496 lines
18 KiB
Python
496 lines
18 KiB
Python
|
"""
|
||
|
|
|
||
|
|
Created on Fri Jul 05 14:05:24 2013
|
||
|
|
Aug 15 2020: add brunnermunzel, rank_compare_2indep
|
||
|
|
|
||
|
|
Author: Josef Perktold
|
||
|
|
"""
|
||
|
|
from statsmodels.compat.python import lzip
|
||
|
|
import numpy as np
|
||
|
|
from numpy.testing import (assert_allclose, assert_almost_equal,
|
||
|
|
assert_approx_equal, assert_)
|
||
|
|
|
||
|
|
from scipy import stats
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from statsmodels.stats.contingency_tables import (
|
||
|
|
mcnemar, cochrans_q, SquareTable)
|
||
|
|
from statsmodels.sandbox.stats.runs import (Runs,
|
||
|
|
runstest_1samp, runstest_2samp)
|
||
|
|
from statsmodels.sandbox.stats.runs import mcnemar as sbmcnemar
|
||
|
|
from statsmodels.stats.nonparametric import (
|
||
|
|
rank_compare_2indep, rank_compare_2ordinal, prob_larger_continuous,
|
||
|
|
cohensd2problarger)
|
||
|
|
from statsmodels.tools.testing import Holder
|
||
|
|
|
||
|
|
|
||
|
|
def _expand_table(table):
|
||
|
|
'''expand a 2 by 2 contingency table to observations
|
||
|
|
'''
|
||
|
|
return np.repeat([[1, 1], [1, 0], [0, 1], [0, 0]], table.ravel(), axis=0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_mcnemar_exact():
|
||
|
|
f_obs1 = np.array([[101, 121], [59, 33]])
|
||
|
|
f_obs2 = np.array([[101, 70], [59, 33]])
|
||
|
|
f_obs3 = np.array([[101, 80], [59, 33]])
|
||
|
|
f_obs4 = np.array([[101, 30], [60, 33]])
|
||
|
|
f_obs5 = np.array([[101, 10], [30, 33]])
|
||
|
|
f_obs6 = np.array([[101, 10], [10, 33]])
|
||
|
|
|
||
|
|
#vassar college online computation
|
||
|
|
res1 = 0.000004
|
||
|
|
res2 = 0.378688
|
||
|
|
res3 = 0.089452
|
||
|
|
res4 = 0.00206
|
||
|
|
res5 = 0.002221
|
||
|
|
res6 = 1.
|
||
|
|
stat = mcnemar(f_obs1, exact=True)
|
||
|
|
assert_almost_equal([stat.statistic, stat.pvalue], [59, res1], decimal=6)
|
||
|
|
stat = mcnemar(f_obs2, exact=True)
|
||
|
|
assert_almost_equal([stat.statistic, stat.pvalue], [59, res2], decimal=6)
|
||
|
|
stat = mcnemar(f_obs3, exact=True)
|
||
|
|
assert_almost_equal([stat.statistic, stat.pvalue], [59, res3], decimal=6)
|
||
|
|
stat = mcnemar(f_obs4, exact=True)
|
||
|
|
assert_almost_equal([stat.statistic, stat.pvalue], [30, res4], decimal=6)
|
||
|
|
stat = mcnemar(f_obs5, exact=True)
|
||
|
|
assert_almost_equal([stat.statistic, stat.pvalue], [10, res5], decimal=6)
|
||
|
|
stat = mcnemar(f_obs6, exact=True)
|
||
|
|
assert_almost_equal([stat.statistic, stat.pvalue], [10, res6], decimal=6)
|
||
|
|
|
||
|
|
|
||
|
|
def test_mcnemar_chisquare():
|
||
|
|
f_obs1 = np.array([[101, 121], [59, 33]])
|
||
|
|
f_obs2 = np.array([[101, 70], [59, 33]])
|
||
|
|
f_obs3 = np.array([[101, 80], [59, 33]])
|
||
|
|
|
||
|
|
#> mcn = mcnemar.test(matrix(c(101, 121, 59, 33),nrow=2))
|
||
|
|
res1 = [2.067222e01, 5.450095e-06]
|
||
|
|
res2 = [0.7751938, 0.3786151]
|
||
|
|
res3 = [2.87769784, 0.08981434]
|
||
|
|
|
||
|
|
stat = mcnemar(f_obs1, exact=False)
|
||
|
|
assert_allclose([stat.statistic, stat.pvalue], res1, rtol=1e-6)
|
||
|
|
stat = mcnemar(f_obs2, exact=False)
|
||
|
|
assert_allclose([stat.statistic, stat.pvalue], res2, rtol=1e-6)
|
||
|
|
stat = mcnemar(f_obs3, exact=False)
|
||
|
|
assert_allclose([stat.statistic, stat.pvalue], res3, rtol=1e-6)
|
||
|
|
|
||
|
|
# test correction = False
|
||
|
|
res1 = [2.135556e01, 3.815136e-06]
|
||
|
|
res2 = [0.9379845, 0.3327967]
|
||
|
|
res3 = [3.17266187, 0.07488031]
|
||
|
|
|
||
|
|
res = mcnemar(f_obs1, exact=False, correction=False)
|
||
|
|
assert_allclose([res.statistic, res.pvalue], res1, rtol=1e-6)
|
||
|
|
res = mcnemar(f_obs2, exact=False, correction=False)
|
||
|
|
assert_allclose([res.statistic, res.pvalue], res2, rtol=1e-6)
|
||
|
|
res = mcnemar(f_obs3, exact=False, correction=False)
|
||
|
|
assert_allclose([res.statistic, res.pvalue], res3, rtol=1e-6)
|
||
|
|
|
||
|
|
|
||
|
|
def test_mcnemar_vectorized(reset_randomstate):
|
||
|
|
ttk = np.random.randint(5,15, size=(2,2,3))
|
||
|
|
with pytest.warns(FutureWarning):
|
||
|
|
res = sbmcnemar(ttk, exact=False)
|
||
|
|
with pytest.warns(FutureWarning):
|
||
|
|
res1 = lzip(*[sbmcnemar(ttk[:, :, i], exact=False) for i in range(3)])
|
||
|
|
assert_allclose(res, res1, rtol=1e-13)
|
||
|
|
|
||
|
|
with pytest.warns(FutureWarning):
|
||
|
|
res = sbmcnemar(ttk, exact=False, correction=False)
|
||
|
|
with pytest.warns(FutureWarning):
|
||
|
|
res1 = lzip(*[sbmcnemar(ttk[:, :, i], exact=False, correction=False)
|
||
|
|
for i in range(3)])
|
||
|
|
assert_allclose(res, res1, rtol=1e-13)
|
||
|
|
|
||
|
|
with pytest.warns(FutureWarning):
|
||
|
|
res = sbmcnemar(ttk, exact=True)
|
||
|
|
with pytest.warns(FutureWarning):
|
||
|
|
res1 = lzip(*[sbmcnemar(ttk[:, :, i], exact=True) for i in range(3)])
|
||
|
|
assert_allclose(res, res1, rtol=1e-13)
|
||
|
|
|
||
|
|
|
||
|
|
def test_symmetry_bowker():
|
||
|
|
table = np.array([0, 3, 4, 4, 2, 4, 1, 2, 4, 3, 5, 3, 0, 0, 2, 2, 3, 0, 0,
|
||
|
|
1, 5, 5, 5, 5, 5]).reshape(5, 5)
|
||
|
|
|
||
|
|
res = SquareTable(table, shift_zeros=False).symmetry()
|
||
|
|
mcnemar5_1 = dict(statistic=7.001587, pvalue=0.7252951, parameters=(10,),
|
||
|
|
distr='chi2')
|
||
|
|
assert_allclose([res.statistic, res.pvalue],
|
||
|
|
[mcnemar5_1['statistic'], mcnemar5_1['pvalue']],
|
||
|
|
rtol=1e-7)
|
||
|
|
|
||
|
|
res = SquareTable(1 + table, shift_zeros=False).symmetry()
|
||
|
|
mcnemar5_1b = dict(statistic=5.355988, pvalue=0.8661652, parameters=(10,),
|
||
|
|
distr='chi2')
|
||
|
|
assert_allclose([res.statistic, res.pvalue],
|
||
|
|
[mcnemar5_1b['statistic'], mcnemar5_1b['pvalue']],
|
||
|
|
rtol=1e-7)
|
||
|
|
|
||
|
|
table = np.array([2, 2, 3, 6, 2, 3, 4, 3, 6, 6, 6, 7, 1, 9, 6, 7, 1, 1, 9,
|
||
|
|
8, 0, 1, 8, 9, 4]).reshape(5, 5)
|
||
|
|
|
||
|
|
res = SquareTable(table, shift_zeros=False).symmetry()
|
||
|
|
mcnemar5_2 = dict(statistic=18.76432, pvalue=0.04336035, parameters=(10,),
|
||
|
|
distr='chi2')
|
||
|
|
assert_allclose([res.statistic, res.pvalue],
|
||
|
|
[mcnemar5_2['statistic'], mcnemar5_2['pvalue']],
|
||
|
|
rtol=1.5e-7)
|
||
|
|
|
||
|
|
res = SquareTable(1 + table, shift_zeros=False).symmetry()
|
||
|
|
mcnemar5_2b = dict(statistic=14.55256, pvalue=0.1492461, parameters=(10,),
|
||
|
|
distr='chi2')
|
||
|
|
assert_allclose([res.statistic, res.pvalue],
|
||
|
|
[mcnemar5_2b['statistic'], mcnemar5_2b['pvalue']],
|
||
|
|
rtol=1e-7)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cochransq():
|
||
|
|
#example from dataplot docs, Conovover p. 253
|
||
|
|
#http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/cochran.htm
|
||
|
|
x = np.array([[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0],
|
||
|
|
[1, 1, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 0],
|
||
|
|
[0, 0, 1],
|
||
|
|
[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1]])
|
||
|
|
res_qstat = 2.8
|
||
|
|
res_pvalue = 0.246597
|
||
|
|
res = cochrans_q(x)
|
||
|
|
assert_almost_equal([res.statistic, res.pvalue], [res_qstat, res_pvalue])
|
||
|
|
|
||
|
|
#equivalence of mcnemar and cochranq for 2 samples
|
||
|
|
a,b = x[:,:2].T
|
||
|
|
res = cochrans_q(x[:, :2])
|
||
|
|
with pytest.warns(FutureWarning):
|
||
|
|
assert_almost_equal(sbmcnemar(a, b, exact=False, correction=False),
|
||
|
|
[res.statistic, res.pvalue])
|
||
|
|
|
||
|
|
|
||
|
|
def test_cochransq2():
|
||
|
|
# from an example found on web, verifies 13.286
|
||
|
|
data = np.array('''
|
||
|
|
0 0 0 1
|
||
|
|
0 0 0 1
|
||
|
|
0 0 0 1
|
||
|
|
1 1 1 1
|
||
|
|
1 0 0 1
|
||
|
|
0 1 0 1
|
||
|
|
1 0 0 1
|
||
|
|
0 0 0 1
|
||
|
|
0 1 0 0
|
||
|
|
0 0 0 0
|
||
|
|
1 0 0 1
|
||
|
|
0 0 1 1'''.split(), int).reshape(-1, 4)
|
||
|
|
|
||
|
|
res = cochrans_q(data)
|
||
|
|
assert_allclose([res.statistic, res.pvalue], [13.2857143, 0.00405776], rtol=1e-6)
|
||
|
|
|
||
|
|
|
||
|
|
def test_cochransq3():
|
||
|
|
# another example compared to SAS
|
||
|
|
# in frequency weight format
|
||
|
|
dt = [('A', 'S1'), ('B', 'S1'), ('C', 'S1'), ('count', int)]
|
||
|
|
dta = np.array([('F', 'F', 'F', 6),
|
||
|
|
('U', 'F', 'F', 2),
|
||
|
|
('F', 'F', 'U', 16),
|
||
|
|
('U', 'F', 'U', 4),
|
||
|
|
('F', 'U', 'F', 2),
|
||
|
|
('U', 'U', 'F', 6),
|
||
|
|
('F', 'U', 'U', 4),
|
||
|
|
('U', 'U', 'U', 6)], dt)
|
||
|
|
|
||
|
|
cases = np.array([[0, 0, 0],
|
||
|
|
[1, 0, 0],
|
||
|
|
[0, 0, 1],
|
||
|
|
[1, 0, 1],
|
||
|
|
[0, 1, 0],
|
||
|
|
[1, 1, 0],
|
||
|
|
[0, 1, 1],
|
||
|
|
[1, 1, 1]])
|
||
|
|
count = np.array([ 6, 2, 16, 4, 2, 6, 4, 6])
|
||
|
|
data = np.repeat(cases, count, 0)
|
||
|
|
|
||
|
|
res = cochrans_q(data)
|
||
|
|
assert_allclose([res.statistic, res.pvalue], [8.4706, 0.0145], atol=5e-5)
|
||
|
|
|
||
|
|
def test_runstest(reset_randomstate):
|
||
|
|
#comparison numbers from R, tseries, runs.test
|
||
|
|
#currently only 2-sided used
|
||
|
|
x = np.array([1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1])
|
||
|
|
|
||
|
|
z_twosided = 1.386750
|
||
|
|
pvalue_twosided = 0.1655179
|
||
|
|
|
||
|
|
z_greater = 1.386750
|
||
|
|
pvalue_greater = 0.08275893
|
||
|
|
|
||
|
|
z_less = 1.386750
|
||
|
|
pvalue_less = 0.917241
|
||
|
|
|
||
|
|
#print Runs(x).runs_test(correction=False)
|
||
|
|
assert_almost_equal(np.array(Runs(x).runs_test(correction=False)),
|
||
|
|
[z_twosided, pvalue_twosided], decimal=6)
|
||
|
|
|
||
|
|
|
||
|
|
# compare with runstest_1samp which should have same indicator
|
||
|
|
assert_almost_equal(runstest_1samp(x, correction=False),
|
||
|
|
[z_twosided, pvalue_twosided], decimal=6)
|
||
|
|
|
||
|
|
x2 = x - 0.5 + np.random.uniform(-0.1, 0.1, size=len(x))
|
||
|
|
assert_almost_equal(runstest_1samp(x2, cutoff=0, correction=False),
|
||
|
|
[z_twosided, pvalue_twosided], decimal=6)
|
||
|
|
|
||
|
|
assert_almost_equal(runstest_1samp(x2, cutoff='mean', correction=False),
|
||
|
|
[z_twosided, pvalue_twosided], decimal=6)
|
||
|
|
assert_almost_equal(runstest_1samp(x2, cutoff=x2.mean(), correction=False),
|
||
|
|
[z_twosided, pvalue_twosided], decimal=6)
|
||
|
|
|
||
|
|
# check median
|
||
|
|
assert_almost_equal(runstest_1samp(x2, cutoff='median', correction=False),
|
||
|
|
runstest_1samp(x2, cutoff=np.median(x2), correction=False),
|
||
|
|
decimal=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)
|
||
|
|
|
||
|
|
|
||
|
|
def test_brunnermunzel_one_sided():
|
||
|
|
# copied from scipy with adjustment
|
||
|
|
x = [1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1]
|
||
|
|
y = [3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4]
|
||
|
|
significant = 13
|
||
|
|
|
||
|
|
# revere direction to match our definition
|
||
|
|
x, y = y, x
|
||
|
|
|
||
|
|
# Results are compared with R's lawstat package.
|
||
|
|
u1, p1 = rank_compare_2indep(x, y
|
||
|
|
).test_prob_superior(alternative='smaller')
|
||
|
|
u2, p2 = rank_compare_2indep(y, x
|
||
|
|
).test_prob_superior(alternative='larger')
|
||
|
|
u3, p3 = rank_compare_2indep(x, y
|
||
|
|
).test_prob_superior(alternative='larger')
|
||
|
|
u4, p4 = rank_compare_2indep(y, x
|
||
|
|
).test_prob_superior(alternative='smaller')
|
||
|
|
|
||
|
|
assert_approx_equal(p1, p2, significant=significant)
|
||
|
|
assert_approx_equal(p3, p4, significant=significant)
|
||
|
|
assert_(p1 != p3)
|
||
|
|
assert_approx_equal(u1, 3.1374674823029505,
|
||
|
|
significant=significant)
|
||
|
|
assert_approx_equal(u2, -3.1374674823029505,
|
||
|
|
significant=significant)
|
||
|
|
assert_approx_equal(u3, 3.1374674823029505,
|
||
|
|
significant=significant)
|
||
|
|
assert_approx_equal(u4, -3.1374674823029505,
|
||
|
|
significant=significant)
|
||
|
|
|
||
|
|
# Note: scipy and lawstat tail is reversed compared to test statistic
|
||
|
|
assert_approx_equal(p3, 0.0028931043330757342,
|
||
|
|
significant=significant)
|
||
|
|
assert_approx_equal(p1, 0.99710689566692423,
|
||
|
|
significant=significant)
|
||
|
|
|
||
|
|
|
||
|
|
def test_brunnermunzel_two_sided():
|
||
|
|
# copied from scipy with adjustment
|
||
|
|
x = [1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1]
|
||
|
|
y = [3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4]
|
||
|
|
significant = 13
|
||
|
|
|
||
|
|
# revere direction to match our definition
|
||
|
|
x, y = y, x
|
||
|
|
|
||
|
|
# Results are compared with R's lawstat package.
|
||
|
|
res1 = rank_compare_2indep(x, y)
|
||
|
|
u1, p1 = res1
|
||
|
|
t1 = res1.test_prob_superior(alternative='two-sided')
|
||
|
|
res2 = rank_compare_2indep(y, x)
|
||
|
|
u2, p2 = res2
|
||
|
|
t2 = res2.test_prob_superior(alternative='two-sided')
|
||
|
|
|
||
|
|
assert_approx_equal(p1, p2, significant=significant)
|
||
|
|
assert_approx_equal(u1, 3.1374674823029505,
|
||
|
|
significant=significant)
|
||
|
|
assert_approx_equal(u2, -3.1374674823029505,
|
||
|
|
significant=significant)
|
||
|
|
assert_approx_equal(p2, 0.0057862086661515377,
|
||
|
|
significant=significant)
|
||
|
|
|
||
|
|
assert_allclose(t1[0], u1, rtol=1e-13)
|
||
|
|
assert_allclose(t2[0], u2, rtol=1e-13)
|
||
|
|
assert_allclose(t1[1], p1, rtol=1e-13)
|
||
|
|
assert_allclose(t2[1], p2, rtol=1e-13)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rank_compare_2indep1():
|
||
|
|
# Example from Munzel and Hauschke 2003
|
||
|
|
# data is given by counts, expand to observations
|
||
|
|
levels = [-2, -1, 0, 1, 2]
|
||
|
|
new = [24, 37, 21, 19, 6]
|
||
|
|
active = [11, 51, 22, 21, 7]
|
||
|
|
x1 = np.repeat(levels, new)
|
||
|
|
x2 = np.repeat(levels, active)
|
||
|
|
|
||
|
|
# using lawstat
|
||
|
|
# > brunner.munzel.test(xn, xa) #brunnermunzel.test(x, y)
|
||
|
|
res2_t = Holder(statistic=1.1757561456582,
|
||
|
|
df=204.2984239868,
|
||
|
|
pvalue=0.2410606649547,
|
||
|
|
ci=[0.4700629827705593, 0.6183882855872511],
|
||
|
|
prob=0.5442256341789052)
|
||
|
|
|
||
|
|
res = rank_compare_2indep(x1, x2, use_t=False)
|
||
|
|
assert_allclose(res.statistic, -res2_t.statistic, rtol=1e-13)
|
||
|
|
assert_allclose(res.prob1, 1 - res2_t.prob, rtol=1e-13)
|
||
|
|
assert_allclose(res.prob2, res2_t.prob, rtol=1e-13)
|
||
|
|
tt = res.test_prob_superior()
|
||
|
|
# TODO: return HolderTuple
|
||
|
|
# assert_allclose(tt.statistic, res2_t.statistic)
|
||
|
|
# TODO: check sign/direction in lawstat
|
||
|
|
assert_allclose(tt[0], -res2_t.statistic, rtol=1e-13)
|
||
|
|
|
||
|
|
ci = res.conf_int(alpha=0.05)
|
||
|
|
# we compare normal confint with t confint, lower rtol
|
||
|
|
assert_allclose(ci, 1 - np.array(res2_t.ci)[::-1], rtol=0.005)
|
||
|
|
# test consistency of test and confint
|
||
|
|
res_lb = res.test_prob_superior(value=ci[0])
|
||
|
|
assert_allclose(res_lb[1], 0.05, rtol=1e-13)
|
||
|
|
res_ub = res.test_prob_superior(value=ci[1])
|
||
|
|
assert_allclose(res_ub[1], 0.05, rtol=1e-13)
|
||
|
|
|
||
|
|
# test consistency of tost and confint
|
||
|
|
# lower margin is binding, alternative larger
|
||
|
|
res_tost = res.tost_prob_superior(ci[0], ci[1] * 1.05)
|
||
|
|
assert_allclose(res_tost.results_larger.pvalue, 0.025, rtol=1e-13)
|
||
|
|
assert_allclose(res_tost.pvalue, 0.025, rtol=1e-13)
|
||
|
|
|
||
|
|
# upper margin is binding, alternative smaller
|
||
|
|
res_tost = res.tost_prob_superior(ci[0] * 0.85, ci[1])
|
||
|
|
assert_allclose(res_tost.results_smaller.pvalue, 0.025, rtol=1e-13)
|
||
|
|
assert_allclose(res_tost.pvalue, 0.025, rtol=1e-13)
|
||
|
|
|
||
|
|
# use t-distribution
|
||
|
|
# our ranking is defined as reversed from lawstat, and BM article
|
||
|
|
# revere direction to match our definition
|
||
|
|
x1, x2 = x2, x1
|
||
|
|
res = rank_compare_2indep(x1, x2, use_t=True)
|
||
|
|
assert_allclose(res.statistic, res2_t.statistic, rtol=1e-13)
|
||
|
|
tt = res.test_prob_superior()
|
||
|
|
# TODO: return HolderTuple
|
||
|
|
# assert_allclose(tt.statistic, res2_t.statistic)
|
||
|
|
# TODO: check sign/direction in lawstat, reversed from ours
|
||
|
|
assert_allclose(tt[0], res2_t.statistic, rtol=1e-13)
|
||
|
|
assert_allclose(tt[1], res2_t.pvalue, rtol=1e-13)
|
||
|
|
assert_allclose(res.pvalue, res2_t.pvalue, rtol=1e-13)
|
||
|
|
assert_allclose(res.df, res2_t.df, rtol=1e-13)
|
||
|
|
|
||
|
|
ci = res.conf_int(alpha=0.05)
|
||
|
|
assert_allclose(ci, res2_t.ci, rtol=1e-11)
|
||
|
|
# test consistency of test and confint
|
||
|
|
res_lb = res.test_prob_superior(value=ci[0])
|
||
|
|
assert_allclose(res_lb[1], 0.05, rtol=1e-11)
|
||
|
|
res_ub = res.test_prob_superior(value=ci[1])
|
||
|
|
assert_allclose(res_ub[1], 0.05, rtol=1e-11)
|
||
|
|
|
||
|
|
# test consistency of tost and confint
|
||
|
|
# lower margin is binding, alternative larger
|
||
|
|
res_tost = res.tost_prob_superior(ci[0], ci[1] * 1.05)
|
||
|
|
assert_allclose(res_tost.results_larger.pvalue, 0.025, rtol=1e-10)
|
||
|
|
assert_allclose(res_tost.pvalue, 0.025, rtol=1e-10)
|
||
|
|
|
||
|
|
# upper margin is binding, alternative smaller
|
||
|
|
res_tost = res.tost_prob_superior(ci[0] * 0.85, ci[1])
|
||
|
|
assert_allclose(res_tost.results_smaller.pvalue, 0.025, rtol=1e-10)
|
||
|
|
assert_allclose(res_tost.pvalue, 0.025, rtol=1e-10)
|
||
|
|
|
||
|
|
# extras
|
||
|
|
# cohen's d
|
||
|
|
esd = res.effectsize_normal()
|
||
|
|
p = prob_larger_continuous(stats.norm(loc=esd), stats.norm)
|
||
|
|
# round trip
|
||
|
|
assert_allclose(p, res.prob1, rtol=1e-13)
|
||
|
|
|
||
|
|
# round trip with cohen's d
|
||
|
|
pc = cohensd2problarger(esd)
|
||
|
|
assert_allclose(pc, res.prob1, rtol=1e-13)
|
||
|
|
|
||
|
|
ci_tr = res.confint_lintransf(1, -1)
|
||
|
|
assert_allclose(ci_tr, 1 - np.array(res2_t.ci)[::-1], rtol=0.005)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rank_compare_ord():
|
||
|
|
# compare ordinal count version with full version
|
||
|
|
# Example from Munzel and Hauschke 2003
|
||
|
|
# data is given by counts, expand to observations
|
||
|
|
levels = [-2, -1, 0, 1, 2]
|
||
|
|
new = [24, 37, 21, 19, 6]
|
||
|
|
active = [11, 51, 22, 21, 7]
|
||
|
|
x1 = np.repeat(levels, new)
|
||
|
|
x2 = np.repeat(levels, active)
|
||
|
|
|
||
|
|
for use_t in [False, True]:
|
||
|
|
res2 = rank_compare_2indep(x1, x2, use_t=use_t)
|
||
|
|
res1 = rank_compare_2ordinal(new, active, use_t=use_t)
|
||
|
|
assert_allclose(res2.prob1, res1.prob1, rtol=1e-13)
|
||
|
|
assert_allclose(res2.var_prob, res1.var_prob, rtol=1e-13)
|
||
|
|
s1 = str(res1.summary())
|
||
|
|
s2 = str(res2.summary())
|
||
|
|
assert s1 == s2
|
||
|
|
|
||
|
|
|
||
|
|
def test_rank_compare_vectorized():
|
||
|
|
np.random.seed(987126)
|
||
|
|
x1 = np.random.randint(0, 20, (50, 3))
|
||
|
|
x2 = np.random.randint(5, 25, (50, 3))
|
||
|
|
res = rank_compare_2indep(x1, x2)
|
||
|
|
tst = res.test_prob_superior(0.5)
|
||
|
|
tost = res.tost_prob_superior(0.4, 0.6)
|
||
|
|
|
||
|
|
# smoke test for summary
|
||
|
|
res.summary()
|
||
|
|
|
||
|
|
for i in range(3):
|
||
|
|
res_i = rank_compare_2indep(x1[:, i], x2[:, i])
|
||
|
|
assert_allclose(res.statistic[i], res_i.statistic, rtol=1e-14)
|
||
|
|
assert_allclose(res.pvalue[i], res_i.pvalue, rtol=1e-14)
|
||
|
|
assert_allclose(res.prob1[i], res_i.prob1, rtol=1e-14)
|
||
|
|
|
||
|
|
tst_i = res_i.test_prob_superior(0.5)
|
||
|
|
assert_allclose(tst.statistic[i], tst_i.statistic, rtol=1e-14)
|
||
|
|
assert_allclose(tst.pvalue[i], tst_i.pvalue, rtol=1e-14)
|
||
|
|
|
||
|
|
tost_i = res_i.tost_prob_superior(0.4, 0.6)
|
||
|
|
assert_allclose(tost.statistic[i], tost_i.statistic, rtol=1e-14)
|
||
|
|
assert_allclose(tost.pvalue[i], tost_i.pvalue, rtol=1e-14)
|