View source: R/netgraph.netmeta.R
netgraph.netmeta  R Documentation 
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", rotate = 0, highlight = NULL, col.highlight = "red2", scale.highlight = 1, multiarm = FALSE, 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 = 1e04, 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 2D plots (value of 0.0175 corresponds to a difference of 1.75% of the range on x and yaxis). 
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
3dimensional plot should be produced, either 
rotate 
A single numeric with value between 180 and 180 specifying the angle to rotate nodes in a circular network. 
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 multiarm studies should be marked in plot. 
col.multiarm 
Either a function from R package colorspace or grDevice to define colors for multiarm studies or a character vector with colors to highlight multiarm studies. 
alpha.transparency 
The alpha transparency of colors used to highlight multiarm 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
ycoordinate if 
eig3 
A numeric indicating which eigenvector is used as
zcoordinate 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 contentbased. 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 common effects model
comparing two treatments (thickness = "se.common"
)
Proportional to inverse standard error of random effects
model comparing two treatments (thickness = "se.random"
)
Weight from common effects model comparing two treatments
(thickness = "w.common"
)
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.common"
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 counterclockwise 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 multiarm studies should be indicated as colored
polygons. By default, if R package colorspace is available the
sequential_hcl
function is used to
highlight multiarm studies; otherwise the rainbow
is
used.
In order to generate 3D 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 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). 
Gerta Rücker ruecker@imbi.unifreiburg.de, Ulrike Krahn ulrike.krahn@bayer.com, Jochem König koenigjo@unimainz.de, Guido Schwarzer sc@imbi.unifreiburg.de
Hall KM (1970): An rdimensional 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 metaanalyses. 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 metaanalysis. Research Synthesis Methods, 7, 94–107
netmeta
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 multiarm # study colored # netgraph(net1, start = "circle", iterate = TRUE, multiarm = TRUE, col.multiarm = "purple") # Network graph optimized, starting from a circle, with multiarm # study colored and all intermediate iteration steps visible # netgraph(net1, start = "circle", iterate = TRUE, multiarm = TRUE, col.multiarm = "purple", allfigures = TRUE) # Network graph optimized, starting from Laplacian eigenvectors, # with multiarm study colored # netgraph(net1, start = "eigen", multiarm = TRUE, col.multiarm = "purple") # Network graph optimized, starting from different Laplacian # eigenvectors, with multiarm study colored # netgraph(net1, start = "prcomp", multiarm = TRUE, col.multiarm = "purple") # Network graph optimized, starting from random initial layout, # with multiarm study colored # netgraph(net1, start = "random", multiarm = TRUE, 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 3D (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 3D plots. # netgraph(net1, dim = "3d") ## End(Not run)
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