sharpshootR (version 1.6)

plotSoilRelationGraph: Plot a component relation graph

Description

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

Usage

plotSoilRelationGraph(m, s='', plot.style='network', 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', ...)

Arguments

m

adjacency matrix

s

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

plot.style

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

graph.mode

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

spanning.tree

plot the minimim or maximum spaning 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.

del.edges

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

vertex.scaling.method

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

vertex.scaling.factor

scaling factor applied to vertex size

edge.scaling.factor

optional scaling factor applied to edge width

vertex.alpha

optional transparency setting for vertices (0-1)

edge.transparency

optional transparency setting for edges (0-1)

edge.col

edge color, applied to all edges

edge.highlight.col

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

g.layout

an igraph layout function, defaults to layout_with_fr

vertex.label.color

vertex label color

…

further arguments passed to plotting function

Value

an igraph `graph` object is invisibly returned

Details

Vertex size is based on a normalized index of connectivity: "degree" size = sqrt(degree(g)/max(degree(g))) * scaling.factor, or "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 mimimum 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' auxillary 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.

Examples

# NOT RUN {
# 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', plot.style='dendrogram')

# compare methods
m.o <- component.adj.matrix(amador, method='occurrence')

par(mfcol=c(1,2))
plotSoilRelationGraph(m, s='amador', plot.style='dendrogram')
title('community matrix')
plotSoilRelationGraph(m.o, s='amador', plot.style='dendrogram')
title('occurence')

# 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)

# }
# NOT RUN {
if(requireNamespace("curl") &
   curl::has_internet() &
   require(soilDB)) {
  
 # get similar data from soilweb, for the Pardee series
s <- 'pardee'
d <- siblings(s, component.data = TRUE)

# normalize component names
d$sib.data$compname <- tolower(d$sib.data$compname)

# keep only major components
d$sib.data <- subset(d$sib.data, subset=compkind == 'Series')

# build adj. matrix and plot
m <- component.adj.matrix(d$sib.data)
plotSoilRelationGraph(m, s=s, plot.style='dendrogram')

# alter plotting style, see ?plot.phylo
plotSoilRelationGraph(m, s=s, plot.style='dendrogram', type='fan')
plotSoilRelationGraph(m, s=s, plot.style='dendrogram', type='unrooted', use.edge.length=FALSE) 
  
}


# }