rankogram  R Documentation 
This function calculates the probabilities of each treatment being at each possible rank and the SUCRAs (Surface Under the Cumulative RAnking curve) in frequentist network metaanalysis.
rankogram( x, nsim = 1000, common = x$common, random = x$random, small.values = x$small.values, cumulative.rankprob = FALSE, nchar.trts = x$nchar.trts, warn.deprecated = gs("warn.deprecated"), ... ) ## S3 method for class 'rankogram' print( x, common = x$common, random = x$random, cumulative.rankprob = x$cumulative.rankprob, nchar.trts = x$nchar.trts, digits = gs("digits.prop"), legend = TRUE, warn.deprecated = gs("warn.deprecated"), ... )
x 
An object of class 
nsim 
Number of simulations. 
common 
A logical indicating to compute ranking probabilities and SUCRAs for the common effects model. 
random 
A logical indicating to compute ranking probabilities and SUCRAs for the random effects model. 
small.values 
A character string specifying whether small
treatment effects indicate a beneficial ( 
cumulative.rankprob 
A logical indicating whether cumulative ranking probabilites should be printed. 
nchar.trts 
A numeric defining the minimum number of characters used to create unique treatment names. 
warn.deprecated 
A logical indicating whether warnings should be printed if deprecated arguments are used. 
... 
Additional arguments for printing. 
digits 
Minimal number of significant digits, see

legend 
A logical indicating whether a legend should be printed. 
We derive a matrix showing the probability of each treatment being at each possible rank. To this aim, we use resampling from a multivariate normal distribution with estimated network effects as means and corresponding estimated variance covariance matrix. We then summarise them using the ranking metric SUCRAs (Surface Under the Cumulative RAnking curve).
An object of class rankogram
with corresponding print
and plot
function. The object is a list containing the
following components:
ranking.matrix.common 
Numeric matrix giving the probability of each treatment being at each possible rank for the common effects model. 
ranking.common 
SUCRA values for the common effects model. 
ranking.matrix.random 
Numeric matrix giving the probability of each treatment being at each possible rank for the random effects model. 
ranking.random 
SUCRA values for the random effects model. 
cumrank.matrix.common 
Numeric matrix giving the cumulative ranking probability of each treatment for the common effects model. 
cumrank.matrix.random 
Numeric matrix giving the cumulative ranking probability of each treatment for the random effects model. 
nsim, common, random 
As defined above 
,
small.values, x 
As defined above 
,
Theodoros Papakonstantinou dev@tpapak.com, Guido Schwarzer sc@imbi.unifreiburg.de
Salanti G, Ades AE, Ioannidis JP (2011): Graphical methods and numerical summaries for presenting results from multipletreatment metaanalysis: an overview and tutorial. Journal of Clinical Epidemiology, 64, 163–71
netmeta
, netrank
data(Woods2010) p1 < pairwise(treatment, event = r, n = N, studlab = author, data = Woods2010, sm = "OR") net1 < netmeta(p1, small.values = "good") ran1 < rankogram(net1, nsim = 100) ran1 print(ran1, cumulative.rankprob = TRUE) plot(ran1)
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