# consensus_tree

##### 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.

##### 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:

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.fit`

; `hrg-methods`

;
`hrg.game`

, `sample_hrg`

;
`hrg.predict`

, `predict_edges`

;
`hrg_tree`

; `hrg`

,
`hrg.create`

;
`print.igraphHRGConsensus`

;
`print.igraphHRG`

*Documentation reproduced from package igraph, version 1.0.1, License: GPL (>= 2)*