## Not run:
# #
# # The example assumes you have the 'latentnet' package installed.
# #
# # Using Sampson's Monk data, lets fit a
# # simple latent position model
# #
# data(sampson)
# #
# # Get the group labels
# #
# samp.labs <- substr(get.vertex.attribute(samplike,"group"),1,1)
# #
# samp.fit <- ergm(samplike ~ latent(k=2), burnin=10000,
# MCMCsamplesize=2000, interval=30)
# #
# # See if we have convergence in the MCMC
# mcmc.diagnostics(samp.fit)
# #
# # Plot the fit
# #
# plot(samp.fit,label=samp.labs, vertex.col="group")
# #
# # Using Sampson's Monk data, lets fit a latent clustering model
# #
# samp.fit <- ergm(samplike ~ latentcluster(k=2, ngroups=3), burnin=10000,
# MCMCsamplesize=2000, interval=30)
# #
# # See if we have convergence in the MCMC
# mcmc.diagnostics(samp.fit)
# #
# # Lets look at the goodness of fit:
# #
# plot(samp.fit,label=samp.labs, vertex.col="group")
# plot(samp.fit,pie=TRUE,label=samp.labs)
# plot(samp.fit,density=c(2,2))
# plot(samp.fit,contours=5,contour.color="red")
# plot(samp.fit,density=TRUE,drawarrows=TRUE)
# add.contours(samp.fit,nlevels=8,lwd=2)
# points(samp.fit$Z.mkl,pch=19,col=samp.fit$class)
# ## End(Not run)
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