# print.igraphHRG

0th

Percentile

##### Print a hierarchical random graph model to the screen

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.

##### Usage
# S3 method for igraphHRG
print(x, type = c("auto", "tree", "plain"), level = 3,
...)
##### Arguments
x

igraphHRG object to print.

type

How to print the dendrogram, see details below.

level

The number of top levels to print from the dendrogram.

...

##### Value

The hierarchical random graph model itself, invisibly.

Other hierarchical random graph functions: 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