plot.ergmm: 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.

At this time, no plotting non-latent-space model fits is not supported.

Usage

## S3 method for class 'ergmm':
plot(x, \dots, 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

an R object of class ergmm. See documentation for ergmm.

what

Character vector, integer, or a list that specifies the point estimates to be used. Can be one of the follwoing: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[objec

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 NULL and rand.eff is given, limits the scaling of the plotting symbol due to random effect to the given value.

plot.means

Whether cluster means are plotted for latent cluster models. The "+" character is used. Defaults to TRUE.

plot.vars

Whether circles with radius equal to the square root of posterior latent or intracluster variance estimates are plotted. Defaults to TRUE.

suppress.axes

Whether axes should not be drawn. Defaults to FALSE. (Axes are drawn.)

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 TRUE.

suppress.center

Suppresses the plotting of "+" at the origin. Defaults to FALSE.

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 plot.network, whose defaults differ from those of plot.network.

zlim,zlab

Limits and labels for the third latent space dimension or principal component, if use.rgl=TRUE.

labels

Whether vertex labels should be displayed. Defaults to FALSE.

print.formula

Whether the formula based on which the x was fitted should be printed under the main title. Defaults to TRUE.

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 FALSE. If set to TRUE and a 3-dimensional plot is produced, edges are not plotted and argument pi

vertex.3d.cex

Controls the size of the plotting symbol when use.rgl=TRUE.

edge.plot3d

If TRUE (the default) edges or arcs in a 3D plot will be drawn. Otherwise, only vertices and clusters.

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: [object Object],[object Object],[object Object]

...

Other optional arguments passed to the plot.network function.

Value

If applicable, invisibly returns the vertex positions plotted.

Details

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.

Examples

Run this code

#
# 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")

\dontrun{
# 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)
}

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