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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]#

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

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:
otherTreeNode

The tree to compare

sampleint or None

Randomly subsample the tips in common between the trees to compare. This is useful when comparing very large trees.

dist_ffunction

The distance function used to compare two the tip-tip distance matrices

shuffle_ffunction

The shuffling function used if sample is not None

Returns:
float

The distance between the trees

Raises:
ValueError

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

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