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 - Notes - Added in version 1.2.0. - 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() 