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), thenp(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.
- Plotting positions are defined as