class_docstr: Results for Cohen's kappa
  
  Attributes
  ----------
  kappa : cohen's kappa
  var_kappa : variance of kappa
  std_kappa : standard deviation of kappa
  alpha : one-sided probability for confidence interval
  kappa_low : lower (1-alpha) confidence limit
  kappa_upp : upper (1-alpha) confidence limit
  var_kappa0 : variance of kappa under H0: kappa=0
  std_kappa0 : standard deviation of kappa under H0: kappa=0
  z_value : test statistic for H0: kappa=0, is standard normal distributed
  pvalue_one_sided : one sided p-value for H0: kappa=0 and H1: kappa>0
  pvalue_two_sided : two sided p-value for H0: kappa=0 and H1: kappa!=0
  distribution_kappa : asymptotic normal distribution of kappa
  distribution_zero_null : asymptotic normal distribution of kappa under
      H0: kappa=0
  
  The confidence interval for kappa and the statistics for the test of
  H0: kappa=0 are based on the asymptotic normal distribution of kappa.
code: def _initialize(self):
      if "alpha" not in self:
          self["alpha"] = 0.025
          self["alpha_ci"] = _int_ifclose(100 - 0.025 * 200)[1]
  
      self["std_kappa"] = np.sqrt(self["var_kappa"])
      self["std_kappa0"] = np.sqrt(self["var_kappa0"])
  
      self["z_value"] = self["kappa"] / self["std_kappa0"]
  
      self["pvalue_one_sided"] = stats.norm.sf(self["z_value"])
      self["pvalue_two_sided"] = (
          stats.norm.sf(np.abs(self["z_value"])) * 2
      )
  
      delta = stats.norm.isf(self["alpha"]) * self["std_kappa"]
      self["kappa_low"] = self["kappa"] - delta
      self["kappa_upp"] = self["kappa"] + delta
      self["distribution_kappa"] = stats.norm(
          loc=self["kappa"], scale=self["std_kappa"]
      )
      self["distribution_zero_null"] = stats.norm(
          loc=0, scale=self["std_kappa0"]
      )
  
file_path: statsmodels/statsmodels/stats/inter_rater.py
imports: import numpy
  from scipy import stats
label_desc: Use image node_id 1 for calling the KappaResults obj's underlying member method code with example usage: obj._initialize() without return types