class_docstr: Statistical Power calculations for t-test for two independent sample
  
  currently only uses pooled variance
class_docstr_tok: ["Statistical","Power","calculations","for","t-test","for","two","independent","sample","currently","only","uses","pooled","variance"]
docstr: Calculate the power of a t-test for two independent sample
  
  Parameters
  ----------
  effect_size : float
      standardized effect size, difference between the two means divided
      by the standard deviation. `effect_size` has to be positive.
  nobs1 : int or float
      number of observations of sample 1. The number of observations of
      sample two is ratio times the size of sample 1,
      i.e. ``nobs2 = nobs1 * ratio``
  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.
  ratio : float
      ratio of the number of observations in sample 2 relative to
      sample 1. see description of nobs1
      The default for ratio is 1; to solve for ratio given the other
      arguments, it has to be explicitly set to None.
  df : int or float
      degrees of freedom. By default this is None, and the df from the
      ttest with pooled variance is used, ``df = (nobs1 - 1 + nobs2 - 1)``
  alternative : str, 'two-sided' (default), 'larger', 'smaller'
      extra argument to choose whether the power is calculated for a
      two-sided (default) or one sided test. The one-sided test can be
      either 'larger', 'smaller'.
  
  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.
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code: def power(
      self,
      effect_size,
      nobs1,
      alpha,
      ratio=1,
      df=None,
      alternative="two-sided",
  ):
      """Calculate the power of a t-test for two independent sample
  
      Parameters
      ----------
      effect_size : float
          standardized effect size, difference between the two means divided
          by the standard deviation. `effect_size` has to be positive.
      nobs1 : int or float
          number of observations of sample 1. The number of observations of
          sample two is ratio times the size of sample 1,
          i.e. ``nobs2 = nobs1 * ratio``
      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.
      ratio : float
          ratio of the number of observations in sample 2 relative to
          sample 1. see description of nobs1
          The default for ratio is 1; to solve for ratio given the other
          arguments, it has to be explicitly set to None.
      df : int or float
          degrees of freedom. By default this is None, and the df from the
          ttest with pooled variance is used, ``df = (nobs1 - 1 + nobs2 - 1)``
      alternative : str, 'two-sided' (default), 'larger', 'smaller'
          extra argument to choose whether the power is calculated for a
          two-sided (default) or one sided test. The one-sided test can be
          either 'larger', 'smaller'.
  
      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.
  
      """
  
      nobs2 = nobs1 * ratio
      # pooled variance
      if df is None:
          df = nobs1 - 1 + nobs2 - 1
  
      nobs = 1.0 / (1.0 / nobs1 + 1.0 / nobs2)
      # print 'calling ttest power with', (effect_size, nobs, alpha, df, alternative)
      return ttest_power(
          effect_size, nobs, alpha, df=df, alternative=alternative
      )
  
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 TTestIndPower obj's underlying member method code with example usage: obj.power(effect_size, nobs1, alpha, ratio, df, alternative) and returns: ttest_power