scipy.stats.mstats.plotting_positions

scipy.stats.mstats.plotting_positions(data, alpha=0.40000000000000002, beta=0.40000000000000002)

Returns the plotting positions (or empirical percentile points) for the

data. Plotting positions are defined as (i-alpha)/(n-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

Parameters:

x : sequence

Input data, as a sequence or array of dimension at most 2.

prob : sequence

List of quantiles to compute.

alpha : {0.4, float} optional

Plotting positions parameter.

beta : {0.4, float} optional

Plotting positions parameter.