### Navigation

- index
- modules |
- modules |
- next |
- previous |
- SciPy v0.9 Reference Guide (DRAFT) »
- Statistical functions (`scipy.stats`) »
- Statistical functions for masked arrays (`scipy.stats.mstats`) »

# scipy.stats.mstats.plotting_positions

`scipy.stats.mstats.``plotting_positions`(data, alpha=0.40000000000000002, beta=0.40000000000000002)

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

  Plotting positions are defined as `(i-alpha)/(n-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

  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.

### Navigation

- index
- modules |
- modules |
- next |
- previous |
- SciPy v0.9 Reference Guide (DRAFT) »
- Statistical functions (`scipy.stats`) »
- Statistical functions for masked arrays (`scipy.stats.mstats`) »