This function generates a graph of the evidence network.
netgraph(x, seq = x$seq,
labels = rownames(x$TE.fixed),
cex = 1, col = "slateblue", offset = 0.0175, scale = 1.10,
plastic, thickness, lwd = 5, lwd.min = lwd/2.5, lwd.max = lwd*4,
dim = "2d",
highlight = NULL, col.highlight = "red2", lwd.highlight = lwd,
multiarm = any(x$narms > 2), col.multiarm = NULL,
alpha.transparency = 0.5,
points = FALSE, col.points = "red", cex.points = 1, pch.points = 20,
number.of.studies = FALSE,
cex.number.of.studies = cex,
col.number.of.studies = "white",
bg.number.of.studies = "black",
start.layout = ifelse(dim == "2d", "circle", "eigen"),
eig1 = 2, eig2 = 3, eig3 = 4,
iterate, tol = 0.0001, maxit = 500, allfigures = FALSE,
A.matrix = x$A.matrix, N.matrix = sign(A.matrix),
D.matrix = netdistance(N.matrix),
xpos = NULL, ypos = NULL, zpos = NULL,
...)An object of class netmeta (mandatory).
A character or numerical vector specifying the sequence
of treatments arrangement (anticlockwise if
start.layout = "circle").
An optional vector with treatment labels.
The magnification to be used for treatment labels.
Color of lines connecting treatments if argument
plastic = FALSE.
Distance between edges (i.e. treatments) in graph and treatment labels (value of 0.0175 corresponds to a difference of 1.75% of the range on x- and y-axis).
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).
A logical indicating whether the appearance of the
comparisons should be in '3D look' (not to be confused with
argument dim).
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 A.matrix
with information on line width.
A numeric for scaling the line width of comparisons.
Maximum line width in network graph. The connection
with the largest value according to argument thickness will
be set to this value.
Minimum line width in network graph. All connections
with line widths below this values will be set to lwd.min.
A character string indicating whether a 2- or
3-dimensional plot should be produced, either "2d" or
"3d".
A character vector identifying comparisons that
should be marked in the network graph,
e.g. highlight = "treat1:treat2".
Color for highlighting the comparisons given by
highlight.
A numeric for the line width for highlighting
the comparisons given by highlight.
A logical indicating whether multi-arm studies should marked in plot.
Either a function from R library colorspace or grDevice to define colors for multi-arm studies or a character vector with colors to highlight multi-arm studies.
The alpha transparency of colors used to highlight multi-arm studies (0 means transparent and 1 means opaque).
A logical indicating whether points should be printed at nodes (i.e. treatments) of the network graph.
Corresponding color, size, type for points.
A logical indicating whether number of studies should be added to network graph.
The magnification to be used for number of studies.
Color for number of studies.
Color for shadow around number of studies.
A character string indicating which starting
layout is used if iterate = TRUE. If "circle" (default), the
iteration starts with a circular ordering of the vertices; if
"eigen", eigenvectors of the Laplacian matrix are used, calculated
via generic function eigen (spectral decomposition);
if "prcomp", eigenvectors of the Laplacian matrix are calculated
via generic function prcomp (principal component
analysis); if "random", a random layout is used, drawn from a
bivariate normal.
A numeric indicating which eigenvector is used as x
coordinate if start = "eigen" or "prcomp" and
iterate = TRUE. Default is 2, the eigenvector to the
second-smallest eigenvalue of the Laplacian matrix.
A numeric indicating which eigenvector is used as
y-coordinate if start = "eigen" or "prcomp" and
iterate = TRUE. Default is 3, the eigenvector to the
third-smallest eigenvalue of the Laplacian matrix.
A numeric indicating which eigenvector is used as
z-coordinate if start = "eigen" or "prcomp" and
iterate = TRUE. Default is 4, the eigenvector to the
fourth-smallest eigenvalue of the Laplacian matrix.
A logical indicating whether the stress majorization algorithm is carried out for optimization of the layout.
A numeric for the tolerance for convergence if
iterate = TRUE.
An integer defining the maximum number of iteration
steps if iterate = TRUE.
A logical indicating whether all iteration steps
are shown if iterate = TRUE. May slow down calculations if
set to TRUE (especially if plastic = TRUE).
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 seq.
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 seq.
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 seq.
Vector (n) of x coordinates.
Vector (n) of y coordinates.
Vector (n) of z coordinates.
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" (R<U+00FC>cker & 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).
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 effect 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 effect 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.
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).
Hall, K.M. (1970). An r-dimensional quadratic placement algorithm. Management Science, 17, 219--229.
Hu, Y. (2012). Combinatorial Scientific Computing, Chapter Algorithms for Visualizing Large Networks, pages 525--549. Chapman and Hall/CRC Computational Science.
Kamada, T. and Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information Processing Letters, 31(1), 7--15.
Krahn U, Binder H, K<U+00F6>nig J (2013), A graphical tool for locating inconsistency in network meta-analyses. BMC Medical Research Methodology, 13, 35.
Michailidis, G. and de Leeuw, J. (2001). Data visualization through graph drawing. Computational Statistics, 16(3), 435--450.
R<U+00FC>cker G & Schwarzer G (2016), Automated drawing of network plots in network meta-analysis. Research Synthesis Methods, 7, 94--107.
# NOT RUN {
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)
# 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
#
# }
# NOT RUN {
netgraph(net1, start = "circle", iterate = TRUE, col.multiarm = "purple",
allfigures = TRUE)
# }
# NOT RUN {
# 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])
# }
# NOT RUN {
# 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")
# }
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