Plot a component relation graph based on an adjacency or similarity matrix.

  s = "", = c("network", "dendrogram"),
  graph.mode = "upper",
  spanning.tree = NULL,
  del.edges = NULL,
  vertex.scaling.method = "degree",
  vertex.scaling.factor = 2,
  edge.scaling.factor = 1,
  vertex.alpha = 0.65,
  edge.transparency = 1,
  edge.col = grey(0.5),
  edge.highlight.col = "royalblue",
  g.layout = layout_with_fr,
  vertex.label.color = "black",
  delete.singletons = FALSE,



adjacency matrix


central component; an empty character string is interpreted as no central component

plot style ('network', or 'dendrogram'), or 'none' for no graphical output


interpretation of adjacency matrix: 'upper' or 'directed', see details


plot the minimum or maximum spanning tree ('min', 'max'), or, max spanning tree plus edges with weight greater than the n-th quantile specified in spanning.tree. See details and examples.


optionally delete edges with weights less than the specified quantile (0-1)


'degree' (default) or 'distance', see details


scaling factor applied to vertex size


optional scaling factor applied to edge width


optional transparency setting for vertices (0-1)


optional transparency setting for edges (0-1)


edge color, applied to all edges


edge color applied to all edges connecting to component named in s


an igraph layout function, defaults to layout_with_fr


vertex label color


optionally delete vertices with no edges (degree == 0)


further arguments passed to plotting function


an igraph graph object is invisibly returned


Vertex size is based on a normalized index of connectivity:

  • "degree" size = sqrt(degree(g)/max(degree(g))) * scaling.factor

  • "distance" size = sqrt(distance(V->s)/max(distance(V->s))) * scaling.factor, where distance(V->s) is the distance from all nodes to the named series, s.

Edge width can be optionally scaled by edge weight by specifying an edge.scaling.factor value. The maximum spanning tree represents a sub-graph where the sum of edge weights are maximized. The minimum spanning tree represents a sub-graph where the sum of edge weights are minimized. The maximum spanning tree is likely a more useful simplification of the full graph, in which only the strongest relationships (e.g. most common co-occurrences) are preserved.

The maximum spanning tree + edges with weights > n-th quantile is an experimental hybrid. The 'backbone' of the graph is created by the maximum spanning tree, and augmented by 'strong' auxiliary edges--defined by a value between 0 and 1.

The graph.mode argument is passed to igraph::graph_from_adjacency_matrix() and determines how vertex relationships are coded in the adjacency matrix m. Typically, the default value of 'upper' (the upper triangle of m contains adjacency information) is the desired mode. If m contains directional information, set graph.mode to 'directed'. This has the side-effect of altering the default community detection algorithm from igraph::cluster_fast_greedy to igraph::cluster_walktrap.


This function is a work in progress, ideas welcome.


D.E. Beaudette


# load sample data set data(amador) # create weighted adjacency matrix (see ?component.adj.matrix for details) m <- component.adj.matrix(amador) # plot network diagram, with Amador soil highlighted plotSoilRelationGraph(m, s='amador')
# dendrogram representation plotSoilRelationGraph(m, s='amador','dendrogram')
# compare methods m.o <- component.adj.matrix(amador, method='occurrence') par(mfcol=c(1,2)) plotSoilRelationGraph(m, s='amador','dendrogram') title('community matrix') plotSoilRelationGraph(m.o, s='amador','dendrogram')
# investigate max spanning tree plotSoilRelationGraph(m, spanning.tree='max') # investigate max spanning tree + edges with weights > 75-th pctile plotSoilRelationGraph(m, spanning.tree=0.75)
# \donttest{ if(requireNamespace("curl") & curl::has_internet() & require(soilDB)) { # get similar data from soilweb, for the Pardee series s <- 'pardee' d <- siblings(s, = TRUE) # normalize component names d$$compname <- tolower(d$$compname) # keep only major components d$ <- subset(d$, subset=compkind == 'Series') # build adj. matrix and plot m <- component.adj.matrix(d$ plotSoilRelationGraph(m, s=s,'dendrogram') # alter plotting style, see ?plot.phylo plotSoilRelationGraph(m, s=s,'dendrogram', type='fan') plotSoilRelationGraph(m, s=s,'dendrogram', type='unrooted', use.edge.length=FALSE) }
# }