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Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model for big networks

gof.bigergm.Rd

A sample of graphs is randomly drawn from the specified model. The first argument is typically the output of a call to bigergm and the model used for that call is the one fit.

By default, the sample consists of 100 simulated networks, but this sample size (and many other settings) can be changed using the ergm_control argument described above.

Usage

# S3 method for class 'bigergm'
gof(
  object,
  ...,
  type = "full",
  control_within = ergm::control.simulate.formula(),
  seed = NULL,
  nsim = 100,
  compute_geodesic_distance = TRUE,
  start_from_observed = TRUE,
  simulate_sbm = FALSE
)

Arguments

object

An bigergm object.

...

Additional arguments, to be passed to simulate_bigergm, which, in turn, passes the information to simulate_formula. See documentation for bigergm.

type

the type of evaluation to perform. Can take the values full or within. full performs the evaluation on all edges, and within only considers within-block edges.

control_within

MCMC parameters as an instance of control.simulate.formula to be used for the within-block simulations.

seed

the seed to be passed to simulate_bigergm. If NULL, a random seed is used.

nsim

the number of simulations to employ for calculating goodness of fit, default is 100.

compute_geodesic_distance

if TRUE, the distribution of geodesic distances is also computed (considerably increases computation time on large networks. FALSE by default.)

start_from_observed

if TRUE, MCMC uses the observed network as a starting point. If FALSE, MCMC starts from a random network.

simulate_sbm

if TRUE, the between-block connections are simulated from the estimated stochastic block model from the first stage not the estimated ERGM.

Value

gof.bigergm returns a list with two entries. The first entry 'original' is another list of the network stats, degree distribution, edgewise-shared partner distribution, and geodesic distance distribution (if compute_geodesic_distance = TRUE) of the observed network. The second entry is called 'simulated' is also list compiling the network stats, degree distribution, edgewise-shared partner distribution, and geodesic distance distribution (if compute_geodesic_distance = TRUE) of all simulated networks.

Examples

data(toyNet)
# \donttest{
# Specify the model that you would like to estimate.
data(toyNet)
# Specify the model that you would like to estimate.
model_formula <- toyNet ~ edges + nodematch("x") + nodematch("y") + triangle
estimate <- bigergm(model_formula,n_blocks = 4)
gof_res <- gof(estimate,
nsim = 100
)
plot(gof_res)




# }