plot.ergm: Plotting Method for class ERGM

Description

plot.ergm is the plotting method for ergm objects. For latent models, this plots the minimum Kullback-Leibler positions by default. The maximum likelihood positions can be used instead, or pie charts of the posterior probabilities of cluster membership can be shown. It also plots the posterior density of the nodes or a series of contour plots, one for each node. See ergm for more information on how to fit these models.

Usage

plot.ergm(x, ..., mle=FALSE, comp.mat = NULL,
          label = NULL, label.col = "black",
          xlab, ylab, main, label.cex = 0.8, edge.lwd = 1,
          edge.col=1, al = 0.1,
          contours=0, density=FALSE, only.subdens = FALSE, 
          drawarrows=FALSE,
          contour.color=1, plotgraph=FALSE, pie = FALSE, piesize=0.07,
          vertex.col=1, vertex.pch=19, vertex.cex=2,
          mycol=c("black","red","green","blue","cyan",
                  "magenta","orange","yellow","purple"),
          mypch=15:19, mycex=2:10)

Arguments

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

mle

Plots the network using the MLE of the positions for latent models.

pie

For latent clustering models, each node is drawn as a pie chart representing the probabilities of cluster membership.

piesize

The size of the pie charts.

contours

Plots a contours by contours array of the network with one contour per network corresponding to the posterior distribution of each of the nodes.

contour.color

Color of the contour lines.

density

If density=TRUE, plots the density of the posterior position of the nodes. If density=c(nr,nc), plots a nr by nc array of density estimates for each cluster.

only.subdens

If density=c(nr,nc), only plots the densities of the clusters, not the overall density.

drawarrows

If density=TRUE, draws the ties on the density plot.

plotgraph

If density=c(nr,nc), a plot of the network is also shown.

comp.mat

For latent models, the positions are Procrustes transformed to look like comp.mat.

label

A vector of the same length as the number of nodes containing the labels of the nodes.

label.col

The color to be used for plotting the labels.

label.cex

The size of the node labels.

xlab

Title for the x axis.

ylab

Title for the y axis.

main

The main title for the graph.

edge.lwd

The line width for the arrows between nodes.

edge.col

The color of the arrows between nodes.

The length of the arrow heads.

vertex.col

The color of the nodes as defined by mycol. Can be specified as an attribute of the network used in the model.

vertex.pch

The plotting character of the nodes as defined by mypch. Can be specified as an attribute of the network used in the model.

vertex.cex

The size of the nodes as defined by mycex. Can be specified as an attribute of the network used in the model.

mycol

Vector of colors to be used. Defaults to: c("black","red","green","blue","cyan", "magenta","orange","yellow","purple")

mypch

Vector of plotting characters to be used. Defaults to:

mycex

Vector of character expansion values.

...

Other optional arguments to be used by the plot function.

Value

NULL

Details

Plots the results of an ergm fit. More information can be found by looking at the documentation of ergm.

Examples

Run this code

#
# Using Sampson's Monk data, lets fit a 
# simple latent position model
#
data(sampson)
#
# Get the group labels
#
samp.labs <- substr(get.vertex.attribute(samplike,"group"),1,1)
#
samp.fit <- ergm(samplike ~ latent(k=2), burnin=10000,
                 MCMCsamplesize=2000, interval=30)
#
# See if we have convergence in the MCMC
mcmc.diagnostics(samp.fit)
#
# Plot the fit
#
plot(samp.fit,label=samp.labs, vertex.col="group")
#
# Using Sampson's Monk data, lets fit a latent clustering model
#
samp.fit <- ergm(samplike ~ latentcluster(k=2, ngroups=3), burnin=10000,
                 MCMCsamplesize=2000, interval=30)
#
# See if we have convergence in the MCMC
mcmc.diagnostics(samp.fit)
#
# Lets look at the goodness of fit:
#
plot(samp.fit,label=samp.labs, vertex.col="group")
plot(samp.fit,pie=TRUE,label=samp.labs)
plot(samp.fit,density=c(2,2))
plot(samp.fit,contours=5,contour.color="red")
plot(samp.fit,density=TRUE,drawarrows=TRUE)
add.contours(samp.fit,nlevels=8,lwd=2)
points(samp.fit$Z.mkl,pch=19,col=samp.fit$class)