119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
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"""Unit tests for NonlinearDeltaCov and LikelihoodResults._get_wald_nonlinear
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Created on Sun Mar 01 01:05:35 2015
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Author: Josef Perktold
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License: BSD-3
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"""
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import numpy as np
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from numpy.testing import assert_allclose, assert_equal
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from statsmodels.regression.linear_model import OLS
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from statsmodels.stats._delta_method import NonlinearDeltaCov
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class TestDeltacovOLS:
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@classmethod
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def setup_class(cls):
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nobs, k_vars = 100, 4
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x = np.random.randn(nobs, k_vars)
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x[:, 0] = 1
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y = x[:, :-1].sum(1) + np.random.randn(nobs)
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cls.res = OLS(y, x).fit()
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def test_method(self):
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# test Results.method is same as calling function/class
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res = self.res
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x = res.model.exog
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def fun(params):
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return np.dot(x, params)**2
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nl = NonlinearDeltaCov(fun, res.params, res.cov_params())
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nlm = res._get_wald_nonlinear(fun)
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# margeff excludes constant, last parameter in this case
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assert_allclose(nlm.se_vectorized(), nlm.se_vectorized(), rtol=1e-12)
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assert_allclose(nlm.predicted(), nlm.predicted(), rtol=1e-12)
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df = res.df_resid
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t1 = nl.summary(use_t=True, df=df)
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t2 = nlm.summary(use_t=True, df=df)
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assert_equal(str(t1), str(t2))
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def test_ttest(self):
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# check with linear function against t_test
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res = self.res
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x = res.model.exog
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def fun(params):
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return np.dot(x, params)
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nl = NonlinearDeltaCov(fun, res.params, res.cov_params())
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predicted = nl.predicted()
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se = nl.se_vectorized()
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assert_allclose(predicted, fun(res.params), rtol=1e-12)
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assert_allclose(se, np.sqrt(np.diag(nl.cov())), rtol=1e-12)
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tt = res.t_test(x, use_t=False)
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assert_allclose(predicted, tt.effect, rtol=1e-12)
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assert_allclose(se, tt.sd, rtol=1e-12)
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assert_allclose(nl.conf_int(), tt.conf_int(), rtol=1e-12)
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t1 = nl.summary()
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t2 = tt.summary()
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# equal because nl.summary uses also ContrastResults
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assert_equal(str(t1), str(t2))
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# use_t = True
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predicted = nl.predicted()
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se = nl.se_vectorized()
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df = res.df_resid
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tt = res.t_test(x, use_t=True)
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assert_allclose(nl.conf_int(use_t=True, df=df), tt.conf_int(),
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rtol=1e-12, atol=1e-10)
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t1 = nl.summary(use_t=True, df=df)
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t2 = tt.summary()
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# equal because nl.summary uses also ContrastResults
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assert_equal(str(t1), str(t2))
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def test_diff(self):
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res = self.res
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x = res.model.exog
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def fun(params):
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return np.dot(x, params) - np.dot(x[:, 1:], params[1:])
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nl = NonlinearDeltaCov(fun, res.params, res.cov_params())
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# the following two use broadcasting
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assert_allclose(nl.predicted(), res.params[0], rtol=1e-12)
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assert_allclose(nl.se_vectorized(), res.bse[0], rtol=1e-12)
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def test_deltacov_margeff():
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# compare with discrete margins
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import statsmodels.discrete.tests.test_discrete as dt
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tc = dt.TestPoissonNewton()
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tc.setup_class()
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res_poi = tc.res1
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res_poi.model._derivative_exog
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# 2d f doesn't work correctly,
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# se_vectorized and predicted are 2d column vector
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def f(p):
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ex = res_poi.model.exog.mean(0)[None, :]
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fv = res_poi.model._derivative_exog(p, ex)
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return np.squeeze(fv)
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nlp = NonlinearDeltaCov(f, res_poi.params, res_poi.cov_params())
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marg = res_poi.get_margeff(at='mean')
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# margeff excludes constant, last parameter in this case
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assert_allclose(nlp.se_vectorized()[:-1], marg.margeff_se, rtol=1e-13)
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assert_allclose(nlp.predicted()[:-1], marg.margeff, rtol=1e-13)
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nlpm = res_poi._get_wald_nonlinear(f)
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# margeff excludes constant, last parameter in this case
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assert_allclose(nlpm.se_vectorized()[:-1], marg.margeff_se, rtol=1e-13)
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assert_allclose(nlpm.predicted()[:-1], marg.margeff, rtol=1e-13)
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