ggm.plot.graph {GeneTS}R Documentation

Graphical Gaussian Models: Plotting the Network


ggm.make.graph converts an edge list as obtained by ggm.test.edges into a graph object.

show.edge.weights summarizes a graph object by prints a vector of weights for all edges contained in a graph. This function is convenient to gain a first impression of the graph (in particular if the "Rgraphviz" library is not installed).

ggm.plot.graph visualizes the network structure of the graphical Gaussian model using the Rgraphviz network plot package. The correlation coefficients are printed as edge labels.


ggm.make.graph(edge.list, num.nodes)
ggm.plot.graph(gr, node.labels=NULL, show.edge.labels=TRUE, col.pos="black", col.neg="grey", ...)


edge.list a data frame, as obtained by ggm.test.edges, listing all edges to be included in the graph
num.nodes the total number of nodes in the network
gr a graph object
node.labels a vector with labels for each node (optional)
show.edge.labels plot correlation values as edge labels (default: TRUE)
col.pos edge color for positive correlation (default: "black")
col.neg edge color for positive correlation (default: "grey")
... options passed to plot.graph


The network plotting functions require the installation of the "graph" and "Rgraphviz" R packages. These are available from the Bioconductor website ( Note that it is not necessary to install the complete set of Bioconductor packages, only "graph" and "Rgraphviz" are needed by the GeneTS package (however, these may in turn require additional packages from Bioconductor).

ggm.plot.graph is a simple utility function to plots the graph in "neato" format with ellipsoid node shapes. See the documentation of plot.graph in the "Rgraphviz" package for many other options.


ggm.make.graph returns a graph object, suitable for plotting with functions from the "Rgraphviz" library.
show.edge.weights returns a vector of weights for all edges contained in a graph.
ggm.plot.graph plots the network on the current graphic device.


Juliane Schaefer ( and Korbinian Strimmer (

See Also

ggm.test.edges, plot.graph.


# load GeneTS library
# generate random network with 20 nodes and 10 percent edges (=19 edges)
true.pcor <- ggm.simulate.pcor(20, 0.1)

# convert to edge list 
test.results <- ggm.test.edges(true.pcor, eta0=0.9, kappa=1000)[1:19,]

# generate graph object 
# NOTE: this requires the installation of the "graph" package
# (in the following "try" is used to avoid an error if the library is not installed)
try( gr <- ggm.make.graph( test.results, 20) )
try( gr )
try( show.edge.weights(gr) )

# plot network
# NOTE: this requires the installation of the "Rgraphviz" library
try ( ggm.plot.graph(gr))
nlab <- c("A","B","C","D","E","F","G","H","I","J","K",
            "L","M","N","O","P","Q","R","S", "T")
try( ggm.plot.graph(gr, nlab) )

[Package GeneTS version 2.3 Index]