plot.ergmm {latentnetHRT}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 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 ergmm for more information on how to fit these models. Some plotting options depend on the KernSmooth package.

Usage

## S3 method for class 'ergmm':
plot(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

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

Details

Plots the results of an ergmm fit.

More information can be found by looking at the documentation of ergmm.

Value

NULL

See Also

ergmm, network, plot.network, plot, ergmm.add.contours

Examples

## Not run: 
#
# 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 <- ergmm(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 <- ergmm(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)
ergmm.add.contours(samp.fit,nlevels=8,lwd=2)
points(samp.fit$Z.mkl,pch=19,col=samp.fit$class)
## End(Not run)

[Package latentnetHRT version 0.7-18 Index]