scipy.stats.yeojohnson_normmax

scipy.stats.yeojohnson_normmax(x, brack=(-2, 2))

Compute optimal Yeo-Johnson transform parameter for input data, using maximum likelihood estimation.

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.

Returns:

maxlog : float

The optimal transform parameter found.

See also

yeojohnson, yeojohnson_llf, yeojohnson_normplot

Notes

New in version 1.2.0.

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

>>> x = stats.loggamma.rvs(5, size=30) + 5
>>> lmax = stats.yeojohnson_normmax(x)

>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> prob = stats.yeojohnson_normplot(x, -10, 10, plot=ax)
>>> ax.axvline(lmax, color='r')

>>> plt.show()