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. 
 
 
- Plotting positions are defined as