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# scipy.stats.boxcox_llf

`scipy.stats.``boxcox_llf`(lmb, data)

  The boxcox log-likelihood function.

  Parameters:

  lmb : scalar

  > Parameter for Box-Cox transformation. See `boxcox` for details.

  data : array_like

  > Data to calculate Box-Cox log-likelihood for. If data is multi-dimensional, the log-likelihood is calculated along the first axis.

  Returns:

  llf : float or ndarray

  > Box-Cox log-likelihood of data given lmb. A float for 1-D data, an array otherwise.

  See also

  `boxcox`, `probplot`, `boxcox_normplot`, `boxcox_normmax`

  Notes

  The Box-Cox log-likelihood function is defined here as

  \[llf = (\lambda - 1) \sum_i(\log(x_i)) - N/2 \log(\sum_i (y_i - \bar{y})^2 / N),\]

  where `y` is the Box-Cox transformed input data `x`.

  Examples

      >>> from scipy import stats
      >>> import matplotlib.pyplot as plt
      >>> from mpl_toolkits.axes_grid1.inset_locator import inset_axes
      >>> np.random.seed(1245)

  Generate some random variates and calculate Box-Cox log-likelihood values for them for a range of `lmbda` values:

      >>> x = stats.loggamma.rvs(5, loc=10, size=1000)
      >>> lmbdas = np.linspace(-2, 10)
      >>> llf = np.zeros(lmbdas.shape, dtype=float)
      >>> for ii, lmbda in enumerate(lmbdas):
      ...     llf[ii] = stats.boxcox_llf(lmbda, x)

  Also find the optimal lmbda value with `boxcox`:

      >>> x_most_normal, lmbda_optimal = stats.boxcox(x)

  Plot the log-likelihood as function of lmbda. Add the optimal lmbda as a horizontal line to check that that’s really the optimum:

      >>> fig = plt.figure()
      >>> ax = fig.add_subplot(111)
      >>> ax.plot(lmbdas, llf, 'b.-')
      >>> ax.axhline(stats.boxcox_llf(lmbda_optimal, x), color='r')
      >>> ax.set_xlabel('lmbda parameter')
      >>> ax.set_ylabel('Box-Cox log-likelihood')

  Now add some probability plots to show that where the log-likelihood is maximized the data transformed with `boxcox` looks closest to normal:

      >>> locs = [3, 10, 4]  # 'lower left', 'center', 'lower right'
      >>> for lmbda, loc in zip([-1, lmbda_optimal, 9], locs):
      ...     xt = stats.boxcox(x, lmbda=lmbda)
      ...     (osm, osr), (slope, intercept, r_sq) = stats.probplot(xt)
      ...     ax_inset = inset_axes(ax, width="20%", height="20%", loc=loc)
      ...     ax_inset.plot(osm, osr, 'c.', osm, slope*osm + intercept, 'k-')
      ...     ax_inset.set_xticklabels([])
      ...     ax_inset.set_yticklabels([])
      ...     ax_inset.set_title('$\lambda=%1.2f$' % lmbda)

      >>> plt.show()

  (Source code)