simulate.ergm.ego: Simulate from a `ergm.ego` fit.

Description

A wrapper around simulate.formula to simulate networks from an ERGM fit using ergm.ego.

Usage

# S3 method for ergm.ego
simulate(
  object,
  nsim = 1,
  seed = NULL,
  constraints = object$constraints,
  popsize = if (object$popsize == 1) object$ppopsize else object$popsize,
  control = control.simulate.ergm.ego(),
  output = c("network", "stats", "edgelist", "pending_update_network", "ergm_state"),
  ...,
  verbose = FALSE
)

Arguments

object

An ergm.ego fit.

nsim

Number of realizations to simulate.

seed

Random seed.

constraints, …

Additional arguments passed to san and simulate.formula.

popsize

Either network size to which to scale the model for simulation or a data.frame with at least those ego attributes required to estimate the model, to simulate over a specific set of actors.

control

A control.simulate.ergm.ego control list.

output

one of "network", "stats", "edgelist", "pending_update_network", or, for future compatibility, "ergm_state". See help for simulate.ergm() for explanation.

verbose

Verbosity of output.

Value

The ouput has the same format (with the same options) as simulate.formula. If output="stats" is passed, an additional attribute, "ppopsize" is set, giving the actual size of the network reconstructed, when the pop.wt control parameter is set to "round" and "popsize" is not a multiple of the egocentric sample size or the sampling weights.

References

Pavel N. Krivitsky and Martina Morris. Inference for Social Network Models from Egocentrically-Sampled Data, with Application to Understanding Persistent Racial Disparities in HIV Prevalence in the US. Thechnical Report. National Institute for Applied Statistics Research Australia, University of Wollongong, 2015(05-15). 10.1214/16-AOAS1010

Pavel N. Krivitsky, Mark S. Handcock, and Martina Morris. Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models. Statistical Methodology, 2011, 8(4), 319-339. 10.1016/j.stamet.2011.01.005

Examples

Run this code

# NOT RUN {
data(faux.mesa.high)
fmh.ego <- as.egodata(faux.mesa.high)
egofit <- ergm.ego(fmh.ego~edges+degree(0:3)+nodefactor("Race")+nodematch("Race")
               +nodefactor("Sex")+nodematch("Sex")+absdiff("Grade"), 
               popsize=network.size(faux.mesa.high))
colMeans(egosim <- simulate(egofit, popsize=300,nsim=50,
                       output="stats", control=control.simulate.ergm.ego(
                       simulate.control=control.simulate.formula(MCMC.burnin=2e6))))
colMeans(egosim)/attr(egosim,"ppopsize")*network.size(faux.mesa.high)
summary(faux.mesa.high~edges+degree(0:3)+nodefactor("Race")+nodematch("Race")
           +nodefactor("Sex")+nodematch("Sex")+absdiff("Grade"))

# }