summary.gofobject {latentnetHRT} | R Documentation |
summary.gofobject
summaries the diagnostics such as the
degree distribution, geodesic distances, shared partner distributions,
and reachability for the goodness-of-fit of Latent Space random graph
models. See ergmm
for more information on these models.
## S3 method for class 'gofobject': summary(object, ...)
object |
an object of class gofobject ,
typically produced by the
gof.ergmm or gof.formula functions.
See the documentation for these. |
... |
Additional arguments, to be passed to the plot function. |
gof.ergmm
produces a sample of networks randomly drawn from the specified model.
This function produces a print out the summary measures.
none
gof.ergmm, ergmm, network, rergm
## 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) # samp.fit <- ergmm(samplike ~ latent(k=2), burnin=10000, MCMCsamplesize=2000, interval=30) # # Posterior Predictive Checks gofsamplike <- gof.ergmm(samp.fit) summary(gofsamplike) # # 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(gofsamplike) # # And now the odds # plot(gofsamplike, plotodds=TRUE) # # Using Sampson's Monk data, lets fit a latent clustering model # samp.fit <- ergmm(samplike ~ latentcluster(k=2, ngroups=3), burnin=10000, MCMCsamplesize=2000, interval=30) # # Posterior Predictive Checks gofsamplike <- gof.ergmm(samp.fit) summary(gofsamplike) # # 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(gofsamplike) # # And now the odds # plot(gofsamplike, plotodds=TRUE) ## End(Not run)