R: Plotting Method for class ergm

plot.ergm {ergm} | R Documentation

## Plotting Method for class ergm

### Description

`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.

### Usage

    ## 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)

### Arguments

`x` | 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` | 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 `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. By default it is 15 - a red square.
`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 ergm fit.

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

### Value

NULL

### See Also

ergm, network, plot.network, plot, add.contours

### Examples

    ## 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)

[Package ergm version 3.8.0 Index]