# Documentation

### This is machine translation

Translated by
Mouseover text to see original. Click the button below to return to the English verison of the page.

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

# mode

Most frequent values in array

## Syntax

``M = mode(A)``
``M = mode(A,dim)``
``````[M,F] = mode(___)``````
``````[M,F,C] = mode(___)``````

## Description

example

````M = mode(A)` returns the sample mode of `A`, which is the most frequently occurring value in `A`. When there are multiple values occurring equally frequently, `mode` returns the smallest of those values. For complex inputs, the smallest value is the first value in a sorted list. If `A` is a vector, then `mode(A)` returns the most frequent value of `A`.If `A` is a nonempty matrix, then `mode(A)` returns a row vector containing the mode of each column of `A`.If `A` is an empty 0-by-0 matrix, `mode(A)` returns `NaN`.If `A` is a multidimensional array, then `mode(A)` treats the values along the first array dimension whose size does not equal `1` as vectors and returns an array of most frequent values. The size of this dimension becomes `1` while the sizes of all other dimensions remain the same. ```

example

````M = mode(A,dim)` returns the mode of elements along dimension `dim`. For example, if `A` is a matrix, then `mode(A,2)` is a column vector containing the most frequent value of each row```

example

``````[M,F] = mode(___)``` also returns a frequency array `F`, using any of the input arguments in the previous syntaxes. `F` is the same size as `M`, and each element of `F` represents the number of occurrences of the corresponding element of `M`.```

example

``````[M,F,C] = mode(___)``` also returns a cell array `C` of the same size as `M` and `F`. Each element of `C` is a sorted vector of all values that have the same frequency as the corresponding element of `M`.```

## Examples

collapse all

Define a 3-by-4 matrix.

`A = [3 3 1 4; 0 0 1 1; 0 1 2 4]`
```A = 3 3 1 4 0 0 1 1 0 1 2 4 ```

Find the most frequent value of each column.

`M = mode(A)`
```M = 0 0 1 4 ```

Define a 3-by-4 matrix.

`A = [3 3 1 4; 0 0 1 1; 0 1 2 4]`
```A = 3 3 1 4 0 0 1 1 0 1 2 4 ```

Find the most frequent value of each row.

`M = mode(A,2)`
```M = 3 0 0 ```

Create a 1-by-3-by-4 array of integers between `1` and `10`.

`A = gallery('integerdata',10,[1,3,4],1)`
```A = A(:,:,1) = 10 8 10 A(:,:,2) = 6 9 5 A(:,:,3) = 9 6 1 A(:,:,4) = 4 9 5 ```

Find the most frequent values of this 3-D array along the second dimension.

`M = mode(A)`
```M = M(:,:,1) = 10 M(:,:,2) = 5 M(:,:,3) = 1 M(:,:,4) = 4 ```

This operation produces a 1-by-1-by-4 array by finding the most frequent value along the second dimension. The size of the second dimension reduces to `1`.

Compute the mode along the first dimension of `A`.

```M = mode(A,1); isequal(A,M)```
```ans = logical 1 ```

This returns the same array as `A` because the size of the first dimension is `1`.

Define a 3-by-4 matrix.

`A = [3 3 1 4; 0 0 1 1; 0 1 2 4]`
```A = 3 3 1 4 0 0 1 1 0 1 2 4 ```

Find the most frequent value of each column, as well as how often it occurs.

`[M,F] = mode(A)`
```M = 0 0 1 4 ```
```F = 2 1 2 2 ```

`F(1)` is `2` since `M(1)` occurs twice in the first column.

Define a 3-by-4 matrix.

`A = [3 3 1 4; 0 0 1 1; 0 1 2 4]`
```A = 3 3 1 4 0 0 1 1 0 1 2 4 ```

Find the most frequent value of each row, how often it occurs, and which values in that row occur with the same frequency.

`[M,F,C] = mode(A,2)`
```M = 3 0 0 ```
```F = 2 2 1 ```
```C = 3x1 cell array {[ 3]} {2x1 double} {4x1 double} ```

`C{2}` is the 2-by-1 vector `[0;1]` since values `0` and `1` in the second row occur with frequency `F(2)`.

`C{3}` is the 4-by-1 vector `[0;1;2;4]` since all values in the third row occur with frequency `F(3)`.

Define a 1-by-4 vector of 16-bit unsigned integers.

`A = gallery('integerdata',10,[1,4],3,'uint16')`
```A = 1x4 uint16 row vector 6 3 2 3 ```

Find the most frequent value, as well as the number of times it occurs.

`[M,F] = mode(A),`
```M = uint16 3 ```
```F = 2 ```
`class(M)`
```ans = 'uint16' ```

`M` is the same class as the input, `A`.

## Input Arguments

collapse all

Input array, specified as a vector, matrix, or multidimensional array. `A` can be a numeric array, categorical array, datetime array, or duration array.

`NaN` or `NaT` (Not a Time) values in the input array, `A`, are ignored. Undefined values in categorical arrays are similar to `NaN`s in numeric arrays.

Dimension to operate along, specified as a positive integer scalar. If no value is specified, then the default is the first array dimension whose size does not equal 1.

Dimension `dim` indicates the dimension whose length reduces to `1`. The `size(M,dim)` is `1`, while the sizes of all other dimensions remain the same.

Consider a two-dimensional input array, `A`.

• If `dim = 1`, then `mode(A,1)` returns a row vector containing the most frequent value in each column.

• If `dim = 2`, then `mode(A,2)` returns a column vector containing the most frequent value in each row.

`mode` returns `A` if `dim` is greater than `ndims(A)`.

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

## Output Arguments

collapse all

Most frequent values returned as a scalar, vector, matrix, or multidimensional array. When there are multiple values occurring equally frequently, `mode` returns the smallest of those values. For complex inputs, this is taken to be the first value in a sorted list of values.

The class of `M` is the same as the class of the input array, `A`.

Frequency array returned as a scalar, vector, matrix, or multidimensional array. The size of `F` is the same as the size of `M`, and each element of `F` represents the number of occurrences of the corresponding element of `M`.

The class of `F` is always `double`.

Most frequent values with multiplicity returned as a cell array. The size of `C` is the same as the size of `M` and `F`, and each element of `C` is a sorted column vector of all values that have the same frequency as the corresponding element of `M`.

## Tips

• The `mode` function is most useful with discrete or coarsely rounded data. The mode for a continuous probability distribution is defined as the peak of its density function. Applying the `mode` function to a sample from that distribution is unlikely to provide a good estimate of the peak; it would be better to compute a histogram or density estimate and calculate the peak of that estimate. Also, the `mode` function is not suitable for finding peaks in distributions having multiple modes.