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: confidence interval for the overall mean estimate
  
  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
  -------
  ci_eff_fe : tuple of floats
      Confidence interval for mean effects size based on fixed effects
      model with scale=1.
  ci_eff_re : tuple of floats
      Confidence interval for mean effects size based on random effects
      model with scale=1
  ci_eff_fe_wls : tuple of floats
      Confidence interval for mean effects size based on fixed effects
      model with estimated scale corresponding to WLS, ie. HKSJ.
  ci_eff_re_wls : tuple of floats
      Confidence interval for mean effects size based on random effects
      model with estimated scale corresponding to WLS, ie. HKSJ.
      If random effects method is fully iterated, i.e. Paule-Mandel, then
      the estimated scale is 1.
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code: def conf_int(self, alpha=0.05, use_t=None):
      """confidence interval for the overall mean estimate
  
      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
      -------
      ci_eff_fe : tuple of floats
          Confidence interval for mean effects size based on fixed effects
          model with scale=1.
      ci_eff_re : tuple of floats
          Confidence interval for mean effects size based on random effects
          model with scale=1
      ci_eff_fe_wls : tuple of floats
          Confidence interval for mean effects size based on fixed effects
          model with estimated scale corresponding to WLS, ie. HKSJ.
      ci_eff_re_wls : tuple of floats
          Confidence interval for mean effects size based on random effects
          model with estimated scale corresponding to WLS, ie. HKSJ.
          If random effects method is fully iterated, i.e. Paule-Mandel, then
          the estimated scale is 1.
  
      """
      if use_t is None:
          use_t = self.use_t
  
      if use_t is False:
          crit = stats.norm.isf(alpha / 2)
      else:
          crit = stats.t.isf(alpha / 2, self.df_resid)
  
      sgn = np.asarray([-1, 1])
      m_fe = self.mean_effect_fe
      m_re = self.mean_effect_re
      ci_eff_fe = m_fe + sgn * crit * self.sd_eff_w_fe
      ci_eff_re = m_re + sgn * crit * self.sd_eff_w_re
  
      ci_eff_fe_wls = m_fe + sgn * crit * np.sqrt(self.var_hksj_fe)
      ci_eff_re_wls = m_re + sgn * crit * np.sqrt(self.var_hksj_re)
  
      return ci_eff_fe, ci_eff_re, ci_eff_fe_wls, ci_eff_re_wls
  
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 3 for calling the CombineResults obj's underlying member method code with example usage: obj.conf_int(alpha, use_t) and returns: ci_eff_fe,  ci_eff_re,  ci_eff_fe_wls,  ci_eff_re_wls