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, ...)measmeas values)par)plotmvagdist.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.
gdist.plotdiff, gclust.boxstats, gclust.centralgraph#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)))Run the code above in your browser using DataLab