133 lines
4.5 KiB
Python
133 lines
4.5 KiB
Python
"""
|
|
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
|