Add unit test for OrdinalLogit
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@ -33,7 +33,7 @@ def test_beta_ratio_vs_jsaffer_pdf():
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y = dist.pdf(x)
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# Compare with expected values from jsaffer implementation
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expected = np.load('tests/beta_ratio_vs_jsaffer.npy', allow_pickle=False)[0]
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expected = np.load('tests/data/beta_ratio_vs_jsaffer.npy', allow_pickle=False)[0]
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assert y == approx(expected)
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def test_beta_ratio_vs_jsaffer_cdf():
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@ -48,7 +48,7 @@ def test_beta_ratio_vs_jsaffer_cdf():
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y = dist.cdf(x)
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# Compare with expected values from jsaffer implementation
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expected = np.load('tests/beta_ratio_vs_jsaffer.npy', allow_pickle=False)[1]
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expected = np.load('tests/data/beta_ratio_vs_jsaffer.npy', allow_pickle=False)[1]
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assert y == approx(expected)
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def _gen_beta_ratio_vs_jsaffer():
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@ -62,7 +62,7 @@ def _gen_beta_ratio_vs_jsaffer():
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y1 = np.vectorize(lambda w: float(beta_quotient_distribution.pdf_bb_ratio(a1, a2, b1, b2, w)))(x)
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y2 = np.vectorize(lambda w: float(beta_quotient_distribution.cdf_bb_ratio(a1, a2, b1, b2, w)))(x)
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np.save('tests/beta_ratio_vs_jsaffer.npy', np.array([y1, y2]), allow_pickle=False)
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np.save('tests/data/beta_ratio_vs_jsaffer.npy', np.array([y1, y2]), allow_pickle=False)
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def test_beta_ratio_mean_vs_empirical():
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"""Compare beta_ratio.mean (via beta_ratio._munp) with empirical mean"""
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@ -0,0 +1,64 @@
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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 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: OrdinalLogit | Df. Model: 5
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Method: Maximum Likelihood | Df. Residuals: 395
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Date: {0:%Y-%m-%d} | Pseudo R²: 0.03
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Time: {0:%H:%M:%S} | LL-Model: -358.51
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Std. Errors: Non-Robust | 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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@ -858,6 +858,8 @@ def regress(
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# Get full name to display
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if model_class is sm.Logit:
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full_name = 'Logistic Regression'
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elif model_class is OrdinalLogit:
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full_name = 'Ordinal Logistic Regression'
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else:
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full_name = '{} Regression'.format(model_class.__name__)
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if fit_kwargs.get('cov_type', 'nonrobust') != 'nonrobust':
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@ -1004,6 +1006,39 @@ class OrdinalLogit(statsmodels.miscmodels.ordinal_model.OrderedModel):
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The implementation subclasses statsmodels' native *OrderedModel*, but substitutes an alternative parameterisation used by R and Stata.
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The the native statsmodels implementation, the first cutoff term is the true cutoff, and further cutoff terms are log differences between consecutive cutoffs.
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In this parameterisation, cutoff terms are represented directly in the model.
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**Example:**
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.. code-block::
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df = pd.DataFrame(...)
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yli.regress(yli.OrdinalLogit, df, 'apply', 'pared + public + gpa', exp=False)
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.. code-block:: text
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Ordinal Logistic Regression Results
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===============================================================
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Dep. Variable: apply | No. Observations: 400
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Model: OrdinalLogit | Df. Model: 5
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Method: Maximum Likelihood | Df. Residuals: 395
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Date: 2022-12-02 | Pseudo R²: 0.03
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Time: 21:30:38 | LL-Model: -358.51
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Std. Errors: Non-Robust | 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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------------------------------------------------------------
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The output summarises the result of the regression.
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The parameters shown under "(Cutoffs)" are the cutoff values in the latent variable parameterisation of ordinal regression.
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Note that because `exp=False` was passed, the parameter estimates are not automatically exponentiated.
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"""
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def __init__(self, endog, exog, **kwargs):
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