skbio.tree.TreeNode.compare_tip_distances¶

TreeNode.
compare_tip_distances
(other, sample=None, dist_f=<function distance_from_r>, shuffle_f=<builtin method shuffle of numpy.random.mtrand.RandomState object>)[source]¶ Compares self to other using tiptotip 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 nonmatching 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 tiptip distance matrices
shuffle_f (function) – The shuffling function used if sample is not None
 Returns
The distance between the trees
 Return type
 Raises
ValueError – A ValueError is raised if there does not exist common tips between the trees
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