class_docstr: Results for rank comparison
  
  This is a subclass of HolderTuple that includes results from intermediate
  computations, as well as methods for hypothesis tests, confidence intervals
  and summary.
class_docstr_tok: ["Results","for","rank","comparison","This","is","a","subclass","of","HolderTuple","that","includes","results","from","intermediate","computations",",","as","well","as","methods","for","hypothesis","tests",",","confidence","intervals","and","summary","."]
docstr: Confidence interval for probability that sample 1 has larger values
  
  Confidence interval is for the shifted probability
  
      P(x1 > x2) + 0.5 * P(x1 = x2) - value
  
  Parameters
  ----------
  value : float
      Value, default 0, shifts the confidence interval,
      e.g. ``value=0.5`` centers the confidence interval at zero.
  alpha : float
      Significance level for the confidence interval, coverage is
      ``1-alpha``
  alternative : str
      The alternative hypothesis, H1, has to be one of the following
  
         * 'two-sided' : H1: ``prob - value`` not equal to 0.
         * 'larger' :   H1: ``prob - value > 0``
         * 'smaller' :  H1: ``prob - value < 0``
  
  Returns
  -------
  lower : float or ndarray
      Lower confidence limit. This is -inf for the one-sided alternative
      "smaller".
  upper : float or ndarray
      Upper confidence limit. This is inf for the one-sided alternative
      "larger".
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code: def conf_int(self, value=None, alpha=0.05, alternative="two-sided"):
      """
      Confidence interval for probability that sample 1 has larger values
  
      Confidence interval is for the shifted probability
  
          P(x1 > x2) + 0.5 * P(x1 = x2) - value
  
      Parameters
      ----------
      value : float
          Value, default 0, shifts the confidence interval,
          e.g. ``value=0.5`` centers the confidence interval at zero.
      alpha : float
          Significance level for the confidence interval, coverage is
          ``1-alpha``
      alternative : str
          The alternative hypothesis, H1, has to be one of the following
  
             * 'two-sided' : H1: ``prob - value`` not equal to 0.
             * 'larger' :   H1: ``prob - value > 0``
             * 'smaller' :  H1: ``prob - value < 0``
  
      Returns
      -------
      lower : float or ndarray
          Lower confidence limit. This is -inf for the one-sided alternative
          "smaller".
      upper : float or ndarray
          Upper confidence limit. This is inf for the one-sided alternative
          "larger".
  
      """
  
      p0 = value
      if p0 is None:
          p0 = 0
      diff = self.prob1 - p0
      std_diff = np.sqrt(self.var / self.nobs)
  
      if self.use_t is False:
          return _zconfint_generic(diff, std_diff, alpha, alternative)
      else:
          return _tconfint_generic(
              diff, std_diff, self.df, alpha, alternative
          )
  
file_path: statsmodels/statsmodels/stats/nonparametric.py
imports: import numpy
  from scipy import stats
  from scipy.stats import rankdata
  from statsmodels.stats.base import HolderTuple
  from statsmodels.stats.weightstats import _tconfint_generic, _tstat_generic, _zconfint_generic, _zstat_generic
label_desc: Use image node_id 1 for calling the RankCompareResult obj's underlying member method code with example usage: obj.conf_int(value, alpha, alternative) and returns: _zconfint_generic, _tconfint_generic