def test_permutation_t_test():
"""Test T-test based on permutations."""
# 1 sample t-test
np.random.seed(10)
n_samples, n_tests = 30, 5
X = np.random.randn(n_samples, n_tests)
X[:, :2] += 1
T_obs, p_values, H0 = permutation_t_test(X, n_permutations=999, tail=0)
is_significant = p_values < 0.05
assert_array_equal(is_significant, [True, True, False, False, False])
T_obs, p_values, H0 = permutation_t_test(X, n_permutations=999, tail=1)
is_significant = p_values < 0.05
assert_array_equal(is_significant, [True, True, False, False, False])
T_obs, p_values, H0 = permutation_t_test(X, n_permutations=999, tail=-1)
is_significant = p_values < 0.05
assert_array_equal(is_significant, [False, False, False, False, False])
X = np.random.randn(18, 1)
T_obs, p_values, H0 = permutation_t_test(X[:, [0]], n_permutations='all')
T_obs_scipy, p_values_scipy = stats.ttest_1samp(X[:, 0], 0)
assert_almost_equal(T_obs[0], T_obs_scipy, 8)
assert_almost_equal(p_values[0], p_values_scipy, 2)
def test_permutation_t_test():
"""Test T-test based on permutations."""
# 1 sample t-test
np.random.seed(10)
n_samples, n_tests = 30, 5
X = np.random.randn(n_samples, n_tests)
X[:, :2] += 1
t_obs, p_values, H0 = permutation_t_test(X, n_permutations=999, tail=0)
is_significant = p_values < 0.05
assert_array_equal(is_significant, [True, True, False, False, False])
t_obs, p_values, H0 = permutation_t_test(X, n_permutations=999, tail=1)
is_significant = p_values < 0.05
assert_array_equal(is_significant, [True, True, False, False, False])
t_obs, p_values, H0 = permutation_t_test(X, n_permutations=999, tail=-1)
is_significant = p_values < 0.05
assert_array_equal(is_significant, [False, False, False, False, False])
X = np.random.randn(18, 1)
t_obs, p_values, H0 = permutation_t_test(X[:, [0]], n_permutations='all')
t_obs_scipy, p_values_scipy = stats.ttest_1samp(X[:, 0], 0)
assert_almost_equal(t_obs[0], t_obs_scipy, 8)
assert_almost_equal(p_values[0], p_values_scipy, 2)
def test_permutation_t_test():
"""Test T-test based on permutations."""
# 1 sample t-test
np.random.seed(10)
n_samples, n_tests = 30, 5
X = np.random.randn(n_samples, n_tests)
X[:, :2] += 1
t_obs, p_values, H0 = permutation_t_test(X,
n_permutations=999,
tail=0,
seed=0)
assert (p_values > 0).all()
assert len(H0) == 999
is_significant = p_values < 0.05
assert_array_equal(is_significant, [True, True, False, False, False])
t_obs, p_values, H0 = permutation_t_test(X,
n_permutations=999,
tail=1,
seed=0)
assert (p_values > 0).all()
assert len(H0) == 999
is_significant = p_values < 0.05
assert_array_equal(is_significant, [True, True, False, False, False])
t_obs, p_values, H0 = permutation_t_test(X,
n_permutations=999,
tail=-1,
seed=0)
is_significant = p_values < 0.05
assert_array_equal(is_significant, [False, False, False, False, False])
X *= -1
t_obs, p_values, H0 = permutation_t_test(X,
n_permutations=999,
tail=-1,
seed=0)
assert (p_values > 0).all()
assert len(H0) == 999
is_significant = p_values < 0.05
assert_array_equal(is_significant, [True, True, False, False, False])
# check equivalence with spatio_temporal_cluster_test
for connectivity in (sparse.eye(n_tests), False):
t_obs_clust, _, p_values_clust, _ = permutation_cluster_1samp_test(
X, n_permutations=999, seed=0, connectivity=connectivity)
# the cluster tests drop any clusters that don't get thresholded
keep = p_values < 1
assert_allclose(t_obs_clust, t_obs)
assert_allclose(p_values_clust, p_values[keep], atol=1e-2)
X = np.random.randn(18, 1)
t_obs, p_values, H0 = permutation_t_test(X, n_permutations='all')
t_obs_scipy, p_values_scipy = stats.ttest_1samp(X[:, 0], 0)
assert_allclose(t_obs[0], t_obs_scipy, 8)
assert_allclose(p_values[0], p_values_scipy, rtol=1e-2)