summary.gofobject {latentnetHRT}R Documentation

Summaries the Goodness-of-Fit Diagnostics on a Latent Space Graph Model

Description

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.

Usage

## S3 method for class 'gofobject':
summary(object, ...)

Arguments

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.

Details

gof.ergmm produces a sample of networks randomly drawn from the specified model. This function produces a print out the summary measures.

Value

none

See Also

gof.ergmm, ergmm, network, rergm

Examples

## 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)

[Package latentnetHRT version 0.7-18 Index]