ergm (version 4.7.1)

as.network.numeric: Create a Simple Random network of a Given Size

Description

as.network.numeric() creates a random Bernoulli network of the given size as an object of class network.

Usage

# S3 method for numeric
as.network(
  x,
  directed = TRUE,
  hyper = FALSE,
  loops = FALSE,
  multiple = FALSE,
  bipartite = FALSE,
  ignore.eval = TRUE,
  names.eval = NULL,
  edge.check = FALSE,
  density = NULL,
  init = NULL,
  numedges = NULL,
  ...
)

Value

An object of class network

Arguments

x

count; the number of nodes in the network

directed

logical; should edges be interpreted as directed?

hyper

logical; are hyperedges allowed? Currently ignored.

loops

logical; should loops be allowed? Currently ignored.

multiple

logical; are multiplex edges allowed? Currently ignored.

bipartite

count; should the network be interpreted as bipartite? If present (i.e., non-NULL) 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 preceding all events). The edges are then interpreted as nondirected.

ignore.eval

logical; ignore edge values? Currently ignored.

names.eval

optionally, the name of the attribute in which edge values should be stored. Currently ignored.

edge.check

logical; perform consistency checks on new edges?

density

numeric; the probability of a tie for Bernoulli networks. If neither density nor init is given, it defaults to the number of nodes divided by the number of dyads (so the expected number of ties is the same as the number of nodes.)

init

numeric; the log-odds of a tie for Bernoulli networks. It is only used if density is not specified.

numedges

count; if present, sample the Bernoulli network conditional on this number of edges (rather than independently with the specified probability).

...

additional arguments

Details

The network will not have vertex, edge or network attributes. These can be added with operators such as %v%, %n%, %e%.

References

Butts, C.T. 2002. ``Memory Structures for Relational Data in R: Classes and Interfaces'' Working Paper.

See Also

network

Examples

# Draw a random directed network with 25 nodes
g <- network(25)

# Draw a random undirected network with density 0.1
g <- network(25, directed=FALSE, density=0.1)

# Draw a random bipartite network with 4 actors and 6 events and density 0.1
g <- network(10, bipartite=4, directed=FALSE, density=0.1)

# Draw a random directed network with 25 nodes and 50 edges
g <- network(25, numedges=50)