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 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_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)