def test_2d_input_with_missing_values(self):
input_residuals = np.array([
[0.0, 0.0, np.nan],
[1.0, np.nan, 1.0],
[2.0, 2.0, np.nan],
[3.0, 3.0, 3.0],
[4.0, 4.0, 4.0],
[5.0, 5.0, 5.0],
[6.0, 6.0, 6.0],
[7.0, 7.0, 7.0],
[8.0, 8.0, 8.0],
])
expected_statistic = np.array([
(8.0**2 + 7.0**2 + 6.0**2) / (0.0**2 + 1.0**2 + 2.0**2),
(8.0**2 + 7.0**2 + 6.0**2) / (0.0**2 + 2.0**2),
np.nan,
])
expected_pvalue = np.array([
2 * min(
self.f.cdf(expected_statistic[0], 3, 3),
self.f.sf(expected_statistic[0], 3, 3),
),
2 * min(
self.f.cdf(expected_statistic[1], 3, 2),
self.f.sf(expected_statistic[1], 3, 2),
),
np.nan,
])
actual_statistic, actual_pvalue = breakvar_heteroskedasticity_test(
input_residuals)
assert_equal(actual_statistic, expected_statistic)
assert_equal(actual_pvalue, expected_pvalue)
def test_alternative(self, alternative, expected_statistic,
expected_pvalue):
input_residuals = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
actual_statistic, actual_pvalue = breakvar_heteroskedasticity_test(
input_residuals,
alternative=alternative,
)
assert actual_statistic == expected_statistic
assert actual_pvalue == expected_pvalue
def test_subset_length(self, subset_length, expected_statistic,
expected_pvalue):
input_residuals = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
actual_statistic, actual_pvalue = breakvar_heteroskedasticity_test(
input_residuals,
subset_length=subset_length,
)
assert actual_statistic == expected_statistic
assert actual_pvalue == expected_pvalue
def test_use_chi2(self):
input_residuals = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
expected_statistic = (4.0**2 + 5.0**2) / (0.0**2 + 1.0**2)
expected_pvalue = 2 * min(self.chi2.cdf(2 * expected_statistic, 2),
self.chi2.sf(2 * expected_statistic, 2))
actual_statistic, actual_pvalue = breakvar_heteroskedasticity_test(
input_residuals,
use_f=False,
)
assert actual_statistic == expected_statistic
assert actual_pvalue == expected_pvalue
def test_1d_input(self):
input_residuals = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
expected_statistic = (4.0**2 + 5.0**2) / (0.0**2 + 1.0**2)
# ~ F(2, 2), two-sided test
expected_pvalue = 2 * min(self.f.cdf(expected_statistic, 2, 2),
self.f.sf(expected_statistic, 2, 2))
actual_statistic, actual_pvalue = breakvar_heteroskedasticity_test(
input_residuals)
assert actual_statistic == expected_statistic
assert actual_pvalue == expected_pvalue
