plot.rankogram: Plot rankograms

In netmeta: Network Meta-Analysis using Frequentist Methods

Plot rankograms

Description

This function produces a rankogram, i.e., an image plot of ranking probabilities for all treatments.

Usage

## S3 method for class 'rankogram'
plot(
  x,
  type = if (cumulative.rankprob) "step" else "bar",
  pooled = ifelse(x$random, "random", "common"),
  sort = TRUE,
  trts,
  cumulative.rankprob = x$cumulative.rankprob,
  ylim,
  ylab,
  nchar.trts = x$nchar.trts,
  ...
)

Arguments

x	An object of class rankogram.
type	A character string specifying whether a "bar" chart, a "line" graph, or "step" functions should be drawn. Can be abbreviated.
pooled	A character string indicating whether results for the common ("common") or random effects model ("random") should be plotted. Can be abbreviated.
sort	A logical indicating whether treatments should be sorted by decreasing SUCRAs.
trts	Treatment(s) to show in rankogram.
cumulative.rankprob	A logical indicating whether cumulative ranking probabilites should be shown.
ylim	The y limits (min, max) of the plot.
ylab	A label for the y-axis.
nchar.trts	A numeric defining the minimum number of characters used to create unique treatment names.
...	Additional graphical arguments (ignored at the moment).

Details

This function produces plots of (cumulative) ranking probabilities for all treatments as a bar graph, a line graph or as step functions (argument type). All plots will be shown in a single figure if R package gridExtra is installed. Otherwise, separate figures will be created for treatments.

By default (argument pooled), results for the random effects model are shown if a network meta-analysis was conducted for both the common and random effects model.

Treatments are sorted according to their mean effects if argument sort = TRUE (default). A subset of treatments can be specified using argument trts.

Cumulative ranking probabilites are shown if cumulative.rankprob = TRUE. By default, step functions are shown for cumulative ranking probabilites.

Author(s)

Theodoros Papakonstantinou [e-mail], Guido Schwarzer [e-mail]

References

Salanti G, Ades AE, Ioannidis JP (2011): Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. Journal of Clinical Epidemiology, 64, 163–71

See Also

rankogram, dat.woods2010

Examples

pw1 <- pairwise(treatment, event = r, n = N, studlab = author,
  data = dat.woods2010, sm = "OR")
net1 <- netmeta(pw1, small.values = "good")

set.seed(1909) # get reproducible results
ran1 <- rankogram(net1, nsim = 100)
ran1

plot(ran1)
plot(ran1, type = "l")
plot(ran1, cumulative.rankprob = TRUE)

netmeta documentation built on April 3, 2025, 6:12 p.m.