scipy.stats.mstats.plotting_positions

scipy.stats.mstats.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:

• 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.

scipy.stats.mstats.idealfourths scipy.stats.mstats.meppf