400 lines
16 KiB
Python
400 lines
16 KiB
Python
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import io
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import os
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import pytest
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import numpy as np
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from numpy.testing import assert_allclose, assert_equal
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import pandas as pd
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import patsy
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from statsmodels.api import families
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from statsmodels.tools.sm_exceptions import (
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ValueWarning,
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)
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from statsmodels.othermod.betareg import BetaModel
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from .results import results_betareg as resultsb
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links = families.links
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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res_dir = os.path.join(cur_dir, "results")
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# betareg(I(food/income) ~ income + persons, data = FoodExpenditure)
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_income_estimates_mean = """\
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varname Estimate StdError zvalue Pr(>|z|)
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(Intercept) -0.62254806 0.223853539 -2.781051 5.418326e-03
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income -0.01229884 0.003035585 -4.051556 5.087819e-05
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persons 0.11846210 0.035340667 3.352005 8.022853e-04"""
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_income_estimates_precision = """\
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varname Estimate StdError zvalue Pr(>|z|)
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(phi) 35.60975 8.079598 4.407366 1.046351e-05
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"""
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_methylation_estimates_mean = """\
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varname Estimate StdError zvalue Pr(>|z|)
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(Intercept) 1.44224 0.03401 42.404 2e-16
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genderM 0.06986 0.04359 1.603 0.109
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CpGCpG_1 0.60735 0.04834 12.563 2e-16
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CpGCpG_2 0.97355 0.05311 18.331 2e-16"""
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_methylation_estimates_precision = """\
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varname Estimate StdError zvalue Pr(>|z|)
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(Intercept) 8.22829 1.79098 4.594 4.34e-06
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age -0.03471 0.03276 -1.059 0.289"""
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expected_income_mean = pd.read_table(
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io.StringIO(_income_estimates_mean), sep=r"\s+")
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expected_income_precision = pd.read_table(
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io.StringIO(_income_estimates_precision), sep=r"\s+")
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expected_methylation_mean = pd.read_table(
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io.StringIO(_methylation_estimates_mean), sep=r"\s+")
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expected_methylation_precision = pd.read_table(
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io.StringIO(_methylation_estimates_precision), sep=r"\s+")
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income = pd.read_csv(os.path.join(res_dir, 'foodexpenditure.csv'))
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methylation = pd.read_csv(os.path.join(res_dir, 'methylation-test.csv'))
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def check_same(a, b, eps, name):
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assert np.allclose(a, b, rtol=0.01, atol=eps), \
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("different from expected", name, list(a), list(b))
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def assert_close(a, b, eps):
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assert np.allclose(a, b, rtol=0.01, atol=eps), (list(a), list(b))
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class TestBetaModel:
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@classmethod
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def setup_class(cls):
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model = "I(food/income) ~ income + persons"
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cls.income_fit = BetaModel.from_formula(model, income).fit()
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model = cls.model = "methylation ~ gender + CpG"
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Z = cls.Z = patsy.dmatrix("~ age", methylation)
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mod = BetaModel.from_formula(model, methylation, exog_precision=Z,
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link_precision=links.Identity())
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cls.meth_fit = mod.fit()
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mod = BetaModel.from_formula(model, methylation, exog_precision=Z,
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link_precision=links.Log())
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cls.meth_log_fit = mod.fit()
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def test_income_coefficients(self):
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rslt = self.income_fit
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assert_close(rslt.params[:-1], expected_income_mean['Estimate'], 1e-3)
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assert_close(rslt.tvalues[:-1], expected_income_mean['zvalue'], 0.1)
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assert_close(rslt.pvalues[:-1], expected_income_mean['Pr(>|z|)'], 1e-3)
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def test_income_precision(self):
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rslt = self.income_fit
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# note that we have to exp the phi results for now.
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assert_close(np.exp(rslt.params[-1:]),
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expected_income_precision['Estimate'], 1e-3)
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# yield check_same, rslt.tvalues[-1:],
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# expected_income_precision['zvalue'], 0.1, "z-score"
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assert_close(rslt.pvalues[-1:],
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expected_income_precision['Pr(>|z|)'], 1e-3)
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def test_methylation_coefficients(self):
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rslt = self.meth_fit
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assert_close(rslt.params[:-2],
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expected_methylation_mean['Estimate'], 1e-2)
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assert_close(rslt.tvalues[:-2],
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expected_methylation_mean['zvalue'], 0.1)
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assert_close(rslt.pvalues[:-2],
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expected_methylation_mean['Pr(>|z|)'], 1e-2)
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def test_methylation_precision(self):
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# R results are from log link_precision
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rslt = self.meth_log_fit
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assert_allclose(rslt.params[-2:],
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expected_methylation_precision['Estimate'],
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atol=1e-5, rtol=1e-10)
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# expected_methylation_precision['Estimate']
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# yield check_same, links.logit()(rslt.params[-2:]),
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# expected_methylation_precision['Estimate'], 1e-3, "estimate"
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# yield check_same, rslt.tvalues[-2:],
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# expected_methylation_precision['zvalue'], 0.1, "z-score"
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def test_precision_formula(self):
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m = BetaModel.from_formula(self.model, methylation,
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exog_precision_formula='~ age',
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link_precision=links.Identity())
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rslt = m.fit()
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assert_close(rslt.params, self.meth_fit.params, 1e-10)
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assert isinstance(rslt.params, pd.Series)
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with pytest.warns(ValueWarning, match="unknown kwargs"):
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BetaModel.from_formula(self.model, methylation,
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exog_precision_formula='~ age',
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link_precision=links.Identity(),
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junk=False)
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def test_scores(self):
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model, Z = self.model, self.Z
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for link in (links.Identity(), links.Log()):
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mod2 = BetaModel.from_formula(model, methylation, exog_precision=Z,
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link_precision=link)
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rslt_m = mod2.fit()
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# evaluate away from optimum to get larger score
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analytical = rslt_m.model.score(rslt_m.params * 1.01)
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numerical = rslt_m.model._score_check(rslt_m.params * 1.01)
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assert_allclose(analytical, numerical, rtol=1e-6, atol=1e-6)
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assert_allclose(link.inverse(analytical[3:]),
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link.inverse(numerical[3:]), rtol=5e-7, atol=5e-6)
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def test_results_other(self):
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rslt = self.meth_fit
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distr = rslt.get_distribution()
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mean, var = distr.stats()
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assert_allclose(rslt.fittedvalues, mean, rtol=1e-13)
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assert_allclose(rslt.model._predict_var(rslt.params), var, rtol=1e-13)
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resid = rslt.model.endog - mean
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assert_allclose(rslt.resid, resid, rtol=1e-12)
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assert_allclose(rslt.resid_pearson, resid / np.sqrt(var), rtol=1e-12)
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class TestBetaMeth():
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@classmethod
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def setup_class(cls):
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formula = "methylation ~ gender + CpG"
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mod = BetaModel.from_formula(formula, methylation,
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exog_precision_formula="~ age",
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link_precision=links.Log())
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cls.res1 = mod.fit(cov_type="eim")
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cls.res2 = resultsb.results_meth
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def test_basic(self):
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res1 = self.res1
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res2 = self.res2
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k_mean = 4
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p, se, zv, pv = res2.table_mean.T
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assert_allclose(res1.params[:k_mean], p, rtol=1e-6)
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assert_allclose(res1.bse[:k_mean], se, rtol=1e-6)
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assert_allclose(res1.tvalues[:k_mean], zv, rtol=1e-6)
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assert_allclose(res1.pvalues[:k_mean], pv, rtol=1e-5)
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p, se, zv, pv = res2.table_precision.T
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assert_allclose(res1.params[k_mean:], p, rtol=1e-6)
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assert_allclose(res1.bse[k_mean:], se, rtol=1e-6)
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assert_allclose(res1.tvalues[k_mean:], zv, rtol=1e-6)
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assert_allclose(res1.pvalues[k_mean:], pv, rtol=1e-5)
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assert_allclose(res1.llf, res2.loglik, rtol=1e-10)
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assert_allclose(res1.aic, res2.aic, rtol=1e-10)
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assert_allclose(res1.bic, res2.bic, rtol=1e-10)
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# dofferent definitions for prsquared
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assert_allclose(res1.prsquared, res2.pseudo_r_squared, atol=0.01)
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assert_equal(res1.df_resid, res2.df_residual)
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assert_equal(res1.nobs, res2.nobs)
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# null model compared to R betareg and lmtest
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df_c = res1.df_resid_null - res1.df_resid
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assert_equal(res1.k_null, 2)
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# > lrt = lrtest(res_meth_null, res_meth) # results from R
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pv = 7.21872953868659e-18
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lln = 60.88809589492269
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llf = 104.14802840534323
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chisq = 86.51986502084107
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dfc = 4
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# stats.chi2.sf(86.51986502093865, 4)
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assert_equal(df_c, dfc)
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assert_allclose(res1.llf, llf, rtol=1e-10)
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assert_allclose(res1.llnull, lln, rtol=1e-10)
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assert_allclose(res1.llr, chisq, rtol=1e-10)
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assert_allclose(res1.llr_pvalue, pv, rtol=1e-6)
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def test_resid(self):
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res1 = self.res1
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res2 = self.res2
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assert_allclose(res1.fittedvalues, res2.resid['fittedvalues'],
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rtol=1e-8)
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assert_allclose(res1.resid, res2.resid['response'],
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atol=1e-8, rtol=1e-8)
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def test_oim(self):
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# estimate with default oim, cov_type nonrobust
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res1 = self.res1.model.fit()
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res2 = self.res2
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k_mean = 4
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# R betareg uses numerical derivatives from bfgs, has lower precision
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p, se, zv, pv = res2.table_mean_oim.T
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assert_allclose(res1.params[:k_mean], p, rtol=1e-6)
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assert_allclose(res1.bse[:k_mean], se, rtol=1e-5)
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assert_allclose(res1.tvalues[:k_mean], zv, rtol=1e-5)
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assert_allclose(res1.pvalues[:k_mean], pv, atol=1e-6, rtol=1e-5)
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p, se, zv, pv = res2.table_precision_oim.T
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assert_allclose(res1.params[k_mean:], p, rtol=1e-6)
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assert_allclose(res1.bse[k_mean:], se, rtol=1e-3)
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assert_allclose(res1.tvalues[k_mean:], zv, rtol=1e-3)
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assert_allclose(res1.pvalues[k_mean:], pv, rtol=1e-2)
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def test_predict_distribution(self):
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res1 = self.res1
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mean = res1.predict()
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var_ = res1.model._predict_var(res1.params)
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distr = res1.get_distribution()
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m2, v2 = distr.stats()
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assert_allclose(mean, m2, rtol=1e-13)
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assert_allclose(var_, v2, rtol=1e-13)
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# from R: > predict(res_meth, type="variance")
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var_r6 = [
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3.1090848852102e-04, 2.4509604000073e-04, 3.7199753140565e-04,
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2.8088261358738e-04, 2.7561111800350e-04, 3.3929220526847e-04]
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n = 6
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assert_allclose(v2[:n], var_r6, rtol=1e-7)
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ex = res1.model.exog[:n]
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ex_prec = res1.model.exog_precision[:n]
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mean6 = res1.predict(ex, transform=False)
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prec = res1.predict(which="precision")
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# todo: prec6 wrong exog if not used as keyword, no exception raised
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prec6 = res1.predict(exog_precision=ex_prec, which="precision",
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transform=False)
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var6 = res1.model._predict_var(res1.params, exog=ex,
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exog_precision=ex_prec)
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assert_allclose(mean6, mean[:n], rtol=1e-13)
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assert_allclose(prec6, prec[:n], rtol=1e-13)
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assert_allclose(var6, var_[:n], rtol=1e-13)
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assert_allclose(var6, var_r6, rtol=1e-7)
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distr6 = res1.model.get_distribution(res1.params,
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exog=ex, exog_precision=ex_prec)
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m26, v26 = distr6.stats()
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assert_allclose(m26, m2[:n], rtol=1e-13)
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assert_allclose(v26, v2[:n], rtol=1e-13)
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distr6f = res1.get_distribution(exog=ex, exog_precision=ex_prec,
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transform=False)
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m26, v26 = distr6f.stats()
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assert_allclose(m26, m2[:n], rtol=1e-13)
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assert_allclose(v26, v2[:n], rtol=1e-13)
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# check formula transform works for predict, currently mean only
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df6 = methylation.iloc[:6]
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mean6f = res1.predict(df6)
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# todo: prec6 wrong exog if not used as keyword, no exception raised
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# formula not supported for exog_precision in predict
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# prec6f = res1.predict(exog_precision=ex_prec, which="precision")
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assert_allclose(mean6f, mean[:n], rtol=1e-13)
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# assert_allclose(prec6f, prec[:n], rtol=1e-13)
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distr6f = res1.get_distribution(exog=df6, exog_precision=ex_prec)
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m26, v26 = distr6f.stats()
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assert_allclose(m26, m2[:n], rtol=1e-13)
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assert_allclose(v26, v2[:n], rtol=1e-13)
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# check that we don't have pandas in distr
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assert isinstance(distr6f.args[0], np.ndarray)
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# minimal checks for get_prediction
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pma = res1.get_prediction(which="mean", average=True)
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dfma = pma.summary_frame()
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assert_allclose(pma.predicted, mean.mean(), rtol=1e-13)
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assert_equal(dfma.shape, (1, 4))
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pm = res1.get_prediction(exog=df6, which="mean", average=False)
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|
|
dfm = pm.summary_frame()
|
||
|
|
assert_allclose(pm.predicted, mean6, rtol=1e-13)
|
||
|
|
assert_equal(dfm.shape, (6, 4))
|
||
|
|
pv = res1.get_prediction(exog=df6, exog_precision=ex_prec,
|
||
|
|
which="var", average=False)
|
||
|
|
dfv = pv.summary_frame()
|
||
|
|
assert_allclose(pv.predicted, var6, rtol=1e-13)
|
||
|
|
assert_equal(dfv.shape, (6, 4))
|
||
|
|
# smoke tests
|
||
|
|
res1.get_prediction(which="linear", average=False)
|
||
|
|
res1.get_prediction(which="precision", average=True)
|
||
|
|
res1.get_prediction(exog_precision=ex_prec, which="precision",
|
||
|
|
average=False)
|
||
|
|
res1.get_prediction(which="linear-precision", average=True)
|
||
|
|
|
||
|
|
# test agg_weights
|
||
|
|
pm = res1.get_prediction(exog=df6, which="mean", average=True)
|
||
|
|
dfm = pm.summary_frame()
|
||
|
|
aw = np.zeros(len(res1.model.endog))
|
||
|
|
aw[:6] = 1
|
||
|
|
aw /= aw.mean()
|
||
|
|
pm6 = res1.get_prediction(exog=df6, which="mean", average=True)
|
||
|
|
dfm6 = pm6.summary_frame()
|
||
|
|
pmw = res1.get_prediction(which="mean", average=True, agg_weights=aw)
|
||
|
|
dfmw = pmw.summary_frame()
|
||
|
|
assert_allclose(pmw.predicted, pm6.predicted, rtol=1e-13)
|
||
|
|
assert_allclose(dfmw, dfm6, rtol=1e-13)
|
||
|
|
|
||
|
|
|
||
|
|
class TestBetaIncome():
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
|
||
|
|
formula = "I(food/income) ~ income + persons"
|
||
|
|
exog_prec = patsy.dmatrix("~ persons", income)
|
||
|
|
mod_income = BetaModel.from_formula(
|
||
|
|
formula,
|
||
|
|
income,
|
||
|
|
exog_precision=exog_prec,
|
||
|
|
link_precision=links.Log()
|
||
|
|
)
|
||
|
|
res_income = mod_income.fit(method="newton")
|
||
|
|
|
||
|
|
mod_restricted = BetaModel.from_formula(
|
||
|
|
formula,
|
||
|
|
income,
|
||
|
|
link_precision=links.Log()
|
||
|
|
)
|
||
|
|
res_restricted = mod_restricted.fit(method="newton")
|
||
|
|
|
||
|
|
cls.res1 = res_income
|
||
|
|
cls.resr = res_restricted
|
||
|
|
|
||
|
|
def test_score_test(self):
|
||
|
|
res1 = self.res1
|
||
|
|
resr = self.resr
|
||
|
|
params_restr = np.concatenate([resr.params, [0]])
|
||
|
|
r_matrix = np.zeros((1, len(params_restr)))
|
||
|
|
r_matrix[0, -1] = 1
|
||
|
|
exog_prec_extra = res1.model.exog_precision[:, 1:]
|
||
|
|
|
||
|
|
from statsmodels.base._parameter_inference import score_test
|
||
|
|
sc1 = score_test(res1, params_constrained=params_restr,
|
||
|
|
k_constraints=1)
|
||
|
|
sc2 = score_test(resr, exog_extra=(None, exog_prec_extra))
|
||
|
|
assert_allclose(sc2[:2], sc1[:2])
|
||
|
|
|
||
|
|
sc1_hc = score_test(res1, params_constrained=params_restr,
|
||
|
|
k_constraints=1, r_matrix=r_matrix, cov_type="HC0")
|
||
|
|
sc2_hc = score_test(resr, exog_extra=(None, exog_prec_extra),
|
||
|
|
cov_type="HC0")
|
||
|
|
assert_allclose(sc2_hc[:2], sc1_hc[:2])
|
||
|
|
|
||
|
|
def test_influence(self):
|
||
|
|
# currently only smoke test
|
||
|
|
res1 = self.res1
|
||
|
|
from statsmodels.stats.outliers_influence import MLEInfluence
|
||
|
|
|
||
|
|
influ0 = MLEInfluence(res1)
|
||
|
|
influ = res1.get_influence()
|
||
|
|
attrs = ['cooks_distance', 'd_fittedvalues', 'd_fittedvalues_scaled',
|
||
|
|
'd_params', 'dfbetas', 'hat_matrix_diag', 'resid_studentized'
|
||
|
|
]
|
||
|
|
for attr in attrs:
|
||
|
|
getattr(influ, attr)
|
||
|
|
|
||
|
|
frame = influ.summary_frame()
|
||
|
|
frame0 = influ0.summary_frame()
|
||
|
|
assert_allclose(frame, frame0, rtol=1e-13, atol=1e-13)
|