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Create a consensus tree from several hierarchical random graph models

Source: R/hrg.R
consensus_tree.Rd

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.

Usage

consensus_tree(graph, hrg = NULL, start = FALSE, num.samples = 10000)

Arguments

graph

The graph the models were fitted to.

hrg

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.

start

Logical, whether to start the fitting/sampling from the supplied igraphHRG object, or from a random starting point.

num.samples

Number of samples to use for consensus generation or missing edge prediction.

Value

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:

parents

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.

weights

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.

See also

Other hierarchical random graph functions: fit_hrg(), hrg(), hrg-methods, hrg_tree(), predict_edges(), print.igraphHRG(), print.igraphHRGConsensus(), sample_hrg()

igraph_hrg_consensus().