ergm.ego (version 1.1.0)

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$popsize == 0 || is(object$popsize, "AsIs"))
    object$ppopsize else object$popsize,
  control = control.simulate.ergm.ego(),
  output = c("network", "stats", "edgelist", "pending_update_network", "ergm_state"),
  ...,
  basis = NULL,
  verbose = FALSE
)

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.

Arguments

object

An ergm.ego fit.

nsim

Number of realizations to simulate.

seed

Seed value (integer) for the random number generator. See set.seed.

constraints, ...

Additional arguments passed to san and simulate.formula.

popsize, basis

A network size to which to scale the model for simulation; a data.frame with at least those ego attributes used to estimate the model to simulate over a specific set of actors; or a network object to use as is. basis is provided for consistency with ergm(), ergm.ego(), simulate.ergm(), and others. If both are specified, popsize overrules.

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

A logical or an integer to control the amount of progress and diagnostic information to be printed. FALSE/0 produces minimal output, with higher values producing more detail. Note that very high values (5+) may significantly slow down processing.

Author

Pavel N. Krivitsky

References

• Pavel N. Krivitsky and Martina Morris (2017). "Inference for social network models from egocentrically sampled data, with application to understanding persistent racial disparities in HIV prevalence in the US." Annals of Applied Statistics, 11(1): 427–455. tools:::Rd_expr_doi("10.1214/16-AOAS1010")

• Pavel N. Krivitsky, Martina Morris, and Michał Bojanowski (2019). "Inference for Exponential-Family Random Graph Models from Egocentrically-Sampled Data with Alter–Alter Relations." NIASRA Working Paper 08-19. https://www.uow.edu.au/niasra/publications/

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

See Also

simulate.formula, simulate.ergm

Examples

data(faux.mesa.high)
data(fmhfit)
colMeans(egosim <- simulate(fmhfit, popsize=300,nsim=50,
                       output="stats", control=control.simulate.ergm.ego(
                       simulate=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"))