100 lines
4.7 KiB
Python
100 lines
4.7 KiB
Python
# 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 pandas as pd
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import yli
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def test_ordinallogit_ucla():
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"""Compare yli.regress with yli.OrdinalLogit for UCLA example at https://stats.oarc.ucla.edu/r/dae/ordinal-logistic-regression/"""
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df = pd.read_stata('tests/data/ucla_ologit.dta')
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result = yli.regress(yli.OrdinalLogit, df, 'apply', 'pared + public + gpa', exp=False)
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assert result.terms['pared'].beta.point == approx(1.04769, abs=0.0001)
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assert result.terms['public'].beta.point == approx(-0.05879, abs=0.001)
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assert result.terms['gpa'].beta.point == approx(0.61594, abs=0.001)
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assert result.terms['(Cutoffs)'].categories['unlikely/somewhat likely'].beta.point == approx(2.20391, abs=0.001)
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assert result.terms['(Cutoffs)'].categories['somewhat likely/very likely'].beta.point == approx(4.29936, abs=0.001)
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# Confidence intervals compared with Stata 16
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# . ologit apply pared public gpa
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assert result.terms['(Cutoffs)'].categories['unlikely/somewhat likely'].beta.ci_lower == approx(0.6754621, abs=0.001)
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assert result.terms['(Cutoffs)'].categories['unlikely/somewhat likely'].beta.ci_upper == approx(3.731184, abs=0.001)
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assert result.terms['(Cutoffs)'].categories['somewhat likely/very likely'].beta.ci_lower == approx(2.72234, abs=0.001)
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assert result.terms['(Cutoffs)'].categories['somewhat likely/very likely'].beta.ci_upper == approx(5.875195, abs=0.001)
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expected_summary = ''' Ordinal Logistic Regression Results
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==========================================================
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Dep. Variable: apply | No. Observations: 400
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Model: Ordinal Logit | Df. Model: 5
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Date: {0:%Y-%m-%d} | Df. Residuals: 395
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Time: {0:%H:%M:%S} | Pseudo R²: 0.03
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Std. Errors: Non-Robust | LL-Model: -358.51
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| LL-Null: -370.60
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| p (LR): <0.001*
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============================================================
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β (95% CI) p
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------------------------------------------------------------
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pared 1.05 (0.53 - 1.57) <0.001*
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public -0.06 (-0.64 - 0.53) 0.84
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gpa 0.62 (0.10 - 1.13) 0.02*
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(Cutoffs)
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unlikely/somewhat likely 2.20 (0.68 - 3.73) 0.005*
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somewhat likely/very likely 4.30 (2.72 - 5.88) <0.001*
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------------------------------------------------------------'''.format(result.fitted_dt)
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assert result.summary() == expected_summary
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def test_brant_ucla():
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"""Compare RegressionResult.brant with R brant library for UCLA example at https://stats.oarc.ucla.edu/r/dae/ordinal-logistic-regression/"""
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df = pd.read_stata('tests/data/ucla_ologit.dta')
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result = yli.regress(yli.OrdinalLogit, df, 'apply', 'pared + public + gpa', exp=False)
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brant_result = result.brant()
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# df <- read.dta("https://stats.idre.ucla.edu/stat/data/ologit.dta")
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# m <- polr(apply ~ pared + public + gpa, data=df, Hess=TRUE)
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# brant(m)
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assert brant_result.tests['Omnibus'].statistic == approx(4.34, abs=0.01)
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assert brant_result.tests['Omnibus'].dof == 3
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assert brant_result.tests['Omnibus'].pvalue == approx(0.23, abs=0.01)
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assert brant_result.tests['pared'].statistic == approx(0.13, abs=0.01)
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assert brant_result.tests['pared'].dof == 1
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assert brant_result.tests['pared'].pvalue == approx(0.72, abs=0.01)
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assert brant_result.tests['public'].statistic == approx(3.44, abs=0.01)
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assert brant_result.tests['public'].dof == 1
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assert brant_result.tests['public'].pvalue == approx(0.06, abs=0.01)
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assert brant_result.tests['gpa'].statistic == approx(0.18, abs=0.01)
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assert brant_result.tests['gpa'].dof == 1
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assert brant_result.tests['gpa'].pvalue == approx(0.67, abs=0.01)
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expected_summary = ''' χ² df p
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Omnibus 4.34 3 0.23
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pared 0.13 1 0.72
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public 3.44 1 0.06
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gpa 0.18 1 0.67 '''
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assert brant_result.summary() == expected_summary
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