133 lines
4.5 KiB
Python
133 lines
4.5 KiB
Python
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"""
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Created on Thu Aug 3 21:08:49 2017
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Author: Josef Perktold
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"""
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import numpy as np
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from numpy.testing import assert_allclose
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# load data into module namespace
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from statsmodels.datasets.cpunish import load
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from statsmodels.discrete.discrete_model import (
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NegativeBinomial,
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NegativeBinomialP,
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Poisson,
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)
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import statsmodels.discrete.tests.results.results_count_margins as res_stata
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from statsmodels.tools.tools import add_constant
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cpunish_data = load()
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cpunish_data.exog = np.asarray(cpunish_data.exog)
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cpunish_data.endog = np.asarray(cpunish_data.endog)
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cpunish_data.exog[:,3] = np.log(cpunish_data.exog[:,3])
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exog = add_constant(cpunish_data.exog, prepend=False)
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endog = cpunish_data.endog - 1 # avoid zero-truncation
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exog /= np.round(exog.max(0), 3)
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class CheckMarginMixin:
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rtol_fac = 1
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def test_margins_table(self):
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res1 = self.res1
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sl = self.res1_slice
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rf = self.rtol_fac
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assert_allclose(self.margeff.margeff, self.res1.params[sl], rtol=1e-5 * rf)
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assert_allclose(self.margeff.margeff_se, self.res1.bse[sl], rtol=1e-6 * rf)
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assert_allclose(self.margeff.pvalues, self.res1.pvalues[sl], rtol=5e-6 * rf)
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assert_allclose(self.margeff.conf_int(), res1.margins_table[sl, 4:6],
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rtol=1e-6 * rf)
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class TestPoissonMargin(CheckMarginMixin):
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@classmethod
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def setup_class(cls):
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# here we do not need to check convergence from default start_params
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start_params = [14.1709, 0.7085, -3.4548, -0.539, 3.2368, -7.9299,
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-5.0529]
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mod_poi = Poisson(endog, exog)
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res_poi = mod_poi.fit(start_params=start_params)
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#res_poi = mod_poi.fit(maxiter=100)
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marge_poi = res_poi.get_margeff()
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cls.res = res_poi
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cls.margeff = marge_poi
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cls.rtol_fac = 1
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cls.res1_slice = slice(None, None, None)
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cls.res1 = res_stata.results_poisson_margins_cont
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class TestPoissonMarginDummy(CheckMarginMixin):
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@classmethod
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def setup_class(cls):
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# here we do not need to check convergence from default start_params
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start_params = [14.1709, 0.7085, -3.4548, -0.539, 3.2368, -7.9299,
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-5.0529]
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mod_poi = Poisson(endog, exog)
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res_poi = mod_poi.fit(start_params=start_params)
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marge_poi = res_poi.get_margeff(dummy=True)
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cls.res = res_poi
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cls.margeff = marge_poi
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cls.res1_slice = [0, 1, 2, 3, 5, 6]
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cls.res1 = res_stata.results_poisson_margins_dummy
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class TestNegBinMargin(CheckMarginMixin):
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@classmethod
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def setup_class(cls):
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# here we do not need to check convergence from default start_params
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start_params = [13.1996, 0.8582, -2.8005, -1.5031, 2.3849, -8.5552,
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-2.88, 1.14]
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mod = NegativeBinomial(endog, exog)
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res = mod.fit(start_params=start_params, method='nm', maxiter=2000)
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marge = res.get_margeff()
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cls.res = res
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cls.margeff = marge
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cls.res1_slice = slice(None, None, None)
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cls.res1 = res_stata.results_negbin_margins_cont
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cls.rtol_fac = 5e1
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# negbin has lower agreement with Stata in this case
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class TestNegBinMarginDummy(CheckMarginMixin):
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@classmethod
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def setup_class(cls):
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# here we do not need to check convergence from default start_params
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start_params = [13.1996, 0.8582, -2.8005, -1.5031, 2.3849, -8.5552,
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-2.88, 1.14]
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mod = NegativeBinomial(endog, exog)
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res = mod.fit(start_params=start_params, method='nm', maxiter=2000)
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marge = res.get_margeff(dummy=True)
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cls.res = res
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cls.margeff = marge
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cls.res1_slice = cls.res1_slice = [0, 1, 2, 3, 5, 6]
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cls.res1 = res_stata.results_negbin_margins_dummy
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cls.rtol_fac = 5e1
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class TestNegBinPMargin(CheckMarginMixin):
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# this is the same as the nb2 version above for NB-P, p=2
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@classmethod
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def setup_class(cls):
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# here we do not need to check convergence from default start_params
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start_params = [13.1996, 0.8582, -2.8005, -1.5031, 2.3849, -8.5552,
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-2.88, 1.14]
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mod = NegativeBinomialP(endog, exog) # checks also that default p=2
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res = mod.fit(start_params=start_params, method='nm', maxiter=2000)
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marge = res.get_margeff()
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cls.res = res
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cls.margeff = marge
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cls.res1_slice = slice(None, None, None)
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cls.res1 = res_stata.results_negbin_margins_cont
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cls.rtol_fac = 5e1
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# negbin has lower agreement with Stata in this case
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