| netgraph.crossnma.model {crossnma} | R Documentation |
Produce a network plot
Description
Create a network plot of the cross network meta-analysis or meta-regression
Usage
## S3 method for class 'crossnma.model'
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,
...
)
Arguments
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 2-D plots (value of 0.0175 corresponds to a difference of 1.75% of the range on x- and y-axis). |
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
|
Value
A data frame containing the following columns:
labels |
Treatment labels. |
seq |
Sequence of treatment labels. |
xpos |
Position of treatment / edge on x-axis. |
ypos |
Position of treatment / edge on y-axis. |
zpos |
Position of treatment / edge on z-axis (for 3-D plots). |
xpos.labels |
Position of treatment labels on x-axis (for 2-D plots). |
ypos.labels |
Position of treatment labels on y-axis (for 2-D plots). |
adj.x |
Adjustment for treatment label on x-axis. |
adj.y |
Adjustment for treatment label on y-axis. |
adj.z |
Adjustment for treatment label on z-axis (for 3-D plots). |
Author(s)
Tasnim Hamza tasnim.hamza@ispm.unibe.ch, Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de
See Also
Examples
# We conduct a network meta-analysis assuming a random-effects
# model.
# The data comes from randomized-controlled trials and
# non-randomized studies (combined naively)
head(ipddata) # participant-level data
stddata # study-level 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")
# Create network plot
netgraph(mod, plastic = FALSE, cex.points = 7, adj = 0.5)