network.make.graph: Graphical Gaussian Models: Plotting the Network
In GeneNet: Modeling and Inferring Gene Networks

Description Usage Arguments Details Value Author(s) See Also Examples

network.make.dot converts an edge list as obtained by network.test.edges into a "dot" file that can directly be used for plotting the network with graphviz.

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

edge.info shows the edge weights and the edge directions.

node.degree shows number of edges connected to a node (bi-directional/undirected edges are counted only once).

num.nodes shows the number of nodes.

network.make.dot(filename, edge.list, node.labels, main=NULL, show.edge.labels=FALSE)
network.make.graph(edge.list, node.labels, drop.singles=FALSE)
edge.info(gr)
node.degree(gr)
num.nodes(gr)

`filename`	name of file containg the "dot" commands for graphviz
`edge.list`	a data frame, as obtained by `network.test.edges`, listing all edges to be included in the graph
`node.labels`	a vector with labels for each node (will be converted to type character)
`main`	title included in plot
`show.edge.labels`	plot correlation values as edge labels (default: FALSE)
`drop.singles`	remove unconnected nodes
`gr`	a graph object

For network plotting the software "graphviz" is employed (https://www.graphviz.org).

For the functions network.plot.graph and network.make.graph the "graph" and "Rgraphviz" packages from the Bioconductor project (https://www.bioconductor.org) is required.

network.make.dot produces a "dot" network description file that can directly be fed into graphviz in order to produce a plot of a network.

network.make.graph returns a graph object, suitable for plotting with functions from the "Rgraphviz" library.

edge.info returns a list containing vector of weights for all edges contained in a graph, and a vector listing the directions of the edges (using Rgraphviz conventions "forward" for directed edge, and "none" for bi-directional/undirected edge).

num.nodes returns the number of nodes.

Juliane Sch\"afer, Rainer Opgen-Rhein, and Korbinian Strimmer (https://strimmerlab.github.io).

network.test.edges, plot.graph.

# load GeneNet library
library("GeneNet")
 
# 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.list.edges(true.pcor)[1:19,]

########  use graphviz directly to produce a plot ##########

# uncomment for actual use!

# nlab <- LETTERS[1:20]
# ggm.make.dot(filename="test.dot", test.results, nlab, main = "A graph") 
# system("fdp -T svg -o test.svg test.dot") # SVG format


########  use Rgraphviz produce a plot ##########

# uncomment for actual use!

# nlab <- LETTERS[1:20]
# gr <- network.make.graph( test.results, nlab) 
# gr 
# num.nodes(gr)
# edge.info(gr)
# gr2 <- network.make.graph( test.results, nlab, drop.singles=TRUE) 
# gr2 
# num.nodes(gr2)
# edge.info(gr2)

# plot network
# NOTE: this requires the installation of the "Rgraphviz" library
# library("Rgraphviz")
# plot(gr, "fdp")
# plot(gr2, "fdp")

## for a full example with beautified Rgraphviz plot see 
## the example scripts provide with GeneNet (e.g. arabidopis-net.R)