Network graph
This function generates a graph of the evidence network.
## S3 method for class 'netmeta' netgraph( x, seq = x$seq, labels = x$trts, cex = 1, adj = NULL, srt.labels = 0, offset = if (!is.null(adj) && all(unique(adj) == 0.5)) 0 else 0.0175, scale = 1.1, col = "slateblue", plastic, thickness, lwd = 5, lwd.min = lwd/2.5, lwd.max = lwd * 4, dim = "2d", highlight = NULL, col.highlight = "red2", scale.highlight = 1, multiarm = any(x$narms > 2), col.multiarm = NULL, alpha.transparency = 0.5, points = FALSE, col.points = "red", cex.points = 1, pch.points = 20, bg.points = "gray", number.of.studies = FALSE, cex.number.of.studies = cex, col.number.of.studies = "white", bg.number.of.studies = "black", pos.number.of.studies = 0.5, start.layout = ifelse(dim == "2d", "circle", "eigen"), eig1 = 2, eig2 = 3, eig3 = 4, iterate, tol = 1e-04, maxit = 500, allfigures = FALSE, A.matrix = x$A.matrix, N.matrix = sign(A.matrix), D.matrix = netdistance(N.matrix), xpos = NULL, ypos = NULL, zpos = NULL, figure = TRUE, ... )
x |
An object of class |
seq |
A character or numerical vector specifying the sequence
of treatments arrangement (anticlockwise if |
labels |
An optional vector with treatment labels. |
cex |
The magnification to be used for 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. |
srt.labels |
The character string |
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). |
scale |
Additional space added outside of edges (i.e. treatments). Increase this value for larger treatment labels (value of 1.10 corresponds to an additional space of 10% around the network graph). |
col |
A single color (or vector of colors) for lines
connecting treatments (edges) if argument |
plastic |
A logical indicating whether the appearance of the
comparisons should be in '3D look' (not to be confused with
argument |
thickness |
Either a character variable to determine the
method to plot line widths (see Details) or a matrix of the same
dimension and row and column names as argument |
lwd |
A numeric for scaling the line width of comparisons. |
lwd.min |
Minimum line width in network graph. All connections
with line widths below this values will be set to |
lwd.max |
Maximum line width in network graph. The connection
with the largest value according to argument |
dim |
A character string indicating whether a 2- or
3-dimensional plot should be produced, either |
highlight |
A character vector identifying comparisons that
should be marked in the network graph, e.g. |
col.highlight |
Color(s) to highlight the comparisons given by
|
scale.highlight |
Scaling factor(s) for the line width(s) to
highlight the comparisons given by |
multiarm |
A logical indicating whether multi-arm studies should be marked in plot. |
col.multiarm |
Either a function from R package colorspace or grDevice to define colors for multi-arm studies or a character vector with colors to highlight multi-arm studies. |
alpha.transparency |
The alpha transparency of colors used to highlight multi-arm studies (0 means transparent and 1 means opaque). |
points |
A logical indicating whether points should be printed at nodes (i.e. treatments) of the network graph. |
col.points, cex.points, pch.points, bg.points |
Corresponding color, size, type, and background color for points. Can be a vector with length equal to the number of treatments. |
number.of.studies |
A logical indicating whether number of studies should be added to network graph. |
cex.number.of.studies |
The magnification to be used for number of studies. |
col.number.of.studies |
Color for number of studies. |
bg.number.of.studies |
Color for shadow around number of studies. |
pos.number.of.studies |
A single value (or vector of values) in [0, 1] specifying the position of the number of studies on the lines connecting treatments (edges). Length of the vector must be equal to the number of edges. |
start.layout |
A character string indicating which starting
layout is used if |
eig1 |
A numeric indicating which eigenvector is used as x
coordinate if |
eig2 |
A numeric indicating which eigenvector is used as
y-coordinate if |
eig3 |
A numeric indicating which eigenvector is used as
z-coordinate if |
iterate |
A logical indicating whether the stress majorization algorithm is carried out for optimization of the layout. |
tol |
A numeric for the tolerance for convergence if
|
maxit |
An integer defining the maximum number of iteration
steps if |
allfigures |
A logical indicating whether all iteration steps
are shown if |
A.matrix |
Adjacency matrix (nxn) characterizing
the structure of the network graph. Row and column names must be
the same set of values as provided by argument |
N.matrix |
Neighborhood matrix (nxn) replacing
A.matrix if neighborhood is to be specified differently from node
adjacency in the network graph, for example content-based. Row
and column names must be the same set of values as provided by
argument |
D.matrix |
Distance matrix (nxn) replacing
A.matrix and N.matrix if distances should be provided
directly. Row and column names must be the same set of values as
provided by argument |
xpos |
Vector (n) of x coordinates. |
ypos |
Vector (n) of y coordinates. |
zpos |
Vector (n) of z coordinates. |
figure |
A logical indicating whether network graph should be shown. |
... |
Additional graphical arguments. |
The network is laid out in the plane, where the nodes in the graph
layout correspond to the treatments and edges display the observed
treatment comparisons. For the default setting, nodes are placed on
a circle. Other starting layouts are "eigen", "prcomp", and
"random" (Rücker & Schwarzer 2015). If iterate = TRUE, the
layout is further optimized using the stress majorization
algorithm. This algorithm specifies an 'ideal' distance (e.g., the
graph distance) between two nodes in the plane. In the optimal
layout, these distances are best approximated in the sense of least
squares. Starting from an initial layout, the optimum is
approximated in an iterative process called stress majorization
(Kamada and Kawai 1989, Michailidis and de Leeuw 2001, Hu
2012). The starting layout can be chosen as a circle or coming from
eigenvectors of the Laplacian matrix (corresponding to Hall's
algorithm, Hall 1970), calculated in different ways, or
random. Moreover, it can be chosen whether the iteration steps are
shown (argument allfigures = TRUE).
An optimized circular presentation which typically has a reduced
(sometimes minimal) number of crossings can be achieved by using
argument seq = "optimal" in combination with argument
start.layout. Note, is is not possible of prespecify the
best value for argument start.layout for any situation as
the result depends on the network structure.
Argument thickness providing the line width of the nodes
(comparisons) can be a matrix of the same dimension as argument
A.matrix or any of the following character variables:
Same line width (argument lwd) for all comparisons
(thickness = "equal")
Proportional to number of studies comparing two treatments
(thickness = "number.of.studies")
Proportional to inverse standard error of fixed effects model
comparing two treatments (thickness = "se.fixed")
Proportional to inverse standard error of random effects
model comparing two treatments (thickness = "se.random")
Weight from fixed effects model comparing two treatments
(thickness = "w.fixed")
Weight from random effects model comparing two treatments
(thickness = "w.random")
Only evidence from direct treatment comparisons is considered to
determine the line width if argument thickness is equal to
any but the first method. By default, thickness = "se.fixed"
is used if start.layout = "circle", iterate = FALSE,
and plastic = TRUE. Otherwise, the same line width is used.
Argument srt.labels can be used to specific the rotation (in
degrees) of the treatment labels. If srt.labels is equal to
"orthogonal", treatment labels are orthogonal to the
circle. If srt.labels is a single numeric, all labels are
rotated by this degree. If srt.labels is a numeric vector,
it must be of the same length as the number of treatments and
labels are rotated counter-clockwise starting on the right
side. Finally, if srt.labels is a named numeric vector, it
must be of the same length as the number of treatments and the
names must be equal to the treatment names (and treatment labels
are rotated according to the specified values).
Further, a couple of graphical parameters can be specified, such as
color and appearance of the edges (treatments) and the nodes
(comparisons), whether special comparisons should be highlighted
and whether multi-arm studies should be indicated as colored
polygons. By default, if R package colorspace is available the
sequential_hcl function is used to
highlight multi-arm studies; otherwise the rainbow is
used.
In order to generate 3-D plots (argument dim = "3d"), R
package rgl is necessary. Note, under macOS the X.Org X
Window System must be available (see
https://www.xquartz.org).
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). |
Gerta Rücker ruecker@imbi.uni-freiburg.de, Ulrike Krahn ulrike.krahn@bayer.com, Jochem König koenigjo@uni-mainz.de, Guido Schwarzer sc@imbi.uni-freiburg.de
Hall KM (1970): An r-dimensional quadratic placement algorithm. Management Science, 17, 219–29
Hu Y (2012): Combinatorial Scientific Computing, Chapter Algorithms for Visualizing Large Networks, pages 525–49. Chapman and Hall / CRC, Computational Science.
Kamada T, Kawai S (1989): An algorithm for drawing general undirected graphs. Information Processing Letters, 31, 7–15
Krahn U, Binder H, König J (2013): A graphical tool for locating inconsistency in network meta-analyses. BMC Medical Research Methodology, 13, 35
Michailidis G, de Leeuw J (2001): Data visualization through graph drawing. Computational Statistics, 16, 435–50
Rücker G, Schwarzer G (2016): Automated drawing of network plots in network meta-analysis. Research Synthesis Methods, 7, 94–107
data(Senn2013)
# Generation of an object of class 'netmeta' with reference
# treatment 'plac'
#
net1 <- netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013, sm = "MD", reference = "plac")
# Network graph with default settings
#
netgraph(net1)
## Not run:
# Network graph with specified order of the treatments and one
# highlighted comparison
#
trts <- c("plac", "benf", "migl", "acar", "sulf",
"metf", "rosi", "piog", "sita", "vild")
netgraph(net1, highlight = "rosi:plac", seq = trts)
# Same network graph using argument 'seq' in netmeta function
#
net2 <- netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013, sm = "MD", reference = "plac",
seq = trts)
netgraph(net2, highlight = "rosi:plac")
# Network graph optimized, starting from a circle, with multi-arm
# study colored
#
netgraph(net1, start = "circle", iterate = TRUE, col.multiarm = "purple")
# Network graph optimized, starting from a circle, with multi-arm
# study colored and all intermediate iteration steps visible
#
netgraph(net1, start = "circle", iterate = TRUE, col.multiarm = "purple",
allfigures = TRUE)
# Network graph optimized, starting from Laplacian eigenvectors,
# with multi-arm study colored
#
netgraph(net1, start = "eigen", col.multiarm = "purple")
# Network graph optimized, starting from different Laplacian
# eigenvectors, with multi-arm study colored
#
netgraph(net1, start = "prcomp", col.multiarm = "purple")
# Network graph optimized, starting from random initial layout,
# with multi-arm study colored
#
netgraph(net1, start = "random", col.multiarm = "purple")
# Network graph without plastic look and one highlighted comparison
#
netgraph(net1, plastic = FALSE, highlight = "rosi:plac")
# Network graph without plastic look and comparisons with same
# thickness
#
netgraph(net1, plastic = FALSE, thickness = FALSE)
# Network graph with changed labels and specified order of the
# treatments
#
netgraph(net1, seq = c(1, 3, 5, 2, 9, 4, 7, 6, 8, 10),
labels = LETTERS[1:10])
# Rotate treatment labels (orthogonal to circle)
#
netgraph(net1, srt.labels = "o")
# Network graph in 3-D (opens a new device, where you may rotate and
# zoom the plot using the mouse / the mouse wheel).
# The rgl package must be installed for 3-D plots.
#
netgraph(net1, dim = "3d")
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.