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Create a consensus tree from several hierarchical random graph models


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)



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.


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 occured 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-methods, hrg_tree(), hrg(), predict_edges(), print.igraphHRGConsensus(), print.igraphHRG(), sample_hrg()

[Package igraph version 1.2.5 Index]