Exemplo n.º 1
0
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)
Exemplo n.º 2
0
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)
Exemplo n.º 3
0
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()