# quantileCut

##### Cut by quantiles

Cuts a variable into equal sized categories

##### Usage

`quantileCut(x,n,...)`

##### Arguments

- x
- A vector containing the observations.
- n
- Number of categories
- ...
- Additional arguments to cut

##### Details

It is sometimes convenient (though not always wise) to split a continuous numeric variable `x`

into a set of `n`

discrete categories that contain an approximately equal number of cases. The `quantileCut`

function does exactly this. The actual categorisation is done by the `cut`

function. However, instead of selecting ranges of equal sizes (the default behaviour in `cut`

), the `quantileCut`

function uses the `quantile`

function to select unequal sized ranges so as to ensure that each of the categories contains the same number of observations. The intended purpose of the function is to assist in exploratory data analysis; it is not generally a good idea to use the output of `quantileCut`

function as a factor in an analysis of variance, for instance, since the factor levels are not interpretable and will almost certainly violate homogeneity of variance.

##### Value

`n`

levels. The factor levels are determined in the same way as for the `cut`

function, and can be specified manually using the `labels`

argument, which is passed to the `cut`

function.##### Warning

This package is under development, and has been released only due to teaching constraints. Until this notice disappears from the help files, you should assume that everything in the package is subject to change. Backwards compatibility is NOT guaranteed. Functions may be deleted in future versions and new syntax may be inconsistent with earlier versions. For the moment at least, this package should be treated with extreme caution.

##### See Also

##### Examples

`library(lsr)`

```
### An example illustrating why care is needed ###
dataset <- c( 0,1,2, 3,4,5, 7,10,15 ) # note the uneven spread of data
x <- quantileCut( dataset, 3 ) # cut into 3 equally frequent bins
table(x) # tabulate
# For comparison purposes, here is the behaviour of the more standard cut
# function when applied to the same data:
y <- cut( dataset, 3 )
table(y)
```

*Documentation reproduced from package lsr, version 0.5, License: GPL-3*