def test_null_constrained(estimate_mean, estimate_scale, estimate_prob):
# Create a mixed population of Z-scores: 1000 standard normal and
# 20 uniformly distributed between 3 and 4.
grid = np.linspace(0.001, 0.999, 1000)
z0 = norm.ppf(grid)
z1 = np.linspace(3, 4, 20)
zs = np.concatenate((z0, z1))
emp_null = NullDistribution(zs,
estimate_mean=estimate_mean,
estimate_scale=estimate_scale,
estimate_null_proportion=estimate_prob)
if not estimate_mean:
assert_allclose(emp_null.mean, 0, atol=1e-5, rtol=1e-5)
if not estimate_scale:
assert_allclose(emp_null.sd, 1, atol=1e-5, rtol=1e-2)
if not estimate_prob:
assert_allclose(emp_null.null_proportion, 1, atol=1e-5, rtol=1e-2)
# consistency check
assert_allclose(emp_null.pdf(np.r_[-1, 0, 1]),
norm.pdf(np.r_[-1, 0, 1],
loc=emp_null.mean,
scale=emp_null.sd),
rtol=1e-13)
def test_null_constrained():
# Create a mixed population of Z-scores: 1000 standard normal and
# 20 uniformly distributed between 3 and 4.
grid = np.linspace(0.001, 0.999, 1000)
z0 = norm.ppf(grid)
z1 = np.linspace(3, 4, 20)
zs = np.concatenate((z0, z1))
for estimate_mean in False,True:
for estimate_scale in False,True:
for estimate_prob in False,True:
emp_null = NullDistribution(zs, estimate_mean=estimate_mean,
estimate_scale=estimate_scale,
estimate_null_proportion=estimate_prob)
if not estimate_mean:
assert_allclose(emp_null.mean, 0, atol=1e-5, rtol=1e-5)
if not estimate_scale:
assert_allclose(emp_null.sd, 1, atol=1e-5, rtol=1e-2)
if not estimate_prob:
assert_allclose(emp_null.null_proportion, 1, atol=1e-5, rtol=1e-2)
# consistency check
assert_allclose(emp_null.pdf(np.r_[-1, 0, 1]),
norm.pdf(np.r_[-1, 0, 1], loc=emp_null.mean,
scale=emp_null.sd),
rtol=1e-13)
