| summary.gofobject {latentnet} | 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)