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# scipy.stats.power_divergence

`scipy.stats.``power_divergence`(f_obs, f_exp=None, ddof=0, axis=0, lambda_=None)

  Cressie-Read power divergence statistic and goodness of fit test.

  This function tests the null hypothesis that the categorical data has the given frequencies, using the Cressie-Read power divergence statistic.

  > Parameters: | f_obs : array_like Observed frequencies in each category. f_exp : array_like, optional Expected frequencies in each category. By default the categories are assumed to be equally likely. ddof : int, optional “Delta degrees of freedom”: adjustment to the degrees of freedom for the p-value. The p-value is computed using a chi-squared distribution with `k - 1 - ddof` degrees of freedom, where k is the number of observed frequencies. The default value of ddof is 0. axis : int or None, optional The axis of the broadcast result of f_obs and f_exp along which to apply the test. If axis is None, all values in f_obs are treated as a single data set. Default is 0. lambda_ : float or str, optional lambda_ gives the power in the Cressie-Read power divergence statistic. The default is 1. For convenience, lambda_ may be assigned one of the following strings, in which case the corresponding numerical value is used:
  >     String              Value   Description
  >     "pearson"             1     Pearson's chi-squared statistic.
  >                                 In this case, the function is
  >                                 equivalent to `stats.chisquare`.
  >     "log-likelihood"      0     Log-likelihood ratio. Also known as
  >                                 the G-test [R354]_.
  >     "freeman-tukey"      -1/2   Freeman-Tukey statistic.
  >     "mod-log-likelihood" -1     Modified log-likelihood ratio.
  >     "neyman"             -2     Neyman's statistic.
  >     "cressie-read"        2/3   The power recommended in [R356]_.
  Returns: | statistic : float or ndarray The Cressie-Read power divergence test statistic. The value is a float if axis is None or if` f_obs and f_exp are 1-D. pvalue : float or ndarray The p-value of the test. The value is a float if ddof and the return value `stat` are scalars.

  See also

  `chisquare`

  Notes

  This test is invalid when the observed or expected frequencies in each category are too small. A typical rule is that all of the observed and expected frequencies should be at least 5.

  When lambda_ is less than zero, the formula for the statistic involves dividing by f_obs, so a warning or error may be generated if any value in f_obs is 0.

  Similarly, a warning or error may be generated if any value in f_exp is zero when lambda_ >= 0.

  The default degrees of freedom, k-1, are for the case when no parameters of the distribution are estimated. If p parameters are estimated by efficient maximum likelihood then the correct degrees of freedom are k-1-p. If the parameters are estimated in a different way, then the dof can be between k-1-p and k-1. However, it is also possible that the asymptotic distribution is not a chisquare, in which case this test is not appropriate.

  This function handles masked arrays. If an element of f_obs or f_exp is masked, then data at that position is ignored, and does not count towards the size of the data set.

  New in version 0.13.0.

  References

  [R352] | Lowry, Richard. “Concepts and Applications of Inferential Statistics”. Chapter 8. http://faculty.vassar.edu/lowry/ch8pt1.html

  [R353] | “Chi-squared test”, http://en.wikipedia.org/wiki/Chi-squared_test

  [R354] | “G-test”, http://en.wikipedia.org/wiki/G-test

  [R355] | Sokal, R. R. and Rohlf, F. J. “Biometry: the principles and practice of statistics in biological research”, New York: Freeman (1981)

  [R356] | Cressie, N. and Read, T. R. C., “Multinomial Goodness-of-Fit Tests”, J. Royal Stat. Soc. Series B, Vol. 46, No. 3 (1984), pp. 440-464.

  Examples

  (See `chisquare` for more examples.)

  When just f_obs is given, it is assumed that the expected frequencies are uniform and given by the mean of the observed frequencies. Here we perform a G-test (i.e. use the log-likelihood ratio statistic):

      >>> from scipy.stats import power_divergence
      >>> power_divergence([16, 18, 16, 14, 12, 12], lambda_='log-likelihood')
      (2.006573162632538, 0.84823476779463769)

  The expected frequencies can be given with the f_exp argument:

      >>> power_divergence([16, 18, 16, 14, 12, 12],
      ...                  f_exp=[16, 16, 16, 16, 16, 8],
      ...                  lambda_='log-likelihood')
      (3.5, 0.62338762774958223)

  When f_obs is 2-D, by default the test is applied to each column.

      >>> obs = np.array([[16, 18, 16, 14, 12, 12], [32, 24, 16, 28, 20, 24]]).T
      >>> obs.shape
      (6, 2)
      >>> power_divergence(obs, lambda_="log-likelihood")
      (array([ 2.00657316,  6.77634498]), array([ 0.84823477,  0.23781225]))

  By setting `axis=None`, the test is applied to all data in the array, which is equivalent to applying the test to the flattened array.

      >>> power_divergence(obs, axis=None)
      (23.31034482758621, 0.015975692534127565)
      >>> power_divergence(obs.ravel())
      (23.31034482758621, 0.015975692534127565)

  ddof is the change to make to the default degrees of freedom.

      >>> power_divergence([16, 18, 16, 14, 12, 12], ddof=1)
      (2.0, 0.73575888234288467)

  The calculation of the p-values is done by broadcasting the test statistic with ddof.

      >>> power_divergence([16, 18, 16, 14, 12, 12], ddof=[0,1,2])
      (2.0, array([ 0.84914504,  0.73575888,  0.5724067 ]))

  f_obs and f_exp are also broadcast. In the following, f_obs has shape (6,) and f_exp has shape (2, 6), so the result of broadcasting f_obs and f_exp has shape (2, 6). To compute the desired chi-squared statistics, we must use `axis=1`:

      >>> power_divergence([16, 18, 16, 14, 12, 12],
      ...                  f_exp=[[16, 16, 16, 16, 16, 8],
      ...                         [8, 20, 20, 16, 12, 12]],
      ...                  axis=1)
      (array([ 3.5 ,  9.25]), array([ 0.62338763,  0.09949846]))