def test_weightstats_2(self):
x1, x2 = self.x1, self.x2
w1, w2 = self.w1, self.w2
d1 = DescrStatsW(x1)
d1w = DescrStatsW(x1, weights=w1)
d2w = DescrStatsW(x2, weights=w2)
x1r = d1w.asrepeats()
x2r = d2w.asrepeats()
# print 'random weights'
# print ttest_ind(x1, x2, weights=(w1, w2))
# print stats.ttest_ind(x1r, x2r)
assert_almost_equal(
ttest_ind(x1, x2, weights=(w1, w2))[:2], stats.ttest_ind(x1r, x2r),
14)
#not the same as new version with random weights/replication
# assert x1r.shape[0] == d1w.sum_weights
# assert x2r.shape[0] == d2w.sum_weights
assert_almost_equal(x2r.mean(0), d2w.mean, 14)
assert_almost_equal(x2r.var(), d2w.var, 14)
assert_almost_equal(x2r.std(), d2w.std, 14)
#note: the following is for 1d
assert_almost_equal(np.cov(x2r, bias=1), d2w.cov, 14)
#assert_almost_equal(np.corrcoef(np.x2r), d2w.corrcoef, 19)
#TODO: exception in corrcoef (scalar case)
#one-sample tests
# print d1.ttest_mean(3)
# print stats.ttest_1samp(x1, 3)
# print d1w.ttest_mean(3)
# print stats.ttest_1samp(x1r, 3)
assert_almost_equal(d1.ttest_mean(3)[:2], stats.ttest_1samp(x1, 3), 11)
assert_almost_equal(
d1w.ttest_mean(3)[:2], stats.ttest_1samp(x1r, 3), 11)
def test_ztest_ztost():
# compare weightstats with separately tested proportion ztest ztost
import statsmodels.stats.proportion as smprop
x1 = [0, 1]
w1 = [5, 15]
res2 = smprop.proportions_ztest(15, 20., value=0.5)
d1 = DescrStatsW(x1, w1)
res1 = d1.ztest_mean(0.5)
assert_allclose(res1, res2, rtol=0.03, atol=0.003)
d2 = DescrStatsW(x1, np.array(w1)*21./20)
res1 = d2.ztest_mean(0.5)
assert_almost_equal(res1, res2, decimal=12)
res1 = d2.ztost_mean(0.4, 0.6)
res2 = smprop.proportions_ztost(15, 20., 0.4, 0.6)
assert_almost_equal(res1[0], res2[0], decimal=12)
x2 = [0, 1]
w2 = [10, 10]
# d2 = DescrStatsW(x1, np.array(w1)*21./20)
d2 = DescrStatsW(x2, w2)
res1 = ztest(d1.asrepeats(), d2.asrepeats())
res2 = smprop.proportions_chisquare(np.asarray([15, 10]),
np.asarray([20., 20]))
# TODO: check this is this difference expected?, see test_proportion
assert_allclose(res1[1], res2[1], rtol=0.03)
res1a = CompareMeans(d1, d2).ztest_ind()
assert_allclose(res1a[1], res2[1], rtol=0.03)
assert_almost_equal(res1a, res1, decimal=12)
def test_ztest_ztost():
# compare weightstats with separately tested proportion ztest ztost
import statsmodels.stats.proportion as smprop
x1 = [0, 1]
w1 = [5, 15]
res2 = smprop.proportions_ztest(15, 20., value=0.5)
d1 = DescrStatsW(x1, w1)
res1 = d1.ztest_mean(0.5)
assert_allclose(res1, res2, rtol=0.03, atol=0.003)
d2 = DescrStatsW(x1, np.array(w1) * 21. / 20)
res1 = d2.ztest_mean(0.5)
assert_almost_equal(res1, res2, decimal=12)
res1 = d2.ztost_mean(0.4, 0.6)
res2 = smprop.proportions_ztost(15, 20., 0.4, 0.6)
assert_almost_equal(res1[0], res2[0], decimal=12)
x2 = [0, 1]
w2 = [10, 10]
#d2 = DescrStatsW(x1, np.array(w1)*21./20)
d2 = DescrStatsW(x2, w2)
res1 = ztest(d1.asrepeats(), d2.asrepeats())
res2 = smprop.proportions_chisquare(np.asarray([15, 10]),
np.asarray([20., 20]))
#TODO: check this is this difference expected?, see test_proportion
assert_allclose(res1[1], res2[1], rtol=0.03)
res1a = CompareMeans(d1, d2).ztest_ind()
assert_allclose(res1a[1], res2[1], rtol=0.03)
assert_almost_equal(res1a, res1, decimal=12)
def test_weightstats_3(self):
x1_2d, x2_2d = self.x1_2d, self.x2_2d
w1, w2 = self.w1, self.w2
d1w_2d = DescrStatsW(x1_2d, weights=w1)
d2w_2d = DescrStatsW(x2_2d, weights=w2)
x1r_2d = d1w_2d.asrepeats()
x2r_2d = d2w_2d.asrepeats()
assert_almost_equal(x2r_2d.mean(0), d2w_2d.mean, 14)
assert_almost_equal(x2r_2d.var(0), d2w_2d.var, 14)
assert_almost_equal(x2r_2d.std(0), d2w_2d.std, 14)
assert_almost_equal(np.cov(x2r_2d.T, bias=1), d2w_2d.cov, 14)
assert_almost_equal(np.corrcoef(x2r_2d.T), d2w_2d.corrcoef, 14)
# print d1w_2d.ttest_mean(3)
# #scipy.stats.ttest is also vectorized
# print stats.ttest_1samp(x1r_2d, 3)
t, p, d = d1w_2d.ttest_mean(3)
assert_almost_equal([t, p], stats.ttest_1samp(x1r_2d, 3), 11)
#print [stats.ttest_1samp(xi, 3) for xi in x1r_2d.T]
cm = CompareMeans(d1w_2d, d2w_2d)
ressm = cm.ttest_ind()
resss = stats.ttest_ind(x1r_2d, x2r_2d)
assert_almost_equal(ressm[:2], resss, 14)
def test_weightstats_3(self):
x1_2d, x2_2d = self.x1_2d, self.x2_2d
w1, w2 = self.w1, self.w2
d1w_2d = DescrStatsW(x1_2d, weights=w1)
d2w_2d = DescrStatsW(x2_2d, weights=w2)
x1r_2d = d1w_2d.asrepeats()
x2r_2d = d2w_2d.asrepeats()
assert_almost_equal(x2r_2d.mean(0), d2w_2d.mean, 14)
assert_almost_equal(x2r_2d.var(0), d2w_2d.var, 14)
assert_almost_equal(x2r_2d.std(0), d2w_2d.std, 14)
assert_almost_equal(np.cov(x2r_2d.T, bias=1), d2w_2d.cov, 14)
assert_almost_equal(np.corrcoef(x2r_2d.T), d2w_2d.corrcoef, 14)
# print d1w_2d.ttest_mean(3)
# #scipy.stats.ttest is also vectorized
# print stats.ttest_1samp(x1r_2d, 3)
t, p, d = d1w_2d.ttest_mean(3)
assert_almost_equal([t, p], stats.ttest_1samp(x1r_2d, 3), 11)
# print [stats.ttest_1samp(xi, 3) for xi in x1r_2d.T]
cm = CompareMeans(d1w_2d, d2w_2d)
ressm = cm.ttest_ind()
resss = stats.ttest_ind(x1r_2d, x2r_2d)
assert_almost_equal(ressm[:2], resss, 14)
def test_weightstats_2(self):
x1, x2 = self.x1, self.x2
w1, w2 = self.w1, self.w2
d1 = DescrStatsW(x1)
d1w = DescrStatsW(x1, weights=w1)
d2w = DescrStatsW(x2, weights=w2)
x1r = d1w.asrepeats()
x2r = d2w.asrepeats()
# print 'random weights'
# print ttest_ind(x1, x2, weights=(w1, w2))
# print stats.ttest_ind(x1r, x2r)
assert_almost_equal(ttest_ind(x1, x2, weights=(w1, w2))[:2],
stats.ttest_ind(x1r, x2r), 14)
# not the same as new version with random weights/replication
# assert x1r.shape[0] == d1w.sum_weights
# assert x2r.shape[0] == d2w.sum_weights
assert_almost_equal(x2r.mean(0), d2w.mean, 14)
assert_almost_equal(x2r.var(), d2w.var, 14)
assert_almost_equal(x2r.std(), d2w.std, 14)
# note: the following is for 1d
assert_almost_equal(np.cov(x2r, bias=1), d2w.cov, 14)
# assert_almost_equal(np.corrcoef(np.x2r), d2w.corrcoef, 19)
# TODO: exception in corrcoef (scalar case)
# one-sample tests
# print d1.ttest_mean(3)
# print stats.ttest_1samp(x1, 3)
# print d1w.ttest_mean(3)
# print stats.ttest_1samp(x1r, 3)
assert_almost_equal(d1.ttest_mean(3)[:2], stats.ttest_1samp(x1, 3), 11)
assert_almost_equal(d1w.ttest_mean(3)[:2],
stats.ttest_1samp(x1r, 3), 11)
def test_weightstats_2(self):
x1, x2 = self.x1, self.x2
w1, w2 = self.w1, self.w2
d1 = DescrStatsW(x1)
d1w = DescrStatsW(x1, weights=w1)
d2w = DescrStatsW(x2, weights=w2)
x1r = d1w.asrepeats()
x2r = d2w.asrepeats()
# print 'random weights'
# print ttest_ind(x1, x2, weights=(w1, w2))
# print stats.ttest_ind(x1r, x2r)
assert_almost_equal(ttest_ind(x1, x2, weights=(w1, w2))[:2],
stats.ttest_ind(x1r, x2r), 14)
#not the same as new version with random weights/replication
# assert x1r.shape[0] == d1w.sum_weights
# assert x2r.shape[0] == d2w.sum_weights
assert_almost_equal(x2r.var(), d2w.var, 14)
assert_almost_equal(x2r.std(), d2w.std, 14)
#one-sample tests
# print d1.ttest_mean(3)
# print stats.ttest_1samp(x1, 3)
# print d1w.ttest_mean(3)
# print stats.ttest_1samp(x1r, 3)
assert_almost_equal(d1.ttest_mean(3)[:2], stats.ttest_1samp(x1, 3), 11)
assert_almost_equal(d1w.ttest_mean(3)[:2], stats.ttest_1samp(x1r, 3), 11)
class TestWeightstats2d_nobs(CheckWeightstats2dMixin):
@classmethod
def setup_class(self):
np.random.seed(9876789)
n1, n2 = 20, 30
m1, m2 = 1, 1.2
x1 = m1 + np.random.randn(n1, 3)
x2 = m2 + np.random.randn(n2, 3)
w1 = np.random.randint(1, 4, n1)
w2 = np.random.randint(1, 4, n2)
self.x1, self.x2 = x1, x2
self.w1, self.w2 = w1, w2
self.d1w = DescrStatsW(x1, weights=w1, ddof=0)
self.d2w = DescrStatsW(x2, weights=w2, ddof=1)
self.x1r = self.d1w.asrepeats()
self.x2r = self.d2w.asrepeats()
def test_weightstats_3(self):
x1_2d, x2_2d = self.x1_2d, self.x2_2d
w1, w2 = self.w1, self.w2
d1w_2d = DescrStatsW(x1_2d, weights=w1)
d2w_2d = DescrStatsW(x2_2d, weights=w2)
x1r_2d = d1w_2d.asrepeats()
x2r_2d = d2w_2d.asrepeats()
# print d1w_2d.ttest_mean(3)
# #scipy.stats.ttest is also vectorized
# print stats.ttest_1samp(x1r_2d, 3)
t,p,d = d1w_2d.ttest_mean(3)
assert_almost_equal([t, p], stats.ttest_1samp(x1r_2d, 3), 11)
#print [stats.ttest_1samp(xi, 3) for xi in x1r_2d.T]
ressm = CompareMeans(d1w_2d, d2w_2d).ttest_ind()
resss = stats.ttest_ind(x1r_2d, x2r_2d)
assert_almost_equal(ressm[:2], resss, 14)
class TestWeightstats2d_nobs(CheckWeightstats2dMixin):
@classmethod
def setup_class(self):
np.random.seed(9876789)
n1, n2 = 20,30
m1, m2 = 1, 1.2
x1 = m1 + np.random.randn(n1, 3)
x2 = m2 + np.random.randn(n2, 3)
w1 = np.random.randint(1,4, n1)
w2 = np.random.randint(1,4, n2)
self.x1, self.x2 = x1, x2
self.w1, self.w2 = w1, w2
self.d1w = DescrStatsW(x1, weights=w1, ddof=0)
self.d2w = DescrStatsW(x2, weights=w2, ddof=1)
self.x1r = self.d1w.asrepeats()
self.x2r = self.d2w.asrepeats()
