View source: R/forest.netcomplex.R
forest.netcomplex  R Documentation 
Draws a forest plot in the active graphics window (using grid graphics system).
## S3 method for class 'netcomplex' forest( x, pooled = ifelse(x$random, "random", "common"), leftcols = "studlab", leftlabs = NULL, rightcols = c("effect", "ci", "statistic", "pval"), rightlabs = c(NA, NA, "z", "pvalue"), nchar.comps = x$nchar.trts, digits = gs("digits.forest"), digits.stat = gs("digits.stat"), digits.pval = gs("digits.pval"), smlab = NULL, backtransf = x$backtransf, lab.NA = ".", weight.study = "same", ... ) ## S3 method for class 'netcomplex' plot(x, ...)
x 
An object of class 
pooled 
A character string indicating whether results for the
common ( 
leftcols 
A character vector specifying (additional) columns
to be plotted on the left side of the forest plot or a logical
value (see 
leftlabs 
A character vector specifying labels for
(additional) columns on left side of the forest plot (see

rightcols 
A character vector specifying (additional) columns
to be plotted on the right side of the forest plot or a logical
value (see 
rightlabs 
A character vector specifying labels for
(additional) columns on right side of the forest plot (see

nchar.comps 
A numeric defining the minimum number of characters used to create unique names for components. 
digits 
Minimal number of significant digits for treatment
effects and confidence intervals, see 
digits.stat 
Minimal number of significant digits for tests
of overall effect, see 
digits.pval 
Minimal number of significant digits for pvalue
of overall effects, see 
smlab 
A label printed at top of figure. By default, text indicating either common or random effects model is printed. 
backtransf 
A logical indicating whether results should be
back transformed in forest plots. If 
lab.NA 
A character string to label missing values. 
weight.study 
A character string indicating weighting used to determine size of squares or diamonds. 
... 
Additional arguments for 
A forest plot, also called confidence interval plot, is drawn in
the active graphics window. For more information see help page of
forest.meta
function.
Guido Schwarzer sc@imbi.unifreiburg.de
netcomplex
, netcomb
,
discomb
, forest.meta
data(Linde2016) # Only consider studies including Facetoface PST (to reduce # runtime of example) # face < subset(Linde2016, id %in% c(16, 24, 49, 118)) # Conduct random effects network metaanalysis # net1 < netmeta(lnOR, selnOR, treat1, treat2, id, data = face, ref = "placebo", sm = "OR", common = FALSE) # Additive model for treatment components (with placebo as inactive # treatment) # nc1 < netcomb(net1, inactive = "placebo") # Some complex interventions # ints < c("F + TCA", "F + Plac", "SSRI + Plac + TCA") netcomplex(nc1, ints) # forest(netcomplex(nc1, ints)) forest(netcomplex(nc1, ints), nchar.comps = 4) # Component effects # forest(netcomplex(nc1, nc1$comps))
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