plot.ergmm {latentnet} | R Documentation |
Plotting Method for class ERGMM
Description
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.
Usage
## 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,
edge.plot3d = TRUE,
suppress.center = FALSE,
density.par = list()
)
Arguments
x |
|
... |
Other optional arguments passed to the
|
what |
Character vector, integer, or a list that specifies the point estimates to be used. Can be one of the follwoing:
|
main , vertex.cex , vertex.col , xlim , ylim , vertex.sides |
Arguments passed
to |
zlim , zlab |
Limits and labels for the third latent space dimension or
principal component, if |
object.scale , pad , edge.col , xlab , ylab |
Arguments passed to
|
cluster.col |
A vector of colors used to distinguish clusters in a latent cluster model. |
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. |
zoom.on |
If given a list of vertex indices, sets the plotting region to the smallest that can fit those vertices. |
pie |
For latent clustering models, each node is drawn as a pie chart representing the probabilities of cluster membership. |
labels |
Whether vertex labels should be displayed. Defaults to
|
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 |
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
|
edge.plot3d |
If |
suppress.center |
Suppresses the plotting of "+" at the origin.
Defaults to |
density.par |
A list of optional parameters for density plots:
|
Details
At this time, no plotting non-latent-space model fits is not supported.
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.
Value
If applicable, invisibly returns the vertex positions plotted.
See Also
ergmm
,ergmm.object
, network
,
plot.network
, plot
Examples
#
# 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
# Store the burn-in.
samp.fit <- ergmm(samplike ~ euclidean(d=2, G=3)+rreceiver,
control=ergmm.control(store.burnin=TRUE))
#
# See if we have convergence in the MCMC
mcmc.diagnostics(samp.fit)
# We can also plot the burn-in:
for(i in samp.fit$control$pilot.runs) mcmc.diagnostics(samp.fit,burnin=i)
#
# 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")
plot(samp.fit,what=5,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)