def test_corr():
"Test stats.corr"
ds = datasets.get_uts()
y = ds.eval("uts.x[:,:3]")
x = ds.eval('Y.x')
n_cases = len(y)
df = n_cases - 2
corr = stats.corr(y, x)
p = stats.rtest_p(corr, df)
for i in range(len(corr)):
r_sp, p_sp = scipy.stats.pearsonr(y[:, i], x)
assert_almost_equal(corr[i], r_sp)
assert_almost_equal(p[i], p_sp)
# NaN
with warnings.catch_warnings(): # divide by 0
warnings.simplefilter("ignore")
eq_(stats.corr(np.arange(10), np.zeros(10)), 0)
# perm
y_perm = np.empty_like(y)
for perm in permute_order(n_cases, 2):
y_perm[perm] = y
stats.corr(y, x, corr, perm)
for i in range(len(corr)):
r_sp, _ = scipy.stats.pearsonr(y_perm[:, i], x)
assert_almost_equal(corr[i], r_sp)
def test_permutation():
"""Test permutation"""
v = Var(np.arange(6))
res = np.empty((5, 6))
for i, y in enumerate(resample(v, samples=5)):
res[i] = y.x
# with unit
s = Factor('abc', tile=2)
for i, y in enumerate(resample(v, samples=5, unit=s)):
res[i] = y.x
# check we have only appropriate cells
cols = [np.unique(res[:, i]) for i in range(res.shape[1])]
for i in range(3):
eq_(len(np.setdiff1d(cols[i], [i, i + 3])), 0)
for i in range(3, 6):
eq_(len(np.setdiff1d(cols[i], [i, i - 3])), 0)
# check we have some variability
eq_(max(map(len, cols)), 2)
# make sure sequence is stable
eq_(list(map(tuple, permute_order(4, 3))), [(2, 3, 1, 0), (2, 1, 3, 0),
(0, 2, 3, 1)])
def test_corr():
"Test stats.corr"
ds = datasets.get_uts()
y = ds.eval("uts.x[:,:3]")
x = ds.eval('Y.x')
n_cases = len(y)
df = n_cases - 2
corr = stats.corr(y, x)
p = stats.rtest_p(corr, df)
for i in range(len(corr)):
r_sp, p_sp = scipy.stats.pearsonr(y[:, i], x)
assert corr[i] == pytest.approx(r_sp)
assert p[i] == pytest.approx(p_sp)
# NaN
with warnings.catch_warnings(): # divide by 0
warnings.simplefilter("ignore")
assert stats.corr(np.arange(10), np.zeros(10)) == 0
# perm
y_perm = np.empty_like(y)
for perm in permute_order(n_cases, 2):
y_perm[perm] = y
stats.corr(y, x, corr, perm)
for i in range(len(corr)):
r_sp, _ = scipy.stats.pearsonr(y_perm[:, i], x)
assert corr[i] == pytest.approx(r_sp)
def test_corr():
"Test stats.corr"
ds = datasets.get_uts()
y = ds.eval("uts.x[:,:3]")
x = ds.eval('Y.x')
n_cases = len(y)
df = n_cases - 2
corr = stats.corr(y, x)
p = stats.rtest_p(corr, df)
for i in xrange(len(corr)):
r_sp, p_sp = scipy.stats.pearsonr(y[:, i], x)
assert_almost_equal(corr[i], r_sp)
assert_almost_equal(p[i], p_sp)
# NaN
r = stats.corr(np.arange(10), np.zeros(10))
eq_(r, 0)
# perm
y_perm = np.empty_like(y)
for perm in permute_order(n_cases, 2):
y_perm[perm] = y
stats.corr(y, x, corr, perm)
for i in xrange(len(corr)):
r_sp, _ = scipy.stats.pearsonr(y_perm[:, i], x)
assert_almost_equal(corr[i], r_sp)
def test_permutation():
"""Test permutation"""
v = Var(np.arange(6))
res = np.empty((5, 6))
for i, y in enumerate(resample(v, samples=5)):
res[i] = y.x
# with unit
s = Factor('abc', tile=2)
for i, y in enumerate(resample(v, samples=5, unit=s)):
res[i] = y.x
# check we have only appropriate cells
cols = [np.unique(res[:, i]) for i in range(res.shape[1])]
for i in range(3):
eq_(len(np.setdiff1d(cols[i], [i, i + 3])), 0)
for i in range(3, 6):
eq_(len(np.setdiff1d(cols[i], [i, i - 3])), 0)
# check we have some variability
eq_(max(map(len, cols)), 2)
# make sure sequence is stable
eq_(list(map(tuple, permute_order(4, 3))),
[(2, 3, 1, 0), (2, 1, 3, 0), (0, 2, 3, 1)])
def test_anova_perm():
"Test permutation argument for ANOVA"
ds = datasets.get_uts()
y = ds['uts'].x
y_perm = np.empty_like(y)
n_cases, n_tests = y.shape
# balanced anova
aov = glm._BalancedFixedNDANOVA(ds.eval('A*B'))
r1 = aov.preallocate(y.shape[1:])
for perm in permute_order(n_cases, 2):
aov.map(y, perm)
r2 = r1.copy()
y_perm[perm] = y
aov.map(y_perm)
assert_allclose(r2, r1, 1e-6, 1e-6)
# full repeated measures anova
aov = glm._BalancedMixedNDANOVA(ds.eval('A*B*rm'))
r1 = aov.preallocate(y.shape[1:])
for perm in permute_order(n_cases, 2):
aov.map(y, perm)
r2 = r1.copy()
y_perm[perm] = y
aov.map(y_perm)
assert_allclose(r2, r1, 1e-6, 1e-6)
# incremental anova
ds = ds[1:]
y = ds['uts'].x
y_perm = np.empty_like(y)
n_cases, n_tests = y.shape
aov = glm._IncrementalNDANOVA(ds.eval('A*B'))
r1 = aov.preallocate(y.shape[1:])
for perm in permute_order(n_cases, 2):
aov.map(y, perm)
r2 = r1.copy()
y_perm[perm] = y
aov.map(y_perm)
assert_allclose(r2, r1, 1e-6, 1e-6)
def test_anova_perm():
"Test permutation argument for ANOVA"
ds = datasets.get_uts()
y = ds['uts'].x
y_perm = np.empty_like(y)
n_cases, n_tests = y.shape
# balanced anova
aov = glm._BalancedFixedNDANOVA(ds.eval('A*B'))
r1 = aov.preallocate(y.shape)
for perm in permute_order(n_cases, 2):
aov.map(y, perm)
r2 = r1.copy()
y_perm[perm] = y
aov.map(y_perm)
assert_allclose(r2, r1, 1e-6, 1e-6)
# full repeated measures anova
aov = glm._FullNDANOVA(ds.eval('A*B*rm'))
r1 = aov.preallocate(y.shape)
for perm in permute_order(n_cases, 2):
aov.map(y, perm)
r2 = r1.copy()
y_perm[perm] = y
aov.map(y_perm)
assert_allclose(r2, r1, 1e-6, 1e-6)
# incremental anova
ds = ds[1:]
y = ds['uts'].x
y_perm = np.empty_like(y)
n_cases, n_tests = y.shape
aov = glm._IncrementalNDANOVA(ds.eval('A*B'))
r1 = aov.preallocate(y.shape)
for perm in permute_order(n_cases, 2):
aov.map(y, perm)
r2 = r1.copy()
y_perm[perm] = y
aov.map(y_perm)
assert_allclose(r2, r1, 1e-6, 1e-6)
def test_t_ind():
"Test independent samples t-test"
ds = datasets.get_uts(True)
y = ds.eval("utsnd.x")
n_cases = len(y)
n = n_cases / 2
t = stats.t_ind(y, n, n)
p = stats.ttest_p(t, n_cases - 2)
t_sp, p_sp = scipy.stats.ttest_ind(y[:n], y[n:])
assert_equal(t, t_sp)
assert_equal(p, p_sp)
assert_allclose(stats.ttest_t(p, n_cases - 2), np.abs(t))
# permutation
y_perm = np.empty_like(y)
for perm in permute_order(n_cases, 2):
stats.t_ind(y, n, n, out=t, perm=perm)
y_perm[perm] = y
t_sp, _ = scipy.stats.ttest_ind(y_perm[:n], y_perm[n:])
assert_allclose(t, t_sp)
def test_t_ind():
"Test independent samples t-test"
ds = datasets.get_uts(True)
y = ds.eval("utsnd.x")
n_cases = len(y)
n = n_cases / 2
t = stats.t_ind(y, n, n)
p = stats.ttest_p(t, n_cases - 2)
t_sp, p_sp = scipy.stats.ttest_ind(y[:n], y[n:])
assert_equal(t, t_sp)
assert_equal(p, p_sp)
assert_allclose(stats.ttest_t(p, n_cases - 2), np.abs(t))
# permutation
y_perm = np.empty_like(y)
for perm in permute_order(n_cases, 2):
stats.t_ind(y, n, n, out=t, perm=perm)
y_perm[perm] = y
t_sp, _ = scipy.stats.ttest_ind(y_perm[:n], y_perm[n:])
assert_allclose(t, t_sp)
