some new features

.venv/lib/python3.12/site-packages/statsmodels/discrete/tests/test_margins.py

@@ -0,0 +1,132 @@
+"""
+Created on Thu Aug  3 21:08:49 2017
+
+Author: Josef Perktold
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose
+
+# load data into module namespace
+from statsmodels.datasets.cpunish import load
+from statsmodels.discrete.discrete_model import (
+    NegativeBinomial,
+    NegativeBinomialP,
+    Poisson,
+)
+import statsmodels.discrete.tests.results.results_count_margins as res_stata
+from statsmodels.tools.tools import add_constant
+
+cpunish_data = load()
+cpunish_data.exog = np.asarray(cpunish_data.exog)
+cpunish_data.endog = np.asarray(cpunish_data.endog)
+cpunish_data.exog[:,3] = np.log(cpunish_data.exog[:,3])
+exog = add_constant(cpunish_data.exog, prepend=False)
+endog = cpunish_data.endog - 1 # avoid zero-truncation
+exog /= np.round(exog.max(0), 3)
+
+class CheckMarginMixin:
+    rtol_fac = 1
+
+    def test_margins_table(self):
+        res1 = self.res1
+        sl = self.res1_slice
+        rf = self.rtol_fac
+        assert_allclose(self.margeff.margeff, self.res1.params[sl], rtol=1e-5 * rf)
+        assert_allclose(self.margeff.margeff_se, self.res1.bse[sl], rtol=1e-6 * rf)
+        assert_allclose(self.margeff.pvalues, self.res1.pvalues[sl], rtol=5e-6 * rf)
+        assert_allclose(self.margeff.conf_int(), res1.margins_table[sl, 4:6],
+                        rtol=1e-6 * rf)
+
+
+class TestPoissonMargin(CheckMarginMixin):
+
+    @classmethod
+    def setup_class(cls):
+        # here we do not need to check convergence from default start_params
+        start_params = [14.1709, 0.7085, -3.4548, -0.539, 3.2368,  -7.9299,
+                        -5.0529]
+        mod_poi = Poisson(endog, exog)
+        res_poi = mod_poi.fit(start_params=start_params)
+        #res_poi = mod_poi.fit(maxiter=100)
+        marge_poi = res_poi.get_margeff()
+        cls.res = res_poi
+        cls.margeff = marge_poi
+
+        cls.rtol_fac = 1
+        cls.res1_slice = slice(None, None, None)
+        cls.res1 = res_stata.results_poisson_margins_cont
+
+
+class TestPoissonMarginDummy(CheckMarginMixin):
+
+    @classmethod
+    def setup_class(cls):
+        # here we do not need to check convergence from default start_params
+        start_params = [14.1709, 0.7085, -3.4548, -0.539, 3.2368,  -7.9299,
+                        -5.0529]
+        mod_poi = Poisson(endog, exog)
+        res_poi = mod_poi.fit(start_params=start_params)
+        marge_poi = res_poi.get_margeff(dummy=True)
+        cls.res = res_poi
+        cls.margeff = marge_poi
+
+        cls.res1_slice = [0, 1, 2, 3, 5, 6]
+        cls.res1 = res_stata.results_poisson_margins_dummy
+
+
+class TestNegBinMargin(CheckMarginMixin):
+
+    @classmethod
+    def setup_class(cls):
+        # here we do not need to check convergence from default start_params
+        start_params = [13.1996, 0.8582, -2.8005, -1.5031, 2.3849, -8.5552,
+                        -2.88, 1.14]
+        mod = NegativeBinomial(endog, exog)
+        res = mod.fit(start_params=start_params, method='nm', maxiter=2000)
+        marge = res.get_margeff()
+        cls.res = res
+        cls.margeff = marge
+
+        cls.res1_slice = slice(None, None, None)
+        cls.res1 = res_stata.results_negbin_margins_cont
+        cls.rtol_fac = 5e1
+        # negbin has lower agreement with Stata in this case
+
+
+class TestNegBinMarginDummy(CheckMarginMixin):
+
+    @classmethod
+    def setup_class(cls):
+        # here we do not need to check convergence from default start_params
+        start_params = [13.1996, 0.8582, -2.8005, -1.5031, 2.3849, -8.5552,
+                        -2.88, 1.14]
+        mod = NegativeBinomial(endog, exog)
+        res = mod.fit(start_params=start_params, method='nm', maxiter=2000)
+        marge = res.get_margeff(dummy=True)
+        cls.res = res
+        cls.margeff = marge
+
+        cls.res1_slice = cls.res1_slice = [0, 1, 2, 3, 5, 6]
+        cls.res1 = res_stata.results_negbin_margins_dummy
+        cls.rtol_fac = 5e1
+
+
+class TestNegBinPMargin(CheckMarginMixin):
+    # this is the same as the nb2 version above for NB-P, p=2
+
+    @classmethod
+    def setup_class(cls):
+        # here we do not need to check convergence from default start_params
+        start_params = [13.1996, 0.8582, -2.8005, -1.5031, 2.3849, -8.5552,
+                        -2.88, 1.14]
+        mod = NegativeBinomialP(endog, exog)   # checks also that default p=2
+        res = mod.fit(start_params=start_params, method='nm', maxiter=2000)
+        marge = res.get_margeff()
+        cls.res = res
+        cls.margeff = marge
+
+        cls.res1_slice = slice(None, None, None)
+        cls.res1 = res_stata.results_negbin_margins_cont
+        cls.rtol_fac = 5e1
+        # negbin has lower agreement with Stata in this case