# 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