View source: R/forest.netmeta.R
forest.netmeta  R Documentation 
Draws a forest plot in the active graphics window (using grid graphics system).
## S3 method for class 'netmeta' forest( x, pooled = ifelse(x$random, "random", "common"), reference.group = x$reference.group, baseline.reference = x$baseline.reference, labels = x$trts, equal.size = TRUE, leftcols = "studlab", leftlabs, rightcols = c("effect", "ci"), rightlabs, digits = gs("digits.forest"), small.values = x$small.values, nsim = 1000, digits.prop = 2, smlab = NULL, sortvar = x$seq, backtransf = x$backtransf, lab.NA = ".", add.data, drop.reference.group = FALSE, col.by = "black", print.subgroup.name = FALSE, ... ) ## S3 method for class 'netmeta' plot(x, ...)
x 
An object of class 
pooled 
A character string indicating whether results for the
common ( 
reference.group 
Reference treatment(s). 
baseline.reference 
A logical indicating whether results should be expressed as comparisons of other treatments versus the reference treatment (default) or vice versa. 
labels 
An optional vector with treatment labels. 
equal.size 
A logical indicating whether all squares should be of equal size. Otherwise, the square size is proportional to the precision of estimates. 
leftcols 
A character vector specifying columns to be plotted on the left 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). 
rightcols 
A character vector specifying columns to be plotted on the right side of the forest plot or a logical value (see Details). 
rightlabs 
A character vector specifying labels for (additional) columns on right side of the forest plot (see Details). 
digits 
Minimal number of significant digits for treatment
effects and confidence intervals, see 
small.values 
A character string specifying whether small
treatment effects indicate a beneficial ( 
nsim 
Number of simulations to calculate SUCRAs. 
digits.prop 
Minimal number of significant digits for
Pscores, SUCRAs and direct evidence proportions, see

smlab 
A label printed at top of figure. By default, text indicating either common or random effects model is printed. 
sortvar 
An optional vector used to sort treatments (must be of same length as the total number of treatments). 
backtransf 
A logical indicating whether results should be
back transformed in forest plots. If 
lab.NA 
A character string to label missing values. 
add.data 
An optional data frame with additional columns to print in forest plot (see Details). 
drop.reference.group 
A logical indicating whether the reference group should be printed in the forest plot. 
col.by 
The colour to print information on subgroups. 
print.subgroup.name 
A logical indicating whether the name of the grouping variable should be printed in front of the group labels. 
... 
Additional arguments for 
A forest plot, also called confidence interval plot, is drawn in the active graphics window.
Argument sortvar
can be either a numeric or character vector
with length of number of treatments. If sortvar
is numeric
the order
function is utilised internally to
determine the order of values. If sortvar
is character it
must be a permutation of the treatment names. It is also possible
to provide either sortvar = Pscore
, sortvar =
"Pscore"
, sortvar = Pscore
, or sortvar = "Pscore"
in order to sort treatments according to the ranking generated by
netrank
which is called internally. It is also
possible to use "SUCRA" instead of "Pscore". Similar expressions
are possible to sort by treatment comparisons (sortvar = TE
,
etc.), standard error (sortvar = seTE
), number of studies
with direct treatment comparisons (sortvar = k
), and direct
evidence proportion (sortvar = prop.direct
, see also
netmeasures
).
The arguments leftcols
and rightcols
can be used to
specify columns which are plotted on the left and right side of the
forest plot, respectively. The following columns are available:
Name  Definition 
"studlab"  Treatments 
"TE"  Network estimates (either from common or random effects model) 
"seTE"  Corresponding standard errors 
"Pscore"  Pscores (see netrank ) 
"SUCRA"  SUCRAs (see netrank ) 
"n.trts"  Number of participants per treatment arm 
"k"  Number of studies in pairwise comparisons 
"prop.direct"  Direct evidence proportions (see
netmeasures ) 
"effect"  (Backtransformed) network estimates 
"ci"  Confidence intervals 
"effect.ci"  (Backtransformed) network estimates and confidence intervals 
As a sidenote, the rather odd column name "studlab"
to
describe the treatment comparisons comes from internally calling
forest.meta
which uses study labels as the essential
information.
Argument add.data
can be used to add additional columns to
the forest plot. This argument must be a data frame with row names
equal to the treatment names in R object x
, i.e.,
x$trts
.
See help page of forest.meta
for more information on
the generation of forest plots and additional arguments.
Guido Schwarzer sc@imbi.unifreiburg.de
forest.meta
data(Senn2013) ## Not run: # Conduct network metaanalysis # net1 < netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "MD") forest(net1, ref = "plac") forest(net1, xlim = c(1.5, 1), ref = "plac", xlab = "HbA1c difference", rightcols = FALSE) ## End(Not run) # Random effects effect model # net2 < netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "MD", common = FALSE) forest(net2, xlim = c(1.5, 1), ref = "plac", xlab = "HbA1c difference") ## Not run: # Add column with PScores on right side of forest plot # forest(net2, xlim = c(1.5, 1), ref = "plac", xlab = "HbA1c difference", rightcols = c("effect", "ci", "Pscore"), just.addcols = "right") # Add column with PScores on left side of forest plot # forest(net2, xlim = c(1.5, 1), ref = "plac", xlab = "HbA1c difference", leftcols = c("studlab", "Pscore"), just.addcols = "right") # Sort forest plot by descending PScore # forest(net2, xlim = c(1.5, 1), ref = "plac", xlab = "HbA1c difference", rightcols = c("effect", "ci", "Pscore"), just.addcols = "right", sortvar = Pscore) # Drop reference group and sort by and print number of studies with # direct treatment comparisons # forest(net2, xlim = c(1.5, 1), ref = "plac", xlab = "HbA1c difference", leftcols = c("studlab", "k"), leftlabs = c("Contrast\nto Placebo", "Direct\nComparisons"), sortvar = k, drop = TRUE, smlab = "Random Effects Model") ## End(Not run)
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