def setUp(self):
self.result = StatResult("This long description",
"This",
"stat",
3,
0.3,
alpha=0.05)
def test_missing_formatter(self):
res = StatResult("Custom test", None, pval=0.05, stat=None,
stat_str=None)
with self.assertRaisesRegex(ValueError, "PValueFormat"):
annotation = Annotation(("group1", "group2"),
res)
print(annotation.text)
def simple_text(result: StatResult,
pvalue_format,
pvalue_thresholds,
short_test_name=True) -> str:
"""
Generates simple text for test name and pvalue.
:param result: StatResult instance
:param pvalue_format: format string for pvalue
:param pvalue_thresholds: String to display per pvalue range
:param short_test_name: whether to display the test (short) name
:returns: simple annotation
"""
# Sort thresholds
thresholds = sorted(pvalue_thresholds, key=lambda x: x[0])
text = (f"{result.test_short_name} "
if short_test_name and result.test_short_name else "")
for threshold in thresholds:
if result.pvalue < threshold[0]:
pval_text = "p ≤ {}".format(threshold[1])
break
else:
pval_text = "p = {}".format(pvalue_format).format(result.pvalue)
return result.adjust(text + pval_text)
def __call__(self, group_data1, group_data2, alpha=0.05,
**stat_params):
stat, pval = self._func(group_data1, group_data2, *self.args,
**{**self.kwargs, **stat_params})[:2]
return StatResult(self._test_long_name, self._test_short_name,
self._stat_name, stat, pval, alpha=alpha)
def setUp(self) -> None:
self.benjamini_hochberg = ComparisonsCorrection("Benjamini-Hochberg")
self.stat_result = StatResult("Test X",
"X",
"Stat",
1,
0.02,
alpha=0.05)
self.stat_result.correction_method = self.benjamini_hochberg.name
def _get_custom_results(self, group_struct1, pvalues) -> StatResult:
pvalue = self._value_for_group_struct(pvalues, group_struct1)
if self.has_type0_comparisons_correction():
pvalue = self.comparisons_correction(pvalue, len(pvalues))
result = StatResult('Custom statistical test',
self.test_short_name,
None,
None,
pval=pvalue,
alpha=self.alpha)
return result
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