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Module « scipy.stats.mstats »

Fonction plotting_positions - module scipy.stats.mstats

Signature de la fonction plotting_positions

def plotting_positions(data, alpha=0.4, beta=0.4) 

Description

help(scipy.stats.mstats.plotting_positions)

Returns plotting positions (or empirical percentile points) for the data.

Plotting positions are defined as ``(i-alpha)/(n+1-alpha-beta)``, where:
    - i is the rank order statistics
    - n is the number of unmasked values along the given axis
    - `alpha` and `beta` are two parameters.

Typical values for `alpha` and `beta` are:
    - (0,1)    : ``p(k) = k/n``, linear interpolation of cdf (R, type 4)
    - (.5,.5)  : ``p(k) = (k-1/2.)/n``, piecewise linear function
      (R, type 5)
    - (0,0)    : ``p(k) = k/(n+1)``, Weibull (R type 6)
    - (1,1)    : ``p(k) = (k-1)/(n-1)``, in this case,
      ``p(k) = mode[F(x[k])]``. That's R default (R type 7)
    - (1/3,1/3): ``p(k) = (k-1/3)/(n+1/3)``, then
      ``p(k) ~ median[F(x[k])]``.
      The resulting quantile estimates are approximately median-unbiased
      regardless of the distribution of x. (R type 8)
    - (3/8,3/8): ``p(k) = (k-3/8)/(n+1/4)``, Blom.
      The resulting quantile estimates are approximately unbiased
      if x is normally distributed (R type 9)
    - (.4,.4)  : approximately quantile unbiased (Cunnane)
    - (.35,.35): APL, used with PWM
    - (.3175, .3175): used in scipy.stats.probplot

Parameters
----------
data : array_like
    Input data, as a sequence or array of dimension at most 2.
alpha : float, optional
    Plotting positions parameter. Default is 0.4.
beta : float, optional
    Plotting positions parameter. Default is 0.4.

Returns
-------
positions : MaskedArray
    The calculated plotting positions.

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