# IQrange

##### The Interquartile Range

Computes (standardized) interquartile range of the `x`

values.

- Keywords
- robust, distribution, univar

##### Usage

```
IQrange(x, na.rm = FALSE, type = 7)
sIQR(x, na.rm = FALSE, type = 7, constant = 2*qnorm(0.75))
```

##### Arguments

- x
a numeric vector.

- na.rm
logical. Should missing values be removed?

- type
an integer between 1 and 9 selecting one of nine quantile algorithms; for more details see

`quantile`

.- constant
standardizing contant; see details below.

##### Details

This function `IQrange`

computes quartiles as
`IQR(x) = quantile(x,3/4) - quantile(x,1/4)`

.
The function is identical to function `IQR`

. It was added
before the `type`

argument was introduced to function `IQR`

in 2010 (r53643, r53644).

For normally \(N(m,1)\) distributed \(X\), the expected value of
`IQR(X)`

is `2*qnorm(3/4) = 1.3490`

, i.e., for a normal-consistent
estimate of the standard deviation, use `IQR(x) / 1.349`

. This is implemented
in function `sIQR`

(standardized IQR).

##### References

Tukey, J. W. (1977). *Exploratory Data Analysis.* Reading: Addison-Wesley.

##### See Also

##### Examples

```
# NOT RUN {
IQrange(rivers)
## identical to
IQR(rivers)
## other quantile algorithms
IQrange(rivers, type = 4)
IQrange(rivers, type = 5)
## standardized IQR
sIQR(rivers)
## right-skewed data distribution
sd(rivers)
mad(rivers)
## for normal data
x <- rnorm(100)
sd(x)
sIQR(x)
mad(x)
# }
```

*Documentation reproduced from package MKdescr, version 0.4, License: LGPL-3*