Coerce a Network Object to Matrix Form
as.matrix methods attempt to coerce their input to a matrix in adjacency, incidence, or edgelist form. Edge values (from a stored attribute) may be used if present.
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## S3 method for class 'network' as.matrix(x, matrix.type = NULL, attrname = NULL, ...) ## S3 method for class 'adjacency' as.matrix.network(x, attrname=NULL, expand.bipartite = FALSE, ...) ## S3 method for class 'edgelist' as.matrix.network(x, attrname=NULL, as.sna.edgelist = FALSE, ...) ## S3 method for class 'incidence' as.matrix.network(x, attrname=NULL, ...)
an object of class
optionally, the name of an edge attribute to use for edge values
logical; should the edgelist be returned in sna edglist form?
If no matrix type is specified,
which.matrix.type will be used to make an educated guess based on the shape of
x. Where edge values are not specified, a dichotomous matrix will be assumed.
Edgelists returned by these methods are by default in a slightly different form from the
sna edgelist standard, but do contain the
sna extended matrix attributes (see
as.network.matrix). They should typically be compatible with
sna library functions. To ensure compatibility, the
as.sna.edgelist argument can be set (which returns an exact
sna edgelist). The
as.edgelist function also returns a similar edgelist matrix but with an enforced sorting.
attrname attribute is used to include a charcter attribute, the resulting edgelist matrix will be character rather than numeric.
Note that adjacency matrices may also be obtained using the extraction operator. See the relevant man page for details.
An adjacency, incidence, or edgelist matrix
Butts, C. T. (2008). “network: a Package for Managing Relational Data in R.” Journal of Statistical Software, 24(2). http://www.jstatsoft.org/v24/i02/
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#Create a random network m <- matrix(rbinom(25,1,0.5),5,5) diag(m) <- 0 g <- network(m) #Coerce to matrix form as.matrix.network(g,matrix.type="adjacency") as.matrix.network(g,matrix.type="incidence") as.matrix.network(g,matrix.type="edgelist") #Can also use the extraction operator g[,] #Get entire adjacency matrix g[1:5,6:10] #Obtain a submatrix
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