| edgeset.constructors {network} | R Documentation |
These functions convert relational data in matrix form to network edge sets.
network.adjacency(x, g, ignore.eval = TRUE, names.eval = NULL, ...) network.edgelist(x, g, ignore.eval = TRUE, names.eval = NULL, ...) network.incidence(x, g, ignore.eval = TRUE, names.eval = NULL, ...) network.bipartite(x, g, ignore.eval = TRUE, names.eval = NULL, ...)
x |
a matrix containing edge information |
g |
an object of class network |
ignore.eval |
logical; ignore edge values? |
names.eval |
the edge attribute under which to store edge values, if any |
... |
additional arguments to add.edge |
Each of the above functions takes a network and a matrix as input, and returns a network object whose edgeset reflects the given information. network.adjacency takes x to be an adjacency matrix; code.edgelist takes x to be an edgelist matrix; and network.incidence takes x to be an incidence matrix. network.bipartite takes x to be an adjacency matrix where the rows are thge actors and the columns are the events; If ignore.eval==FALSE, (non-zero) edge values are stored as edgewise attributes with name names.eval. Any additional command line parameters are passed to add.edge.
An object of class network
Handling of missing data is not yet fully implemented.
Carter T. Butts buttsc@uci.edu and David Hunter dhunter@stat.psu.edu
Butts, C.T. 2002. ``Memory Structures for Relational Data in R: Classes and Interfaces'' Working Paper.
network, network.initialize, add.edge
#Create an arbitrary adjacency matrix m<-matrix(rbinom(25,1,0.5),5,5) diag(m)<-0 g<-network.initialize(5) #Initialize the network g<-network.adjacency(m,g) #Import the edge data