netgraph.crossnma {crossnma}  R Documentation 
Create a network plot of the cross network metaanalysis or metaregression
## S3 method for class 'crossnma'
netgraph(
x,
labels,
adj = NULL,
offset = if (!is.null(adj) && all(unique(adj) == 0.5)) 0 else 0.0175,
points = !missing(cex.points),
cex.points = 1,
...
)
x 
An object produced by 
labels 
An optional vector with treatment labels. 
adj 
One, two, or three values in [0, 1] (or a vector / matrix with length / number of rows equal to the number of treatments) specifying the x (and optionally y and z) adjustment for treatment labels. 
offset 
Distance between edges (i.e. treatments) in graph and treatment labels for 2D plots (value of 0.0175 corresponds to a difference of 1.75% of the range on x and yaxis). 
points 
A logical indicating whether points should be printed at nodes (i.e. treatments) of the network graph. 
cex.points 
Corresponding size for points. Can be a vector with length equal to the number of treatments. 
... 
... Additional arguments (passed on to

A data frame containing the following columns:
labels 
Treatment labels. 
seq 
Sequence of treatment labels. 
xpos 
Position of treatment / edge on xaxis. 
ypos 
Position of treatment / edge on yaxis. 
zpos 
Position of treatment / edge on zaxis (for 3D plots). 
xpos.labels 
Position of treatment labels on xaxis (for 2D plots). 
ypos.labels 
Position of treatment labels on yaxis (for 2D plots). 
adj.x 
Adjustment for treatment label on xaxis. 
adj.y 
Adjustment for treatment label on yaxis. 
adj.z 
Adjustment for treatment label on zaxis (for 3D plots). 
Tasnim Hamza tasnim.hamza@ispm.unibe.ch
## Not run:
# We conduct a network metaanalysis assuming a randomeffects
# model.
# The data comes from randomizedcontrolled trials and
# nonrandomized studies (combined naively)
head(ipddata) # participantlevel data
stddata # studylevel data
# Create a JAGS model
mod < crossnma.model(treat, id, relapse, n, design,
prt.data = ipddata, std.data = stddata,
reference = "A", trt.effect = "random", method.bias = "naive")
# Fit JAGS model
set.seed(1909)
fit < crossnma(mod)
# Create network plot
netgraph(fit, plastic = FALSE, cex.points = 7, adj = 0.5)
## End(Not run)