| gof {ergm} | R Documentation |
gof calculates p-values for geodesic
distance, degree, and reachability summaries to
diagnose the goodness-of-fit of exponential family random graph
models. See ergm for more information on these models.
## Default S3 method:
gof(object,...)
## S3 method for class 'formula'
gof(object,
...,
coef=NULL,
GOF=NULL,
constraints=~.,
control=control.gof.formula(),
verbose=FALSE)
## S3 method for class 'ergm'
gof(object,
...,
coef=NULL,
GOF=NULL,
constraints=NULL,
control=control.gof.ergm(),
verbose=FALSE)
object |
an R object. Either a formula or an |
... |
Additional arguments, to be passed to lower-level functions in the future. |
coef |
When given either
a formula or an object of class ergm, |
GOF |
formula; an R formula object, of the form
|
constraints |
A one-sided formula specifying one or more constraints
on the support of the distribution of the networks being
modeled. See the help for similarly-named argument in
|
control |
A list to control parameters, constructed using
|
verbose |
Provide verbose information on the progress of the simulation. |
A sample of graphs is randomly drawn from the specified model.
The first argument is typically
the output of a call to ergm and the model
used for that call is the one fit.
A plot of the summary measures is plotted.
More information can be found by looking at the documentation of
ergm.
For GOF = ~model, the model's observed sufficient statistics are plotted
as quantiles of the simulated sample. In a good fit, the observed statistics should
be near the sample median (0.5).
For gof.ergm and gof.formula, default behavior depends on
the directedness of the network involved; if undirected then degree,
espartners, and distance are used as default properties to examine. If
the network in question is directed, “degree” in the above is replaced
by idegree and odegree.
gof, gof.ergm, and gof.formula
return an object of class gofobject.
This is a list of the tables of statistics and p-values.
This is typically plotted using plot.gofobject.
ergm, network, simulate.ergm, summary.ergm, plot.gofobject
data(florentine) gest <- ergm(flomarriage ~ edges + kstar(2)) gest summary(gest) # test the gof.ergm function gofflo <- gof(gest) gofflo summary(gofflo) # Plot all three on the same page # with nice margins par(mfrow=c(1,3)) par(oma=c(0.5,2,1,0.5)) plot(gofflo) # And now the log-odds plot(gofflo, plotlogodds=TRUE) # Use the formula version of gof gofflo2 <-gof(flomarriage ~ edges + kstar(2), coef=c(-1.6339, 0.0049)) plot(gofflo2)