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: |
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Returns: | The distance between the trees |
Return type: | float |
Raises: |
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See also
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