Module « scipy.stats.mstats »

Fonction spearmanr - module scipy.stats.mstats

Signature de la fonction spearmanr

def spearmanr(x, y=None, use_ties=True, axis=None, nan_policy='propagate', alternative='two-sided') 

Description

spearmanr.__doc__

    Calculates a Spearman rank-order correlation coefficient and the p-value
    to test for non-correlation.

    The Spearman correlation is a nonparametric measure of the linear
    relationship between two datasets. Unlike the Pearson correlation, the
    Spearman correlation does not assume that both datasets are normally
    distributed. Like other correlation coefficients, this one varies
    between -1 and +1 with 0 implying no correlation. Correlations of -1 or
    +1 imply a monotonic relationship. Positive correlations imply that
    as `x` increases, so does `y`. Negative correlations imply that as `x`
    increases, `y` decreases.

    Missing values are discarded pair-wise: if a value is missing in `x`, the
    corresponding value in `y` is masked.

    The p-value roughly indicates the probability of an uncorrelated system
    producing datasets that have a Spearman correlation at least as extreme
    as the one computed from these datasets. The p-values are not entirely
    reliable but are probably reasonable for datasets larger than 500 or so.

    Parameters
    ----------
    x, y : 1D or 2D array_like, y is optional
        One or two 1-D or 2-D arrays containing multiple variables and
        observations. When these are 1-D, each represents a vector of
        observations of a single variable. For the behavior in the 2-D case,
        see under ``axis``, below.
    use_ties : bool, optional
        DO NOT USE.  Does not do anything, keyword is only left in place for
        backwards compatibility reasons.
    axis : int or None, optional
        If axis=0 (default), then each column represents a variable, with
        observations in the rows. If axis=1, the relationship is transposed:
        each row represents a variable, while the columns contain observations.
        If axis=None, then both arrays will be raveled.
    nan_policy : {'propagate', 'raise', 'omit'}, optional
        Defines how to handle when input contains nan. 'propagate' returns nan,
        'raise' throws an error, 'omit' performs the calculations ignoring nan
        values. Default is 'propagate'.
    alternative : {'two-sided', 'less', 'greater'}, optional
        Defines the alternative hypothesis. Default is 'two-sided'.
        The following options are available:

        * 'two-sided': the correlation is nonzero
        * 'less': the correlation is negative (less than zero)
        * 'greater':  the correlation is positive (greater than zero)

        .. versionadded:: 1.7.0

    Returns
    -------
    correlation : float
        Spearman correlation coefficient
    pvalue : float
        2-tailed p-value.

    References
    ----------
    [CRCProbStat2000] section 14.7