Repository: scipy/scipy
File: scipy/stats/_morestats.py (Python)

Code before:


FlignerResult = namedtuple('FlignerResult', ('statistic', 'pvalue'))


@xp_capabilities(np_only=True)
@_axis_nan_policy_factory(FlignerResult, n_samples=None)
def fligner(*samples, center='median', proportiontocut=0.05, axis=0):
    r"""Perform Fligner-Killeen test for equality of variance.

    Fligner's test tests the null hypothesis that all input samples
    are from populations with equal variances.  Fligner-Killeen's test is
    distribution free when populations are identical [2]_.

    Parameters
    ----------
    sample1, sample2, ... : array_like
        Arrays of sample data.  Need not be the same length.
    center : {'mean', 'median', 'trimmed'}, optional
        Which statistics to use to center data points within each sample. Default
        is 'median'.
    proportiontocut : float, optional
        When `center` is 'trimmed', this gives the proportion of data points
        to cut from each end. (See `scipy.stats.trim_mean`.)
        Default is 0.05.
    axis : int or tuple of ints, default: None
        If an int or tuple of ints, the axis or axes of the input along which
        to compute the statistic. The statistic of each axis-slice (e.g. row)
        of the input will appear in a corresponding element of the output.
        If ``None``, the input will be raveled before computing the statistic.

    Returns
    -------
    statistic : float
        The test statistic.
    pvalue : float
        The p-value for the hypothesis test.

    See Also
    --------
    bartlett : A parametric test for equality of k variances in normal samples
    levene : A robust parametric test for equality of k variances
    :ref:`hypothesis_fligner` : Extended example

    Notes
    -----
    As with Levene's test there are three variants of Fligner's test that
    differ by the measure of central tendency used in the test.  See `levene`
    for more information.

    Conover et al. (1981) examine many of the existing parametric and

Review comment (suggestion):
```suggestion
    axis : int or tuple of ints, default: 0
```

Code after:


FlignerResult = namedtuple('FlignerResult', ('statistic', 'pvalue'))


@xp_capabilities(np_only=True)
@_axis_nan_policy_factory(FlignerResult, n_samples=None)
def fligner(*samples, center='median', proportiontocut=0.05, axis=0):
    r"""Perform Fligner-Killeen test for equality of variance.

    Fligner's test tests the null hypothesis that all input samples
    are from populations with equal variances.  Fligner-Killeen's test is
    distribution free when populations are identical [2]_.

    Parameters
    ----------
    sample1, sample2, ... : array_like
        Arrays of sample data.  Need not be the same length.
    center : {'mean', 'median', 'trimmed'}, optional
        Which statistics to use to center data points within each sample. Default
        is 'median'.
    proportiontocut : float, optional
        When `center` is 'trimmed', this gives the proportion of data points
        to cut from each end. (See `scipy.stats.trim_mean`.)
        Default is 0.05.
    axis : int or tuple of ints, default: 0
        If an int or tuple of ints, the axis or axes of the input along which
        to compute the statistic. The statistic of each axis-slice (e.g. row)
        of the input will appear in a corresponding element of the output.
        If ``None``, the input will be raveled before computing the statistic.

    Returns
    -------
    statistic : float
        The test statistic.
    pvalue : float
        The p-value for the hypothesis test.

    See Also
    --------
    bartlett : A parametric test for equality of k variances in normal samples
    levene : A robust parametric test for equality of k variances
    :ref:`hypothesis_fligner` : Extended example

    Notes
    -----
    As with Levene's test there are three variants of Fligner's test that
    differ by the measure of central tendency used in the test.  See `levene`
    for more information.

    Conover et al. (1981) examine many of the existing parametric and