Module « scipy.stats »

Fonction yeojohnson_normmax - module scipy.stats

Signature de la fonction yeojohnson_normmax

def yeojohnson_normmax(x, brack=(-2, 2)) 

Description

yeojohnson_normmax.__doc__

Compute optimal Yeo-Johnson transform parameter.

    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
    -----
    .. versionadded:: 1.2.0

    Examples
    --------
    >>> 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()