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Module « scipy.stats »

Fonction boxcox_normplot - module scipy.stats

Signature de la fonction boxcox_normplot

def boxcox_normplot(x, la, lb, plot=None, N=80) 

Description

help(scipy.stats.boxcox_normplot)

Compute parameters for a Box-Cox normality plot, optionally show it.

A Box-Cox normality plot shows graphically what the best transformation
parameter is to use in `boxcox` to obtain a distribution that is close
to normal.

Parameters
----------
x : array_like
    Input array.
la, lb : scalar
    The lower and upper bounds for the ``lmbda`` values to pass to `boxcox`
    for Box-Cox transformations.  These are also the limits of the
    horizontal axis of the plot if that is generated.
plot : object, optional
    If given, plots the quantiles and least squares fit.
    `plot` is an object that has to have methods "plot" and "text".
    The `matplotlib.pyplot` module or a Matplotlib Axes object can be used,
    or a custom object with the same methods.
    Default is None, which means that no plot is created.
N : int, optional
    Number of points on the horizontal axis (equally distributed from
    `la` to `lb`).

Returns
-------
lmbdas : ndarray
    The ``lmbda`` values for which a Box-Cox transform was done.
ppcc : ndarray
    Probability Plot Correlation Coefficient, as obtained from `probplot`
    when fitting the Box-Cox transformed input `x` against a normal
    distribution.

See Also
--------
probplot, boxcox, boxcox_normmax, boxcox_llf, ppcc_max

Notes
-----
Even if `plot` is given, the figure is not shown or saved by
`boxcox_normplot`; ``plt.show()`` or ``plt.savefig('figname.png')``
should be used after calling `probplot`.

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

Generate some non-normally distributed data, and create a Box-Cox plot:

>>> x = stats.loggamma.rvs(5, size=500) + 5
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> prob = stats.boxcox_normplot(x, -20, 20, plot=ax)

Determine and plot the optimal ``lmbda`` to transform ``x`` and plot it in
the same plot:

>>> _, maxlog = stats.boxcox(x)
>>> ax.axvline(maxlog, color='r')

>>> plt.show()

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