def test_large_x(self):
# for large values of x and n, the series used to compute the cdf
# converges slowly.
# this leads to bug in R package goftest and MAPLE code that is
# the basis of the implemenation in scipy
# note: cdf = 1 for x >= 1000/3 and n = 1000
assert_(0.99999 < _cdf_cvm(333.3, 1000) < 1.0)
assert_(0.99999 < _cdf_cvm(333.3) < 1.0)
def test_low_p(self):
# _cdf_cvm can return values larger than 1. In that case, we just
# return a p-value of zero.
n = 12
res = cramervonmises(np.ones(n) * 0.8, 'norm')
assert_(_cdf_cvm(res.statistic, n) > 1.0)
assert_equal(res.pvalue, 0)
def test_cdf_support(self):
# cdf has support on [1/(12*n), n/3]
assert_equal(_cdf_cvm([1 / (12 * 533), 533 / 3], 533), [0, 1])
assert_equal(_cdf_cvm([1 / (12 * (27 + 1)), (27 + 1) / 3], 27), [0, 1])
def test_cdf_inf(self):
assert_allclose(_cdf_cvm([0.02480, 0.03656, 0.11888, 1.16204]),
[0.01, 0.05, 0.5, 0.999],
atol=1e-4)
def test_cdf_1000(self):
assert_allclose(_cdf_cvm([0.02481, 0.03658, 0.11889, 1.16120], 1000),
[0.01, 0.05, 0.5, 0.999],
atol=1e-4)
def test_cdf_10(self):
assert_allclose(_cdf_cvm([0.02657, 0.03830, 0.12068, 0.56643], 10),
[0.01, 0.05, 0.5, 0.975],
atol=1e-4)
def test_cdf_4(self):
assert_allclose(_cdf_cvm([0.02983, 0.04111, 0.12331, 0.94251], 4),
[0.01, 0.05, 0.5, 0.999],
atol=1e-4)
def test_cdf_large_n(self):
# test that asymptotic cdf and cdf for large samples are close
assert_allclose(_cdf_cvm([0.02480, 0.03656, 0.11888, 1.16204, 100],
10000),
_cdf_cvm([0.02480, 0.03656, 0.11888, 1.16204, 100]),
atol=1e-4)
