69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
# scipy-yli: Helpful SciPy utilities and recipes
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# Copyright © 2022–2023 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 pandas as pd
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import yli
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def test_pearsonr_ol11_15():
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"""Compare yli.pearsonr for Ott & Longnecker (2016) example 11.15"""
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df = pd.DataFrame({
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'y': [41, 39, 47, 51, 43, 40, 57, 46, 50, 59, 61, 52],
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'x': [24, 30, 33, 35, 36, 36, 37, 37, 38, 40, 43, 49]
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})
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result = yli.pearsonr(df, 'y', 'x')
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assert result.statistic.point == approx(0.646, abs=0.001)
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assert result.pvalue == approx(0.0234, abs=0.0001)
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expected_summary = 'r (95% CI) = 0.65 (0.11–0.89); p = 0.02*'
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assert result.summary() == expected_summary
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assert result._repr_html_() == '<i>r</i> (95% CI) = 0.65 (0.11–0.89); <i>p</i> = 0.02*'
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def test_pearsonr_ol11_16():
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"""Compare yli.pearsonr for Ott & Longnecker (2016) example 11.16"""
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df = pd.DataFrame({
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'Eggs': [27, 32, 39, 48, 59, 67, 71, 65, 73, 67, 78, 72, 81, 74, 83, 75, 84, 77, 83, 76, 82, 75, 78, 77, 75, 73, 71, 70, 68, 65],
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'Weight': [2.1, 2.3, 2.4, 2.5, 2.9, 3.1, 3.2, 3.3, 3.4, 3.4, 3.5, 3.5, 3.5, 3.6, 3.6, 3.6, 3.6, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0, 4.3, 4.4, 4.7, 4.8, 4.9, 5.0, 5.1]
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})
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result = yli.pearsonr(df, 'Eggs', 'Weight')
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assert result.statistic.point == approx(0.606, abs=0.001)
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assert result.statistic.ci_lower == approx(0.314, abs=0.001)
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assert result.statistic.ci_upper == approx(0.793, abs=0.001)
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def test_spearman_ol11_17():
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"""Compare yli.spearman for Ott & Longnecker (2016) example 11.17"""
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df = pd.DataFrame({
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'Profit': [2.5, 6.2, 3.1, 4.6, 7.3, 4.5, 6.1, 11.6, 10.0, 14.2, 16.1, 19.5],
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'Quality': [50, 57, 61, 68, 77, 80, 82, 85, 89, 91, 95, 99]
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})
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result = yli.spearman(df, 'Profit', 'Quality')
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assert result.statistic.point == approx(0.874, abs=0.001)
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expected_summary = 'ρ (95% CI) = 0.87 (0.60–0.96); p < 0.001*' # NB: The confidence intervals are unvalidated
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assert result.summary() == expected_summary
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assert result._repr_html_() == '<i>ρ</i> (95% CI) = 0.87 (0.60–0.96); <i>p</i> < 0.001*'
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