| plot.gofobject {latentnetHRT} | R Documentation |
plot.gofobject plots diagnostics such as the
degree distribution, geodesic distances, shared partner distributions,
and reachability for the posterior predictive goodness-of-fit of Latent Space random graph
models. See ergmm for more information on these models.
## S3 method for class 'gofobject':
plot(x, ...,
cex.axis=0.7, plotodds=FALSE,
main = "Goodness-of-fit diagnostics",
normalize.reachability=FALSE,
verbose=FALSE)
x |
an object of class gofobject,
typically produced by the
gof.ergmm or gof.formula functions.
See the documentation for these. |
cex.axis |
Character expansion of the axis labels relative to that for the plot. |
plotodds |
Plot the odds of a dyad having given characteristics (e.g., reachability, minimum geodesic distance, shared partners). This is an alternative to the probability of a dyad having the same property. |
main |
Title for the goodness-of-fit plots. |
normalize.reachability |
Should the reachability proportion be normalized to make it more comparable with the other geodesic distance proportions. |
verbose |
Provide verbose information on the progress of the plotting. |
... |
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 plot of the summary measures.
none
ergm, network, rergm.ergmm, summary.ergmm
## 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)
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)
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)