`scipy.stats._mstats_basic:plotting_positions`

Signature

def 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:

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

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.