Module « scipy.stats »

Fonction mood - module scipy.stats

Signature de la fonction mood

def mood(x, y, axis=0, alternative='two-sided') 

Description

mood.__doc__

Perform Mood's test for equal scale parameters.

    Mood's two-sample test for scale parameters is a non-parametric
    test for the null hypothesis that two samples are drawn from the
    same distribution with the same scale parameter.

    Parameters
    ----------
    x, y : array_like
        Arrays of sample data.
    axis : int, optional
        The axis along which the samples are tested.  `x` and `y` can be of
        different length along `axis`.
        If `axis` is None, `x` and `y` are flattened and the test is done on
        all values in the flattened arrays.
    alternative : {'two-sided', 'less', 'greater'}, optional
        Defines the alternative hypothesis. Default is 'two-sided'.
        The following options are available:

        * 'two-sided': the scales of the distributions underlying `x` and `y`
          are different.
        * 'less': the scale of the distribution underlying `x` is less than
          the scale of the distribution underlying `y`.
        * 'greater': the scale of the distribution underlying `x` is greater
          than the scale of the distribution underlying `y`.

        .. versionadded:: 1.7.0

    Returns
    -------
    z : scalar or ndarray
        The z-score for the hypothesis test.  For 1-D inputs a scalar is
        returned.
    p-value : scalar ndarray
        The p-value for the hypothesis test.

    See Also
    --------
    fligner : A non-parametric test for the equality of k variances
    ansari : A non-parametric test for the equality of 2 variances
    bartlett : A parametric test for equality of k variances in normal samples
    levene : A parametric test for equality of k variances

    Notes
    -----
    The data are assumed to be drawn from probability distributions ``f(x)``
    and ``f(x/s) / s`` respectively, for some probability density function f.
    The null hypothesis is that ``s == 1``.

    For multi-dimensional arrays, if the inputs are of shapes
    ``(n0, n1, n2, n3)``  and ``(n0, m1, n2, n3)``, then if ``axis=1``, the
    resulting z and p values will have shape ``(n0, n2, n3)``.  Note that
    ``n1`` and ``m1`` don't have to be equal, but the other dimensions do.

    Examples
    --------
    >>> from scipy import stats
    >>> rng = np.random.default_rng()
    >>> x2 = rng.standard_normal((2, 45, 6, 7))
    >>> x1 = rng.standard_normal((2, 30, 6, 7))
    >>> z, p = stats.mood(x1, x2, axis=1)
    >>> p.shape
    (2, 6, 7)

    Find the number of points where the difference in scale is not significant:

    >>> (p > 0.1).sum()
    78

    Perform the test with different scales:

    >>> x1 = rng.standard_normal((2, 30))
    >>> x2 = rng.standard_normal((2, 35)) * 10.0
    >>> stats.mood(x1, x2, axis=1)
    (array([-5.76174136, -6.12650783]), array([8.32505043e-09, 8.98287869e-10]))