scipy.stats.mstats.meppf#

scipy.stats.mstats.meppf(data, alpha=0.4, beta=0.4)[source]#

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.