148 lines
6.5 KiB
Python
148 lines
6.5 KiB
Python
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import pytest
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import numpy as np
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from numpy.testing import assert_equal, assert_allclose
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from .._discrete_distns import nchypergeom_fisher, hypergeom
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from scipy.stats._odds_ratio import odds_ratio
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from .data.fisher_exact_results_from_r import data
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class TestOddsRatio:
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@pytest.mark.parametrize('parameters, rresult', data)
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def test_results_from_r(self, parameters, rresult):
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alternative = parameters.alternative.replace('.', '-')
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result = odds_ratio(parameters.table)
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# The results computed by R are not very accurate.
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if result.statistic < 400:
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or_rtol = 5e-4
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ci_rtol = 2e-2
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else:
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or_rtol = 5e-2
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ci_rtol = 1e-1
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assert_allclose(result.statistic,
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rresult.conditional_odds_ratio, rtol=or_rtol)
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ci = result.confidence_interval(parameters.confidence_level,
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alternative)
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assert_allclose((ci.low, ci.high), rresult.conditional_odds_ratio_ci,
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rtol=ci_rtol)
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# Also do a self-check for the conditional odds ratio.
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# With the computed conditional odds ratio as the noncentrality
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# parameter of the noncentral hypergeometric distribution with
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# parameters table.sum(), table[0].sum(), and table[:,0].sum() as
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# total, ngood and nsample, respectively, the mean of the distribution
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# should equal table[0, 0].
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cor = result.statistic
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table = np.array(parameters.table)
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total = table.sum()
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ngood = table[0].sum()
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nsample = table[:, 0].sum()
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# nchypergeom_fisher does not allow the edge cases where the
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# noncentrality parameter is 0 or inf, so handle those values
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# separately here.
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if cor == 0:
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nchg_mean = hypergeom.support(total, ngood, nsample)[0]
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elif cor == np.inf:
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nchg_mean = hypergeom.support(total, ngood, nsample)[1]
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else:
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nchg_mean = nchypergeom_fisher.mean(total, ngood, nsample, cor)
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assert_allclose(nchg_mean, table[0, 0], rtol=1e-13)
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# Check that the confidence interval is correct.
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alpha = 1 - parameters.confidence_level
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if alternative == 'two-sided':
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if ci.low > 0:
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sf = nchypergeom_fisher.sf(table[0, 0] - 1,
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total, ngood, nsample, ci.low)
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assert_allclose(sf, alpha/2, rtol=1e-11)
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if np.isfinite(ci.high):
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cdf = nchypergeom_fisher.cdf(table[0, 0],
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total, ngood, nsample, ci.high)
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assert_allclose(cdf, alpha/2, rtol=1e-11)
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elif alternative == 'less':
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if np.isfinite(ci.high):
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cdf = nchypergeom_fisher.cdf(table[0, 0],
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total, ngood, nsample, ci.high)
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assert_allclose(cdf, alpha, rtol=1e-11)
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else:
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# alternative == 'greater'
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if ci.low > 0:
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sf = nchypergeom_fisher.sf(table[0, 0] - 1,
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total, ngood, nsample, ci.low)
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assert_allclose(sf, alpha, rtol=1e-11)
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@pytest.mark.parametrize('table', [
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[[0, 0], [5, 10]],
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[[5, 10], [0, 0]],
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[[0, 5], [0, 10]],
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[[5, 0], [10, 0]],
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])
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def test_row_or_col_zero(self, table):
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result = odds_ratio(table)
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assert_equal(result.statistic, np.nan)
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ci = result.confidence_interval()
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assert_equal((ci.low, ci.high), (0, np.inf))
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@pytest.mark.parametrize("case",
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[[0.95, 'two-sided', 0.4879913, 2.635883],
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[0.90, 'two-sided', 0.5588516, 2.301663]])
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def test_sample_odds_ratio_ci(self, case):
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# Compare the sample odds ratio confidence interval to the R function
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# oddsratio.wald from the epitools package, e.g.
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# > library(epitools)
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# > table = matrix(c(10, 20, 41, 93), nrow=2, ncol=2, byrow=TRUE)
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# > result = oddsratio.wald(table)
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# > result$measure
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# odds ratio with 95% C.I.
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# Predictor estimate lower upper
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# Exposed1 1.000000 NA NA
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# Exposed2 1.134146 0.4879913 2.635883
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confidence_level, alternative, ref_low, ref_high = case
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table = [[10, 20], [41, 93]]
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result = odds_ratio(table, kind='sample')
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assert_allclose(result.statistic, 1.134146, rtol=1e-6)
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ci = result.confidence_interval(confidence_level, alternative)
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assert_allclose([ci.low, ci.high], [ref_low, ref_high], rtol=1e-6)
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@pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
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def test_sample_odds_ratio_one_sided_ci(self, alternative):
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# can't find a good reference for one-sided CI, so bump up the sample
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# size and compare against the conditional odds ratio CI
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table = [[1000, 2000], [4100, 9300]]
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res = odds_ratio(table, kind='sample')
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ref = odds_ratio(table, kind='conditional')
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assert_allclose(res.statistic, ref.statistic, atol=1e-5)
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assert_allclose(res.confidence_interval(alternative=alternative),
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ref.confidence_interval(alternative=alternative),
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atol=2e-3)
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@pytest.mark.parametrize('kind', ['sample', 'conditional'])
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@pytest.mark.parametrize('bad_table', [123, "foo", [10, 11, 12]])
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def test_invalid_table_shape(self, kind, bad_table):
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with pytest.raises(ValueError, match="Invalid shape"):
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odds_ratio(bad_table, kind=kind)
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def test_invalid_table_type(self):
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with pytest.raises(ValueError, match='must be an array of integers'):
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odds_ratio([[1.0, 3.4], [5.0, 9.9]])
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def test_negative_table_values(self):
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with pytest.raises(ValueError, match='must be nonnegative'):
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odds_ratio([[1, 2], [3, -4]])
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def test_invalid_kind(self):
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with pytest.raises(ValueError, match='`kind` must be'):
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odds_ratio([[10, 20], [30, 14]], kind='magnetoreluctance')
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def test_invalid_alternative(self):
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result = odds_ratio([[5, 10], [2, 32]])
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with pytest.raises(ValueError, match='`alternative` must be'):
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result.confidence_interval(alternative='depleneration')
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@pytest.mark.parametrize('level', [-0.5, 1.5])
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def test_invalid_confidence_level(self, level):
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result = odds_ratio([[5, 10], [2, 32]])
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with pytest.raises(ValueError, match='must be between 0 and 1'):
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result.confidence_interval(confidence_level=level)
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