gof.ergmm {latentnetHRT} | R Documentation |
gof
computes and plots posterior predictive checks for various
networks statistics (e.g., degree, and reachability) to
diagnose the goodness-of-fit of Latent Position or Latent Cluster Position
models. See ergmm
for more information on these models.
## Default S3 method: gof(object,...) ## S3 method for class 'ergmm': gof(object, ..., nsim=100, GOF=~degree+espartners, verbose=FALSE)
object |
an ergmm object.
See documentation for ergmm . |
nsim |
The number of posterior draws to use for the posterior predictive checks. This is the size of the sample of networks to be randomly drawn from the posterior distribution specified by the object. |
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"),
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 specified model.
The first argument is
the output of a call to ergmm
and the model
used for that call is the one evalauted.
A plot of the summary measures is plotted.
gof
, gof.ergmm
, and gof.formula
return an object of class gofobject
.
This is a list of the tables of statistics and posterior
``p-values".
This is typically plotted using plot.gofobject
.
ergmm, network, rergm, summary.ergmm, plot.gofobject
## Not run: # # Using Sampson's Monk data, lets fit a # simple latent position model # data(sampson) # # Get the group labels # group <- get.vertex.attribute(samplike,"group") samp.labs <- substr(group,1,1) # # Fit the two-dimensional latent social space model # # This may take a few minutes ... # samp.fit <- ergmm(samplike ~ latent(k=2), burnin=10000, MCMCsamplesize=2000, interval=30) # # Posterior Predictive Checks gofsamplike <- gof.ergmm(samp.fit, GOF=~idegree) gofsamplike # # Place both on the same page # with nice margins # par(mfrow=c(1,2)) par(oma=c(0.5,2,1,0.5)) # plot(gofsamplike) # # And now the odds # plot(gofsamplike, plotodds=TRUE) # # Using Sampson's Monk data, lets # fit the two-dimensional clustered latent social space model # # The ngroups parameter fits 3 groups # # This may take a few minutes ... # samp.fit <- ergmm(samplike ~ latentcluster(k=2, ngroups=3), burnin=10000, MCMCsamplesize=2000, interval=30) # # Posterior Predictive Checks gofsamplike <- gof.ergmm(samp.fit, GOF=~idegree) gofsamplike # plot(gofsamplike) # # And now the odds # plot(gofsamplike, plotodds=TRUE) ## End(Not run)