class_docstr: class for trimmed and winsorized one sample statistics
  
  axis is None, i.e. ravelling, is not supported
  
  Parameters
  ----------
  data : array-like
      The data, observations to analyze.
  fraction : float in (0, 0.5)
      The fraction of observations to trim at each tail.
      The number of observations trimmed at each tail is
      ``int(fraction * nobs)``
  is_sorted : boolean
      Indicator if data is already sorted. By default the data is sorted
      along ``axis``.
  axis : int
      The axis of reduce operations. By default axis=0, that is observations
      are along the zero dimension, i.e. rows if 2-dim.
docstr: One sample t-test for trimmed or Winsorized mean
  
  Parameters
  ----------
  value : float
      Value of the mean under the Null hypothesis
  transform : {'trimmed', 'winsorized'}
      Specified whether the mean test is based on trimmed or winsorized
      data.
  alternative : {'two-sided', 'larger', 'smaller'}
  
  
  Notes
  -----
  p-value is based on the approximate t-distribution of the test
  statistic. The approximation is valid if the underlying distribution
  is symmetric.
docstr_tok: ["One","sample","t-test","for","trimmed","or","Winsorized","mean","Parameters","--","--","--","--","--","value",":","float","Value","of","the","mean","under","the","Null","hypothesis","transform",":","{","'trimmed","'",",","'winsorized","'","}","Specified","whether","the","mean","test","is","based","on","trimmed","or","winsorized","data",".","alternative",":","{","'two-sided","'",",","'larger","'",",","'smaller","'","}","Notes","--","--","-","p-value","is","based","on","the","approximate","t-distribution","of","the","test","statistic",".","The","approximation","is","valid","if","the","underlying","distribution","is","symmetric","."]
code: def ttest_mean(
      self, value=0, transform="trimmed", alternative="two-sided"
  ):
      """
      One sample t-test for trimmed or Winsorized mean
  
      Parameters
      ----------
      value : float
          Value of the mean under the Null hypothesis
      transform : {'trimmed', 'winsorized'}
          Specified whether the mean test is based on trimmed or winsorized
          data.
      alternative : {'two-sided', 'larger', 'smaller'}
  
  
      Notes
      -----
      p-value is based on the approximate t-distribution of the test
      statistic. The approximation is valid if the underlying distribution
      is symmetric.
      """
      import statsmodels.stats.weightstats as smws
  
      df = self.nobs_reduced - 1
      if transform == "trimmed":
          mean_ = self.mean_trimmed
          std_ = self.std_mean_trimmed
      elif transform == "winsorized":
          mean_ = self.mean_winsorized
          std_ = self.std_mean_winsorized
      else:
          raise ValueError(
              "transform can only be 'trimmed' or 'winsorized'"
          )
  
      res = smws._tstat_generic(
          mean_, 0, std_, df, alternative=alternative, diff=value
      )
      return res + (df,)
  
file_path: statsmodels/statsmodels/stats/robust_compare.py
imports: import numbers
  import numpy
label_desc: Use image node_id 9 for calling the TrimmedMean obj's underlying member method code with example usage: obj.ttest_mean(value, transform, alternative) and returns: unknown