def test_get_validated_comparisons_correction_to_results(self):
raw_p_values = [6.477e-01, 4.690e-02, 2.680e-02]
expected = [False, False, False]
cc = get_validated_comparisons_correction("BH")
with warnings.catch_warnings():
warnings.simplefilter('ignore')
observed = cc(raw_p_values)
npt.assert_allclose(observed, expected)
def comparisons_correction(self, comparisons_correction):
"""
:param comparisons_correction: Method for multiple comparisons
correction. One of `statsmodels` `multipletests` methods
(w/ default FWER), or a `ComparisonsCorrection` instance.
"""
self._check_has_plotter()
self._comparisons_correction = get_validated_comparisons_correction(
comparisons_correction)
def test_get_validated_comparisons_correction_none_to_none(self):
self.assertIsNone(get_validated_comparisons_correction(None))
def test_get_validated_comparisons_correction_str_to_instance(self):
self.assertIsInstance(get_validated_comparisons_correction("BH"),
ComparisonsCorrection)
def apply_test(
group_data1,
group_data2,
test: Union[StatTest, str] = None,
comparisons_correction: Union[ComparisonsCorrection, str] = None,
num_comparisons: int = 1,
alpha: float = 0.05,
**stats_params
):
"""Get formatted result of two-sample statistical test.
:param group_data1: data
:param group_data2: data
:param test: Union[StatTest, str]: Statistical test to run.
Either a `StatTest` instance or one of:
- `Levene`
- `Mann-Whitney`
- `Mann-Whitney-gt`
- `Mann-Whitney-ls`
- `t-test_ind`
- `t-test_welch`
- `t-test_paired`
- `Wilcoxon`
- `Kruskal`
:param comparisons_correction: Union[ComparisonsCorrection, str]:
(Default value = None)
Method to use for multiple comparisons correction. Either a
`ComparisonsCorrection` instance or one of (interfacing statsmodels):
- Bonferroni
- Holm-Bonferroni
- Benjamini-Hochberg
- Benjamini-Yekutieli
:param num_comparisons: int: (Default value = 1)
Number of comparisons to use for multiple comparisons correction.
:param alpha: float: (Default value = 0.05)
Used for pvalue interpretation in case of comparisons_correction.
:param stats_params: Additional keyword arguments to pass to the test
function
"""
# Check arguments.
if (isinstance(comparisons_correction, ComparisonsCorrection)
or comparisons_correction is None):
pass
else:
comparisons_correction = \
get_validated_comparisons_correction(comparisons_correction)
if test is None:
result = StatResult(None, '', None, None, np.nan)
else:
if isinstance(test, StatTest):
get_stat_result = test
else:
get_stat_result = StatTest.from_library(test)
result = get_stat_result(
group_data1, group_data2, alpha=alpha, **stats_params)
# Optionally, run multiple comparisons correction that can independently be
# applied to each pval
if comparisons_correction is not None and comparisons_correction.type == 0:
result.pvalue = comparisons_correction(result.pvalue, num_comparisons)
result.correction_method = comparisons_correction.name
return result