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 of a linear transformation of prob1
  
  This computes the confidence interval for
  
      d = const + slope * prob1
  
  Default values correspond to Somers' d.
  
  Parameters
  ----------
  const, slope : float
      Constant and slope for linear (affine) transformation.
  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 confint_lintransf(
      self, const=-1, slope=2, alpha=0.05, alternative="two-sided"
  ):
      """confidence interval of a linear transformation of prob1
  
      This computes the confidence interval for
  
          d = const + slope * prob1
  
      Default values correspond to Somers' d.
  
      Parameters
      ----------
      const, slope : float
          Constant and slope for linear (affine) transformation.
      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".
  
      """
  
      low_p, upp_p = self.conf_int(alpha=alpha, alternative=alternative)
      low = const + slope * low_p
      upp = const + slope * upp_p
      if slope < 0:
          low, upp = upp, low
      return low, upp
  
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 4 for calling the RankCompareResult obj's underlying member method code with example usage: obj.confint_lintransf(const, slope, alpha, alternative) and returns: low,  upp