plot.ergm {ergm} | R Documentation |
plot.ergm
is the plotting method for ergm
objects.
It plots the MCMC diagnostics via the
mcmc.diagnostics
function.
See ergm
for more information on how to fit these
models.
## S3 method for class 'ergm' 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, plotnetwork=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)
x |
|
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 |
For latent models, 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. |
plotnetwork |
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 network. |
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 |
vertex.pch |
The plotting character of the nodes as defined by
|
vertex.cex |
The size of the nodes as defined by |
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. |
Plots the results of an ergm fit.
More information can be found by looking at the documentation of
ergm
.
NULL
ergm, network, plot.network, plot, add.contours
## Not run: # # The example assumes you have the 'latentnet' package installed. # # 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) ## End(Not run)