def test_permutation():
dat = np.random.multivariate_normal([2, 6], [[.5, 2], [.5, 3]], 1000)
x = dat[:, 0]
y = dat[:, 1]
stats = two_sample_permutation(x, y, tail=1)
assert (stats['mean'] < -2) & (stats['mean'] > -6) & (stats['p'] < .001)
stats = one_sample_permutation(x - y, tail=1)
assert (stats['mean'] < -2) & (stats['mean'] > -6) & (stats['p'] < .001)
stats = correlation_permutation(x, y, metric='pearson', tail=1)
assert (stats['correlation'] > .4) & (stats['correlation'] <
.85) & (stats['p'] < .001)
stats = correlation_permutation(x, y, metric='spearman', tail=1)
assert (stats['correlation'] > .4) & (stats['correlation'] <
.85) & (stats['p'] < .001)
stats = correlation_permutation(x, y, metric='kendall', tail=2)
assert (stats['correlation'] > .4) & (stats['correlation'] <
.85) & (stats['p'] < .001)
# with pytest.raises(ValueError):
# correlation_permutation(x, y, metric='kendall',tail=3)
# with pytest.raises(ValueError):
# correlation_permutation(x, y, metric='doesntwork',tail=3)
s = np.random.normal(0, 1, 10000)
two_sided = _calc_pvalue(all_p=s, stat=1.96, tail=2)
upper_p = _calc_pvalue(all_p=s, stat=1.96, tail=1)
lower_p = _calc_pvalue(all_p=s, stat=-1.96, tail=1)
sum_p = upper_p + lower_p
np.testing.assert_almost_equal(two_sided, sum_p)
def test_permutation():
dat = np.random.multivariate_normal([2, 6], [[.5, 2], [.5, 3]], 1000)
x = dat[:, 0]
y = dat[:, 1]
stats = two_sample_permutation(x, y, tail=1, n_permute=1000)
assert (stats['mean'] < -2) & (stats['mean'] > -6) & (stats['p'] < .001)
stats = one_sample_permutation(x - y, tail=1, n_permute=1000)
assert (stats['mean'] < -2) & (stats['mean'] > -6) & (stats['p'] < .001)
stats = correlation_permutation(x, y, metric='pearson', tail=1)
assert (stats['correlation'] > .4) & (stats['correlation'] <
.85) & (stats['p'] < .001)
stats = correlation_permutation(x, y, metric='spearman', tail=1)
assert (stats['correlation'] > .4) & (stats['correlation'] <
.85) & (stats['p'] < .001)
stats = correlation_permutation(x, y, metric='kendall', tail=2)
assert (stats['correlation'] > .4) & (stats['correlation'] <
.85) & (stats['p'] < .001)
# with pytest.raises(ValueError):
# correlation_permutation(x, y, metric='kendall',tail=3)
# with pytest.raises(ValueError):
# correlation_permutation(x, y, metric='doesntwork',tail=3)
s = np.random.normal(0, 1, 10000)
two_sided = _calc_pvalue(all_p=s, stat=1.96, tail=2)
upper_p = _calc_pvalue(all_p=s, stat=1.96, tail=1)
lower_p = _calc_pvalue(all_p=s, stat=-1.96, tail=1)
sum_p = upper_p + lower_p
np.testing.assert_almost_equal(two_sided, sum_p)
# Test matrix_permutation
dat = np.random.multivariate_normal([2, 6], [[.5, 2], [.5, 3]], 190)
x = squareform(dat[:, 0])
y = squareform(dat[:, 1])
stats = matrix_permutation(x, y, n_permute=1000)
assert (stats['correlation'] > .4) & (stats['correlation'] <
.85) & (stats['p'] < .001)
# Test jackknife_permutation
dat = np.random.multivariate_normal(
[5, 10, 15, 25, 35, 45],
[[1, .2, .5, .7, .8, .9], [.2, 1, .4, .1, .1, .1],
[.5, .4, 1, .1, .1, .1], [.7, .1, .1, 1, .3, .6],
[.8, .1, .1, .3, 1, .5], [.9, .1, .1, .6, .5, 1]], 200)
dat = dat + np.random.randn(dat.shape[0], dat.shape[1]) * .5
data1 = pairwise_distances(dat[0:100, :].T, metric='correlation')
data2 = pairwise_distances(dat[100:, :].T, metric='correlation')
stats = jackknife_permutation(data1, data2)
print(stats)
assert (stats['correlation'] >= .4) & (stats['correlation'] <=
.99) & (stats['p'] <= .05)
def similarity(self, data, plot=False, **kwargs):
''' Calculate similarity between two Adjacency matrices.
Default is to use spearman correlation and permutation test.'''
if not isinstance(data, Adjacency):
data2 = Adjacency(data)
else:
data2 = data.copy()
if self.is_single_matrix:
if plot:
plot_stacked_adjacency(self, data)
return correlation_permutation(self.data, data2.data, **kwargs)
else:
if plot:
_, a = plt.subplots(len(self))
for i in a:
plot_stacked_adjacency(self, data, ax=i)
return [correlation_permutation(x.data, data2.data, **kwargs) for x in self]
def test_permutation():
dat = np.random.multivariate_normal([2, 6], [[.5, 2], [.5, 3]], 100)
x = dat[:, 0]
y = dat[:, 1]
stats = two_sample_permutation(x, y)
assert (stats['mean'] < -2) & (stats['mean'] > -6)
assert stats['p'] < .001
print(stats)
stats = one_sample_permutation(x - y)
assert (stats['mean'] < -2) & (stats['mean'] > -6)
assert stats['p'] < .001
print(stats)
stats = correlation_permutation(x, y)
assert (stats['correlation'] > .4) & (stats['correlation'] < .85)
assert stats['p'] < .001
stats = correlation_permutation(x, y, metric='kendall')
assert (stats['correlation'] > .4) & (stats['correlation'] < .85)
assert stats['p'] < .001
def test_permutation():
dat = np.random.multivariate_normal([2, 6], [[0.5, 2], [0.5, 3]], 1000)
x = dat[:, 0]
y = dat[:, 1]
stats = two_sample_permutation(x, y, tail=1, n_permute=1000)
assert (stats["mean"] < -2) & (stats["mean"] > -6) & (stats["p"] < 0.001)
stats = one_sample_permutation(x - y, tail=1, n_permute=1000)
assert (stats["mean"] < -2) & (stats["mean"] > -6) & (stats["p"] < 0.001)
for method in ["permute", "circle_shift", "phase_randomize"]:
for metric in ["spearman", "kendall", "pearson"]:
stats = correlation_permutation(x,
y,
metric=metric,
method=method,
n_permute=500,
tail=1)
assert ((stats["correlation"] > 0.4)
& (stats["correlation"] < 0.85)
& (stats["p"] < 0.05))
# with pytest.raises(ValueError):
# correlation_permutation(x, y, metric='kendall',tail=3)
# with pytest.raises(ValueError):
# correlation_permutation(x, y, metric='doesntwork',tail=3)
s = np.random.normal(0, 1, 10000)
two_sided = _calc_pvalue(all_p=s, stat=1.96, tail=2)
upper_p = _calc_pvalue(all_p=s, stat=1.96, tail=1)
lower_p = _calc_pvalue(all_p=s, stat=-1.96, tail=1)
sum_p = upper_p + lower_p
np.testing.assert_almost_equal(two_sided, sum_p, decimal=3)
# Test matrix_permutation
dat = np.random.multivariate_normal([2, 6], [[0.5, 2], [0.5, 3]], 190)
x = squareform(dat[:, 0])
y = squareform(dat[:, 1])
stats = matrix_permutation(x, y, n_permute=1000)
assert ((stats["correlation"] > 0.4)
& (stats["correlation"] < 0.85)
& (stats["p"] < 0.001))
