igraphHRG objects can be printed to the screen in two forms: as
a tree or as a list, depending on the type argument of the
print function. By default the auto type is used, which selects
tree for small graphs and simple (=list) for bigger
ones. The tree format looks like
this:
Hierarchical random graph, at level 3: g1 p= 0 '- g15 p=0.33 1 '- g13 p=0.88 6 3 9 4 2 10 7 5 8 '- g8 p= 0.5 '- g16 p= 0.2 20 14 17 19 11 15 16 13 '- g5 p= 0 12 18
This is a graph with 20 vertices, and the
top three levels of the fitted hierarchical random graph are
printed. The root node of the HRG is always vertex group #1
(‘g1’ in the the printout). Vertex pairs in the left
subtree of g1 connect to vertices in the right subtree with
probability zero, according to the fitted model. g1 has two
subgroups, g15 and g8. g15 has a subgroup of a
single vertex (vertex 1), and another larger subgroup that contains
vertices 6, 3, etc. on lower levels, etc.
The plain printing is simpler and faster to produce, but less
visual:
Hierarchical random graph: g1 p=0.0 -> g12 g10 g2 p=1.0 -> 7 10 g3 p=1.0 -> g18 14 g4 p=1.0 -> g17 15 g5 p=0.4 -> g15 17 g6 p=0.0 -> 1 4 g7 p=1.0 -> 11 16 g8 p=0.1 -> g9 3 g9 p=0.3 -> g11 g16 g10 p=0.2 -> g4 g5 g11 p=1.0 -> g6 5 g12 p=0.8 -> g8 8 g13 p=0.0 -> g14 9 g14 p=1.0 -> 2 6 g15 p=0.2 -> g19 18 g16 p=1.0 -> g13 g2 g17 p=0.5 -> g7 13 g18 p=1.0 -> 12 19 g19 p=0.7 -> g3 20
It lists the two subgroups of each internal node, in as many columns as the screen width allows.
# S3 method for igraphHRG
print(x, type = c("auto", "tree", "plain"), level = 3, ...)igraphHRG object to print.
How to print the dendrogram, see details below.
The number of top levels to print from the dendrogram.
Additional arguments, not used currently.
The hierarchical random graph model itself, invisibly.
Other hierarchical random graph functions:
consensus_tree(),
fit_hrg(),
hrg-methods,
hrg_tree(),
hrg(),
predict_edges(),
print.igraphHRGConsensus(),
sample_hrg()