class_docstr: Results obtained from Stata 11
class_docstr_tok: ["Results","obtained","from","Stata","11"]
code: def negativebinomial_nb2_bfgs():
      # R 2.15.1 MASS 7.3-22 glm.nb()
      obj = Namespace()
      obj.nobs = 20190
      obj.params = [
          -0.0579469537244314,
          -0.267787718814838,
          0.0412060770911646,
          -0.0381376804392121,
          0.268915772213171,
          0.0381637446219235,
          -0.0441338846217674,
          0.0172521803400544,
          0.177960787443151,
          0.663556087183864,
          # lnalpha from stata
          1.292953339909746,
      ]
      # alpha and stderr from stata
      obj.lnalpha_std_err = 0.0143932
      obj.lnalpha = 0.256929012449
      obj.bse = [
          0.00607085853920512,
          0.0226125368090765,
          0.00405542008282773,
          0.00344455937127785,
          0.0298855063286547,
          0.00142421904710063,
          0.0199374393307107,
          0.0358416931939136,
          0.0741013728607101,
          0.0250354082637892,
          # from stata
          0.0186098,
      ]
      obj.z = [
          -9.54510030998327,
          -11.8424447940467,
          10.1607419822296,
          -11.071860382846,
          8.99820030672628,
          26.7962605187844,
          -2.21361850384595,
          0.481343898758222,
          2.40158556546135,
          26.5047040652267,
      ]
      obj.pvalues = [
          1.35975947860026e-21,
          2.35486776488278e-32,
          2.96808970292151e-24,
          1.71796558863781e-28,
          2.2944789508802e-19,
          3.57231639404726e-158,
          0.0268550333379416,
          0.630272102021494,
          0.0163241908407114,
          8.55476622951356e-155,
      ]
      obj.fittedvalues = [
          0.892904166867786,
          0.892904166867786,
          0.892904166867786,
          0.892904166867786,
          0.892904166867786,
          0.937038051489553,
          0.937038051489553,
          0.937038051489553,
          0.937038051489553,
          0.937038051489553,
      ]
      # obj.aic = 86789.3241530713    # This is what R reports
      obj.aic = 86789.32415307125484  # from Stata
      obj.df_resid = 20180
      obj.df_model = 9
      # R conf_int: 1.96 * bse, not profile likelihood via R's confint()
      obj.conf_int = [
          # from Stata
          [-0.0698826, -0.0460113],
          [-0.3122654, -0.2233101],
          [0.0330781, 0.049334],
          [-0.0448006, -0.0314748],
          [0.2102246, 0.3276069],
          [0.0352959, 0.0410316],
          [-0.0834356, -0.0048321],
          [-0.0535908, 0.0880951],
          [0.0324115, 0.3235101],
          [0.6150055, 0.7121067],
          # from Stata
          [1.256989, 1.329947],
      ]
      obj.bic = 86876.36652289562335  # stata
      obj.llnull = -44199.27443563430279  # stata
      obj.llr = 1631.224718197351  # stata
      obj.llf = -43383.66207653563  # stata
      obj.df_model = 9.0
      obj.llr_pvalue = 0.0
      return obj
  
imports: import os
  import numpy
label_desc: Use image node_id 3 for calling the RandHIE obj's underlying member method code with example usage: obj.negativebinomial_nb2_bfgs() and returns: obj