statsmodels.sandbox.stats.multicomp.homogeneous_subsets¶

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:¶

valsarray_like

values that are pairwise compared

dcritarray_like or float

critical distance for rejecting, either float, or 2-dimensional array with distances on the upper triangle.

Returns:¶

rejslist of pairs

list of pair-indices with (strictly) larger than critical difference

nrejslist of pairs

list of pair-indices with smaller than critical difference

llilist of tuples

list of subsets with smaller than critical difference

restree

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.])]