class_docstr: Statistical Power calculations F-test for one factor balanced ANOVA
  
  This is based on Cohen's f as effect size measure.
  
  See Also
  --------
  statsmodels.stats.oneway.effectsize_oneway
class_docstr_tok: ["Statistical","Power","calculations","F-test","for","one","factor","balanced","ANOVA","This","is","based","on","Cohen","'s","f","as","effect","size","measure",".","See","Also","--","--","--","--","statsmodels.stats.oneway.effectsize_oneway"]
docstr: experimental, test failure in solve_power for effect_size
          
docstr_tok: ["experimental",",","test","failure","in","solve_power","for","effect_size"]
code: def _solve_effect_size(
      self,
      effect_size=None,
      nobs=None,
      alpha=None,
      power=None,
      k_groups=2,
  ):
      """experimental, test failure in solve_power for effect_size"""
  
      def func(x):
          effect_size = x
          return self._power_identity(
              effect_size=effect_size,
              nobs=nobs,
              alpha=alpha,
              k_groups=k_groups,
              power=power,
          )
  
      val, r = optimize.brentq(func, 1e-8, 1 - 1e-8, full_output=True)
      if not r.converged:
          print(r)
      return val
  
imports: import warnings
  import numpy
  from scipy import stats, optimize, special
  from statsmodels.tools.rootfinding import brentq_expanding
label_desc: Use image node_id 3 for calling the FTestAnovaPower obj's underlying member method code with example usage: obj._solve_effect_size(effect_size, nobs, alpha, power, k_groups) and returns: val, self