# quantile

Quantiles of data set

## Syntax

``Q = quantile(A,p)``
``Q = quantile(A,n)``
``Q = quantile(___,"all")``
``Q = quantile(___,dim)``
``Q = quantile(___,vecdim)``
``Q = quantile(___,"Method",method)``

## Description

example

````Q = quantile(A,p)` returns quantiles of elements in input data `A` for the cumulative probability or probabilities `p` in the interval [0,1]. If `A` is a vector, then `Q` is a scalar or a vector with the same length as `p`. `Q(i)` contains the `p(i)` quantile.If `A` is a matrix, then `Q` is a row vector or a matrix, where the number of rows of `Q` is equal to `length(p)`. The `i`th row of `Q` contains the `p(i)` quantiles of each column of `A`.If `A` is a multidimensional array, then `Q` contains the quantiles computed along the first array dimension of size greater than 1. ```

example

````Q = quantile(A,n)` returns quantiles for `n` evenly spaced cumulative probabilities (1/(`n` + 1), 2/(`n` + 1), ..., `n`/(`n` + 1)) for integer `n` > 1. If `A` is a vector, then `Q` is a scalar or a vector with length `n`.If `A` is a matrix, then `Q` is a matrix with `n` rows.If `A` is a multidimensional array, then `Q` contains the quantiles computed along the first array dimension of size greater than 1. ```

example

````Q = quantile(___,"all")` returns quantiles of all the elements of `A` for either of the first two syntaxes.```

example

````Q = quantile(___,dim)` operates along the dimension `dim` for either of the first two syntaxes. For example, if `A` is a matrix, then `quantile(A,p,2)` operates on the elements in each row.```

example

````Q = quantile(___,vecdim)` operates along the dimensions specified in the vector `vecdim` for either of the first two syntaxes. For example, if `A` is a matrix, then `quantile(A,n,[1 2])` operates on all the elements of `A` because every element of a matrix is contained in the array slice defined by dimensions 1 and 2.```

example

````Q = quantile(___,"Method",method)` returns either exact or approximate quantiles based on the value of `method`, using any of the input argument combinations in the previous syntaxes.```

## Examples

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Calculate the quantiles of a data set for specified probabilities.

Generate a data set of size 7.

```rng default % for reproducibility A = randn(1,7)```
```A = 1×7 0.5377 1.8339 -2.2588 0.8622 0.3188 -1.3077 -0.4336 ```

Calculate the 0.3 quantile of the elements of `A`.

`Q = quantile(A,0.3)`
```Q = -0.7832 ```

Calculate the quantiles of the elements of `A` for the cumulative probabilities 0.025, 0.25, 0.5, 0.75, and 0.975.

`Q = quantile(A,[0.025 0.25 0.5 0.75 0.975])`
```Q = 1×5 -2.2588 -1.0892 0.3188 0.7810 1.8339 ```

Calculate the quantiles of a data set for a given number of probabilities.

Generate a data set of size 7.

```rng default % for reproducibility A = randn(1,7)```
```A = 1×7 0.5377 1.8339 -2.2588 0.8622 0.3188 -1.3077 -0.4336 ```

Calculate four evenly spaced quantiles of the elements of `A`.

`Q = quantile(A,4)`
```Q = 1×4 -1.4028 -0.2079 0.4720 0.9593 ```

Using `Q = quantile(A,[0.2,0.4,0.6,0.8])` is another way to return the four evenly spaced quantiles.

Calculate the quantiles along the columns and rows of a data matrix for specified probabilities.

Generate a 4-by-6 data matrix.

```rng default % for reproducibility A = randn(4,6)```
```A = 4×6 0.5377 0.3188 3.5784 0.7254 -0.1241 0.6715 1.8339 -1.3077 2.7694 -0.0631 1.4897 -1.2075 -2.2588 -0.4336 -1.3499 0.7147 1.4090 0.7172 0.8622 0.3426 3.0349 -0.2050 1.4172 1.6302 ```

Calculate the 0.3 quantile for each column of `A`.

`Q = quantile(A,0.3,1)`
```Q = 1×6 -0.3013 -0.6958 1.5336 -0.1056 0.9491 0.1078 ```

`quantile` returns a row vector `Q` when calculating one quantile for each column in `A`. `-0.3013` is the 0.3 quantile of the first column of `A` with elements 0.5377, 1.8339, -2.2588, and 0.8622. Because the default value of `dim` is 1, `Q = quantile(A,0.3)` returns the same result.

Calculate the 0.3 quantile for each row of `A`.

`Q = quantile(A,0.3,2)`
```Q = 4×1 0.3844 -0.8642 -1.0750 0.4985 ```

`quantile` returns a column vector `Q` when calculating one quantile for each row in `A`. `0.3844` is the 0.3 quantile of the first row of `A` with elements 0.5377, 0.3188, 3.5784, 0.7254, -0.1241, and 0.6715.

Calculate evenly spaced quantiles along the columns and rows of a data matrix.

Generate a 6-by-7 data matrix.

```rng default % for reproducibility A = randi(10,6,7)```
```A = 6×7 9 3 10 8 7 8 7 10 6 5 10 8 1 4 2 10 9 7 8 3 10 10 10 2 1 4 1 1 7 2 5 9 7 1 5 1 10 10 10 2 9 4 ```

Calculate the quantiles for each column of `A` for three evenly spaced cumulative probabilities.

`Q = quantile(A,3,1)`
```Q = 3×7 2.0000 3.0000 5.0000 7.0000 4.0000 1.0000 4.0000 8.0000 8.0000 7.0000 8.5000 7.0000 2.0000 4.5000 10.0000 10.0000 10.0000 10.0000 8.0000 8.0000 7.0000 ```

Each column of matrix `Q` contains the quantiles for the corresponding column in `A`. `2`, `8`, and 10 are the quantiles of the first column of `A` with elements 9, 10, 2, 10, 7, and 1. `Q = quantile(A,3)` returns the same result because the default value of `dim` is 1.

Calculate the quantiles for each row of `A` for three evenly spaced cumulative probabilities.

`Q = quantile(A,3,2)`
```Q = 6×3 7.0000 8.0000 8.7500 4.2500 6.0000 9.5000 4.0000 8.0000 9.7500 1.0000 2.0000 8.5000 2.7500 5.0000 7.0000 2.5000 9.0000 10.0000 ```

Each row of matrix `Q` contains the three evenly spaced quantiles for the corresponding row in `A`. `7`, `8`, and `8.75` are the quantiles of the first row of `A` with elements 9, 3, 10, 8, 7, 8, and 7.

Calculate the quantiles of a multidimensional array for specified probabilities by using `"all"` and the `vecdim` inputs.

Create a 3-by-5-by-2 array. Specify a vector of probabilities.

`A = reshape(1:30,[3 5 2])`
```A = A(:,:,1) = 1 4 7 10 13 2 5 8 11 14 3 6 9 12 15 A(:,:,2) = 16 19 22 25 28 17 20 23 26 29 18 21 24 27 30 ```
`p = [0.25 0.75];`

Calculate the 0.25 and 0.75 quantiles of all the elements of `A`.

`Qall = quantile(A,p,"all")`
```Qall = 2×1 8 23 ```

`Qall(1)` is the 0.25 quantile of `A`, and `Qall(2)` is the 0.75 quantile of `A`.

Calculate the 0.25 and 0.75 quantiles for each page of `A` by specifying dimensions 1 and 2 as the operating dimensions.

`Qpage = quantile(A,p,[1 2])`
```Qpage = Qpage(:,:,1) = 4.2500 11.7500 Qpage(:,:,2) = 19.2500 26.7500 ```

`Qpage(1,1,1)` is the 0.25 quantile of the first page of `A`, and `Qpage(2,1,1)` is the 0.75 quantile of the first page of `A`.

Calculate the 0.25 and 0.75 quantiles of the elements in each `A(i,:,:)` slice by specifying dimensions 2 and 3 as the operating dimensions.

`Qrow = quantile(A,p,[2 3])`
```Qrow = 3×2 7 22 8 23 9 24 ```

`Qrow(3,1)` is the 0.25 quantile of the elements in `A(3,:,:)`, and `Qrow(3,2)` is the 0.75 quantile of the elements in `A(3,:,:)`.

Find the median and quartiles of a vector with an even number of elements.

Create a data vector.

`A = [2 5 6 10 11 13]`
```A = 1×6 2 5 6 10 11 13 ```

Calculate the median of the elements of `A`.

`Q = quantile(A,0.5)`
```Q = 8 ```

Calculate the quartiles of the elements of `A`.

`Q = quantile(A,[0.25, 0.5, 0.75])`
```Q = 1×3 5 8 11 ```

Using `Q = quantile(A,3)` is another way to compute the quartiles of the elements of `A`.

These results might be different from the textbook definitions because `quantile` uses Linear Interpolation to find the median and quartiles.

Find the median and quartiles of a vector with an odd number of elements.

Create a data vector.

`A = [2 4 6 8 10 12 14]`
```A = 1×7 2 4 6 8 10 12 14 ```

Calculate the median of the elements of `A`.

`Q = quantile(A,0.50)`
```Q = 8 ```

Calculate the quartiles of the elements of `A`.

`Q = quantile(A,[0.25, 0.5, 0.75])`
```Q = 1×3 4.5000 8.0000 11.5000 ```

Using `Q = quantile(A,3)` is another way to compute the quartiles of `A`.

These results might be different from the textbook definitions because `quantile` uses Linear Interpolation to find the median and quartiles.

Calculate exact and approximate quantiles of a tall column vector for a given probability.

When you perform calculations on tall arrays, MATLAB® uses either a parallel pool (default if you have Parallel Computing Toolbox™) or the local MATLAB session. To run the example using the local MATLAB session when you have Parallel Computing Toolbox, change the global execution environment by using the `mapreducer` function.

`mapreducer(0)`

Create a datastore for the `airlinesmall` data set. Treat `"NA"` values as missing data so that `datastore` replaces them with `NaN` values. Specify to work with the `ArrTime` variable.

```ds = datastore("airlinesmall.csv","TreatAsMissing","NA", ... "SelectedVariableNames","ArrTime");```

Create a tall table `tt` on top of the datastore, and extract the data from the tall table into a tall vector `A`.

`tt = tall(ds)`
```tt = Mx1 tall table ArrTime _______ 735 1124 2218 1431 746 1547 1052 1134 : : ```
`A = tt{:,:}`
```A = Mx1 tall double column vector 735 1124 2218 1431 746 1547 1052 1134 : : ```

Calculate the exact quantile of `A` for cumulative probability `p = 0.5`. Because `A` is a tall column vector and `p` is a scalar, `quantile` returns the exact quantile value by default.

```p = 0.5; Qexact = quantile(A,p)```
```Qexact = tall double ? ```

Calculate the approximate quantile of `A` for `p = 0.5`. Specify `method` as `"approximate"` to use an approximation algorithm based on T-Digest for computing the quantiles.

`Qapprox = quantile(A,p,"Method","approximate")`
```Qapprox = MxNx... tall double array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : : ```

Evaluate the tall arrays and bring the results into memory by using `gather`.

`[Qexact,Qapprox] = gather(Qexact,Qapprox)`
```Evaluating tall expression using the Local MATLAB Session: - Pass 1 of 4: Completed in 0.93 sec - Pass 2 of 4: Completed in 0.24 sec - Pass 3 of 4: Completed in 0.42 sec - Pass 4 of 4: Completed in 0.26 sec Evaluation completed in 2.5 sec ```
```Qexact = 1522 ```
```Qapprox = 1.5220e+03 ```

The values of the exact quantile and the approximate quantile are the same to the four digits shown.

Calculate exact and approximate quantiles of a tall matrix for specified cumulative probabilities along different dimensions.

When you perform calculations on tall arrays, MATLAB® uses either a parallel pool (default if you have Parallel Computing Toolbox™) or the local MATLAB session. To run the example using the local MATLAB session when you have Parallel Computing Toolbox, change the global execution environment by using the `mapreducer` function.

`mapreducer(0)`

Create a tall matrix `A` containing a subset of variables stored in `varnames` from the `airlinesmall` data set. See Quantiles of Tall Vector for Given Probability for details about the steps to extract data from a tall array.

```varnames = ["ArrDelay","ArrTime","DepTime","ActualElapsedTime"]; ds = datastore("airlinesmall.csv","TreatAsMissing","NA", ... "SelectedVariableNames",varnames); tt = tall(ds); A = tt{:,varnames}```
```A = Mx4 tall double matrix 8 735 642 53 8 1124 1021 63 21 2218 2055 83 13 1431 1332 59 4 746 629 77 59 1547 1446 61 3 1052 928 84 11 1134 859 155 : : : : : : : : ```

When operating along a dimension that is not 1, the `quantile` function calculates the exact quantiles only so that it can perform the computation efficiently using a sorting-based algorithm (see Algorithms) instead of an approximation algorithm based on T-Digest.

Calculate the exact quantiles of `A` along the second dimension for the vector `p` of cumulative probabilities 0.25, 0.5, and 0.75.

```p = [0.25 0.5 0.75]; Qexact = quantile(A,p,2)```
```Qexact = MxNx... tall double array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : : ```

When the function operates along the first dimension and `p` is a vector of cumulative probabilities, you must use the approximation algorithm based on t-digest to compute the quantiles. Using the sorting-based algorithm to find quantiles along the first dimension of a tall array is computationally intensive.

Calculate the approximate quantiles of `A` along the first dimension for the cumulative probabilities 0.25, 0.5, and 0.75. Because the default dimension is 1, you do not need to specify a value for `dim`.

`Qapprox = quantile(A,p,"Method","approximate")`
```Qapprox = MxNx... tall double array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : : ```

Evaluate the tall arrays and bring the results into memory by using `gather`.

`[Qexact,Qapprox] = gather(Qexact,Qapprox);`
```Evaluating tall expression using the Local MATLAB Session: - Pass 1 of 1: Completed in 1.6 sec Evaluation completed in 2 sec ```

Show the first five rows of the exact quantiles of `A` (along the second dimension) for the cumulative probabilities 0.25, 0.5, and 0.75.

`Qexact(1:5,:)`
```ans = 5×3 103 × 0.0305 0.3475 0.6885 0.0355 0.5420 1.0725 0.0520 1.0690 2.1365 0.0360 0.6955 1.3815 0.0405 0.3530 0.6875 ```

Each row of the matrix `Qexact` contains the three quantiles of the corresponding row in `A`. For example, `30.5`, `347.5`, and `688.5` are the 0.25, 0.5, and 0.75 quantiles, respectively, of the first row in `A`.

Show the approximate quantiles of `A` (along the first dimension) for the cumulative probabilities 0.25, 0.5, and 0.75.

`Qapprox`
```Qapprox = 3×4 103 × -0.0070 1.1149 0.9322 0.0700 0 1.5220 1.3350 0.1020 0.0110 1.9180 1.7400 0.1510 ```

Each column of the matrix `Qapprox` contains to the three quantiles of the corresponding column in `A`. For example, the first column of `Qapprox` with elements –7, 0, and 11 contains the quantiles for the first column of `A`.

Calculate exact and approximate quantiles along different dimensions of a tall matrix for a given number of evenly spaced cumulative probabilities.

When you perform calculations on tall arrays, MATLAB® uses either a parallel pool (default if you have Parallel Computing Toolbox™) or the local MATLAB session. To run the example using the local MATLAB session when you have Parallel Computing Toolbox, change the global execution environment by using the `mapreducer` function.

`mapreducer(0)`

Create a tall matrix `A` containing a subset of variables stored in `varnames` from the `airlinesmall` data set. See Quantiles of Tall Vector for Given Probability for details about the steps to extract data from a tall array.

```varnames = ["ArrDelay","ArrTime","DepTime","ActualElapsedTime"]; ds = datastore("airlinesmall.csv","TreatAsMissing","NA", ... "SelectedVariableNames",varnames); tt = tall(ds); A = tt{:,varnames}```
```A = Mx4 tall double matrix 8 735 642 53 8 1124 1021 63 21 2218 2055 83 13 1431 1332 59 4 746 629 77 59 1547 1446 61 3 1052 928 84 11 1134 859 155 : : : : : : : : ```

To calculate quantiles for evenly spaced cumulative probabilities along the first dimension, you must use the approximation algorithm based on T-Digest. Using the sorting-based algorithm (see Algorithms) to find quantiles along the first dimension of a tall array is computationally intensive.

Calculate the quantiles for three evenly spaced cumulative probabilities along the first dimension of `A`. Because the default dimension is 1, you do not need to specify a value for `dim`. Specify the `method` as `"approximate"` to use the approximation algorithm.

`Qapprox = quantile(A,3,"Method","approximate")`
```Qapprox = MxNx... tall double array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : : ```

To calculate quantiles for evenly spaced cumulative probabilities along any other dimension (`dim` is not `1`), `quantile` calculates the exact quantiles only, so that it can perform the computation efficiently by using the sorting-based algorithm.

Calculate the quantiles for three evenly spaced cumulative probabilities along the second dimension of `A`. Because `dim` is not 1, `quantile` returns the exact quantiles by default.

`Qexact = quantile(A,3,2)`
```Qexact = MxNx... tall double array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : : ```

Evaluate the tall arrays and bring the results into memory by using `gather`.

`[Qapprox,Qexact] = gather(Qapprox,Qexact);`
```Evaluating tall expression using the Local MATLAB Session: - Pass 1 of 1: Completed in 1.1 sec Evaluation completed in 1.2 sec ```

Show the approximate quantiles of `A` (along the first dimension) for the three evenly spaced cumulative probabilities.

`Qapprox`
```Qapprox = 3×4 103 × -0.0070 1.1149 0.9321 0.0700 0 1.5220 1.3350 0.1020 0.0110 1.9180 1.7400 0.1510 ```

Each column of the matrix `Qapprox` contains the quantiles of the corresponding column in `A`. For example, the first column of `Qapprox` with elements –7, 0, and 11 contains the quantiles for the first column of `A`.

Show the first five rows of the exact quantiles of `A` (along the second dimension) for the three evenly spaced cumulative probabilities.

`Qexact(1:5,:)`
```ans = 5×3 103 × 0.0305 0.3475 0.6885 0.0355 0.5420 1.0725 0.0520 1.0690 2.1365 0.0360 0.6955 1.3815 0.0405 0.3530 0.6875 ```

Each row of the matrix `Qexact` contains the three evenly spaced quantiles of the corresponding row in `A`. For example, `30.5`, `347.5`, and `688.5` are the 0.25, 0.5, and 0.75 quantiles, respectively, of the first row in `A`.

## Input Arguments

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Input array, specified as a vector, matrix, or multidimensional array.

Data Types: `single` | `double`

Cumulative probabilities for which to compute quantiles, specified as a scalar or vector of scalars from 0 to 1.

Example: 0.3

Example: [0.25, 0.5, 0.75]

Example: (0:0.25:1)

Data Types: `single` | `double`

Number of probabilities for which to compute quantiles, specified as a positive integer scalar. `quantile` returns `n` quantiles that divide the data set into evenly distributed `n`+1 segments.

Data Types: `single` | `double`

Dimension to operate along, specified as a positive integer scalar. If you do not specify the dimension, then the default is the first array dimension of size greater than 1.

Consider an input matrix `A` and a vector of cumulative probabilities `p`:

• `Q = quantile(A,p,1)` computes quantiles of the columns in `A` for the cumulative probabilities in `p`. Because 1 is the specified operating dimension, `Q` has `length(p)` rows.

• `Q = quantile(A,p,2)` computes quantiles of the rows in `A` for the cumulative probabilities in `p`. Because 2 is the specified operating dimension, `Q` has `length(p)` columns.

Consider an input matrix `A` and a vector of evenly spaced probabilities `n`:

• `Q = quantile(A,n,1)` computes quantiles of the columns in `A` for the `n` evenly spaced cumulative probabilities. Because 1 is the specified operating dimension, `Q` has `n` rows.

• `Q = quantile(A,n,2)` computes quantiles of the rows in `A` for the `n` evenly spaced cumulative probabilities. Because 2 is the specified operating dimension, `Q` has `n` columns.

Dimension `dim` indicates the dimension of `Q` whose length is equal to `length(p)` or `n`.

Data Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64`

Vector of dimensions to operate along, specified as a vector of positive integers. Each element represents a dimension of the input data.

The size of the output `Q` in the smallest specified operating dimension is equal to `length(p)` or `n`. The size of `Q` in the other operating dimensions specified in `vecdim` is 1. The size of `Q` in all dimensions not specified in `vecdim` remains the same as the input data.

Consider a 2-by-3-by-3 input array `A` and the cumulative probabilities `p`. `quantile(A,p,[1 2])` returns a `length(p)`-by-1-by-3 array because 1 and 2 are the operating dimensions and `min([1 2]) = 1`. Each page of the returned array contains the quantiles of the elements on the corresponding page of `A`.

Data Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64`

Method for calculating quantiles, specified as one of these values:

• `"exact"` — Calculate exact quantiles with an algorithm that uses sorting.

• `"approximate"` — Calculate approximate quantiles with an algorithm that uses T-Digest.

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### Linear Interpolation

Linear interpolation uses linear polynomials to find yi = f(xi), the values of the underlying function Y = f(X) at the points in the vector or array x. Given the data points (x1, y1) and (x2, y2), where y1 = f(x1) and y2 = f(x2), linear interpolation finds y = f(x) for a given x between x1 and x2 as

`$y=f\left(x\right)={y}_{1}+\frac{\left(x-{x}_{1}\right)}{\left({x}_{2}-{x}_{1}\right)}\left({y}_{2}-{y}_{1}\right).$`

Similarly, if the 1.5/n quantile is y1.5/n and the 2.5/n quantile is y2.5/n, then linear interpolation finds the 2.3/n quantile y2.3/n as

`${y}_{\frac{2.3}{n}}={y}_{\frac{1.5}{n}}+\frac{\left(\frac{2.3}{n}-\frac{1.5}{n}\right)}{\left(\frac{2.5}{n}-\frac{1.5}{n}\right)}\left({y}_{\frac{2.5}{n}}-{y}_{\frac{1.5}{n}}\right).$`

### T-Digest

T-digest  is a probabilistic data structure that is a sparse representation of the empirical cumulative distribution function (CDF) of a data set. T-digest is useful for computing approximations of rank-based statistics (such as percentiles and quantiles) from online or distributed data in a way that allows for controllable accuracy, particularly near the tails of the data distribution.

For data that is distributed in different partitions, t-digest computes quantile estimates (and percentile estimates) for each data partition separately, and then combines the estimates while maintaining a constant-memory bound and constant relative accuracy of computation ($q\left(1-q\right)$ for the qth quantile). For these reasons, t-digest is practical for working with tall arrays.

To estimate quantiles of an array that is distributed in different partitions, first build a t-digest in each partition of the data. A t-digest clusters the data in the partition and summarizes each cluster by a centroid value and an accumulated weight that represents the number of samples contributing to the cluster. T-digest uses large clusters (widely spaced centroids) to represent areas of the CDF that are near `q = 0.5` and uses small clusters (tightly spaced centroids) to represent areas of the CDF that are near ```q = 0``` and `q = 1`.

T-digest controls the cluster size by using a scaling function that maps a quantile q to an index k with a compression parameter δ. That is,

`$k\left(q,\delta \right)=\delta \cdot \left(\frac{{\mathrm{sin}}^{-1}\left(2q-1\right)}{\pi }+\frac{1}{2}\right),$`

where the mapping k is monotonic with minimum value k(0,δ) = 0 and maximum value k(1,δ) = δ. This figure shows the scaling function for δ = 10. The scaling function translates the quantile q to the scaling factor k in order to give variable-size steps in q. As a result, cluster sizes are unequal (larger around the center quantiles and smaller near `q = 0` and ```q = 1```). The smaller clusters allow for better accuracy near the edges of the data.

To update a t-digest with a new observation that has a weight and location, find the cluster closest to the new observation. Then, add the weight and update the centroid of the cluster based on the weighted average, provided that the updated weight of the cluster does not exceed the size limitation.

You can combine independent t-digests from each partition of the data by taking a union of the t-digests and merging their centroids. To combine t-digests, first sort the clusters from all the independent t-digests in decreasing order of cluster weights. Then, merge neighboring clusters, when they meet the size limitation, to form a new t-digest.

Once you form a t-digest that represents the complete data set, you can estimate the endpoints (or boundaries) of each cluster in the t-digest and then use interpolation between the endpoints of each cluster to find accurate quantile estimates.

## Algorithms

For an n-element vector `A`, `quantile` computes quantiles by using a sorting-based algorithm:

1. The sorted elements in `A` are taken as the (0.5/n), (1.5/n), ..., ([n – 0.5]/n) quantiles. For example:

• For a data vector of five elements such as {6, 3, 2, 10, 1}, the sorted elements {1, 2, 3, 6, 10} respectively correspond to the 0.1, 0.3, 0.5, 0.7, and 0.9 quantiles.

• For a data vector of six elements such as {6, 3, 2, 10, 8, 1}, the sorted elements {1, 2, 3, 6, 8, 10} respectively correspond to the (0.5/6), (1.5/6), (2.5/6), (3.5/6), (4.5/6), and (5.5/6) quantiles.

2. `quantile` uses Linear Interpolation to compute quantiles for probabilities between (0.5/n) and ([n – 0.5]/n).

3. For the quantiles corresponding to the probabilities outside that range, `quantile` assigns the minimum or maximum values of the elements in `A`.

`quantile` treats `NaN`s as missing values and removes them.

 Langford, E. “Quartiles in Elementary Statistics”, Journal of Statistics Education. Vol. 14, No. 3, 2006.

 Dunning, T., and O. Ertl. “Computing Extremely Accurate Quantiles Using T-Digests.” August 2017.