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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
from numpy.testing import assert_array_equal, assert_allclose
import numpy as np
from scipy import stats, sparse
from mne.stats import permutation_cluster_1samp_test
from mne.stats.permutations import (permutation_t_test, _ci,
bootstrap_confidence_interval)
from mne.utils import run_tests_if_main, check_version
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)
def test_ci():
"""Test confidence intervals."""
# isolated test of CI functions
arr = np.linspace(0, 1, 1000)[..., np.newaxis]
assert_allclose(_ci(arr, method="parametric"),
_ci(arr, method="bootstrap"), rtol=.005)
assert_allclose(bootstrap_confidence_interval(arr, stat_fun="median",
random_state=0),
bootstrap_confidence_interval(arr, stat_fun="mean",
random_state=0),
rtol=.1)
# smoke test for new API
if check_version('numpy', '1.17'):
random_state = np.random.default_rng(0)
bootstrap_confidence_interval(arr, random_state=random_state)
run_tests_if_main()
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