statsmodels.sandbox.stats.multicomp.homogeneous_subsets
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statsmodels.sandbox.stats.multicomp.homogeneous_subsets(vals, dcrit)
[source] -
recursively check all pairs of vals for minimum distance
step down method as in Newman-Keuls and Ryan procedures. This is not a closed procedure since not all partitions are checked.
Parameters: vals : array_like
values that are pairwise compared
dcrit : array_like or float
critical distance for rejecting, either float, or 2-dimensional array with distances on the upper triangle.
Returns: rejs : list of pairs
list of pair-indices with (strictly) larger than critical difference
nrejs : list of pairs
list of pair-indices with smaller than critical difference
lli : list of tuples
list of subsets with smaller than critical difference
res : tree
result of all comparisons (for checking)
this follows description in SPSS notes on Post-Hoc Tests :
Because of the recursive structure, some comparisons are made several :
times, but only unique pairs or sets are returned. :
Examples
>>> m = [0, 2, 2.5, 3, 6, 8, 9, 9.5,10 ] >>> rej, nrej, ssli, res = homogeneous_subsets(m, 2) >>> set_partition(ssli) ([(5, 6, 7, 8), (1, 2, 3), (4,)], [0]) >>> [np.array(m)[list(pp)] for pp in set_partition(ssli)[0]] [array([ 8. , 9. , 9.5, 10. ]), array([ 2. , 2.5, 3. ]), array([ 6.])]
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