`scipy.stats._morestats:yeojohnson_normmax`

source: /scipy/stats/_morestats.py :1839

Signature

def yeojohnson_normmax ( x , brack = None )

Summary

Compute optimal Yeo-Johnson transform parameter.

Extended Summary

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. If None, optimize.fminbound is used with bounds that avoid overflow.

Returns

maxlog : float: The optimal transform parameter found.

Notes

Array API Standard Support

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 dev-arrayapi 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()

✓

Aliases

scipy.stats.yeojohnson_normmax