def test_too_few_permutations(self):
obs = p_value_to_str(self.p_value, 9)
self.assertEqual(obs, 'Too few permutations to compute p-value '
'(permutations = 9)')
obs = p_value_to_str(self.p_value, 1)
self.assertEqual(obs, 'Too few permutations to compute p-value '
'(permutations = 1)')
obs = p_value_to_str(self.p_value, 0)
self.assertEqual(obs, 'Too few permutations to compute p-value '
'(permutations = 0)')
def test_valid_input(self):
obs = p_value_to_str(self.p_value, 100)
self.assertEqual(obs, '0.12')
obs = p_value_to_str(self.p_value, 250)
self.assertEqual(obs, '0.12')
obs = p_value_to_str(self.p_value, 1000)
self.assertEqual(obs, '0.119')
obs = p_value_to_str(0.0055623489, 999)
self.assertEqual(obs, '0.006')
def pwmantel(dms, labels=None, strict=True, lookup=None, method='pearson',
permutations=999, alternative='two-sided'):
"""Run Mantel tests for every pair of distance matrices.
Runs a Mantel test for each pair of distance matrices and collates the
results in a data frame. Distance matrices do not need to be in the same
ID order (contrary to how the ``mantel`` function behaves). Distance
matrices will be re-ordered prior to running each pairwise test, and if
``strict=False``, IDs that don't match between a pair of distance matrices
will be dropped prior to running the test (otherwise a ``ValueError`` will
be raised if there are non-matching IDs between any pair of distance
matrices).
Parameters
----------
dms : iterable of DistanceMatrix objects
DistanceMatrix instances to perform pairwise Mantel tests upon.
labels : iterable of str or int, optional
Labels for each ``DistanceMatrix`` in `dms`. These are
used in the results data frame to identify the pair of distance
matrices used in a pairwise Mantel test. If ``None``, defaults to
monotonically-increasing integers starting at zero.
strict : bool, optional
If ``True``, raises a ``ValueError`` if IDs are found that do not exist
in both distance matrices for the current pairwise test. If ``False``,
these "extra" (nonmatching) IDs are discarded before running the
pairwise Mantel test.
lookup : dict, optional
Maps each ID in the distance matrices to a new ID. Used to match up IDs
across distance matrices prior to running the Mantel test. If the IDs
already match between the distance matrices in `dms`, this parameter is
not necessary.
method : {'pearson', 'spearman'}
Correlation method. See ``mantel`` function for more details.
permutations : int, optional
Number of permutations. See ``mantel`` function for more details.
alternative : {'two-sided', 'greater', 'less'}
Alternative hypothesis. See ``mantel`` function for more details.
Returns
-------
pandas.DataFrame
Data frame containing the results of each pairwise test (one per row).
Includes the number of objects considered in each test as column ``n``
(after applying `lookup` and filtering non-matching IDs if
``strict=False``). Column ``p-value`` has the p-values formatted as
strings with the correct number of decimal places, or ``N/A`` if a
p-value could not be computed.
See Also
--------
mantel
"""
num_dms = len(dms)
if num_dms < 2:
raise ValueError("Must provide at least two distance matrices.")
for dm in dms:
if not isinstance(dm, DistanceMatrix):
raise TypeError("Must provide DistanceMatrix instances as input.")
if labels is None:
labels = range(num_dms)
else:
if num_dms != len(labels):
raise ValueError("Number of labels must match the number of "
"distance matrices.")
if len(set(labels)) != len(labels):
raise ValueError("Labels must be unique.")
num_combs = scipy.misc.comb(num_dms, 2, exact=True)
results_dtype = [('dm1', object), ('dm2', object), ('statistic', float),
('p-value', object), ('n', int), ('method', object),
('permutations', int), ('alternative', object)]
results = np.empty(num_combs, dtype=results_dtype)
for i, pair in enumerate(combinations(zip(labels, dms), 2)):
(xlabel, x), (ylabel, y) = pair
x, y = _order_dms(x, y, strict=strict, lookup=lookup)
stat, p_val = mantel(x, y, method=method, permutations=permutations,
alternative=alternative)
p_val_str = p_value_to_str(p_val, permutations)
results[i] = (xlabel, ylabel, stat, p_val_str, x.shape[0], method,
permutations, alternative)
return pd.DataFrame.from_records(results, index=('dm1', 'dm2'))
def _format_data(self):
p_value_str = p_value_to_str(self.p_value, self.permutations)
return (self.short_method_name, '%d' % self.sample_size,
'%d' % len(self.groups), str(self.statistic), p_value_str,
'%d' % self.permutations)
def test_missing_or_invalid_p_value(self):
obs = p_value_to_str(None, 0)
self.assertEqual(obs, 'N/A')
obs = p_value_to_str(np.nan, 0)
self.assertEqual(obs, 'N/A')
