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: Forest plot with means and confidence intervals
  
  Parameters
  ----------
  ax : None or matplotlib axis instance
      If ax is provided, then the plot will be added to it.
  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.
  use_exp : bool
      If `use_exp` is True, then the effect size and confidence limits
      will be exponentiated. This transform log-odds-ration into
      odds-ratio, and similarly for risk-ratio.
  ax : AxesSubplot, optional
      If given, this axes is used to plot in instead of a new figure
      being created.
  kwds : optional keyword arguments
      Keywords are forwarded to the dot_plot function that creates the
      plot.
  
  Returns
  -------
  fig : Matplotlib figure instance
  
  See Also
  --------
  dot_plot
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code: def plot_forest(
      self, alpha=0.05, use_t=None, use_exp=False, ax=None, **kwds
  ):
      """Forest plot with means and confidence intervals
  
      Parameters
      ----------
      ax : None or matplotlib axis instance
          If ax is provided, then the plot will be added to it.
      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.
      use_exp : bool
          If `use_exp` is True, then the effect size and confidence limits
          will be exponentiated. This transform log-odds-ration into
          odds-ratio, and similarly for risk-ratio.
      ax : AxesSubplot, optional
          If given, this axes is used to plot in instead of a new figure
          being created.
      kwds : optional keyword arguments
          Keywords are forwarded to the dot_plot function that creates the
          plot.
  
      Returns
      -------
      fig : Matplotlib figure instance
  
      See Also
      --------
      dot_plot
  
      """
      from statsmodels.graphics.dotplots import dot_plot
  
      res_df = self.summary_frame(alpha=alpha, use_t=use_t)
      if use_exp:
          res_df = np.exp(res_df[["eff", "ci_low", "ci_upp"]])
      hw = np.abs(res_df[["ci_low", "ci_upp"]] - res_df[["eff"]].values)
      fig = dot_plot(
          points=res_df["eff"],
          intervals=hw,
          lines=res_df.index,
          line_order=res_df.index,
          **kwds,
      )
      return fig
  
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 7 for calling the CombineResults obj's underlying member method code with example usage: obj.plot_forest(alpha, use_t, use_exp, ax) and returns: fig