skbio.tree.TreeNode.compare_tip_distances

TreeNode.compare_tip_distances(other, sample=None, dist_f=<function distance_from_r>, shuffle_f=<built-in method shuffle of numpy.random.mtrand.RandomState object>)[source]

Compares self to other using tip-to-tip distance matrices.

State: Experimental as of 0.4.0.

Value returned is dist_f(m1, m2) for the two matrices. Default is to use the Pearson correlation coefficient, with +1 giving a distance of 0 and -1 giving a distance of +1 (the maximum possible value). Depending on the application, you might instead want to use distance_from_r_squared, which counts correlations of both +1 and -1 as identical (0 distance).

Note: automatically strips out the names that don’t match (this is necessary for this method because the distance between non-matching names and matching names is undefined in the tree where they don’t match, and because we need to reorder the names in the two trees to match up the distance matrices).

Parameters

  • other (TreeNode) – The tree to compare

  • sample (int or None) – Randomly subsample the tips in common between the trees to compare. This is useful when comparing very large trees.

  • dist_f (function) – The distance function used to compare two the tip-tip distance matrices

  • shuffle_f (function) – The shuffling function used if sample is not None

Returns

The distance between the trees

Return type

float

Raises

ValueError – A ValueError is raised if there does not exist common tips between the trees

See also

compare_subsets(), compare_rfd()

Examples

>>> from skbio import TreeNode
>>> # note, only three common taxa between the trees
>>> tree1 = TreeNode.read(["((a:1,b:1):2,(c:0.5,X:0.7):3);"])
>>> tree2 = TreeNode.read(["(((a:1,b:1,Y:1):2,c:3):1,Z:4);"])
>>> dist = tree1.compare_tip_distances(tree2)
>>> print("%.9f" % dist)
0.000133446