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# scipy.stats.boxcox_normmax

`scipy.stats.``boxcox_normmax`(x, brack=(-2.0, 2.0), method='pearsonr')

  Compute optimal Box-Cox transform parameter for input data.

  Parameters: | x : array_like Input array. brack : 2-tuple, optional The starting interval for a downhill bracket search with optimize.brent. Note that this is in most cases not critical; the final result is allowed to be outside this bracket. method : str, optional The method to determine the optimal transform parameter (`boxcox` `lmbda` parameter). Options are: ‘pearsonr’ (default) Maximizes the Pearson correlation coefficient between `y = boxcox(x)` and the expected values for `y` if x would be normally-distributed. ‘mle’ Minimizes the log-likelihood `boxcox_llf`. This is the method used in `boxcox`. ‘all’ Use all optimization methods available, and return all results. Useful to compare different methods.
  Returns: | maxlog : float or ndarray The optimal transform parameter found. An array instead of a scalar for `method='all'`.

  See also

  `boxcox`, `boxcox_llf`, `boxcox_normplot`

  Examples

      >>> from scipy import stats
      >>> import matplotlib.pyplot as plt
      >>> np.random.seed(1234)  # make this example reproducible

  Generate some data and determine optimal `lmbda` in various ways:

      >>> x = stats.loggamma.rvs(5, size=30) + 5
      >>> y, lmax_mle = stats.boxcox(x)
      >>> lmax_pearsonr = stats.boxcox_normmax(x)

      >>> lmax_mle
      7.177...
      >>> lmax_pearsonr
      7.916...
      >>> stats.boxcox_normmax(x, method='all')
      array([ 7.91667384,  7.17718692])

      >>> fig = plt.figure()
      >>> ax = fig.add_subplot(111)
      >>> prob = stats.boxcox_normplot(x, -10, 10, plot=ax)
      >>> ax.axvline(lmax_mle, color='r')
      >>> ax.axvline(lmax_pearsonr, color='g', ls='--')

      >>> plt.show()

  (Source code)