues should be printed in scientific notation, e.g., 1.2345e01 instead of 0.12345. 
big.mark 
A character used as thousands separator. 
zero.pval 
A logical specifying whether pvalues should be printed with a leading zero. 
JAMA.pval 
A logical specifying whether pvalues for test of overall effect should be printed according to JAMA reporting standards. 
warn.deprecated 
A logical indicating whether warnings should be printed if deprecated arguments are used. 
... 
Additional graphical arguments. 
A forest plot, also called confidence interval plot, is drawn in
the active graphics window. The forest functions in R package
meta are based on the grid graphics system. In order to
print the forest plot, resize the graphics window and either use
dev.copy2eps
or dev.copy2pdf
. Another
possibility is to create a file using pdf
,
png
, or svg
and to specify the width and
height of the graphic (see Examples).
By default, treatment estimates and confidence intervals are plotted in the following way:
For an individual study, a square with treatment estimate in
the center and confidence interval as line extending either side
of the square (type.study = "square"
)
For metaanalysis results, a diamond with treatment estimate
in the center and right and left side corresponding to lower and
upper confidence limits (type.common = "diamond"
,
type.random = "diamond"
, and type.subgroup = "diamond"
)
In a forest plot, size of the squares typically reflects the precision of
individual treatment estimates based either on the common effect
(weight.study = "common"
) or random effects metaanalysis
(weight.study = "random"
). Information from metaanalysis object
x
is utilised if argument weight.study
is missing. Weights
from the common effect model are used if argument x$common
is
TRUE
; weights from the random effects model are used if argument
x$random
is TRUE
and x$common
is FALSE
.
The same square sizes are used if weight.study = "same"
.
A prediction interval for treatment effect of a new study (Higgins
et al., 2009) is given in the forest plot if arguments
prediction
and random
are TRUE
. For
graphical presentation of prediction intervals the approach by
Guddat et al. (2012) is used.
Argument leftcols
can be used to specify columns which are
printed on the left side of the forest plot. By default, i.e. if
argument leftcols
is NULL
and layout = "meta"
,
and depending on the class of the metaanalysis object (which is
defined by the R function used to generate the object) a different
set of columns is printed on the left
side of the forest plot:
Function  Value of argument 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") 
metamean  c("studlab", "n", "mean", "sd")

metaprop  c("studlab", "event", "n") 
metarate  c("studlab", "event", "time", "n")

metacum  "studlab" 
metainf  "studlab"

For threelevel models, the cluster variable is printed next to the
study labels (value "cluster"
in argument leftcols
).
By default, study labels and labels for pooled estimates and
heterogeneity statistics will be printed in the first column on the
left side of the forest plot. The character string "studlab"
is used to identify study labels as this is the name of the list
element of a metaanalysis object.
If the character string "studlab"
is not provided in
leftcols
and rightcols
, the first additional
variable specified by the user is used as study labels (and labels
for pooled estimates are printed in this column). Additional
variables are any variables not mentioned in the section on
predefined column names below. For example, leftcols =
"studlab"
and leftcols = "study"
would result in the same
forest plot if the variable "study"
was used in the command
to conduct the metaanalysis. If no additional variable is provided
by the user, no study labels will be printed.
Depending on the number of columns printed on the left side of the
forest plot, information on heterogeneity measures or statistical
tests (see below) can be overlapping with the xaxis. Argument
addrows.below.overall
can be used to specify the number of
empty rows that are printed between metaanalysis results and
information on heterogeneity measures and statistical tests. By
default, no additional rows are added to the forest plot. If
addrows.below.overall = NULL
, the function tries to add a
sufficient number of empty rows to prevent overlapping
text. Another possibility is to manually increase the space between
the columns on the left side (argument colgap.left
) or
between the columns on the left side and the forest plot (argument
colgap.forest.left
).
Argument rightcols
can be used to
specify columns which are printed on the right side of the
forest plot. If argument rightcols
is
FALSE
, no columns will be printed on the right side. By
default, i.e. if argument rightcols
is
NULL
and layout = "meta"
, the following
columns will be printed on the right side
of the forest plot:
Metaanalysis results  Value of argument rightcols 
No summary  c("effect", "ci") 
Only common effect model  c("effect", "ci", "w.common")

Only random effects model  c("effect", "ci", "w.random")

Both models  c("effect", "ci", "w.common", "w.random")

By default, estimated treatment effect and corresponding confidence
interval will be printed. Depending on arguments common
and
random
, weights of the common effect and/or random effects
model will be given too.
For an object of class metacum
or
metainf
the following columns will be printed:
c("effect", "ci", "pval", "tau2", "tau", "I2")
. This
information corresponds to the printout with
print.meta
.
The arguments leftlabs
and rightlabs
can be used to
specify column headings which are printed on left or right side of
the forest plot. For certain columns predefined labels exist which
are used by default, i.e., if arguments leftlabs
and
rightlabs
are NULL
:
Column:  studlab  TE  seTE 
cluster  n.e  n.c 
Label:  "Study"  "TE"  "seTE"  "Cluster"  "Total"  "Total" 
Column:  n  event.e  event.c 
event  mean.e  mean.c 
Label:  "Total"  "Events"  "Events"  "Events"  "Mean"  "Mean" 
Column:  sd.e  sd.c  time.e
 time.c  effect  
Label:  "SD"  "SD"  "Time"  "Time" 
x$sm  
Column:  ci  effect.ci 
w.common  w.random  
Label:  x$level "%CI"  effect+ci  "W(common)"  "W(random)" 
For other columns, the column name will be used as a label if no
column label is defined. 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 which
should use default labels (see Examples).
In pairwise metaanalysis comparing two groups (i.e.,
metabin
, metacont
,
metainc
, and metagen
depending on the
outcome), arguments label.e
and label.c
are used to
label columns belonging to the two treatment groups. By default,
labels defined in the metaanalysis object are used. The columns
where treatment labels are attached can be changed using arguments
label.e.attach
and label.c.attach
.
Argument hetstat
can be a character string to specify where
to print heterogeneity information:
row with results for common effect model (hetstat =
"common"
),
row with results for random effects model (hetstat =
"random"
).
Otherwise, information on heterogeneity measures is printed below
the metaanalysis results if argument overall.hetstat = TRUE
(default). The heterogeneity measures to print can be specified
(see list of arguments following overall.hetstat
).
In addition, the following arguments can be used to print results for various statistical tests:
Argument  Statistical test 
test.overall.common  Test for overall effect (common effect model) 
test.overall.random  Test for overall effect (random effects model) 
test.effect.subgroup.common  Test for effect in subgroup (CE model) 
test.effect.subgroup.random  Test for effect in subgroup (RE model) 
test.subgroup.common  Test for subgroup differences (CE model) 
test.subgroup.random  Test for subgroup differences (RE model) 
By default, these arguments are FALSE
with exception of
tests for subgroup differences which are TRUE
. R function
settings.meta
can be used to change this default for
the entire R session. For example, use the following command to
always print results of tests for an overall effect:
settings.meta(test.overall = TRUE)
.
Argument subgroup
determines whether summary results are
printed for subgroups. A logical vector of length equal to the
number of subgroups can be provided to determine which subgroup
summaries are printed. By default, only subgroup results based on
at least two studies are printed which is identical to use argument
subgroup = k.w > 1
. The order of the logical vector
corresponds to the order of subgroups in list element 'subgroup.levels' of a
metaanalysis object. Argument subgroup = k.w >= 1
can be
used to show results for all subgroups (including those with a
single study).
The following arguments can be used in a similar way:
subgroup.hetstat
(heterogeneity statistic in
subgroups),
prediction.subgroup
(prediction interval in
subgroups),
test.effect.subgroup
(test for effect in subgroups),
test.effect.subgroup.common
(test for effect in
subgroups, common effect model),
test.effect.subgroup.random
(test for effect in
subgroups, random effects model).
Arguments text.common
, text.random
, and
text.predict
can be used to change the label to identify
overall results (common effect and random effects model as well as
prediction interval). By default the following text is printed:
"Common effect model" (argument text.common
)
"Random effects model" (text.random
)
"Prediction interval" (text.predict
)
If confidence interval levels are different for individual studies,
metaanalysis, and prediction interval (arguments level
,
level.ma
, level.predict
in metaanalysis functions,
e.g., metabin
), additional information is printed,
e.g., " (99%CI)" for a 99% confidence interval in the
metaanalysis.
Argument pscale
can be used to rescale single proportions or
risk differences, e.g., pscale = 1000
means that proportions
are expressed as events per 1000 observations. This is useful in
situations with (very) low event probabilities.
Argument irscale
can be used to rescale single rates or rate
differences, e.g., irscale = 1000
means that rates are
expressed as events per 1000 time units, e.g., personyears. This is
useful in situations with (very) low rates. Argument irunit
can be used to specify the time unit used in individual studies
(default: "personyears"). This information is printed in summaries
and forest plots if argument irscale
is not equal to 1.
If argument layout = "RevMan5"
(and arguments leftcols
and
rightcols
are NULL
), the layout for forest plots used for
Cochrane reviews (which are generated with Review Manager 5,
https://training.cochrane.org/onlinelearning/coresoftware/revman)
is reproduced:
All columns are printed on the left side of the forest plot
(see arguments leftcols
and rightcols
)
Tests for overall effect and subgroup differences are printed
(test.overall
, test.effect.subgroup
,
test.subgroup
)
Diamonds representing metaanalysis results are printed in
black (diamond.common
, diamond.random
)
Colour of squares depends on the metaanalysis object
(col.square
, col.square.lines
)
Information on effect measure and metaanalysis method is
printed above the forest plot (smlab
)
Label "Study or Subgroup" is printed for metaanalysis with
subgroups (leftlabs
)
If argument layout = "JAMA"
(and arguments leftcols
and
rightcols
are NULL
), instructions for authors of the
Journal of the American Medical Association, see
https://jamanetwork.com/journals/jama/pages/instructionsforauthors/,
are taken into account:
Graph labels on right and left side are printed in bold font
at top of forest plot (see arguments bottom.lr
and
ff.lr
)
Information on effect measure and level of confidence
interval is printed at bottom of forest plot (xlab
)
Tests for overall effect are printed (test.overall
)
Diamonds representing metaanalysis results are printed in
lightblue (diamond.common
, diamond.random
)
Squares representing individual study results are printed in
darkblue (col.square
, col.square.lines
)
Betweenstudy variance \tau^2
is not printed
Empty rows are omitted (addrow
, addrow.overall
,
addrow.subgroups
)
Label "Source" is printed instead of "Study" (leftlabs
)
Pvalues are printed without leading zeros (zero.pval
)
Pvalues are rounded to three digits (for 0.001 < p \le
0.01) or two digits (p > 0.01) (JAMA.pval
)
Study labels according to JAMA guidelines can be generated using
labels.meta
.
The following changes are conducted if argument
layout = "subgroup"
(and arguments leftcols
and
rightcols
are NULL
) and a subgroup analysis was
conducted:
Individual study results are omitted (see argument
study.results
)
Total number of observations is not printed
(pooled.totals
)
Label "Subgroup" is printed instead of "Study"
(leftlabs
)
Guido Schwarzer guido.schwarzer@uniklinikfreiburg.de
Guddat C, Grouven U, Bender R, Skipka G (2012): A note on the graphical presentation of prediction intervals in randomeffects metaanalyses. Systematic Reviews, 1, 34
Higgins JPT, Thompson SG, Spiegelhalter DJ (2009): A reevaluation of randomeffects metaanalysis. Journal of the Royal Statistical Society: Series A, 172, 13759
metabin
, metacont
,
metagen
, forest.metabind
,
settings.meta
, labels.meta
data(Olkin1995)
m1 < metabin(ev.exp, n.exp, ev.cont, n.cont,
data = Olkin1995, subset = c(41, 47, 51, 59),
sm = "RR", method = "I",
studlab = paste(author, year))
## Not run:
# Do standard (symmetric) forest plot
#
forest(m1)
## End(Not run)
# Layout of forest plot similar to Review Manager 5
#
# Furthermore, add labels on both sides of forest plot and
# prediction interval
#
forest(m1, layout = "RevMan5", common = FALSE,
label.right = "Favours control", col.label.right = "red",
label.left = "Favours experimental", col.label.left = "green",
prediction = TRUE)
## Not run:
# Create a PDF file forestm1.pdf with the forest plot
#
pdf("forestm1.pdf", width = 10, height = 3)
forest(m1)
dev.off()
# Define equivalence limits: 0.75 and 1 / 0.75
#
forest(m1, layout = "RevMan5", common = FALSE,
lower.equi = 0.75, upper.equi = 1 / 0.75, fill.equi = "lightgray")
# Fill areas with beneficial and detrimental effects
#
forest(m1, layout = "RevMan5", common = FALSE,
lower.equi = 0.75, upper.equi = 1 / 0.75,
fill.lower.equi = c("green", "lightgray"),
fill.upper.equi = c("lightgray", "red"))
# Define thresholds for small, moderate and large effects
# and use hcl.colors() to define colours to fill areas
#
thresholds < c(0.25, 0.5, 0.75)
n.cols < length(thresholds) + 1
forest(m1, layout = "RevMan5", common = FALSE,
label.right = "Undesirable effect",
label.left = "Desirable effect",
lty.equi = 3, col.equi = "darkgray",
lower.equi = thresholds, upper.equi = 1 / rev(thresholds),
fill.lower.equi =
hcl.colors(n.cols, palette = "Blues 2", alpha = 0.6),
fill.upper.equi =
hcl.colors(n.cols, palette = "Oranges", alpha = 0.6, rev = TRUE))
# Conduct subgroup metaanalysis
#
m2 < update(m1,
subgroup = ifelse(year < 1987, "Before 1987", "1987 and later"),
print.subgroup.name = FALSE)
# Show summary results for subgroups with at least two studies
#
forest(m2, sortvar = TE, random = FALSE)
# Show results for all subgroups
#
forest(m2, sortvar = TE, random = FALSE, subgroup = k.w >= 1)
# Forest plot specifying argument xlim
#
forest(m1, xlim = c(0.01, 10))
# Print results of test for overall effect
#
forest(m1, test.overall.common = TRUE, test.overall.random = TRUE)
# Forest plot with 'classic' layout used in R package meta,
# version < 1.60
#
forest(m1, col.square = "black", hetstat = FALSE)
# Change set of columns printed on left side of forest plot
# (resulting in overlapping text)
#
forest(m1, random = FALSE, leftcols = "studlab")
# Use argument 'calcwidth.hetstat' to consider text for heterogeneity
# measures in width of column with study labels
#
forest(m1, random = FALSE, leftcols = "studlab",
calcwidth.hetstat = TRUE)
# Use argument 'addrows.below.overall' to manually add two empty
# rows
#
forest(m1, random = FALSE, leftcols = "studlab", addrows = 2)
# Do not print columns on right side of forest plot
#
forest(m1, rightcols = FALSE)
# Change study label to "Author"
#
forest(m1, random = FALSE, leftlabs = c("Author", NA, NA, NA, NA))
# Just give effect estimate and 95% confidence interval on right
# side of forest plot (in one column)
#
forest(m1, rightcols = c("effect.ci"))
# Just give effect estimate and 95% confidence interval on right
# side of forest plot
#
forest(m1, 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(m1,
leftcols = c("studlab", "n.e", "event.e", "n.c", "event.c"),
label.e.attach = "event.e", label.c.attach = "event.c")
# Specify column labels only for variables 'year' and 'author'
# (and define digits for additional variables)
#
forest(m1,
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(m1,
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(m1,
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(m1,
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.common = "plain", ff.hetstat = "plain")
# Change some colours
#
forest(m1,
col.diamond = "green", col.diamond.lines = "red",
col.study = c("green", "blue", "red", "orange"),
col.square = "pink", col.square.lines = "black")
# Sort by weight in common effect model
#
forest(m1, sortvar = w.common, random = FALSE)
# Sort by decreasing weight in common effect model
#
forest(m1, sortvar = w.common, random = FALSE)
# Sort by size of treatment effect
#
forest(m1, sortvar = TE, random = FALSE)
# Sort by size of treatment effect
#
forest(m1, sortvar = TE, random = FALSE)
# Sort by decreasing year of publication
#
forest(m1, sortvar = year, random = FALSE)
# Print results of test for subgroup differences (random effects
# model)
#
forest(m2, sortvar = TE, common = FALSE)
# Print only subgroup results
#
forest(m2, layout = "subgroup")
# Print only subgroup results (and consider text for tests of
# subgroup differences in width of subgroup column)
#
forest(m2, layout = "subgroup", calcwidth.tests = TRUE)
# Print only subgroup results (and consider text for heterogeneity
# in width of subgroup column)
#
forest(m2, layout = "subgroup", calcwidth.hetstat = TRUE)
## End(Not run)
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