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scipy.stats.mstats.

# plotting_positions

scipy.stats.mstats.plotting_positions(data, alpha=0.4, beta=0.4)

  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:
    dataarray_like

      Input data, as a sequence or array of dimension at most 2.

    alphafloat, optional

      Plotting positions parameter. Default is 0.4.

    betafloat, optional

      Plotting positions parameter. Default is 0.4.

  Returns:
    positionsMaskedArray

      The calculated plotting positions.