R: Create a consensus tree from several hierarchical random...

consensus_tree {igraph} | R Documentation

## Create a consensus tree from several hierarchical random graph models

### Description

`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 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 1.2.4.1 Index]