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: Calculate the power of a F-test for one factor ANOVA.
  
  Parameters
  ----------
  effect_size : float
      standardized effect size. The effect size is here Cohen's f, square
      root of "f2".
  nobs : int or float
      sample size, number of observations.
  alpha : float in interval (0,1)
      significance level, e.g. 0.05, is the probability of a type I
      error, that is wrong rejections if the Null Hypothesis is true.
  k_groups : int or float
      number of groups in the ANOVA or k-sample comparison. Default is 2.
  
  Returns
  -------
  power : float
      Power of the test, e.g. 0.8, is one minus the probability of a
      type II error. Power is the probability that the test correctly
      rejects the Null Hypothesis if the Alternative Hypothesis is true.
docstr_tok: ["Calculate","the","power","of","a","F-test","for","one","factor","ANOVA",".","Parameters","--","--","--","--","--","effect_size",":","float","standardized","effect","size",".","The","effect","size","is","here","Cohen","'s","f",",","square","root","of","``","f2","''",".","nobs",":","int","or","float","sample","size",",","number","of","observations",".","alpha",":","float","in","interval","(","0,1",")","significance","level",",","e.g",".","0.05",",","is","the","probability","of","a","type","I","error",",","that","is","wrong","rejections","if","the","Null","Hypothesis","is","true",".","k_groups",":","int","or","float","number","of","groups","in","the","ANOVA","or","k-sample","comparison",".","Default","is","2",".","Returns","--","--","--","-","power",":","float","Power","of","the","test",",","e.g",".","0.8",",","is","one","minus","the","probability","of","a","type","II","error",".","Power","is","the","probability","that","the","test","correctly","rejects","the","Null","Hypothesis","if","the","Alternative","Hypothesis","is","true","."]
code: def power(self, effect_size, nobs, alpha, k_groups=2):
      """Calculate the power of a F-test for one factor ANOVA.
  
      Parameters
      ----------
      effect_size : float
          standardized effect size. The effect size is here Cohen's f, square
          root of "f2".
      nobs : int or float
          sample size, number of observations.
      alpha : float in interval (0,1)
          significance level, e.g. 0.05, is the probability of a type I
          error, that is wrong rejections if the Null Hypothesis is true.
      k_groups : int or float
          number of groups in the ANOVA or k-sample comparison. Default is 2.
  
      Returns
      -------
      power : float
          Power of the test, e.g. 0.8, is one minus the probability of a
          type II error. Power is the probability that the test correctly
          rejects the Null Hypothesis if the Alternative Hypothesis is true.
  
      """
      return ftest_anova_power(
          effect_size, nobs, alpha, k_groups=k_groups
      )
  
imports: import warnings
  import numpy
  from scipy import stats, optimize, special
  from statsmodels.tools.rootfinding import brentq_expanding
label_desc: Use image node_id 1 for calling the FTestAnovaPower obj's underlying member method code with example usage: obj.power(effect_size, nobs, alpha, k_groups) and returns: ftest_anova_power