% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/forest.R
\name{forest.meta}
\alias{forest.meta}
\alias{forest}
\alias{plot.meta}
\alias{.forestArgs}
\title{Forest plot to display the result of a meta-analysis}
\usage{
\method{forest}{meta}(
  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"),
  rob = x$rob,
  rob.text = "Risk of Bias",
  rob.xpos = 0,
  rob.legend = TRUE,
  rob.only = FALSE,
  xlab = "",
  xlab.pos,
  smlab = NULL,
  smlab.pos,
  xlim,
  allstudies = TRUE,
  weight.study = NULL,
  pscale = x$pscale,
  irscale = x$irscale,
  irunit = x$irunit,
  file = NULL,
  width = gs("width"),
  rows.gr = NULL,
  func.gr = NULL,
  args.gr = NULL,
  dev.off = NULL,
  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),
  fill = gs("fill"),
  leftcols = gs("leftcols"),
  rightcols = gs("rightcols"),
  leftlabs = gs("leftlabs"),
  rightlabs = gs("rightlabs"),
  label.e = x$label.e,
  label.c = x$label.c,
  label.e.attach = gs("label.e.attach"),
  label.c.attach = gs("label.c.attach"),
  label.left = x$label.left,
  label.right = x$label.right,
  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,
  col.label = gs("col.label"),
  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 = gs("col.square.lines"),
  col.circle = gs("col.circle"),
  col.circle.lines = col.circle,
  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.left = x$col.label.left,
  col.label.right = x$col.label.right,
  hetstat = common | random | overall.hetstat,
  overall.hetstat = x$overall.hetstat & !inherits(x, "metamerge"),
  hetlab = gs("hetlab"),
  resid.hetstat = gs("resid.hetstat"),
  resid.hetlab = gs("resid.hetlab"),
  print.I2 = gs("forest.I2"),
  print.I2.ci = gs("forest.I2.ci"),
  print.tau2 = gs("forest.tau2"),
  print.tau2.ci = gs("forest.tau2.ci"),
  print.tau = gs("forest.tau"),
  print.tau.ci = gs("forest.tau.ci"),
  print.Q = gs("forest.Q"),
  print.pval.Q = gs("forest.pval.Q"),
  print.Rb = gs("forest.Rb"),
  print.Rb.ci = gs("forest.Rb.ci"),
  text.subgroup.nohet = gs("text.subgroup.nohet"),
  LRT = gs("LRT"),
  test.overall = gs("test.overall"),
  test.overall.common = common & overall & test.overall,
  test.overall.random = random & overall & test.overall,
  label.test.overall.common,
  label.test.overall.random,
  print.stat = gs("forest.stat"),
  test.subgroup = x$test.subgroup,
  test.subgroup.common = test.subgroup & common,
  test.subgroup.random = test.subgroup & random,
  common.subgroup = common,
  random.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,
  details = gs("forest.details"),
  col.lines = gs("col.lines"),
  header.line,
  col.header.line = col.lines,
  col.jama.line = col.subgroup,
  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,
  fs.rob = fontsize,
  fs.rob.symbols = fontsize,
  fs.details = 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"),
  ff.rob = "plain",
  ff.rob.symbols = "bold",
  ff.details = "plain",
  squaresize = if (layout == "BMJ") 0.9/spacing else 0.8/spacing,
  lwd.square = gs("lwd.square"),
  lwd.diamond = gs("lwd.diamond"),
  arrow.type = gs("arrow.type"),
  arrow.length = gs("arrow.length"),
  plotwidth = if (layout \%in\% c("BMJ", "JAMA")) "8cm" else "6cm",
  colgap = gs("colgap"),
  colgap.left = colgap,
  colgap.right = colgap,
  colgap.studlab = colgap.left,
  colgap.forest = gs("colgap.forest"),
  colgap.forest.left = colgap.forest,
  colgap.forest.right = colgap.forest,
  colgap.rob = "1mm",
  colgap.rob.overall = "2mm",
  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,
  bmj.text = NULL,
  bmj.xpos = 0,
  bmj.sep = " / ",
  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 = gs("digits.pval"),
  digits.pval.Q = gs("digits.pval.Q"),
  digits.Q = gs("digits.Q"),
  digits.tau2 = gs("digits.tau2"),
  digits.tau = gs("digits.tau"),
  digits.I2 = gs("digits.I2"),
  digits.weight = gs("digits.weight"),
  digits.mean = gs("digits.mean"),
  digits.sd = gs("digits.sd"),
  digits.cor = digits,
  digits.time = digits,
  digits.n = 0,
  digits.event = 0,
  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"),
  ...
)

\method{plot}{meta}(x, ...)

.forestArgs()
}
\arguments{
\item{x}{An object of class \code{meta}.}

\item{sortvar}{An optional vector used to sort the individual
studies (must be of same length as \code{x$TE}).}

\item{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 \code{x$TE} then).}

\item{layout}{A character string specifying the layout of the
forest plot (see Details).}

\item{common}{A logical indicating whether common effect estimate
should be plotted.}

\item{random}{A logical indicating whether random effects estimate
should be plotted.}

\item{overall}{A logical indicating whether overall summaries
should be plotted. This argument is useful in a meta-analysis
with subgroups if summaries should only be plotted on group
level.}

\item{text.common}{A character string used in the plot to label the
pooled common effect estimate.}

\item{text.random}{A character string used in the plot to label the
pooled random effects estimate.}

\item{lty.common}{Line type of pooled common effect estimate.}

\item{lty.random}{Line type of pooled random effects estimate.}

\item{col.common}{Line colour of pooled common effect estimate.}

\item{col.random}{Line colour of pooled random effects estimate.}

\item{text.w.common}{A character string used to label weights of
common effect model.}

\item{text.w.random}{A character string used to label weights of
random effects model.}

\item{prediction}{A logical indicating whether a prediction
interval should be printed.}

\item{text.predict}{A character string used in the plot to label
the prediction interval.}

\item{subgroup}{A single logical or logical vector indicating
whether / which subgroup results should be shown in forest
plot. This argument is useful in a meta-analysis with subgroups
if summaries should not be plotted for (some) subgroups.}

\item{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 meta-analysis with subgroups if heterogeneity statistics
should not be printed for (some) subgroups.}

\item{print.subgroup.labels}{A logical indicating whether subgroup
label should be printed.}

\item{subgroup.name}{A character string with a label for the
grouping variable.}

\item{print.subgroup.name}{A logical indicating whether the name of
the grouping variable should be printed in front of the group
labels.}

\item{sep.subgroup}{A character string defining the separator
between label and levels of grouping variable.}

\item{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.}

\item{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.}

\item{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.}

\item{sort.subgroup}{A logical indicating whether groups should be
ordered alphabetically.}

\item{pooled.totals}{A logical indicating whether total number of
observations should be given in the figure.}

\item{pooled.events}{A logical indicating whether total number of
events should be given in the figure.}

\item{pooled.times}{A logical indicating whether total person time
at risk should be given in the figure.}

\item{study.results}{A logical indicating whether results for
individual studies should be shown in the figure (useful to only
plot subgroup results).}

\item{rob}{Risk of bias (RoB) assessment.}

\item{rob.text}{Column heading for RoB table.}

\item{rob.xpos}{A numeric specifying the horizontal position of the
risk of bias label in RoB table heading. The value is a so called
normalised parent coordinate in the horizontal direction (see
\code{\link[grid]{unit}}).}

\item{rob.legend}{A logical specifying whether a legend with RoB
domains should be printed.}

\item{rob.only}{A logical indicating whether the risk of bias
assessment is the only information printed on the right side of
the forest plot.}

\item{xlab}{A label for the x-axis.}

\item{xlab.pos}{A numeric specifying the center of the label on the
x-axis.}

\item{smlab}{A label for the summary measure (printed at top of
figure).}

\item{smlab.pos}{A numeric specifying the center of the label for
the summary measure.}

\item{xlim}{The x limits (min,max) of the plot, or the character
string "symmetric" to produce symmetric forest plots.}

\item{allstudies}{A logical indicating whether studies with
inestimable treatment effects should be included in the forest
plot.}

\item{weight.study}{A character string indicating weighting used to
determine size of squares or diamonds (argument
\code{type.study}) to plot individual study results. One of
missing, \code{"same"}, \code{"common"}, or \code{"random"}, can
be abbreviated. Plot symbols have the same size for all studies
or represent study weights from common effect or random effects
model.}

\item{pscale}{A numeric giving scaling factor for printing of
single event probabilities or risk differences, i.e. if argument
\code{sm} is equal to \code{"PLOGIT"}, \code{"PLN"},
\code{"PRAW"}, \code{"PAS"}, \code{"PFT"}, or \code{"RD"}.}

\item{irscale}{A numeric defining a scaling factor for printing of
single incidence rates or incidence rate differences, i.e. if
argument \code{sm} is equal to \code{"IR"}, \code{"IRLN"},
\code{"IRS"}, \code{"IRFT"}, or \code{"IRD"}.}

\item{irunit}{A character specifying the time unit used to
calculate rates, e.g., person-years.}

\item{file}{File name.}

\item{width}{Width of graphics file.}

\item{rows.gr}{Additional rows in forest plot to change height of
graphics file (e.g., in order to add a title at the top of the
forest plot).}

\item{func.gr}{Name of graphics function, e.g., \code{\link{pdf}}.}

\item{args.gr}{List with additional graphical parameters passed on
to graphics function (argument 'height' cannot be provided as the
height is calculated internally; use instead argument 'rows.gr').}

\item{dev.off}{A logical to specify whether current graphics device
should be shut down, i.e., whether file should be stored.}

\item{ref}{A numerical giving the reference value to be plotted as
a line in the forest plot. No reference line is plotted if
argument \code{ref} is equal to \code{NA}.}

\item{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 \code{lower.equi} is
equal to \code{NA}.}

\item{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 \code{upper.equi} is
equal to \code{NA}.}

\item{lty.equi}{Line type (limits of equivalence).}

\item{col.equi}{Line colour (limits of equivalence).}

\item{fill.equi}{Colour(s) for area between limits of equivalence
or more general limits.}

\item{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).}

\item{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).}

\item{fill}{Colour for background of confidence interval plot.}

\item{leftcols}{A character vector specifying (additional) columns
to be printed on the left side of the forest plot or a logical
value (see Details).}

\item{rightcols}{A character vector specifying (additional) columns
to be printed on the right side of the forest plot or a logical
value (see Details).}

\item{leftlabs}{A character vector specifying labels for
(additional) columns on left side of the forest plot (see
Details).}

\item{rightlabs}{A character vector specifying labels for
(additional) columns on right side of the forest plot (see
Details).}

\item{label.e}{Label to be used for experimental group in table
heading.}

\item{label.c}{Label to be used for control group in table heading.}

\item{label.e.attach}{A character specifying the column name where
label \code{label.e} should be attached to in table heading.}

\item{label.c.attach}{A character specifying the column name where
label \code{label.c} should be attached to in table heading.}

\item{label.left}{Graph label on left side of null effect.}

\item{label.right}{Graph label on right side of null effect.}

\item{bottom.lr}{A logical indicating whether labels on right and
left side should be printed at bottom or top of forest plot.}

\item{lab.NA}{A character string to label missing values.}

\item{lab.NA.effect}{A character string to label missing values in
individual treatment estimates and confidence intervals.}

\item{lab.NA.weight}{A character string to label missing weights.}

\item{lwd}{The line width, see \code{\link{par}}.}

\item{at}{The points at which tick-marks are to be drawn, see
\code{grid.xaxis}.}

\item{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 \code{\link{grid.xaxis}}.}

\item{col.label}{The colour of labels on the x-axis.}

\item{type.study}{A character string or vector specifying how to
plot treatment effects and confidence intervals for individual
studies (see Details).}

\item{type.common}{A character string specifying how to plot
treatment effect and confidence interval for common effect
meta-analysis (see Details).}

\item{type.random}{A character string specifying how to plot
treatment effect and confidence interval for random effects
meta-analysis (see Details).}

\item{type.subgroup}{A character string specifying how to plot
treatment effect and confidence interval for subgroup results
(see Details).}

\item{type.subgroup.common}{A character string specifying how to
plot treatment effect and confidence interval for subgroup
results (common effect model).}

\item{type.subgroup.random}{A character string specifying how to
plot treatment effect and confidence interval for subgroup
results (random effects model).}

\item{col.study}{The colour for individual study results and
confidence limits.}

\item{col.square}{The colour for squares reflecting study's weight
in the meta-analysis.}

\item{col.square.lines}{The colour for the outer lines of squares
reflecting study's weight in the meta-analysis.}

\item{col.circle}{The colour for circles reflecting study weights
in the meta-analysis.}

\item{col.circle.lines}{The colour for the outer lines of circles
reflecting study's weight in the meta-analysis.}

\item{col.inside}{The colour for individual study results and
confidence limits if confidence limits are completely within
squares.}

\item{col.inside.common}{The colour for result of common effect
meta-analysis if confidence limit lies completely within square.}

\item{col.inside.random}{The colour for result of random effects
meta-analysis if confidence limit lies completely within square.}

\item{col.diamond}{The colour of diamonds representing the results
for common effect and random effects models.}

\item{col.diamond.common}{The colour(s) of diamonds for common effect
estimates.}

\item{col.diamond.random}{The colour(s) of diamonds for random effects
estimates.}

\item{col.diamond.lines}{The colour of the outer lines of diamonds
representing the results for common effect and random effects
models.}

\item{col.diamond.lines.common}{The colour(s) of the outer lines of
diamond for common effect estimate.}

\item{col.diamond.lines.random}{The colour(s) of the outer lines of
diamond for random effects estimate.}

\item{col.predict}{Background colour(s) of prediction intervals.}

\item{col.predict.lines}{Colour(s) of outer lines of prediction
intervals.}

\item{col.subgroup}{The colour to print information on subgroups.}

\item{col.label.left}{The colour of the label on the left side of the null
effect.}

\item{col.label.right}{The colour of the label on the right side of the null
effect.}

\item{hetstat}{Either a logical value indicating whether to print
results for heterogeneity measures at all or a character string
(see Details).}

\item{overall.hetstat}{A logical value indicating whether to print
heterogeneity measures for overall treatment comparisons. This
argument is useful in a meta-analysis with subgroups if
heterogeneity statistics should only be printed on subgroup
level.}

\item{hetlab}{Label printed in front of results for heterogeneity
measures.}

\item{resid.hetstat}{A logical value indicating whether to print
measures of residual heterogeneity in a meta-analysis with
subgroups.}

\item{resid.hetlab}{Label printed in front of results for residual
heterogeneity measures.}

\item{print.I2}{A logical value indicating whether to print the
value of the I-squared statistic.}

\item{print.I2.ci}{A logical value indicating whether to print the
confidence interval of the I-squared statistic.}

\item{print.tau2}{A logical value indicating whether to print the
value of the between-study variance \eqn{\tau^2}.}

\item{print.tau2.ci}{A logical value indicating whether to print
the confidence interval of \eqn{\tau^2}.}

\item{print.tau}{A logical value indicating whether to print
\eqn{\tau}, the square root of the between-study variance
\eqn{\tau^2}.}

\item{print.tau.ci}{A logical value indicating whether to print the
confidence interval of \eqn{\tau}.}

\item{print.Q}{A logical value indicating whether to print the
value of the heterogeneity statistic Q.}

\item{print.pval.Q}{A logical value indicating whether to print the
p-value of the heterogeneity statistic Q.}

\item{print.Rb}{A logical value indicating whether to print the
value of the I-squared statistic.}

\item{print.Rb.ci}{A logical value indicating whether to print the
confidence interval of the I-squared statistic.}

\item{text.subgroup.nohet}{A logical value or character string
which is printed to indicate subgroups with less than two studies
contributing to meta-analysis (and thus without
heterogeneity). If FALSE, heterogeneity statistics are printed
(with NAs).}

\item{LRT}{A logical value indicating whether to report
Likelihood-Ratio or Wald-type test of heterogeneity for
generalised linear mixed models.}

\item{test.overall}{A logical value indicating whether to print
results of test for overall effect.}

\item{test.overall.common}{A logical value indicating whether to
print results of test for overall effect (common effect model).}

\item{test.overall.random}{A logical value indicating whether to
print results of test for overall effect (random effects model).}

\item{label.test.overall.common}{Label printed in front of results
of test for overall effect (common effect model).}

\item{label.test.overall.random}{Label printed in front of results
of test for overall effect (random effects model).}

\item{print.stat}{A logical value indicating whether z- or t-value
for test of treatment effect should be printed.}

\item{test.subgroup}{A logical value indicating whether to print
results of test for subgroup differences.}

\item{test.subgroup.common}{A logical value indicating whether to
print results of test for subgroup differences (common effect
model).}

\item{test.subgroup.random}{A logical value indicating whether to
print results of test for subgroup differences (random effects
model).}

\item{common.subgroup}{A single logical or logical vector
indicating whether / which common effect estimates should be
printed for subgroups.}

\item{random.subgroup}{A single logical or logical vector
indicating whether / which random effects estimates should be
printed for subgroups.}

\item{prediction.subgroup}{A single logical or logical vector
indicating whether / which prediction intervals should be printed
for subgroups.}

\item{print.Q.subgroup}{A logical value indicating whether to print
the value of the heterogeneity statistic Q (test for subgroup
differences).}

\item{label.test.subgroup.common}{Label printed in front of results
of test for subgroup differences (common effect model).}

\item{label.test.subgroup.random}{Label printed in front of results
of test for subgroup differences (random effects model).}

\item{test.effect.subgroup}{A single logical or logical vector
indicating whether / which tests for effect in subgroups should
be printed.}

\item{test.effect.subgroup.common}{A single logical or logical
vector indicating whether / which tests for effect in subgroups
should be printed (common effect model).}

\item{test.effect.subgroup.random}{A single logical or logical
vector indicating whether / which tests for effect in subgroups
should be printed (random effects model).}

\item{label.test.effect.subgroup.common}{Label printed in front of
results of test for effect in subgroups (common effect model).}

\item{label.test.effect.subgroup.random}{Label printed in front of
results of test for effect in subgroups (random effects model).}

\item{text.addline1}{Text for first additional line (below
meta-analysis results).}

\item{text.addline2}{Text for second additional line (below
meta-analysis results).}

\item{details}{A logical specifying whether details on statistical
methods should be printed.}

\item{col.lines}{The colour of lines.}

\item{header.line}{A logical value indicating whether to print a
header line or a character string ("both", "below", "").}

\item{col.header.line}{Colour of the header line(s).}

\item{col.jama.line}{Colour of the additional JAMA lines.}

\item{fontsize}{The size of text (in points), see
\code{\link{gpar}}.}

\item{fontfamily}{The font family, see \code{\link{gpar}}.}

\item{fs.heading}{The size of text for column headings, see
\code{\link{gpar}}.}

\item{fs.common}{The size of text for results of common effect
model, see \code{\link{gpar}}.}

\item{fs.random}{The size of text for results of random effects
model, see \code{\link{gpar}}.}

\item{fs.predict}{The size of text for results of prediction
interval, see \code{\link{gpar}}.}

\item{fs.common.labels}{The size of text for label of common effect
model, see \code{\link{gpar}}.}

\item{fs.random.labels}{The size of text for label of random
effects model, see \code{\link{gpar}}.}

\item{fs.predict.labels}{The size of text for label of prediction
interval, see \code{\link{gpar}}.}

\item{fs.study}{The size of text for results of individual studies,
see \code{\link{gpar}}.}

\item{fs.study.labels}{The size of text for labels of individual
studies, see \code{\link{gpar}}.}

\item{fs.hetstat}{The size of text for heterogeneity measures, see
\code{\link{gpar}}.}

\item{fs.test.overall}{The size of text of test for overall effect,
see \code{\link{gpar}}.}

\item{fs.test.subgroup}{The size of text of test of subgroup
differences, see \code{\link{gpar}}.}

\item{fs.test.effect.subgroup}{The size of text of test of effect
in subgroups, see \code{\link{gpar}}.}

\item{fs.addline}{The size of text for additional lines, see
\code{\link{gpar}}.}

\item{fs.axis}{The size of text on x-axis, see \code{\link{gpar}}.}

\item{fs.smlab}{The size of text of label for summary measure, see
\code{\link{gpar}}.}

\item{fs.xlab}{The size of text of label on x-axis, see
\code{\link{gpar}}.}

\item{fs.lr}{The size of text of label on left and right side of
forest plot, see \code{\link{gpar}}.}

\item{fs.rob}{The size of text of risk of bias items in the legend,
see \code{\link{gpar}}.}

\item{fs.rob.symbols}{The size of risk of bias symbols, see
\code{\link{gpar}}.}

\item{fs.details}{The size of text for details on (meta-analysis)
methods, see \code{\link{gpar}}.}

\item{ff.heading}{The fontface for column headings, see
\code{\link{gpar}}.}

\item{ff.common}{The fontface of text for results of common effect
model, see \code{\link{gpar}}.}

\item{ff.random}{The fontface of text for results of random effects
model, see \code{\link{gpar}}.}

\item{ff.predict}{The fontface of text for results of prediction
interval, see \code{\link{gpar}}.}

\item{ff.common.labels}{The fontface of text for label of common
effect model, see \code{\link{gpar}}.}

\item{ff.random.labels}{The fontface of text for label of random
effects model, see \code{\link{gpar}}.}

\item{ff.predict.labels}{The fontface of text for label of
prediction interval, see \code{\link{gpar}}.}

\item{ff.study}{The fontface of text for results of individual
studies, see \code{\link{gpar}}.}

\item{ff.study.labels}{The fontface of text for labels of
individual studies, see \code{\link{gpar}}.}

\item{ff.hetstat}{The fontface of text for heterogeneity measures,
see \code{\link{gpar}}.}

\item{ff.test.overall}{The fontface of text of test for overall
effect, see \code{\link{gpar}}.}

\item{ff.test.subgroup}{The fontface of text for test of subgroup
differences, see \code{\link{gpar}}.}

\item{ff.test.effect.subgroup}{The fontface of text for test of
effect in subgroups, see \code{\link{gpar}}.}

\item{ff.addline}{The fontface of text for additional lines, see
\code{\link{gpar}}.}

\item{ff.axis}{The fontface of text on x-axis, see
\code{\link{gpar}}.}

\item{ff.smlab}{The fontface of text of label for summary measure,
see \code{\link{gpar}}.}

\item{ff.xlab}{The fontface of text of label on x-axis, see
\code{\link{gpar}}.}

\item{ff.lr}{The fontface of text of label on left and right side
of forest plot, see \code{\link{gpar}}.}

\item{ff.rob}{The fontface of text of risk of bias items, see
\code{\link{gpar}}.}

\item{ff.rob.symbols}{The fontface of risk of bias symbols, see
\code{\link{gpar}}.}

\item{ff.details}{The fontface for details on (meta-analysis)
methods, see \code{\link{gpar}}.}

\item{squaresize}{A numeric used to increase or decrease the size
of squares in the forest plot.}

\item{lwd.square}{The line width of the border around squares.}

\item{lwd.diamond}{The line width of the border around diamonds.}

\item{arrow.type}{A character string indicating whether arrows
printed for results outside the forest plot should be
\code{"open"}, or \code{"closed"}, can be abbreviated.}

\item{arrow.length}{The length of arrows in inches.}

\item{plotwidth}{Either a character string, e.g., "8cm", "60mm", or
"3inch", or a \code{\link[grid]{unit}} object specifying width of
the forest plot.}

\item{colgap}{Either a character string or a
\code{\link[grid]{unit}} object specifying gap between columns
printed on left and right side of forest plot.}

\item{colgap.left}{Either a character string or a
\code{\link[grid]{unit}} object specifying gap between columns
printed on left side of forest plot.}

\item{colgap.right}{Either a character string or a
\code{\link[grid]{unit}} object specifying gap between columns
printed on right side of forest plot.}

\item{colgap.studlab}{Either a character string or a
\code{\link[grid]{unit}} object specifying gap between column
with study labels and subsequent column.}

\item{colgap.forest}{Either a character string or a
\code{\link[grid]{unit}} object specifying gap between column
adjacent to forest plot and the forest plot.}

\item{colgap.forest.left}{Either a character string or a
\code{\link[grid]{unit}} object specifying gap between column on
the left side of forest plot and the forest plot.}

\item{colgap.forest.right}{Either a character string or a
\code{\link[grid]{unit}} object specifying gap between column on
the right side of forest plot and the forest plot.}

\item{colgap.rob}{Either a character string or a
\code{\link[grid]{unit}} object specifying gap between risk of
bias columns.}

\item{colgap.rob.overall}{Either a character string or a
\code{\link[grid]{unit}} object specifying gap before column with
overall risk of bias assessment.}

\item{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.}

\item{calcwidth.common}{A logical indicating whether text given in
arguments \code{text.common} and \code{text.common.w} should be
considered to calculate width of the column with study labels.}

\item{calcwidth.random}{A logical indicating whether text given in
arguments \code{text.random} and \code{text.random.w} should be
considered to calculate width of the column with study labels.}

\item{calcwidth.predict}{A logical indicating whether text given in
argument \code{text.predict} and \code{text.predict.w} should be
considered to calculate width of the column with study labels.}

\item{calcwidth.hetstat}{A logical indicating whether text for
heterogeneity statistics should be considered to calculate width
of the column with study labels.}

\item{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.}

\item{calcwidth.subgroup}{A logical indicating whether text with
subgroup labels should be considered to calculate width of the
column with study labels.}

\item{calcwidth.addline}{A logical indicating whether text for
additional lines should be considered to calculate width of the
column with study labels.}

\item{just}{Justification of text in all columns but columns with
study labels and additional variables (possible values: "left",
"right", "center").}

\item{just.studlab}{Justification of text for study labels
(possible values: "left", "right", "center").}

\item{just.addcols}{Justification of text for additional columns
(possible values: "left", "right", "center").}

\item{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.}

\item{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.}

\item{bmj.text}{A character string used in the plot with BMJ layout
to label the group specific information.}

\item{bmj.xpos}{A numeric specifying the horizontal position of the
BMJ label. The value is a so called normalised parent coordinate
in the horizontal direction (see \code{\link[grid]{unit}}).}

\item{bmj.sep}{A character string used to separate sample sizes
from number of events or means / standard deviations.}

\item{spacing}{A numeric determining line spacing in a forest plot.}

\item{addrow}{A logical value indicating whether an empty row is
printed above study results.}

\item{addrow.overall}{A logical value indicating whether an empty
row is printed above overall meta-analysis results.}

\item{addrow.subgroups}{A logical value indicating whether an empty
row is printed between results for subgroups.}

\item{addrows.below.overall}{A numeric value indicating how many
empty rows are printed between meta-analysis results and
heterogeneity statistics and test results.}

\item{new}{A logical value indicating whether a new figure should
be printed in an existing graphics window.}

\item{backtransf}{A logical indicating whether results should be
back transformed in forest plots. If \code{backtransf = TRUE},
results for \code{sm = "OR"} are presented as odds ratios rather
than log odds ratios and results for \code{sm = "ZCOR"} are
presented as correlations rather than Fisher's z transformed
correlations, for example.}

\item{digits}{Minimal number of significant digits for treatment
effects, see \code{print.default}.}

\item{digits.se}{Minimal number of significant digits for standard
errors.}

\item{digits.stat}{Minimal number of significant digits for z- or
t-statistic for test of overall effect.}

\item{digits.pval}{Minimal number of significant digits for p-value
of overall treatment effect.}

\item{digits.pval.Q}{Minimal number of significant digits for
p-value of heterogeneity test.}

\item{digits.Q}{Minimal number of significant digits for
heterogeneity statistic Q.}

\item{digits.tau2}{Minimal number of significant digits for
between-study variance.}

\item{digits.tau}{Minimal number of significant digits for square
root of between-study variance.}

\item{digits.I2}{Minimal number of significant digits for I-squared
statistic.}

\item{digits.weight}{Minimal number of significant digits for
weights.}

\item{digits.mean}{Minimal number of significant digits for means;
only applies to \code{\link{metacont}} objects.}

\item{digits.sd}{Minimal number of significant digits for standard
deviations; only applies to \code{\link{metacont}} objects.}

\item{digits.cor}{Minimal number of significant digits for
correlations; only applies to \code{\link{metacor}} objects.}

\item{digits.time}{Minimal number of significant digits for times;
only applies to \code{\link{metainc}} and \code{\link{metarate}}
objects.}

\item{digits.n}{Minimal number of significant digits for sample
sizes.}

\item{digits.event}{Minimal number of significant digits for event
numbers.}

\item{digits.TE}{Minimal number of significant digits for list
element 'TE'.}

\item{digits.addcols}{A vector or scalar with minimal number of
significant digits for additional columns.}

\item{digits.addcols.right}{A vector or scalar with minimal number
of significant digits for additional columns on right side of
forest plot.}

\item{digits.addcols.left}{A vector or scalar with minimal number
of significant digits for additional columns on left side of
forest plot.}

\item{scientific.pval}{A logical specifying whether p-values should
be printed in scientific notation, e.g., 1.2345e-01 instead of
0.12345.}

\item{big.mark}{A character used as thousands separator.}

\item{zero.pval}{A logical specifying whether p-values should be
printed with a leading zero.}

\item{JAMA.pval}{A logical specifying whether p-values for test of
overall effect should be printed according to JAMA reporting
standards.}

\item{warn.deprecated}{A logical indicating whether warnings should
be printed if deprecated arguments are used.}

\item{\dots}{Additional graphical arguments.}
}
\description{
Draw a forest plot (using grid graphics system) in the active
graphics window or store the forest plot in a file.
}
\details{
A forest plot, also called confidence interval plot, is drawn in
the active graphics window. The forest functions in R package
\bold{meta} are based on the grid graphics system. Resize the
graphics windows if the forest plot is too large or too small for
the graphics window. Alternatively, save the forest plot in a file.

\subsection{Saving forest plots}{

A forest plot can be directly stored in a file using argument
\code{file} or specifying the R function for the graphics device
driver using argument \code{func.gr}, e.g., \code{\link{pdf}}. If
only the filename is provided, the extension is checked and matched
against the most common graphics device drivers.

\tabular{ll}{
\bold{Extension} \tab \bold{Graphics device} \cr
\code{.pdf} \tab \code{\link{pdf}} \cr
\code{.ps} \tab \code{\link{postscript}} \cr
\code{.svg} \tab \code{\link{svg}} \cr
\code{.bmp} \tab \code{\link{bmp}} \cr
\code{.jpg} / \code{.jpeg} \tab \code{\link{jpeg}} \cr
\code{.png} \tab \code{\link{png}} \cr
\code{.tif} / \code{.tiff} \tab \code{\link{tiff}}
}

The height of the graphics device is automatically determined if
the forest plot is saved to a file. Argument \code{rows.gr} can be
used to increase or decrease the number of rows shown in the forest
plot (either to show missing information or to remove
whitespace). The width of the graphics device can be specified with
argument \code{width}, see, for example, \code{\link{pdf}} or
\code{\link{jpeg}}. Other arguments of graphics device functions
can be provided as a list in argument \code{args.gr}.

Alternatively, the (resized) graphics window can be stored to a
file using either \code{\link{dev.copy2eps}} or
\code{\link{dev.copy2pdf}}. It is also possible to manually create
a file using, for example, \code{\link{pdf}}, \code{\link{png}}, or
\code{\link{svg}} and to specify the width and height of the
graphic (see Examples).
}

\subsection{Default layout for studies and pooled effects}{

By default, treatment estimates and confidence intervals are
plotted in the following way:
\itemize{
\item For an individual study, a square with treatment estimate in
  the center and confidence interval as line extending either side
  of the square (\code{type.study = "square"})
\item For meta-analysis results, a diamond with treatment estimate
  in the center and right and left side corresponding to lower and
  upper confidence limits (\code{type.common = "diamond"},
  \code{type.random = "diamond"}, and \code{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
(\code{weight.study = "common"}) or random effects meta-analysis
(\code{weight.study = "random"}). Information from meta-analysis object
\code{x} is utilised if argument \code{weight.study} is missing. Weights
from the common effect model are used if argument \code{x$common} is
\code{TRUE}; weights from the random effects model are used if argument
\code{x$random} is \code{TRUE} and \code{x$common} is \code{FALSE}.
The same square sizes are used if \code{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
\code{prediction} and \code{random} are \code{TRUE}. For
graphical presentation of prediction intervals the approach by
Guddat et al. (2012) is used.
}

\subsection{Columns printed on left side of forest plot}{

Argument \code{leftcols} can be used to specify columns which are
printed on the left side of the forest plot. By default, i.e. if
argument \code{leftcols} is \code{NULL} and \code{layout = "meta"},
and depending on the class of the meta-analysis object (which is
defined by the R function used to generate the object) a different
set of \emph{\bold{columns}} is printed \emph{\bold{on the left
side of the forest plot}}:
\tabular{ll}{
\bold{Function} \tab \bold{Value of argument leftcols} \cr
\code{\link{metabin}} \tab \code{c("studlab", "event.e", "n.e",
  "event.c", "n.c")} \cr
\code{\link{metacont}} \tab \code{c("studlab", "n.e", "mean.e",
  "sd.e", "n.c", "mean.c", "sd.c")} \cr
\code{\link{metacor}} \tab \code{c("studlab", "n")} \cr
\code{\link{metagen}} \tab \code{c("studlab", "TE", "seTE")} \cr
\code{\link{metainc}} \tab \code{c("studlab", "event.e", "time.e",
  "event.c", "time.c")} \cr
\code{\link{metamean}} \tab \code{c("studlab", "n", "mean", "sd")}
  \cr
\code{\link{metaprop}} \tab \code{c("studlab", "event", "n")} \cr
\code{\link{metarate}} \tab \code{c("studlab", "event", "time", "n")}
  \cr
\code{\link{metacum}} \tab \code{"studlab"} \cr
\code{\link{metainf}} \tab \code{"studlab"}
}

For three-level models, the cluster variable is printed next to the
study labels (value \code{"cluster"} in argument \code{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 \code{"studlab"}
is used to identify study labels as this is the name of the list
element of a meta-analysis object.

If the character string \code{"studlab"} is not provided in
\code{leftcols} and \code{rightcols}, the first \emph{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, \code{leftcols =
"studlab"} and \code{leftcols = "study"} would result in the same
forest plot if the variable \code{"study"} was used in the command
to conduct the meta-analysis. If no additional variable is provided
by the user, no study labels will be printed.
}

\subsection{Overlapping information on left side of forest plot}{

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 x-axis. Argument
\code{addrows.below.overall} can be used to specify the number of
empty rows that are printed between meta-analysis results and
information on heterogeneity measures and statistical tests. By
default, no additional rows are added to the forest plot. If
\code{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 \code{colgap.left}) or
between the columns on the left side and the forest plot (argument
\code{colgap.forest.left}).
}

\subsection{Columns printed on right side of forest plot}{

Argument \code{rightcols} can be used to
specify columns which are printed on the right side of the
forest plot. If argument \code{rightcols} is
\code{FALSE}, no columns will be printed on the right side. By
default, i.e. if argument \code{rightcols} is
\code{NULL} and \code{layout = "meta"}, the following
\emph{\bold{columns}} will be printed \emph{\bold{on the right side
of the forest plot}}:
\tabular{ll}{
\bold{Meta-analysis results} \tab \bold{Value of argument
  rightcols} \cr
No summary \tab \code{c("effect", "ci")} \cr
Only common effect model \tab \code{c("effect", "ci", "w.common")}
  \cr
Only random effects model \tab \code{c("effect", "ci", "w.random")}
  \cr
Both models \tab \code{c("effect", "ci", "w.common", "w.random")}
}

By default, estimated treatment effect and corresponding confidence
interval will be printed. Depending on arguments \code{common} and
\code{random}, weights of the common effect and/or random effects
model will be given too.

For an object of class \code{\link{metacum}} or
\code{\link{metainf}} the following columns will be printed:
\code{c("effect", "ci", "pval", "tau2", "tau", "I2")}. This
information corresponds to the printout with
\code{\link{print.meta}}.
}

\subsection{Predefined column names}{

The arguments \code{leftlabs} and \code{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 \code{leftlabs} and
\code{rightlabs} are \code{NULL}:
\tabular{rcccccc}{
Column: \tab \code{studlab} \tab \code{TE} \tab \code{seTE} \tab
  \code{cluster} \tab \code{n.e} \tab \code{n.c} \cr 
Label: \tab "Study" \tab "TE" \tab "seTE" \tab "Cluster" \tab
  "Total" \tab "Total" \cr
\cr
Column: \tab \code{n} \tab \code{event.e} \tab \code{event.c} \tab
  \code{event} \tab \code{mean.e} \tab \code{mean.c} \cr
Label: \tab "Total" \tab "Events" \tab "Events" \tab "Events" \tab
  "Mean" \tab "Mean" \cr
\cr
Column: \tab \code{sd.e} \tab \code{sd.c} \tab \code{time.e}
  \tab \code{time.c} \tab \code{effect} \tab \cr
Label: \tab "SD" \tab "SD" \tab "Time" \tab "Time" \tab
  \code{x$sm} \tab \cr
\cr
Column: \tab \code{ci} \tab \code{effect.ci} \tab
  \code{w.common} \tab \code{w.random} \tab \code{cycles} \tab \cr
Label: \tab \code{x$level}"\%-CI" \tab \emph{effect+ci} \tab
  "W(common)" \tab "W(random)" \tab "Cycles" \tab
}

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 \code{leftlabs}
and \code{rightlabs} must be the same as the number of printed
columns. The value \code{NA} can be used to specify columns which
should use default labels (see Examples).


In pairwise meta-analysis comparing two groups (i.e.,
\code{\link{metabin}}, \code{\link{metacont}},
\code{\link{metainc}}, and \code{\link{metagen}} depending on the
outcome), arguments \code{label.e} and \code{label.c} are used to
label columns belonging to the two treatment groups. By default,
labels defined in the meta-analysis object are used. The columns
where treatment labels are attached can be changed using arguments
\code{label.e.attach} and \code{label.c.attach}.
}

\subsection{Risk of bias assessment}{

A risk of bias (RoB) assessment can be shown in the forest plot by
either using a meta-analysis object with an RoB assessment as main
input or providing a suitable object created with
\code{\link{rob}}. Argument \code{rob = FALSE} can be used to
suppress the print of the risk of bias information.

RoB assessments are shown as the only information on the right side
of the forest plot. Thus, arguments \code{rightcols} and
\code{rightlabs} should not be used. Predefined columns shown by
default on the right side of a forest plot will be moved to the
left side.
}


\subsection{Information on heterogeneity and statistical tests}{

Argument \code{hetstat} can be a character string to specify where
to print heterogeneity information:
\itemize{
\item row with results for common effect model (\code{hetstat =
"common"}),
\item row with results for random effects model (\code{hetstat =
"random"}).
}

Otherwise, information on heterogeneity measures is printed below
the meta-analysis results if argument \code{overall.hetstat = TRUE}
(default). The heterogeneity measures to print can be specified
(see list of arguments following \code{overall.hetstat}).

In addition, the following arguments can be used to print results
for various statistical tests:
\tabular{ll}{
\bold{Argument} \tab \bold{Statistical test} \cr
\code{test.overall.common} \tab Test for overall effect (common
  effect model) \cr
\code{test.overall.random} \tab Test for overall effect (random
  effects model) \cr
\code{test.effect.subgroup.common} \tab Test for effect in subgroup
  (CE model) \cr
\code{test.effect.subgroup.random} \tab Test for effect in subgroup
  (RE model) \cr
\code{test.subgroup.common} \tab Test for subgroup differences (CE
  model) \cr
\code{test.subgroup.random} \tab Test for subgroup differences (RE
  model)
}

By default, these arguments are \code{FALSE} with exception of
tests for subgroup differences which are \code{TRUE}. R function
\code{\link{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:
\code{settings.meta(test.overall = TRUE)}.
}

\subsection{Flexible printing of subgroup results}{

Argument \code{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
\code{subgroup = k.w > 1}. The order of the logical vector
corresponds to the order of subgroups in list element 'subgroup.levels' of a
meta-analysis object. Argument \code{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:

\itemize{
\item \code{subgroup.hetstat} (heterogeneity statistic in
  subgroups),
\item \code{common.subgroup} (common effect estimates in
  subgroups),
\item \code{random.subgroup} (random effects estimates in
  subgroups),
\item \code{prediction.subgroup} (prediction interval in
  subgroups),
\item \code{test.effect.subgroup} (test for effect in subgroups),
\item \code{test.effect.subgroup.common} (test for effect in
  subgroups, common effect model),
\item \code{test.effect.subgroup.random} (test for effect in
  subgroups, random effects model).
}
}

\subsection{Additional general settings}{

Arguments \code{text.common}, \code{text.random}, and
\code{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:
\itemize{
\item "Common effect model" (argument \code{text.common})
\item "Random effects model" (\code{text.random})
\item "Prediction interval" (\code{text.predict})
}

If confidence interval levels are different for individual studies,
meta-analysis, and prediction interval (arguments \code{level},
\code{level.ma}, \code{level.predict} in meta-analysis functions,
e.g., \code{\link{metabin}}), additional information is printed,
e.g., " (99\%-CI)" for a 99\% confidence interval in the
meta-analysis.

Argument \code{pscale} can be used to rescale single proportions or
risk differences, e.g., \code{pscale = 1000} means that proportions
are expressed as events per 1000 observations. This is useful in
situations with (very) low event probabilities.

Argument \code{irscale} can be used to rescale single rates or rate
differences, e.g., \code{irscale = 1000} means that rates are
expressed as events per 1000 time units, e.g., person-years. This is
useful in situations with (very) low rates. Argument \code{irunit}
can be used to specify the time unit used in individual studies
(default: "person-years"). This information is printed in summaries
and forest plots if argument \code{irscale} is not equal to 1.
}

\subsection{Forest plots in RevMan5 layout}{

If argument \code{layout = "RevMan5"} (and arguments \code{leftcols} and
\code{rightcols} are \code{NULL}), the layout for forest plots used for
Cochrane reviews (which are generated with Review Manager 5,
\url{https://training.cochrane.org/online-learning/core-software/revman})
is reproduced:
\enumerate{
\item All columns are printed on the left side of the forest plot
  (see arguments \code{leftcols} and \code{rightcols})
\item Tests for overall effect and subgroup differences are printed
  (\code{test.overall}, \code{test.effect.subgroup},
  \code{test.subgroup})
\item Diamonds representing meta-analysis results are printed in
  black (\code{diamond.common}, \code{diamond.random})
\item Colour of squares depends on the meta-analysis object
  (\code{col.square}, \code{col.square.lines})
\item Information on effect measure and meta-analysis method is
  printed above the forest plot (\code{smlab})
\item Label "Study or Subgroup" is printed for meta-analysis with
  subgroups (\code{leftlabs})
}
}

\subsection{Forest plots in JAMA layout}{

If argument \code{layout = "JAMA"} (and arguments \code{leftcols} and
\code{rightcols} are \code{NULL}), instructions for authors of the
\emph{Journal of the American Medical Association}, see
\url{https://jamanetwork.com/journals/jama/pages/instructions-for-authors/},
are taken into account:
\enumerate{
\item Graph labels on right and left side are printed in bold font
  at top of forest plot (see arguments \code{bottom.lr} and
  \code{ff.lr})
\item Information on effect measure and level of confidence
  interval is printed at bottom of forest plot (\code{xlab})
\item Tests for overall effect are printed (\code{test.overall})
\item Diamonds representing meta-analysis results are printed in
  lightblue (\code{diamond.common}, \code{diamond.random})
\item Squares representing individual study results are printed in
  darkblue (\code{col.square}, \code{col.square.lines})
\item Between-study variance \eqn{\tau^2} is not printed
\item Empty rows are omitted (\code{addrow}, \code{addrow.overall},
  \code{addrow.subgroups})
\item Label "Source" is printed instead of "Study" (\code{leftlabs})
\item P-values are printed without leading zeros (\code{zero.pval})
\item P-values are rounded to three digits (for 0.001 < p \eqn{\le}
  0.01) or two digits (p > 0.01) (\code{JAMA.pval})
}
Study labels according to JAMA guidelines can be generated using
\code{\link{labels.meta}}.
}

\subsection{Forest plots showing results of subgroups}{

The following changes are conducted if argument
\code{layout = "subgroup"} (and arguments \code{leftcols} and
\code{rightcols} are \code{NULL}) and a subgroup analysis was
conducted:
\enumerate{
\item Individual study results are omitted (see argument
  \code{study.results})
\item Total number of observations is not printed
  (\code{pooled.totals})
\item Label "Subgroup" is printed instead of "Study"
  (\code{leftlabs})
}
}
}
\note{
R function \code{.forestArgs} generates a character vector with all
arguments of \code{forest.meta}.
}
\examples{
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))


\dontrun{
# Do standard (symmetric) forest plot
#
forest(m1)
}

# 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.left = "Favours experimental", col.label.left = "green",
  label.right = "Favours control", col.label.right = "red",
  prediction = TRUE)


\dontrun{
# Create PDF files with the forest plot
#
# - specify filename (R function pdf() is used due to extension .pdf)
# - height of the figure is automatically determined
# - width is set to 10 inches
forest(m1, file = "forest-m1-1.pdf", width = 10)
#
# - specify graphics device function
#   (filename "Rplots.pdf" used, see help page of R function pdf())
# - height of the figure is automatically determined
# - width is set to 10 inches
# - set title for PDF file
# - set background of forest plot
forest(m1, func.gr = pdf, width = 10,
  args.gr = list(title = "My Forest Plot", bg = "green"))
#
# - manually specify the height of the figure
pdf("forest-m1-2.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.left = "Desirable effect", 
  label.right = "Undesirable 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 meta-analysis
#
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 < 1.6-0
#
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 = "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)
}

}
\references{
Guddat C, Grouven U, Bender R, Skipka G (2012):
A note on the graphical presentation of prediction intervals in
random-effects meta-analyses.
\emph{Systematic Reviews},
\bold{1}, 34

Higgins JPT, Thompson SG, Spiegelhalter DJ (2009): 
A re-evaluation of random-effects meta-analysis.
\emph{Journal of the Royal Statistical Society: Series A},
\bold{172}, 137-59
}
\seealso{
\code{\link{metabin}}, \code{\link{metacont}},
  \code{\link{metagen}}, \code{\link{forest.metabind}},
  \code{\link{settings.meta}}, \code{\link{labels.meta}}
}
\author{
Guido Schwarzer \email{guido.schwarzer@uniklinik-freiburg.de}
}
\keyword{hplot}
