def test_pearsonr_with_confidence(self):
for noise in [0.01, 0.1, 0.2, 0.5, 1, 2, 5, 10]:
x = np.random.uniform(0, 1, 100)
y = x + np.random.normal(0, noise, 100)
rho, p, lb, ub = stats.pearsonr_with_confidence(x, y)
self.assertGreater(ub, rho)
self.assertLess(lb, rho)
self.assertLess(ub, 1)
self.assertGreater(lb, -1)
if p < 0.05:
self.assertGreater(lb, 0)
else:
self.assertLess(lb, 0)
for noise in [0.01, 0.1, 0.2, 0.5, 1, 2, 5, 10]:
x = np.random.uniform(0, 1, 100)
y = x**4 + np.random.normal(0, noise, 100)
rho, p, lb, ub = stats.pearsonr_with_confidence(x, y)
self.assertGreater(ub, rho)
self.assertLess(lb, rho)
self.assertLess(ub, 1)
self.assertGreater(lb, -1)
if p < 0.05:
self.assertGreater(lb, 0)
else:
self.assertLess(lb, 0)
def test_pearsonr_with_confidence(self):
for noise in [0.01, 0.1, 0.2, 0.5, 1, 2, 5, 10]:
x = np.random.uniform(0, 1, 100)
y = x + np.random.normal(0, noise, 100)
rho, p, lb, ub = stats.pearsonr_with_confidence(x, y)
self.assertGreater(ub, rho)
self.assertLess(lb, rho)
self.assertLess(ub, 1)
self.assertGreater(lb, -1)
if p < 0.05:
self.assertGreater(lb, 0)
else:
self.assertLess(lb, 0)
for noise in [0.01, 0.1, 0.2, 0.5, 1, 2, 5, 10]:
x = np.random.uniform(0, 1, 100)
y = x**4 + np.random.normal(0, noise, 100)
rho, p, lb, ub = stats.pearsonr_with_confidence(x, y)
self.assertGreater(ub, rho)
self.assertLess(lb, rho)
self.assertLess(ub, 1)
self.assertGreater(lb, -1)
if p < 0.05:
self.assertGreater(lb, 0)
else:
self.assertLess(lb, 0)
def test_pearsonr_partial(self):
x = np.random.normal(0, 1, 200)
z = x + np.random.normal(0, .2, x.shape)
y = 3 * z + np.random.normal(0, .1, x.shape)
rho, p, lb, ub = stats.pearsonr_partial_with_confidence(x, y, [z])
# find best fit line
slope, icpt, _, _, _ = scipy.stats.linregress(z, y)
y_prime = y - (slope * z + icpt)
rho_correct, p_correct, lb_correct, ub_correct = stats.pearsonr_with_confidence(x, y_prime)
self.assertAlmostEqual(rho, rho_correct)
self.assertAlmostEqual(p, p_correct)
self.assertAlmostEqual(lb, lb_correct)
self.assertAlmostEqual(ub, ub_correct)
def test_pearsonr_partial(self):
x = np.random.normal(0, 1, 200)
z = x + np.random.normal(0, .2, x.shape)
y = 3 * z + np.random.normal(0, .1, x.shape)
rho, p, lb, ub = stats.pearsonr_partial_with_confidence(x, y, [z])
# find best fit line
slope, icpt, _, _, _ = scipy.stats.linregress(z, y)
y_prime = y - (slope * z + icpt)
rho_correct, p_correct, lb_correct, ub_correct = stats.pearsonr_with_confidence(
x, y_prime)
self.assertAlmostEqual(rho, rho_correct)
self.assertAlmostEqual(p, p_correct)
self.assertAlmostEqual(lb, lb_correct)
self.assertAlmostEqual(ub, ub_correct)
