class_docstr: Results obtained from Stata 11
class_docstr_tok: ["Results","obtained","from","Stata","11"]
code: def poisson():
      obj = Namespace()
      obj.nobs = 20190
      obj.params = [
          -0.052535114675,
          -0.247086797633,
          0.035290201794,
          -0.03457750643,
          0.271713973711,
          0.033941474461,
          -0.012635035534,
          0.054056326828,
          0.206115121809,
          0.700352877227,
      ]
      obj.cov_params = None
      obj.bse = [
          0.00288398915279,
          0.01061725196728,
          0.00182833684966,
          0.00161284852954,
          0.01223913844387,
          0.00056476496963,
          0.00925061122826,
          0.01530987068312,
          0.02627928267502,
          0.01116266712362,
      ]
      predict = np.loadtxt(
          os.path.join(cur_dir, "yhat_poisson.csv"), delimiter=","
      )
      obj.phat = predict[:, 0]
      obj.yhat = predict[:, 1]
      obj.llf = -62419.588535018
      obj.llnull = -66647.181687959
      obj.df_model = 9
      obj.df_resid = obj.nobs - obj.df_model - 1
      obj.llr = 8455.186305881856
      obj.prsquared = 0.0634324369893758
      obj.llr_pvalue = 0
      obj.aic = 124859.17707
      obj.bic = 124938.306497
      obj.z = [
          -18.21612769,
          -23.27219872,
          19.30180524,
          -21.43878101,
          22.20041672,
          60.09840604,
          -1.36585953,
          3.53081538,
          7.84325525,
          62.74063980,
      ]
      obj.conf_int = [
          [-0.0581876, -0.0468826],
          [-0.2678962, -0.2262774],
          [0.0317067, 0.0388737],
          [-0.0377386, -0.0314164],
          [0.2477257, 0.2957022],
          [0.0328346, 0.0350484],
          [-0.0307659, 0.0054958],
          [0.0240495, 0.0840631],
          [0.1546087, 0.2576216],
          [0.6784745, 0.7222313],
      ]
      obj.pvalues = [
          3.84415e-74,
          8.4800e-120,
          5.18652e-83,
          5.8116e-102,
          3.4028e-109,
          0,
          0.1719830562,
          0.0004142808,
          4.39014e-15,
          0,
      ]
  
      # from stata
      # use margins and put i. in front of dummies
      obj.margeff_dummy_overall = [
          -0.15027280560599,
          -0.66568074771099,
          0.10094500919706,
          -0.09890639687842,
          0.77721770295360,
          0.09708707452600,
          -0.03608195237609,
          0.15804581481115,
          0.65104087597053,
      ]
      obj.margeff_dummy_overall_se = [
          0.008273103,
          0.0269856266,
          0.0052466639,
          0.0046317555,
          0.0351582169,
          0.0016652181,
          0.0263736472,
          0.0457480115,
          0.0913901155,
      ]
  
      # just use margins
      obj.margeff_nodummy_overall = [
          -0.15027280560599,
          -0.70677348928158,
          0.10094500919705,
          -0.09890639687842,
          0.77721770295359,
          0.09708707452600,
          -0.03614158359367,
          0.15462412033340,
          0.58957704430148,
      ]
      obj.margeff_nodummy_overall_se = [
          0.008273103,
          0.0305119343,
          0.0052466639,
          0.0046317555,
          0.0351582168,
          0.0016652181,
          0.0264611158,
          0.0437974779,
          0.0752099666,
      ]
      # taken from gretl
      obj.resid = np.loadtxt(
          os.path.join(cur_dir, "poisson_resid.csv"), delimiter=","
      )
      return obj
  
imports: import os
  import numpy
label_desc: Use image node_id 2 for calling the RandHIE obj's underlying member method code with example usage: obj.poisson() and returns: obj