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
(g1g1 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 class 'igraphHRG':
print(x, type = c("auto", "tree", "plain"), level = 3,
...)igraphHRG object to print.consensus_tree,
hrg.consensus; fit_hrg,
hrg.fit; hrg-methods;
hrg.game, sample_hrg;
hrg.predict, predict_edges;
hrg_tree; hrg,
hrg.create;
print.igraphHRGConsensus