# movmean

## Syntax

``M = movmean(A,k)``
``M = movmean(A,[kb kf])``
``M = movmean(___,dim)``
``M = movmean(___,nanflag)``
``M = movmean(___,Name,Value)``

## Description

example

````M = movmean(A,k)` returns an array of local `k`-point mean values, where each mean is calculated over a sliding window of length `k` across neighboring elements of `A`. When `k` is odd, the window is centered about the element in the current position. When `k` is even, the window is centered about the current and previous elements. The window size is automatically truncated at the endpoints when there are not enough elements to fill the window. When the window is truncated, the average is taken over only the elements that fill the window. `M` is the same size as `A`.If `A` is a vector, then `movmean` operates along the length of the vector `A`.If `A` is a multidimensional array, then `movmean` operates along the first dimension of `A` whose size does not equal 1.```

example

````M = movmean(A,[kb kf])` computes the mean with a window of length `kb+kf+1` that includes the element in the current position, `kb` elements backward, and `kf` elements forward.```

example

````M = movmean(___,dim)` specifies the dimension of `A` to operate along for any of the previous syntaxes. For example, if `A` is a matrix, then `movmean(A,k,2)` operates along the columns of `A`, computing the `k`-element sliding mean for each row.```

example

````M = movmean(___,nanflag)` specifies whether to include or omit `NaN` values from the calculation for any of the previous syntaxes. `movmean(A,k,'includenan')` includes all `NaN` values in the calculation while `movmean(A,k,'omitnan')` ignores them and computes the mean over fewer points.```

example

````M = movmean(___,Name,Value)` specifies additional parameters for the moving average using one or more name-value pair arguments. For example, if `x` is a vector of time values, then `movmean(A,k,'SamplePoints',x)` computes the moving average relative to the times in `x`.```

## Examples

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Compute the three-point centered moving average of a row vector. When there are fewer than three elements in the window at the endpoints, take the average over the elements that are available.

```A = [4 8 6 -1 -2 -3 -1 3 4 5]; M = movmean(A,3)```
```M = 1×10 6.0000 6.0000 4.3333 1.0000 -2.0000 -2.0000 -0.3333 2.0000 4.0000 4.5000 ```

Compute the three-point trailing moving average of a row vector. When there are fewer than three elements in the window at the endpoints, take the average over the elements that are available.

```A = [4 8 6 -1 -2 -3 -1 3 4 5]; M = movmean(A,[2 0])```
```M = 1×10 4.0000 6.0000 6.0000 4.3333 1.0000 -2.0000 -2.0000 -0.3333 2.0000 4.0000 ```

Compute the three-point centered moving average for each row of a matrix. The window starts on the first row, slides horizontally to the end of the row, then moves to the second row, and so on. The dimension argument is two, which slides the window across the columns of `A`.

`A = [4 8 6; -1 -2 -3; -1 3 4]`
```A = 3×3 4 8 6 -1 -2 -3 -1 3 4 ```
`M = movmean(A,3,2)`
```M = 3×3 6.0000 6.0000 7.0000 -1.5000 -2.0000 -2.5000 1.0000 2.0000 3.5000 ```

Compute the three-point centered moving average of a row vector containing two `NaN` elements.

```A = [4 8 NaN -1 -2 -3 NaN 3 4 5]; M = movmean(A,3)```
```M = 1×10 6.0000 NaN NaN NaN -2.0000 NaN NaN NaN 4.0000 4.5000 ```

Recalculate the average, but omit the `NaN` values. When `movmean` discards `NaN` elements, it takes the average over the remaining elements in the window.

`M = movmean(A,3,'omitnan')`
```M = 1×10 6.0000 6.0000 3.5000 -1.5000 -2.0000 -2.5000 0 3.5000 4.0000 4.5000 ```

Compute a 3-hour centered moving average of the data in `A` according to the time vector `t`.

```A = [4 8 6 -1 -2 -3]; k = hours(3); t = datetime(2016,1,1,0,0,0) + hours(0:5)```
```t = 1x6 datetime Columns 1 through 3 01-Jan-2016 00:00:00 01-Jan-2016 01:00:00 01-Jan-2016 02:00:00 Columns 4 through 6 01-Jan-2016 03:00:00 01-Jan-2016 04:00:00 01-Jan-2016 05:00:00 ```
`M = movmean(A,k,'SamplePoints',t)`
```M = 1×6 6.0000 6.0000 4.3333 1.0000 -2.0000 -2.5000 ```

Compute the three-point centered moving average of a row vector, but discard any calculation that uses fewer than three points from the output. In other words, return only the averages computed from a full three-element window, discarding endpoint calculations.

```A = [4 8 6 -1 -2 -3 -1 3 4 5]; M = movmean(A,3,'Endpoints','discard')```
```M = 1×8 6.0000 4.3333 1.0000 -2.0000 -2.0000 -0.3333 2.0000 4.0000 ```

## Input Arguments

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

Window length, specified as a numeric or duration scalar. When `k` is a positive integer scalar, the centered average includes the element in the current position plus surrounding neighbors.

For example, `movmean(A,3)` computes an array of local three-point mean values.

Directional window length, specified as a numeric or duration row vector containing two elements. When `kb` and `kf` are positive integer scalars, the calculation is over `kb+kf+1` elements. The calculation includes the element in the current position, `kb` elements before the current position, and `kf` elements after the current position.

For example, `movmean(A,[2 1])` computes an array of local four-point mean values.

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.

Dimension `dim` indicates the dimension that `movmean` operates along, that is, the direction in which the specified window slides.

Consider an `m`-by-`n` input matrix, `A`:

• `movmean(A,k,1)` computes the `k`-element sliding mean for each column of `A` and returns an `m`-by-`n` matrix.

• `movmean(A,k,2)` computes the `k`-element sliding mean for each row of `A` and returns an `m`-by-`n` matrix.

`NaN` condition, specified as one of these values:

• `'includenan'` — Include `NaN` values from the input when computing the mean, resulting in `NaN` output.

• `'omitnan'` — Ignore all `NaN` values in the input. If a window contains only `NaN` values, then `movmean` returns `NaN`.

### Name-Value Arguments

Specify optional pairs of arguments as `Name1=Value1,...,NameN=ValueN`, where `Name` is the argument name and `Value` is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose `Name` in quotes.

Example: `M = movmean(A,k,'Endpoints','fill')`

Method to treat leading and trailing windows, specified as one of these options:

`'Endpoints'` ValueDescription
`'shrink'`Shrink the window size near the endpoints of the input to include only existing elements.
`'discard'`Do not output any averages when the window does not completely overlap with existing elements.
`'fill'`Substitute nonexisting elements with `NaN`.
numeric or logical scalarSubstitute nonexisting elements with a specified numeric or logical value.

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

Sample points for computing averages, specified as a vector. The sample points represent the location of the data in `A`. Sample points do not need to be uniformly sampled. By default, the sample points vector is `[1 2 3 ... ]`.

Moving windows are defined relative to the sample points, which must be sorted and contain unique elements. For example, if `t` is a vector times corresponding to the input data, then `movmean(rand(1,10),3,'SamplePoints',t)` has a window that represents the time interval between `t(i)-1.5` and `t(i)+1.5`.

When the sample points vector has data type `datetime` or `duration`, then the moving window length must have type `duration`.

If the sample points are nonuniformly spaced and the `'Endpoints'` name-value pair is specified, then its value must be `'shrink'`.

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

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### Mean

For a finite-length vector A made up of N scalar observations, the mean is defined as

`$\mu =\frac{1}{N}\sum _{i=1}^{N}{A}_{i}.$`

## Version History

Introduced in R2016a