def test_runstest_2sample():
# regression test, checked with MonteCarlo and looks reasonable
x = [31.8, 32.8, 39.2, 36, 30, 34.5, 37.4]
y = [35.5, 27.6, 21.3, 24.8, 36.7, 30]
y[-1] += 1e-6 #avoid tie that creates warning
groups = np.concatenate((np.zeros(len(x)), np.ones(len(y))))
res = runstest_2samp(x, y)
res1 = (0.022428065200812752, 0.98210649318649212)
assert_allclose(res, res1, rtol=1e-6)
# check as stacked array
res2 = runstest_2samp(x, y)
assert_allclose(res2, res, rtol=1e-6)
xy = np.concatenate((x, y))
res_1s = runstest_1samp(xy)
assert_allclose(res_1s, res1, rtol=1e-6)
# check cutoff
res2_1s = runstest_1samp(xy, xy.mean())
assert_allclose(res2_1s, res_1s, rtol=1e-6)
def test_runstest_2sample():
# regression test, checked with MonteCarlo and looks reasonable
x = [31.8, 32.8, 39.2, 36, 30, 34.5, 37.4]
y = [35.5, 27.6, 21.3, 24.8, 36.7, 30]
y[-1] += 1e-6 #avoid tie that creates warning
groups = np.concatenate((np.zeros(len(x)), np.ones(len(y))))
res = runstest_2samp(x, y)
res1 = (0.022428065200812752, 0.98210649318649212)
assert_allclose(res, res1, rtol=1e-6)
# check as stacked array
res2 = runstest_2samp(x, y)
assert_allclose(res2, res, rtol=1e-6)
xy = np.concatenate((x, y))
res_1s = runstest_1samp(xy)
assert_allclose(res_1s, res1, rtol=1e-6)
# check cutoff
res2_1s = runstest_1samp(xy, xy.mean())
assert_allclose(res2_1s, res_1s, rtol=1e-6)
from statsmodels.sandbox.stats.runs import runstest_2samp from statsmodels.stats.descriptivestats import sign_test import scipy.stats as stats # 分布的检验 from scipy.stats import kstest import numpy as np x = np.random.normal(0, 1, 1000) test_stat = kstest(x, 'norm', args=(x.mean(), x.std())) print(test_stat) stats.anderson() stats.shapiro() stats.ranksums() stats.mannwhitneyu() stats.wilcoxon() stats.ks_2samp() runstest_2samp()