def test_t_two_sample_switch(self):
"""t_two_sample should call t_one_observation if 1 item in sample."""
sample = np.array([4.02, 3.88, 3.34, 3.87, 3.18])
x = np.array([3.02])
np.testing.assert_allclose(t_two_sample(x, sample),
(-1.5637254, 0.1929248))
np.testing.assert_allclose(t_two_sample(sample, x),
(1.5637254, 0.1929248))
# can't do the test if both samples have single item
self.assertEqual(t_two_sample(x, x), (None, None))
# Test special case if t=0.
np.testing.assert_allclose(t_two_sample([2], [1, 2, 3]), (0.0, 1.0))
np.testing.assert_allclose(t_two_sample([1, 2, 3], [2]), (0.0, 1.0))
def test_t_two_sample_switch(self):
"""t_two_sample should call t_one_observation if 1 item in sample."""
sample = np.array([4.02, 3.88, 3.34, 3.87, 3.18])
x = np.array([3.02])
np.testing.assert_allclose(t_two_sample(x, sample),
(-1.5637254, 0.1929248))
np.testing.assert_allclose(t_two_sample(sample, x),
(1.5637254, 0.1929248))
# can't do the test if both samples have single item
self.assertEqual(t_two_sample(x, x), (None, None))
# Test special case if t=0.
np.testing.assert_allclose(t_two_sample([2], [1, 2, 3]), (0.0, 1.0))
np.testing.assert_allclose(t_two_sample([1, 2, 3], [2]), (0.0, 1.0))
def test_t_two_sample(self):
"""t_two_sample should match example on p.225 of Sokal and Rohlf"""
I = np.array([7.2, 7.1, 9.1, 7.2, 7.3, 7.2, 7.5])
II = np.array([8.8, 7.5, 7.7, 7.6, 7.4, 6.7, 7.2])
np.testing.assert_allclose(t_two_sample(I, II),
(-0.1184, 0.45385 * 2),
atol=10e-3)
def test_t_two_sample(self):
"""t_two_sample should match example on p.225 of Sokal and Rohlf"""
I = np.array([7.2, 7.1, 9.1, 7.2, 7.3, 7.2, 7.5])
II = np.array([8.8, 7.5, 7.7, 7.6, 7.4, 6.7, 7.2])
np.testing.assert_allclose(t_two_sample(I, II),
(-0.1184, 0.45385 * 2),
atol=10e-3)
def test_t_two_sample_no_variance(self):
"""t_two_sample should properly handle lists that are invariant"""
# By default should return (None, None) to mimic R's t.test.
x = np.array([1, 1., 1])
y = np.array([0, 0, 0.0])
self.assertEqual(t_two_sample(x, x), (None, None))
self.assertEqual(t_two_sample(x, y), (None, None))
# Test none_on_zero_variance=False on various tail types. We use
# self.assertEqual instead of assert_allclose because the latter
# sees inf and -inf as being equal.
# Two tailed: a < b
self.assertEqual(t_two_sample(y, x, none_on_zero_variance=False),
(float('-inf'), 0.0))
# Two tailed: a > b
self.assertEqual(t_two_sample(x, y, none_on_zero_variance=False),
(float('inf'), 0.0))
# One-tailed 'high': a < b
self.assertEqual(t_two_sample(y, x, tails='high',
none_on_zero_variance=False),
(float('-inf'), 1.0))
# One-tailed 'high': a > b
self.assertEqual(t_two_sample(x, y, tails='high',
none_on_zero_variance=False),
(float('inf'), 0.0))
# One-tailed 'low': a < b
self.assertEqual(t_two_sample(y, x, tails='low',
none_on_zero_variance=False),
(float('-inf'), 0.0))
# One-tailed 'low': a > b
self.assertEqual(t_two_sample(x, y, tails='low',
none_on_zero_variance=False),
(float('inf'), 1.0))
# Should still receive (None, None) if the lists have no variance and
# have the same single value.
self.assertEqual(t_two_sample(x, x, none_on_zero_variance=False),
(None, None))
self.assertEqual(t_two_sample(x, [1, 1], none_on_zero_variance=False),
(None, None))
def test_t_two_sample_no_variance(self):
"""t_two_sample should properly handle lists that are invariant"""
# By default should return (None, None) to mimic R's t.test.
x = np.array([1, 1., 1])
y = np.array([0, 0, 0.0])
self.assertEqual(t_two_sample(x, x), (None, None))
self.assertEqual(t_two_sample(x, y), (None, None))
# Test none_on_zero_variance=False on various tail types. We use
# self.assertEqual instead of assert_allclose because the latter
# sees inf and -inf as being equal.
# Two tailed: a < b
self.assertEqual(t_two_sample(y, x, none_on_zero_variance=False),
(float('-inf'), 0.0))
# Two tailed: a > b
self.assertEqual(t_two_sample(x, y, none_on_zero_variance=False),
(float('inf'), 0.0))
# One-tailed 'high': a < b
self.assertEqual(t_two_sample(y, x, tails='high',
none_on_zero_variance=False),
(float('-inf'), 1.0))
# One-tailed 'high': a > b
self.assertEqual(t_two_sample(x, y, tails='high',
none_on_zero_variance=False),
(float('inf'), 0.0))
# One-tailed 'low': a < b
self.assertEqual(t_two_sample(y, x, tails='low',
none_on_zero_variance=False),
(float('-inf'), 0.0))
# One-tailed 'low': a > b
self.assertEqual(t_two_sample(x, y, tails='low',
none_on_zero_variance=False),
(float('inf'), 1.0))
# Should still receive (None, None) if the lists have no variance and
# have the same single value.
self.assertEqual(t_two_sample(x, x, none_on_zero_variance=False),
(None, None))
self.assertEqual(t_two_sample(x, [1, 1], none_on_zero_variance=False),
(None, None))
