Plots a two-dimensional metric MDS of `d`

, with the corresponding values of `meas`

indicated at each point. Various options are available for controlling how `meas`

is to be displayed.

```
gdist.plotstats(d, meas, siz.lim=c(0, 0.15), rescale="quantile",
display.scale="radius", display.type="circleray", cex=0.5, pch=1,
labels=NULL, pos=1, labels.cex=1, legend=NULL, legend.xy=NULL,
legend.cex=1, ...)
```

d

A matrix containing the inter-graph distances

meas

An nxm matrix containing the graph-level measures; each row must correspond to a graph, and each column must correspond to an index

siz.lim

The minimum and maximum sizes (respectively) of the plotted symbols, given as fractions of the total plotting range

rescale

One of ``quantile'' for ordinal scaling, ``affine'' for max-min scaling, and ``normalize'' for rescaling by maximum value; these determine the scaling rule to be used in sizing the plotting symbols

display.scale

One of ``area'' or ``radius''; this controls the attribute of the plotting symbol which is rescaled by the value of `meas`

display.type

One of ``circle'', ``ray'', ``circleray'', ``poly'', or ``polyray''; this determines the type of plotting symbol used (circles, rays, polygons, or come combination of these)

cex

Character expansion coefficient

pch

Point types for the base plotting symbol (not the expanded symbols which are used to indicate `meas`

values)

labels

Point labels, if desired

pos

Relative position of labels (see `par`

)

labels.cex

Character expansion factor for labels

legend

Add a legend?

legend.xy

x,y coordinates for legend

legend.cex

Character expansion factor for legend

…

Additional arguments to `plot`

None

`gdist.plotstats`

works by performing an MDS (using `cmdscale`

) on `d`

, and then using the values in `meas`

to determine the shape of the points at each MDS coordinate. Typically, these shapes involve rays of varying color and length indicating `meas`

magnitude, with circles and polygons of the appropriate radius and/or error being options as well. Various options are available (described above) to govern the details of the data display; some tinkering may be needed in order to produce an aesthetically pleasing visualization.

The primary use of `gdist.plotstats`

is to explore broad relationships between graph properties and inter-graph distances. This routine complements others in the `gdist`

and `gclust`

family of interstructural visualization tools.

Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Interstructural Analysis.'' CASOS working paper, Carnegie Mellon University.

```
# NOT RUN {
#Generate random graphs with varying density
g<-rgraph(10,20,tprob=runif(20,0,1))
#Get Hamming distances between graphs
g.h<-hdist(g)
#Plot the association of distance, density, and reciprocity
gdist.plotstats(g.h,cbind(gden(g),grecip(g)))
# }
```

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