consensus_tree creates a consensus tree from several fitted
hierarchical random graph models, using phylogeny methods. If the hrg
argument is given and start is set to TRUE, then it starts
sampling from the given HRG. Otherwise it optimizes the HRG log-likelihood
first, and then samples starting from the optimum.
consensus_tree(graph, hrg = NULL, start = FALSE, num.samples = 10000)consensus_tree returns a list of two objects. The first
is an igraphHRGConsensus object, the second is an
igraphHRG object. The igraphHRGConsensus object has the
following members:
For each vertex, the id of its parent vertex is stored, or zero, if the vertex is the root vertex in the tree. The first n vertex ids (from 0) refer to the original vertices of the graph, the other ids refer to vertex groups.
Numeric vector, counts the number of times a given tree
split occurred in the generated network samples, for each internal
vertices. The order is the same as in the parents vector.
The graph the models were fitted to.
A hierarchical random graph model, in the form of an
igraphHRG object. consensus_tree allows this to be
NULL as well, then a HRG is fitted to the graph first, from a
random starting point.
Logical, whether to start the fitting/sampling from the
supplied igraphHRG object, or from a random starting point.
Number of samples to use for consensus generation or missing edge prediction.
Other hierarchical random graph functions:
fit_hrg(),
hrg-methods,
hrg_tree(),
hrg(),
predict_edges(),
print.igraphHRGConsensus(),
print.igraphHRG(),
sample_hrg()