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
- 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-HocTests
Because
of
the
recursive
structure
,some
comparisons
are
made
several
times
,but
only
unique
pairs
orsets
are
returned.
- rejs
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.])]