70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
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# scipy-yli: Helpful SciPy utilities and recipes
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# Copyright © 2022 Lee Yingtong Li (RunasSudo)
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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from pytest import approx
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import numpy as np
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import pandas as pd
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import yli
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def test_chi2_ol10_15():
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"""Compare yli.chi2 for Ott & Longnecker (2016) example 10.15"""
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data = [
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(1, 'Moderate', 15),
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(2, 'Moderate', 32),
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(3, 'Moderate', 18),
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(4, 'Moderate', 5),
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(1, 'Mildly Severe', 8),
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(2, 'Mildly Severe', 29),
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(3, 'Mildly Severe', 23),
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(4, 'Mildly Severe', 18),
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(1, 'Severe', 1),
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(2, 'Severe', 20),
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(3, 'Severe', 25),
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(4, 'Severe', 22)
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]
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df = pd.DataFrame({
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'AgeCategory': np.repeat([d[0] for d in data], [d[2] for d in data]),
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'Severity': np.repeat([d[1] for d in data], [d[2] for d in data])
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})
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result = yli.chi2(df, 'Severity', 'AgeCategory')
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assert result.statistic == approx(27.13, abs=0.01)
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assert result.pvalue == approx(0.00014, abs=0.00001)
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def test_chi2_ol10_18():
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"""Compare yli.chi2 for Ott & Longnecker (2016) example 10.18"""
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data = [
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(False, False, 250),
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(True, False, 750),
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(False, True, 400),
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(True, True, 1600)
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]
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df = pd.DataFrame({
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'Response': np.repeat([d[0] for d in data], [d[2] for d in data]),
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'Stress': np.repeat([d[1] for d in data], [d[2] for d in data])
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})
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result = yli.chi2(df, 'Stress', 'Response')
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assert result.oddsratio.point == approx(1.333, abs=0.001)
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assert result.oddsratio.ci_lower == approx(1.113, abs=0.001)
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assert result.oddsratio.ci_upper == approx(1.596, abs=0.001)
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