Network Objects
Construct, coerce to, test for and print network objects.
is.network(x) as.network(x, ...) network(x, vertex.attr = NULL, vertex.attrnames = NULL, directed = TRUE, hyper = FALSE, loops = FALSE, multiple = FALSE, bipartite = FALSE, ...) network.copy(x) ## S3 method for class 'network' print(x, matrix.type = which.matrix.type(x), mixingmatrices = FALSE, na.omit = TRUE, print.adj = FALSE, ...) ## S3 method for class 'network' summary(object, na.omit = TRUE, mixingmatrices = FALSE, print.adj = TRUE, ...)
x |
for |
... |
additional arguments. |
vertex.attr |
optionally, a list containing vertex attributes. |
vertex.attrnames |
optionally, a list containing vertex attribute names. |
directed |
logical; should edges be interpreted as directed? |
hyper |
logical; are hyperedges allowed? |
loops |
logical; should loops be allowed? |
multiple |
logical; are multiplex edges allowed? |
bipartite |
count; should the network be interpreted as bipartite? If present (i.e., non-NULL, non-FALSE) it is the count of the number of actors in the bipartite network. In this case, the number of nodes is equal to the number of actors plus the number of events (with all actors preceeding all events). The edges are then interpreted as nondirected. Values of bipartite==0 are permited, indicating a bipartite network with zero-sized first partition. |
matrix.type |
one of |
mixingmatrices |
logical; print the mixing matrices for the discrete attributes? |
na.omit |
logical; omit summarization of missing attributes in
|
print.adj |
logical; print the network adjacency structure? |
object |
an object of class |
network constructs a network class object from a matrix
representation. If the matrix.type parameter is not specified, it
will make a guess as to the intended edgeset.constructors function to
call based on the format of these input matrices. If the class of x
is not a matrix, network construction can be dispatched to other methods.
For example, If the ergm package is loaded, network() can
function as a shorthand for as.network.numeric with
x as an integer specifying the number of nodes to be created in the
random graph.
If the ergm package is loaded, network can function as a
shorthand for as.network.numeric if x is an integer specifying
the number of nodes. See the help page for
as.network.numeric in ergm package for details.
network.copy creates a new network object which duplicates its
supplied argument. (Direct assignment with <- should be used rather
than network.copy in most cases.)
as.network tries to coerce its argument to a network, using the
as.network.matrix functions if x is a matrix. (If the argument
is already a network object, it is returned as-is and all other arguments
are ignored.)
is.network tests whether its argument is a network (in the sense that
it has class network).
print.network prints a network object in one of several possible
formats. It also prints the list of global attributes of the network.
summary.network provides similar information.
network, as.network, and print.network all
return a network class object; is.network returns TRUE or FALSE.
Between versions 0.5 and 1.2, direct assignment of a network object
created a pointer to the original object, rather than a copy. As of version
1.2, direct assignment behaves in the same manner as network.copy.
Direct use of the latter is thus superfluous in most situations, and is
discouraged.
Many of the network package functions modify their network object arguments
in-place. For example, set.network.attribute(net,"myVal",5) will have
the same effect as net<-set.network.attribute(net,"myVal",5).
Unfortunately, the current implementation of in-place assignment breaks when
the network argument is an element of a list or a named part of another
object. So set.network.attribute(myListOfNetworks[[1]],"myVal",5)
will silently fail to modify its network argument, likely leading to
incorrect output.
Carter T. Butts buttsc@uci.edu and David Hunter dhunter@stat.psu.edu
Butts, C. T. (2008). “network: a Package for Managing Relational Data in R.” Journal of Statistical Software, 24(2). https://www.jstatsoft.org/v24/i02/
m <- matrix(rbinom(25,1,.4),5,5) diag(m) <- 0 g <- network(m, directed=FALSE) summary(g) h <- network.copy(g) #Note: same as h<-g summary(h)
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