- :
`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

- Method 1: Inverse of empirical distribution function.
- Method 2: Similar to method 1 but with averaging at discontinuities.
- 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.

- Method 4: p(k) = k / n. That is, linear interpolation of the empirical cdf.
- 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.
- Method 6: p(k) = k / (n + 1).
- Method 7: p(k) = (k - 1) / (n - 1).
- Method 8: p(k) = (k - 1/3) / (n + 1/3). The resulting quantile
estimates are approximately median-unbiased regardless of the distribution
of
`x`. - 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