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C++ API

: q = quantile (x)
: q = quantile (x, p)
: q = quantile (x, p, dim)
: q = quantile (x, p, dim, method)

For a sample, x, calculate the quantiles, q, corresponding to the cumulative probability values in p. All non-numeric values (NaNs) of x are ignored.

If x is a matrix, compute the quantiles for each column and return them in a matrix, such that the i-th row of q contains the p(i)th quantiles of each column of x.

If p is unspecified, return the quantiles for [0.00 0.25 0.50 0.75 1.00]. The optional argument dim determines the dimension along which the quantiles are calculated. If dim is omitted it defaults to the first non-singleton dimension.

The methods available to calculate sample quantiles are the nine methods used by R (http://www.r-project.org/). The default value is METHOD = 5.

Discontinuous sample quantile methods 1, 2, and 3

  1. Method 1: Inverse of empirical distribution function.
  2. Method 2: Similar to method 1 but with averaging at discontinuities.
  3. Method 3: SAS definition: nearest even order statistic.

Continuous sample quantile methods 4 through 9, where p(k) is the linear interpolation function respecting each methods’ representative cdf.

  1. Method 4: p(k) = k / n. That is, linear interpolation of the empirical cdf.
  2. Method 5: p(k) = (k - 0.5) / n. That is a piecewise linear function where the knots are the values midway through the steps of the empirical cdf.
  3. Method 6: p(k) = k / (n + 1).
  4. Method 7: p(k) = (k - 1) / (n - 1).
  5. Method 8: p(k) = (k - 1/3) / (n + 1/3). The resulting quantile estimates are approximately median-unbiased regardless of the distribution of x.
  6. Method 9: p(k) = (k - 3/8) / (n + 1/4). The resulting quantile estimates are approximately unbiased for the expected order statistics if x is normally distributed.

Hyndman and Fan (1996) recommend method 8. Maxima, S, and R (versions prior to 2.0.0) use 7 as their default. Minitab and SPSS use method 6. MATLAB uses method 5.

References:

  • Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
  • Hyndman, R. J. and Fan, Y. (1996) Sample quantiles in statistical packages, American Statistician, 50, 361–365.
  • R: A Language and Environment for Statistical Computing; http://cran.r-project.org/doc/manuals/fullrefman.pdf.

Examples: 

x = randi (1000, [10, 1]);  # Create empirical data in range 1-1000
q = quantile (x, [0, 1]);   # Return minimum, maximum of distribution
q = quantile (x, [0.25 0.5 0.75]); # Return quartiles of distribution

See also: prctile.

Package: octave