plot.ergmm {latentnet} | R Documentation |
plot.ergmm
is the plotting method for ergmm
objects. For
latent models, this plots the minimum Kullback-Leibler positions by
default. The maximum likelihood, posterior mean, posterior mode, or a
particular iteration's or configuration's positions
can be used instead, or pie charts of the posterior
probabilities of cluster membership can be shown. See
ergmm
for more information on how to fit these models.
At this time, no plotting non-latent-space model fits is not supported.
## S3 method for class 'ergmm' plot(x, ..., vertex.cex=1, vertex.sides=16*ceiling(sqrt(vertex.cex)), what="mkl", main = NULL, xlab=NULL, ylab=NULL, zlab=NULL, xlim=NULL, ylim=NULL, zlim=NULL, object.scale=formals(plot.network.default)[["object.scale"]], pad=formals(plot.network.default)[["pad"]], cluster.col=c("red","green","blue","cyan","magenta", "orange","yellow","purple"), vertex.col = NULL, print.formula = TRUE, edge.col = 8, Z.ref = NULL, Z.K.ref = NULL, zoom.on = NULL, pie = FALSE, labels=FALSE, rand.eff = NULL, rand.eff.cap = NULL, plot.means = TRUE, plot.vars = TRUE, suppress.axes = FALSE, jitter1D=1, curve1D=TRUE, use.rgl = FALSE, vertex.3d.cex = 1/20, suppress.center=FALSE,density.par=list())
x |
|
what |
Character vector, integer, or a list that specifies the point estimates to be used. Can be one of the follwoing:
|
pie |
For latent clustering models, each node is drawn as a pie chart representing the probabilities of cluster membership. |
rand.eff |
A character vector selecting "sender", "receiver", "sociality", or "total" random effects. Each vertex is scaled such that its area is proportional to the odds ratio due to its selected random effect. |
rand.eff.cap |
If not |
plot.means |
Whether cluster means are plotted for latent cluster
models. The "+" character is used. Defaults to |
plot.vars |
Whether circles with radius equal to the square root
of posterior latent or intracluster variance estimates are
plotted. Defaults to |
suppress.axes |
Whether axes should not be drawn. Defaults
to |
jitter1D |
For 1D latent space fits, it often helps to jitter the positions for visualization. This option controls the amount of jitter. |
curve1D |
Controls whether the edges in 1D latent space fits are
plotted as curves. Defaults to |
suppress.center |
Suppresses the plotting of "+" at the
origin. Defaults to |
cluster.col |
A vector of colors used to distinguish clusters in a latent cluster model. |
main, vertex.cex, vertex.col, xlim, ylim, vertex.sides,
object.scale, pad, edge.col, xlab, ylab |
Arguments passed to
|
zlim,zlab |
Limits and labels for the third latent space
dimension or principal component, if |
labels |
Whether vertex labels should be displayed. Defaults to
|
print.formula |
Whether the formula based on which the |
Z.ref |
If given, rotates the the latent positions to the nearest configuration to this one before plotting. |
Z.K.ref |
If given, relabels the clusters to the nearest configuration to this one before plotting. |
use.rgl |
Whether the package rgl should be used to plot fits for
latent space dimension 3 or higher in 3D. Defaults to
|
vertex.3d.cex |
Controls the size of the plotting symbol when |
zoom.on |
If given a list of vertex indices, sets the plotting region to the smallest that can fit those vertices. |
density.par |
A list of optional parameters for density plots:
|
... |
Other optional arguments passed to the |
Plots the results of an ergmm fit.
More information can be found by looking at the documentation of
ergmm
.
For bipartite networks, the events are marked with a bullet (small black circle) inside the plotting symbol.
If applicable, invisibly returns the vertex positions plotted.
ergmm
,ergmm.object
, network
, plot.network
, plot
# # Using Sampson's Monk data, let's fit a # simple latent position model # data(sampson) # # Using Sampson's Monk data, let's fit a # latent clustering random effects model # samp.fit <- ergmm(samplike ~ euclidean(d=2, G=3)+rreceiver) # # See if we have convergence in the MCMC mcmc.diagnostics(samp.fit) # # Plot the resulting fit. # plot(samp.fit,labels=TRUE,rand.eff="receiver") plot(samp.fit,pie=TRUE,rand.eff="receiver") plot(samp.fit,what="pmean",rand.eff="receiver") plot(samp.fit,what="cloud",rand.eff="receiver") plot(samp.fit,what="density",rand.eff="receiver") ## Not run: # Fit a 3D latent space model to Sampson's Monks samp.fit3 <- ergmm(samplike ~ euclidean(d=3)) # Plot the first two principal components of the # latent space positions plot(samp.fit,use.rgl=FALSE) # Plot the resulting fit in 3D plot(samp.fit,use.rgl=TRUE) ## End(Not run)