simulate.ergmm: Draw from the distribution of an Exponential Random Graph...

In statnet/latentnet: Latent Position and Cluster Models for Statistical Networks

Draw from the distribution of an Exponential Random Graph Mixed Model

Description

If passed a ergmm fit object, simulate is used to simulate networks from the posterior of an exponetial random graph mixed model fit. Alternatively, a ergmm.model can be passed to simulate based on a particular parametr configuration.

Usage

## S3 method for class 'ergmm'
simulate(object, nsim = 1, seed = NULL, ...)

## S3 method for class 'ergmm.model'
simulate(object, nsim = 1, seed = NULL, par, prior = list(), ...)

Arguments

object	either an object of class ergmm for posterior simulation, or an object of class ergmm.model for a specific model.
nsim	number of networks to draw (independently)
seed	random seed to use; defaults to using the current state of the random number generator
...	Additional arguments. Currently unused.
par	a list with the parameter configuration based on which to simulate
prior	a list with the prior distribution parameters that deviate from their defaults

Details

A sample of networks is randomly drawn from the specified model. If a needed value of par is missing, it is generated from its prior distribution.

Value

If nsim = 1, simulate returns an object of class network. Otherwise, an object of class network.series that is a list consisting of the following elements:

$formula	The formula used to generate the sample.
$networks	A list of the generated networks.

See Also

ergmm, network, print.network

Examples

#
# Fit a short MCMC run: just the MCMC.
#
data(sampson)
gest <- ergmm(samplike ~ euclidean(d=2,G=3),
              control=ergmm.control(burnin=100,interval=5,sample.size=100),tofit="mcmc")
#
# Draw from the posterior
#
g.sim <- simulate(gest)
plot(g.sim)
#
# Draw from the first draw from the posterior
#
g.sim <- with(gest,simulate(model,par=sample[[1]],prior=prior))
plot(g.sim)

statnet/latentnet documentation built on Feb. 24, 2024, 4:02 p.m.