gof {latentnet} | R Documentation |
gof
calculates p-values for geodesic
distance, degree, and reachability summaries to
diagnose the goodness-of-fit of exponential family random graph
mixed models. See ergmm
for more information on these models.
## S3 method for class 'ergmm': gof(object, ..., nsim=100, GOF=~idegree+odegree+distance, verbose=FALSE)
object |
an ergmm object (returned by
ergmm ). |
nsim |
The number of simulations to use for the MCMC p-values. This is the size of the sample of graphs to be randomly drawn from the distribution specified by the object on the set of all graphs. |
GOF |
formula; an R formula object, of the form
~ <model terms> specifying the
statistics to use to diagnosis the goodness-of-fit of the model.
They do not need to be in the model formula specified in
formula , and typically are not.
Examples are the degree distribution ("degree"),
minimum geodesic distances ("dist"), and shared partner distributions
("espartners" and "dspartners").
For the details on the possible
<model terms> , see ergm-terms . |
verbose |
Provide verbose information on the progress of the simulation. |
... |
Additional arguments, to be passed to lower-level functions in the future. |
A sample of graphs is randomly drawn from the posterior of the
ergmm
.
A plot of the summary measures is plotted.
More information can be found by looking at the documentation of
ergm
.
gof
and gof.ergmm
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
.
ergmm
,
ergmm (object)
,
ergm
, network
,
simulate.ergmm
, plot.gofobject
# data(sampson) # # test the gof.ergm function # samplike.fit <- ergmm(samplike ~ latent(d=2,G=3),control=ergmm.control(burnin=1000,interval=5)) samplike.fit summary(samplike.fit) # # Plot the probabilities first # monks.gof <- gof(samplike.fit) monks.gof # # Place all three on the same page # with nice margins # par(mfrow=c(1,3)) par(oma=c(0.5,2,1,0.5)) # plot(monks.gof) # # And now the odds # plot(monks.gof, plotlogodds=TRUE)