def test_chi2_mcnemar(self):
"""Test function chi2_mcnemar."""
# Setup
np.random.seed(42)
mean, cov = [0.5, 0.5], [(1, .6), (.6, 1)]
x, y = np.random.multivariate_normal(mean, cov, 30).T
data = pd.DataFrame({'x': x, 'y': y})
mask_class_1 = data > 0.5
data[mask_class_1] = 1
data[~mask_class_1] = 0
# Testing validations
def expect_assertion_error(*params):
with pytest.raises(AssertionError):
pg.chi2_mcnemar(*params)
expect_assertion_error(1, 'x', 'y') # Not a pd.DataFrame
expect_assertion_error(data, x, 'y') # Not a string
expect_assertion_error(data, 'x', y) # Not a string
expect_assertion_error(data, 'x', 'z') # Not a column of data
# Testing happy-day
pg.chi2_mcnemar(data, 'x', 'y')
# Testing NaN incompatibility
data.iloc[0, 0] = np.nan
with pytest.raises(ValueError):
pg.chi2_mcnemar(data, 'x', 'y')
# Testing invalid dichotomous value
data.iloc[0, 0] = 3
with pytest.raises(ValueError):
pg.chi2_mcnemar(data, 'x', 'y')
# Testing error when b == 0 and c == 0
data = pd.DataFrame({'x': [0, 0, 0, 1, 1, 1], 'y': [0, 0, 0, 1, 1, 1]})
with pytest.raises(ValueError):
pg.chi2_mcnemar(data, 'x', 'y')
# Comparing results with R
# 2 x 2 contingency table (dof = 1)
# >>> tbl = table(df$treatment_X, df$treatment_Y)
# >>> mcnemar.test(tbl, correct = TRUE)
_, stats = pg.chi2_mcnemar(df_mcnemar, 'treatment_X', 'treatment_Y')
assert round(stats.at['mcnemar', 'chi2'], 3) == 20.021
assert stats.at['mcnemar', 'dof'] == 1
assert np.isclose(stats.at['mcnemar', 'p-approx'], 7.66e-06)
# Results are compared to the exact2x2 R package
# >>> exact2x2(tbl, paired = TRUE, midp = FALSE)
assert np.isclose(stats.at['mcnemar', 'p-exact'], 3.305e-06)
def expect_assertion_error(*params):
with pytest.raises(AssertionError):
pg.chi2_mcnemar(*params)