simulate.sisnetwork: Simulate a bipartite network using sequential importance...

In networksis: Simulate Bipartite Graphs with Fixed Marginals Through Sequential Importance Sampling

Description

The method simulate.sisnetwork simulates graphs with the same marginals as the passed network or as the rows and columns specified in a sisnetwork object through sequential importance sampling. That is, the degrees of the nodes are fixed and specified.

Usage

## S3 method for class 'sisnetwork'
simulate(object, nsim = 1, seed = NULL, save.networks = FALSE, ...)

Arguments

object	Either a network object or sisnetwork object. If a sisnetwork object, this should be a list with components row and col to specify the row and column degrees. These are the degrees of the type 1 and type 2 nodes, respectively.
nsim	Number of networks to be randomly drawn from the set of all networks.
seed	Seed for random number generator.
save.networks	If this is TRUE, the sampled networks are returned. Otherwise only the last network is returned.
...	Further arguments passed to or used by methods.

Details

A sample of networks is randomly drawn from the space of networks with the same degrees for each node.

Value

simulate.sisnetwork returns an object of class network.series, that is a list consisting of the following elements:

networks	The vector of simulated networks.
log.prob	The vector of the logarithm of the probability of being sampled.
log.graphspace.size	The logarithm of the mean estimate of the number of graphs in the graph space.
log.graphspace.SE	The logarithm of the standard error of the mean estimate of the number of graphs in the graph space.
log.graphspace.size.lne	The logarithm of the lognormal-based estimate of the number of graphs in the graph space.
log.graphspace.SE.lne	The logarithm of the standard error of the lognormal-based estimate of the number of graphs in the graph space.

See Also

network

Examples

bipartite.graph <- matrix(c(1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0), nrow = 3, byrow = TRUE)
example.net <- network(bipartite.graph)

# Specify the set to which each node belongs
example.net %v% "set" <- c(rep(1, 3),rep(2, 4))

# Simulate 100 graphs with the same marginals as 'example.net'
sim <- simulate.sisnetwork(example.net, nsim = 100)

# Estimated graph space size and SE
exp(sim$log.graphspace.size)
exp(sim$log.graphspace.SE)

# Darwin's finches example
data(finch)

sim <- simulate.sisnetwork(finch, nsim = 100, save.networks = TRUE)

# Calculate importance weights from the graph probabilities
importance.weights <- 1 / exp(sim$log.prob)
hist(importance.weights, breaks = 25, xlab = "Inverse Graph Probability", main="")

# Calculate Sanderson's \bar{S}^2
s.bar.squared.vec <- rep(0, 100)

for(i in 1 : 100)
{
   # Extract simulated bipartite graphs
   new.graph <- as.matrix.network(sim$networks[[i]])

   # Calculate custom graph statistic
   s.bar.squared.vec[i] <- (sum((new.graph %*% t(new.graph)) ^ 2) -
   sum(diag((new.graph %*% t(new.graph)) ^ 2))) / (13 * 12)
}

Example output

Loading required package: network
network: Classes for Relational Data
Version 1.13.0.1 created on 2015-08-31.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
                    Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
                    Martina Morris, University of Washington
                    Skye Bender-deMoll, University of Washington
 For citation information, type citation("network").
 Type help("network-package") to get started.

networksis: Simulate Bipartite Graphs with Fixed Marginals Through
Sequential Importance Sampling
Version 2.1-3 created on 2015-04-07.
copyright (c) 2008, Ryan Admiraal, Murdoch University
 Based on "statnet" project software (statnet.org).
 For license and citation information see statnet.org/attribution
 For citation information, type citation("networksis").
 Type help("networksis-package") to get started.

[1] 11.585
[1] 0.1626881

networksis documentation built on May 2, 2019, 8:23 a.m.