def test_pval_bounds(self):
x = np.arange(1,10)
d_ks_n, p_n = lilliefors(x, dist='norm')
d_ks_e, p_e = lilliefors(x, dist='exp')
assert_almost_equal(p_n, 0.200, decimal=7)
assert_almost_equal(p_e, 0.200, decimal=7)
def test_min_nobs(self):
x = np.arange(3.)
with pytest.raises(ValueError):
lilliefors(x, dist='norm', pvalmethod='approx')
x = np.arange(2.)
with pytest.raises(ValueError):
lilliefors(x, dist='exp', pvalmethod='approx')
def test_normal(self):
np.random.seed(3975)
x_n = stats.norm.rvs(size=500)
# R function call:
# require(nortest)
# lillie.test(x_n)
d_ks_norm, p_norm = lilliefors(x_n, dist='norm', pvalmethod='approx')
# shift normal distribution > 0 to exactly mirror R `KScorrect` test
# R `KScorrect` requires all values tested for exponential
# distribution to be > 0
# R function call:
# require(KScorrect)
# LcKS(x_n+abs(min(x_n))+0.001, 'pexp')
d_ks_exp, p_exp = lilliefors(x_n + np.abs(x_n.min()) + 0.001,
dist='exp',
pvalmethod='approx')
# assert normal
assert_almost_equal(d_ks_norm, 0.025957, decimal=3)
assert_almost_equal(p_norm, 0.64175, decimal=3)
# assert exp
assert_almost_equal(d_ks_exp, 0.3436007, decimal=3)
assert_almost_equal(p_exp, 0.001, decimal=3)
def test_pval_bounds(self):
x = stats.norm.ppf((np.arange(10.) + 0.5) / 10)
d_ks_n, p_n = lilliefors(x, dist='norm', pvalmethod='approx')
x = stats.expon.ppf((np.arange(10.) + 0.5) / 10)
d_ks_e, p_e = lilliefors(x, dist='exp', pvalmethod='approx')
assert_almost_equal(p_n, 0.99, decimal=7)
assert_almost_equal(p_e, 0.99, decimal=7)
def stat_test(x):
start = process_time()
_, pval = lilliefors(x, dist='exp')
stat, critval, alpha = anderson(x, dist='expon')
end = process_time()
logmsg('sample_size=%d: Anderson: %s %s %s (%f secs)',
len(x), str(stat), str(critval), str(alpha), end - start)
def test_expon(self):
np.random.seed(3975)
x_e = stats.expon.rvs(size=500)
# R function call:
# require(nortest)
# lillie.test(x_n)
d_ks_norm, p_norm = lilliefors(x_e, dist='norm')
# R function call:
# require(KScorrect)
# LcKS(x_e, 'pexp')
d_ks_exp, p_exp = lilliefors(x_e, dist='exp')
# assert normal
assert_almost_equal(d_ks_norm, 0.15581, decimal=3)
assert_almost_equal(p_norm, 2.2e-16, decimal=3)
# assert exp
assert_almost_equal(d_ks_exp, 0.02763748, decimal=3)
assert_almost_equal(p_exp, 0.200, decimal=3)
def test_normal_table(self):
np.random.seed(3975)
x_n = stats.norm.rvs(size=500)
d_ks_norm, p_norm = lilliefors(x_n, dist='norm', pvalmethod='table')
# assert normal
assert_almost_equal(d_ks_norm, 0.025957, decimal=3)
assert_almost_equal(p_norm, 0.64175, decimal=3)
def test_normal(self):
np.random.seed(3975)
x_n = stats.norm.rvs(size=500)
# R function call:
# require(nortest)
# lillie.test(x_n)
d_ks_norm, p_norm = lilliefors(x_n, dist='norm')
# shift normal distribution > 0 to exactly mirror R `KScorrect` test
# R `KScorrect` requires all values tested for exponential
# distribution to be > 0
# R function call:
# require(KScorrect)
# LcKS(x_n+abs(min(x_n))+0.001, 'pexp')
d_ks_exp, p_exp = lilliefors(x_n+np.abs(x_n.min()) + 0.001, dist='exp')
# assert normal
assert_almost_equal(d_ks_norm, 0.025957, decimal=3)
assert_almost_equal(p_norm, 0.2000, decimal=3)
# assert exp
assert_almost_equal(d_ks_exp, 0.3436007, decimal=3)
assert_almost_equal(p_exp, 0.01, decimal=3)
def test_x_dims(self):
np.random.seed(3975)
x_n = stats.norm.rvs(size=500)
# 1d array
data = x_n
d_ks_norm, p_norm = lilliefors(data, dist='norm', pvalmethod='approx')
assert_almost_equal(d_ks_norm, 0.025957, decimal=3)
assert_almost_equal(p_norm, 0.64175, decimal=3)
# 2d array with size 1 second dimension
data = x_n.reshape(-1, 1)
d_ks_norm, p_norm = lilliefors(data, dist='norm', pvalmethod='approx')
assert_almost_equal(d_ks_norm, 0.025957, decimal=3)
assert_almost_equal(p_norm, 0.64175, decimal=3)
# 2d array with larger second dimension
data = np.array([x_n, x_n]).T
with pytest.raises(ValueError):
lilliefors(data, dist='norm', pvalmethod='approx')
# single-column DataFrame
data = pd.DataFrame(data=x_n)
d_ks_norm, p_norm = lilliefors(data, dist='norm', pvalmethod='approx')
assert_almost_equal(d_ks_norm, 0.025957, decimal=3)
assert_almost_equal(p_norm, 0.64175, decimal=3)
# two-column DataFrame
data = pd.DataFrame(data=[x_n, x_n])
with pytest.raises(ValueError):
lilliefors(data, dist='norm', pvalmethod='approx')
# DataFrame with observations in columns
data = pd.DataFrame(data=x_n.reshape(-1, 1).T)
with pytest.raises(ValueError):
lilliefors(data, dist='norm', pvalmethod='approx')
def test_large_sample(self, reset_randomstate):
x = np.random.randn(10000)
lilliefors(x, pvalmethod='approx')
