class_docstr: Results from combined estimate of means or effect sizes
  
  This currently includes intermediate results that might be removed
class_docstr_tok: ["Results","from","combined","estimate","of","means","or","effect","sizes","This","currently","includes","intermediate","results","that","might","be","removed"]
docstr: Create DataFrame with sample statistics and mean estimates
  
  Parameters
  ----------
  alpha : float in (0, 1)
      Significance level for confidence interval. Nominal coverage is
      ``1 - alpha``.
  use_t : None or bool
      If use_t is None, then the attribute `use_t` determines whether
      normal or t-distribution is used for confidence intervals.
      Specifying use_t overrides the attribute.
      If use_t is false, then confidence intervals are based on the
      normal distribution. If it is true, then the t-distribution is
      used.
  
  Returns
  -------
  res : DataFrame
      pandas DataFrame instance with columns
      ['eff', "sd_eff", "ci_low", "ci_upp", "w_fe","w_re"].
      Rows include statistics for samples and estimates of overall mean.
docstr_tok: ["Create","DataFrame","with","sample","statistics","and","mean","estimates","Parameters","--","--","--","--","--","alpha",":","float","in","(","0",",","1",")","Significance","level","for","confidence","interval",".","Nominal","coverage","is","``","1","-","alpha","``",".","use_t",":","None","or","bool","If","use_t","is","None",",","then","the","attribute","`","use_t","`","determines","whether","normal","or","t-distribution","is","used","for","confidence","intervals",".","Specifying","use_t","overrides","the","attribute",".","If","use_t","is","false",",","then","confidence","intervals","are","based","on","the","normal","distribution",".","If","it","is","true",",","then","the","t-distribution","is","used",".","Returns","--","--","--","-","res",":","DataFrame","pandas","DataFrame","instance","with","columns","[","'eff","'",",","``","sd_eff","''",",","``","ci_low","''",",","``","ci_upp","''",",","``","w_fe","''",",","''","w_re","''","]",".","Rows","include","statistics","for","samples","and","estimates","of","overall","mean","."]
code: def summary_frame(self, alpha=0.05, use_t=None):
      """Create DataFrame with sample statistics and mean estimates
  
      Parameters
      ----------
      alpha : float in (0, 1)
          Significance level for confidence interval. Nominal coverage is
          ``1 - alpha``.
      use_t : None or bool
          If use_t is None, then the attribute `use_t` determines whether
          normal or t-distribution is used for confidence intervals.
          Specifying use_t overrides the attribute.
          If use_t is false, then confidence intervals are based on the
          normal distribution. If it is true, then the t-distribution is
          used.
  
      Returns
      -------
      res : DataFrame
          pandas DataFrame instance with columns
          ['eff', "sd_eff", "ci_low", "ci_upp", "w_fe","w_re"].
          Rows include statistics for samples and estimates of overall mean.
  
      """
      if use_t is None:
          use_t = self.use_t
      labels = list(self.row_names) + [
          "fixed effect",
          "random effect",
          "fixed effect wls",
          "random effect wls",
      ]
      res, col_names = self.summary_array(alpha=alpha, use_t=use_t)
      results = pd.DataFrame(res, index=labels, columns=col_names)
      return results
  
file_path: statsmodels/statsmodels/stats/meta_analysis.py
imports: import numpy
  import pandas
  from scipy import stats
  from statsmodels.stats.base import HolderTuple
label_desc: Use image node_id 6 for calling the CombineResults obj's underlying member method code with example usage: obj.summary_frame(alpha, use_t) and returns: results