skbio.tree.rf_dists#

skbio.tree.rf_dists(trees, ids=None, shared_by_all=True, proportion=False, rooted=False)[source]#

Calculate Robinson-Foulds (RF) distances among trees.

Added in version 0.6.3.

Parameters:

treeslist of TreeNode: Input trees.
idslist of str, optional: Unique identifiers of input trees. If omitted, will use incremental integers “0”, “1”, “2”,…
shared_by_allbool, optional: Calculate the distance between each pair of trees based on taxa shared across all trees (True, default), or shared between the current pair of trees (False).
proportionbool, optional: Whether to return the RF distance as count (False, default) or proportion (True).
rootedbool, optional: Whether to consider the trees as unrooted (False, default) or rooted (True).

Returns:

DistanceMatrix: Matrix of the Robinson-Foulds distances.

See also

TreeNode.compare_rfd

Notes

The Robinson-Foulds (RF) distance [1], a.k.a. symmetric difference, is the number of bipartitions differing between two trees.

This function is equivalent to TreeNode.compare_rfd() for two trees. Refer to the latter for details about the metric and its parameters. However, the current function extends the operation to an arbitrary number of trees and returns a distance matrix for them.

This function is optimized for calculation based on taxa shared across all trees. One can instead set shared_by_all to False to calculate based on taxa shared between each pair of trees, which is however less efficient since bipartitions need to be re-inferred during each comparison.

References

[1]

Robinson, D. F., & Foulds, L. R. (1981). Comparison of phylogenetic trees. Mathematical biosciences, 53(1-2), 131-147.

Examples

>>> from skbio import TreeNode
>>> trees = [TreeNode.read([x]) for x in (
...     "(((a,b),c),d,e);",
...     "((a,(b,c)),d,e);",
...     "((a,b),(c,d),e);",
...     "(a,b,(c,(d,e)));",
... )]
>>> dm = rf_dists(trees, ids=list("ABCD"))
>>> print(dm)
4x4 distance matrix
IDs:
'A', 'B', 'C', 'D'
Data:
[[ 0.  2.  2.  0.]
 [ 2.  0.  4.  2.]
 [ 2.  4.  0.  2.]
 [ 0.  2.  2.  0.]]