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from functools import partial
from itertools import product
import pytest
from numpy.testing import (assert_array_almost_equal, assert_allclose,
assert_array_less)
import numpy as np
import scipy.stats
import mne
from mne.stats.parametric import (f_mway_rm, f_threshold_mway_rm,
_map_effects)
# hardcoded external test results, manually transferred
test_external = {
# SPSS, manually conducted analysis
'spss_fvals': np.array([2.568, 0.240, 1.756]),
'spss_pvals_uncorrected': np.array([0.126, 0.788, 0.186]),
'spss_pvals_corrected': np.array([0.126, 0.784, 0.192]),
# R 15.2
# data generated using this code http://goo.gl/7UcKb
'r_fvals': np.array([2.567619, 0.24006, 1.756380]),
'r_pvals_uncorrected': np.array([0.12557, 0.78776, 0.1864]),
# and https://gist.github.com/dengemann/5539403
'r_fvals_3way': np.array([
0.74783999999999995, # A
0.20895, # B
0.21378, # A:B
0.99404000000000003, # C
0.094039999999999999, # A:C
0.11685, # B:C
2.78749]), # A:B:C
'r_fvals_1way': np.array([0.67571999999999999])
}
def generate_data(n_subjects, n_conditions):
"""Generate testing data."""
rng = np.random.RandomState(42)
data = rng.randn(n_subjects * n_conditions).reshape(
n_subjects, n_conditions)
return data
def test_map_effects():
"""Test ANOVA effects parsing."""
selection, names = _map_effects(n_factors=2, effects='A')
assert names == ['A']
selection, names = _map_effects(n_factors=2, effects=['A', 'A:B'])
assert names == ['A', 'A:B']
selection, names = _map_effects(n_factors=3, effects='A*B')
assert names == ['A', 'B', 'A:B']
# XXX this might be wrong?
selection, names = _map_effects(n_factors=3, effects='A*C')
assert names == ['A', 'B', 'A:B', 'C', 'A:C']
pytest.raises(ValueError, _map_effects, n_factors=2, effects='C')
pytest.raises(ValueError, _map_effects, n_factors=27, effects='all')
def test_f_twoway_rm():
"""Test 2-way anova."""
rng = np.random.RandomState(42)
iter_params = product([4, 10], [2, 15], [4, 6, 8],
['A', 'B', 'A:B'],
[False, True])
_effects = {
4: [2, 2],
6: [2, 3],
8: [2, 4]
}
for params in iter_params:
n_subj, n_obs, n_levels, effects, correction = params
data = rng.random_sample([n_subj, n_levels, n_obs])
fvals, pvals = f_mway_rm(data, _effects[n_levels], effects,
correction=correction)
assert (fvals >= 0).all()
if pvals.any():
assert ((0 <= pvals) & (1 >= pvals)).all()
n_effects = len(_map_effects(n_subj, effects)[0])
assert fvals.size == n_obs * n_effects
if n_effects == 1: # test for principle of least surprise ...
assert fvals.ndim == 1
fvals_ = f_threshold_mway_rm(n_subj, _effects[n_levels], effects)
assert (fvals_ >= 0).all()
assert fvals_.size == n_effects
# check time-frequency input
n_subj, n_freqs, n_times, n_levels = (5, 10, 101, 4)
data = rng.random_sample([n_subj, n_levels, n_freqs, n_times])
fvals, pvals = f_mway_rm(data, _effects[n_levels])
assert fvals.shape[1:] == pvals.shape[1:] == (n_freqs, n_times)
data = rng.random_sample([n_subj, n_levels, 1])
pytest.raises(ValueError, f_mway_rm, data, _effects[n_levels],
effects='C', correction=correction)
data = rng.random_sample([n_subj, n_levels, n_obs, 3])
# check for dimension handling
f_mway_rm(data, _effects[n_levels], effects, correction=correction)
# now check against external software results
test_data = generate_data(n_subjects=20, n_conditions=6)
fvals, pvals = f_mway_rm(test_data, [2, 3])
assert_array_almost_equal(fvals, test_external['spss_fvals'], 3)
assert_array_almost_equal(pvals, test_external['spss_pvals_uncorrected'],
3)
assert_array_almost_equal(fvals, test_external['r_fvals'], 4)
assert_array_almost_equal(pvals, test_external['r_pvals_uncorrected'], 3)
_, pvals = f_mway_rm(test_data, [2, 3], correction=True)
assert_array_almost_equal(pvals, test_external['spss_pvals_corrected'], 3)
test_data = generate_data(n_subjects=20, n_conditions=8)
fvals, _ = f_mway_rm(test_data, [2, 2, 2])
assert_array_almost_equal(fvals, test_external['r_fvals_3way'], 5)
fvals, _ = f_mway_rm(test_data, [8], 'A')
assert_array_almost_equal(fvals, test_external['r_fvals_1way'], 5)
@pytest.mark.parametrize('kind, kwargs', [
('1samp', {}),
('ind', {}), # equal_var=True is the default
('ind', dict(equal_var=True)),
('ind', dict(equal_var=False)),
])
@pytest.mark.parametrize('sigma', (0., 1e-3,))
@pytest.mark.parametrize('seed', [0, 42, 1337])
def test_ttest_equiv(kind, kwargs, sigma, seed):
"""Test t-test equivalence."""
rng = np.random.RandomState(seed)
def theirs(*a, **kw):
f = getattr(scipy.stats, 'ttest_%s' % (kind,))
if kind == '1samp':
func = partial(f, popmean=0, **kwargs)
else:
func = partial(f, **kwargs)
return func(*a, **kw)[0]
ours = partial(getattr(mne.stats, 'ttest_%s_no_p' % (kind,)),
sigma=sigma, **kwargs)
X = rng.randn(3, 4, 5)
if kind == 'ind':
X = [X, rng.randn(30, 4, 5)] # should differ based on equal_var
got = ours(*X)
want = theirs(*X)
else:
got = ours(X)
want = theirs(X)
if sigma == 0.:
assert_allclose(got, want, rtol=1e-7, atol=1e-6)
else:
assert not np.allclose(got, want, rtol=1e-7, atol=1e-6)
# should mostly be similar, but uniformly smaller because we add
# something to the divisor (var)
assert_allclose(got, want, rtol=2e-1, atol=1e-2)
assert_array_less(np.abs(got), np.abs(want))
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