def test_corr(): "Test stats.corr" ds = datasets.get_uts() y = ds.eval("uts.x[:,:3]") x = ds.eval('Y.x') n_cases = len(y) df = n_cases - 2 corr = stats.corr(y, x) p = stats.rtest_p(corr, df) for i in range(len(corr)): r_sp, p_sp = scipy.stats.pearsonr(y[:, i], x) assert corr[i] == pytest.approx(r_sp) assert p[i] == pytest.approx(p_sp) # NaN with warnings.catch_warnings(): # divide by 0 warnings.simplefilter("ignore") assert stats.corr(np.arange(10), np.zeros(10)) == 0 # perm y_perm = np.empty_like(y) for perm in permute_order(n_cases, 2): y_perm[perm] = y stats.corr(y, x, corr, perm) for i in range(len(corr)): r_sp, _ = scipy.stats.pearsonr(y_perm[:, i], x) assert corr[i] == pytest.approx(r_sp)
def test_corr(): "Test stats.corr" ds = datasets.get_uts() y = ds.eval("uts.x[:,:3]") x = ds.eval('Y.x') n_cases = len(y) df = n_cases - 2 corr = stats.corr(y, x) p = stats.rtest_p(corr, df) for i in range(len(corr)): r_sp, p_sp = scipy.stats.pearsonr(y[:, i], x) assert_almost_equal(corr[i], r_sp) assert_almost_equal(p[i], p_sp) # NaN with warnings.catch_warnings(): # divide by 0 warnings.simplefilter("ignore") eq_(stats.corr(np.arange(10), np.zeros(10)), 0) # perm y_perm = np.empty_like(y) for perm in permute_order(n_cases, 2): y_perm[perm] = y stats.corr(y, x, corr, perm) for i in range(len(corr)): r_sp, _ = scipy.stats.pearsonr(y_perm[:, i], x) assert_almost_equal(corr[i], r_sp)
def test_corr(): "Test stats.corr" ds = datasets.get_uts() y = ds.eval("uts.x[:,:3]") x = ds.eval('Y.x') n_cases = len(y) df = n_cases - 2 corr = stats.corr(y, x) p = stats.rtest_p(corr, df) for i in xrange(len(corr)): r_sp, p_sp = scipy.stats.pearsonr(y[:, i], x) assert_almost_equal(corr[i], r_sp) assert_almost_equal(p[i], p_sp) # NaN r = stats.corr(np.arange(10), np.zeros(10)) eq_(r, 0) # perm y_perm = np.empty_like(y) for perm in permute_order(n_cases, 2): y_perm[perm] = y stats.corr(y, x, corr, perm) for i in xrange(len(corr)): r_sp, _ = scipy.stats.pearsonr(y_perm[:, i], x) assert_almost_equal(corr[i], r_sp)