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)