ergm.ego (version 1.1.0)

gof.ergm.ego: Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Model fit to Egocentrically Sampled Data

Description

gof.ergm.ego implements the gof method for ergm.ego fit objects.

An enhanced plotting method is also provided, giving uncertainty bars for the observed statistics as well.

Usage

# S3 method for ergm.ego
gof(
  object,
  ...,
  GOF = c("model", "degree", "espartners"),
  control = control.gof.ergm(),
  verbose = FALSE
)
# S3 method for gof.ergm.ego
plot(x, ..., ego.conf.level = 0.95)

Value

An object of class gof.ergm.ego, inheriting from gof.ergm.

Arguments

object

An ergm.ego fit.

...

Additional arguments. Unused by gof.ergm.ego(), passed to ergm::plot.gof() by plot.gof.ergm.ego()

GOF

A string specifying the statistics whose goodness of fit is to be evaluated. Currently, only “degree”, “espartners” and “model” are implemented; see gof documentation for details.

control

A list to control parameters, constructed using control.gof.formula or control.gof.ergm (which have different defaults).

verbose

Provide verbose information on the progress of the simulation.

x

an object returned by gof.ergm.ego().

ego.conf.level

confidence level for the observed statistic estimates as well.

Author

Pavel N. Krivitsky

References

• David R. Hunter, Steven M. Goodreau, and Mark S. Handcock (2008). "Goodness of Fit of Social Network Models." Journal of the American Statistical Association, 103:481: 248–258. tools:::Rd_expr_doi("10.1198/016214507000000446")

See Also

For examples, see ergm.ego.

Examples

# \donttest{
data(faux.mesa.high)
fmh.ego <- as.egor(faux.mesa.high)

head(fmh.ego)

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))

# Check whether the model "converged":
(modelgof <- gof(egofit, GOF="model"))
plot(modelgof)

# Check whether the model reconstructs the degree distribution:
(deggof <- gof(egofit, GOF="degree"))
plot(deggof)
# }