# Median absolute deviation in R

The `mad`

function in R is used to calculate the median absolute deviation (MAD), which measures the dispersion of a dataset. The MAD is a robust alternative to standard deviation and to interquartile range that is less sensitive to outliers.

## Syntax

The syntax of the `mad`

function is the following:

```
mad(x, center = median(x), constant = 1.4826,
na.rm = FALSE, low = FALSE, high = FALSE)
```

Being:

`x`

: a numeric vector.`center`

: the center of the data used for calculating the MAD. By default, it uses the median of x.`constant`

: a scale factor defaulting to 1.4826, which allows to ensure asymptotically normal consistency.`na.rm`

: a logical value indicating whether missing values should be removed or not. Defaults to`FALSE`

.`low`

: a logical value indicating whether to compute the ālo-medianā. Defaults to`FALSE`

.`high`

: a logical value indicating whether to compute the āhi-medianā. Defaults to`FALSE`

.

## Examples

Given a sample vector named `x`

you can compute its median absolute deviation in R with the `mad`

function as follows:

```
# Sample data
set.seed(19)
x <- rnorm(100)
# MAD
mad(x)
```

`1.057287`

Recall to set `na.rm = TRUE`

if your data contains missing values.

### Center function

The default center function is `median(x)`

but you could also input other function such as `mean(x)`

.

```
# Sample data
set.seed(19)
x <- rnorm(100)
# MAD with custom center function
mad(x, center = mean(x))
```

`1.058821`

### Constant

The default constant of 1.4826 (`1/qnorm(3/4)`

) ensures asymptotically normal consistency. However, you can customize the default value with `constant

```
# Sample data
set.seed(19)
x <- rnorm(100)
# MAD with custom constant
mad(x, constant = 1)
```

`0.7131301`

### Lo-median

By default, when the sample size is even, the function computes the mean of the two central values. However, when `low = TRUE`

the function takes smallest of the two middle values instead of their mean.

```
# Sample data
set.seed(19)
x <- rnorm(100)
# MAD lo-median
mad(x, low = TRUE)
```

`0.7131301`

### Hi-median

When `high = TRUE`

and the sample size is even, the function takes the highest of the middle values instead of their mean.

```
# Sample data
set.seed(19)
x <- rnorm(100)
# MAD hi-median
mad(x, high = TRUE)
```

`1.059213`

Note that its not possible to set both `low`

and `high`

as `TRUE`

at the same time.