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scipy.stats.mstats.

# mquantiles

scipy.stats.mstats.mquantiles(a, prob=(0.25, 0.5, 0.75), alphap=0.4, betap=0.4, axis=None, limit=())

  Computes empirical quantiles for a data array.

  Samples quantile are defined by `Q(p) = (1-gamma)*x[j] + gamma*x[j+1]`, where `x[j]` is the j-th order statistic, and gamma is a function of `j = floor(n*p + m)`, `m = alphap + p*(1 - alphap - betap)` and `g = n*p + m - j`.

  Reinterpreting the above equations to compare to R lead to the equation: `p(k) = (k - alphap)/(n + 1 - alphap - betap)`

  Typical values of (alphap,betap) 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)` : (R type 6)

    - (1,1) : `p(k) = (k-1)/(n-1)`: p(k) = mode[F(x[k])]. (R type 7, R default)

    - (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:
    aarray_like

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

    probarray_like, optional

      List of quantiles to compute.

    alphapfloat, optional

      Plotting positions parameter, default is 0.4.

    betapfloat, optional

      Plotting positions parameter, default is 0.4.

    axisint, optional

      Axis along which to perform the trimming. If None (default), the input array is first flattened.

    limittuple, optional

      Tuple of (lower, upper) values. Values of a outside this open interval are ignored.

  Returns:
    mquantilesMaskedArray

      An array containing the calculated quantiles.

  Notes

  This formulation is very similar to R except the calculation of `m` from `alphap` and `betap`, where in R `m` is defined with each type.

  References

  [1]

  R statistical software: https://www.r-project.org/

  [2]

  R `quantile` function: http://stat.ethz.ch/R-manual/R-devel/library/stats/html/quantile.html

  Examples

      >>> import numpy as np
      >>> from scipy.stats.mstats import mquantiles
      >>> a = np.array([6., 47., 49., 15., 42., 41., 7., 39., 43., 40., 36.])
      >>> mquantiles(a)
      array([ 19.2,  40. ,  42.8])

  Using a 2D array, specifying axis and limit.

      >>> data = np.array([[   6.,    7.,    1.],
      ...                  [  47.,   15.,    2.],
      ...                  [  49.,   36.,    3.],
      ...                  [  15.,   39.,    4.],
      ...                  [  42.,   40., -999.],
      ...                  [  41.,   41., -999.],
      ...                  [   7., -999., -999.],
      ...                  [  39., -999., -999.],
      ...                  [  43., -999., -999.],
      ...                  [  40., -999., -999.],
      ...                  [  36., -999., -999.]])
      >>> print(mquantiles(data, axis=0, limit=(0, 50)))
      [[19.2  14.6   1.45]
       [40.   37.5   2.5 ]
       [42.8  40.05  3.55]]

      >>> data[:, 2] = -999.
      >>> print(mquantiles(data, axis=0, limit=(0, 50)))
      [[19.200000000000003 14.6 --]
       [40.0 37.5 --]
       [42.800000000000004 40.05 --]]