scipy.stats.

yeojohnson_normmax#

scipy.stats.yeojohnson_normmax(x, brack=None)[source]#

Compute optimal Yeo-Johnson transform parameter.

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

Parameters:

xarray_like: Input array.
brack2-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. If None, optimize.fminbound is used with bounds that avoid overflow.

Returns:

maxlogfloat: The optimal transform parameter found.

See also

yeojohnson, yeojohnson_llf, yeojohnson_normplot

Notes

Added in version 1.2.0.

yeojohnson_normmax has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	⛔
PyTorch	⛔	⛔
JAX	⛔	⛔
Dask	⛔	n/a

See Support for the array API standard for more information.

Examples

>>> import numpy as np
>>> from scipy import stats
>>> import matplotlib.pyplot as plt

Generate some data and determine optimal lmbda

>>> rng = np.random.default_rng()
>>> x = stats.loggamma.rvs(5, size=30, random_state=rng) + 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()

../../_images/scipy-stats-yeojohnson_normmax-1.png