Module « scipy.stats »

Fonction yeojohnson - module scipy.stats

Signature de la fonction yeojohnson

def yeojohnson(x, lmbda=None) 

Description

yeojohnson.__doc__

Return a dataset transformed by a Yeo-Johnson power transformation.

    Parameters
    ----------
    x : ndarray
        Input array.  Should be 1-dimensional.
    lmbda : float, optional
        If ``lmbda`` is ``None``, find the lambda that maximizes the
        log-likelihood function and return it as the second output argument.
        Otherwise the transformation is done for the given value.

    Returns
    -------
    yeojohnson: ndarray
        Yeo-Johnson power transformed array.
    maxlog : float, optional
        If the `lmbda` parameter is None, the second returned argument is
        the lambda that maximizes the log-likelihood function.

    See Also
    --------
    probplot, yeojohnson_normplot, yeojohnson_normmax, yeojohnson_llf, boxcox

    Notes
    -----
    The Yeo-Johnson transform is given by::

        y = ((x + 1)**lmbda - 1) / lmbda,                for x >= 0, lmbda != 0
            log(x + 1),                                  for x >= 0, lmbda = 0
            -((-x + 1)**(2 - lmbda) - 1) / (2 - lmbda),  for x < 0, lmbda != 2
            -log(-x + 1),                                for x < 0, lmbda = 2

    Unlike `boxcox`, `yeojohnson` does not require the input data to be
    positive.

    .. versionadded:: 1.2.0


    References
    ----------
    I. Yeo and R.A. Johnson, "A New Family of Power Transformations to
    Improve Normality or Symmetry", Biometrika 87.4 (2000):


    Examples
    --------
    >>> from scipy import stats
    >>> import matplotlib.pyplot as plt

    We generate some random variates from a non-normal distribution and make a
    probability plot for it, to show it is non-normal in the tails:

    >>> fig = plt.figure()
    >>> ax1 = fig.add_subplot(211)
    >>> x = stats.loggamma.rvs(5, size=500) + 5
    >>> prob = stats.probplot(x, dist=stats.norm, plot=ax1)
    >>> ax1.set_xlabel('')
    >>> ax1.set_title('Probplot against normal distribution')

    We now use `yeojohnson` to transform the data so it's closest to normal:

    >>> ax2 = fig.add_subplot(212)
    >>> xt, lmbda = stats.yeojohnson(x)
    >>> prob = stats.probplot(xt, dist=stats.norm, plot=ax2)
    >>> ax2.set_title('Probplot after Yeo-Johnson transformation')

    >>> plt.show()