code: def test_dietox_slopes(self):
      # dietox data from geepack using random intercepts
      #
      # Fit in R using
      #
      # library(geepack)
      # r = lmer(Weight ~ Time + (1 + Time | Pig), data=dietox)
      # r = lmer(Weight ~ Time + (1 + Time | Pig), REML=FALSE, data=dietox)
      #
      # Comments below are the R code used to extract the constants
      # for comparison.
  
      cur_dir = os.path.dirname(os.path.abspath(__file__))
      rdir = os.path.join(cur_dir, "results")
      fname = os.path.join(rdir, "dietox.csv")
  
      # REML
      data = pd.read_csv(fname)
      model = MixedLM.from_formula(
          "Weight ~ Time",
          groups="Pig",
          re_formula="1 + Time",
          data=data,
      )
      result = model.fit(method="cg")
  
      # fixef(r)
      assert_allclose(
          result.fe_params, np.r_[15.738650, 6.939014], rtol=1e-5
      )
  
      # sqrt(diag(vcov(r)))
      assert_allclose(
          result.bse[0:2], np.r_[0.5501253, 0.0798254], rtol=1e-3
      )
  
      # attr(VarCorr(r), "sc")^2
      assert_allclose(result.scale, 6.03745, rtol=1e-3)
  
      # as.numeric(VarCorr(r)[[1]])
      assert_allclose(
          result.cov_re.values.ravel(),
          np.r_[19.4934552, 0.2938323, 0.2938323, 0.4160620],
          rtol=1e-1,
      )
  
      # logLik(r)
      assert_allclose(
          model.loglike(result.params_object), -2217.047, rtol=1e-5
      )
  
      # ML
      data = pd.read_csv(fname)
      model = MixedLM.from_formula(
          "Weight ~ Time",
          groups="Pig",
          re_formula="1 + Time",
          data=data,
      )
      result = model.fit(method="cg", reml=False)
  
      # fixef(r)
      assert_allclose(
          result.fe_params, np.r_[15.73863, 6.93902], rtol=1e-5
      )
  
      # sqrt(diag(vcov(r)))
      assert_allclose(
          result.bse[0:2], np.r_[0.54629282, 0.07926954], rtol=1e-3
      )
  
      # attr(VarCorr(r), "sc")^2
      assert_allclose(result.scale, 6.037441, rtol=1e-3)
  
      #  as.numeric(VarCorr(r)[[1]])
      assert_allclose(
          result.cov_re.values.ravel(),
          np.r_[19.190922, 0.293568, 0.293568, 0.409695],
          rtol=1e-2,
      )
  
      # logLik(r)
      assert_allclose(
          model.loglike(result.params_object), -2215.753, rtol=1e-5
      )
  
imports: from statsmodels.compat.platform import PLATFORM_OSX
  import os
  import csv
  import warnings
  import numpy
  import pandas
  from scipy import sparse
  import pytest
  from statsmodels.regression.mixed_linear_model import MixedLM, MixedLMParams, _smw_solver, _smw_logdet
  from numpy.testing import assert_almost_equal, assert_equal, assert_allclose, assert_
  from statsmodels.base import _penalties
  import statsmodels.tools.numdiff
  from .results import lme_r_results
label_desc: Use image node_id 10 for calling the TestMixedLM obj's underlying member method code with example usage: obj.test_dietox_slopes() without return types