R: Plotting Method for class ERGMM

plot.ergmm {latentnet} | 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, 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.

### Usage

    ## S3 method for class 'ergmm'
    plot(
      x,
      ...,
      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

`x` | an R object of class `ergmm`. See documentation for `ergmm`.
`...` | Other optional arguments passed to the `plot.network` function.
`what` | Character vector, integer, or a list that specifies the point estimates to be used. Can be one of the follwoing: `"mkl"` This is the defult. Plots the Minimum Kulblack-Leibler divergence values. `"start"`,`"burnin.start"` Plots the starting configuration. `"sampling.start"` Plots the starting configuration of the sampling phase (the last burnin configuration). `"mle"` Plots the maximum likelihood estimates. Random effects are treated as fixed. `"pmean"` Plots the posterior means. `"pmode"` Plots the conditional posterior mode. `"cloud"` Plots the “cloud” of latent space position draws, with their cluster colors. `"density"` Plots density and contours of the posterior latent positions, and, in cluster models, each cluster. `list` Plots the configuration contained in the list. integer Plots the configuration of `what`th MCMC draw stored in `x`.
`main, vertex.cex, vertex.col, xlim, ylim, vertex.sides,` | 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`.
`object.scale, pad, edge.col, xlab, ylab` | Arguments passed to `plot.network`, whose defaults differ from those of `plot.network`.
`cluster.col` | A vector of colors used to distinguish clusters in a latent cluster model.
`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.
`zoom.on` | If given a list of vertex indices, sets the plotting region to the smallest that can fit those vertices.
`pie` | For latent clustering models, each node is drawn as a pie chart representing the probabilities of cluster membership.
`labels` | Whether vertex labels should be displayed. Defaults to `FALSE`.
`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`.
`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`, argument `pie` has no effect.
`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.
`suppress.center` | Suppresses the plotting of "+" at the origin. Defaults to `FALSE`.
`density.par` | A list of optional parameters for density plots: `totaldens` Whether the overal density of latent space positions should be plotted. Defaults to `TRUE`. `subdens` Whether the densities of latent space positions broken down by cluster should be plotted. Defaults to `TRUE`. `mfrow` When plotting multiple clusters' densities, passed to `par`

### Details

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

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.

### Value

If applicable, invisibly returns the vertex positions plotted.

### See Also

`ergmm`,`ergmm.object`, `network`, `plot.network`, `plot`

### Examples

    #
    # 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")

    ## Not run:
    # 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)

    ## End(Not run)

[Package latentnet version 2.10.5 Index]