Normalise.numeric {COINr} | R Documentation |

## Normalise a numeric vector

### Description

Normalise a numeric vector using a specified function `f_n`

, with possible reversal of direction
using `direction`

.

### Usage

```
## S3 method for class 'numeric'
Normalise(x, f_n = NULL, f_n_para = NULL, direction = 1, ...)
```

### Arguments

`x` |
Object to be normalised |

`f_n` |
The normalisation method, specified as string which refers to a function of the form |

`f_n_para` |
Supporting list of arguments for |

`direction` |
If |

`...` |
arguments passed to or from other methods. |

### Details

Normalisation is specified using the `f_n`

and `f_n_para`

arguments. In these, `f_n`

should be a character
string which is the name of a normalisation
function. For example, `f_n = "n_minmax"`

calls the `n_minmax()`

function. `f_n_para`

is a list of any
further arguments to `f_n`

. This means that any function can be passed to `Normalise()`

, as long as its
first argument is `x`

, a numeric vector, and it returns a numeric vector of the same length. See `n_minmax()`

for an example.

COINr has a number of built-in normalisation functions of the form `n_*()`

. See online documentation
for details.

`f_n_para`

is *required* to be a named list. So e.g. if we define a function `f1(x, arg1, arg2)`

then we should
specify `f_n = "f1"`

, and `f_n_para = list(arg1 = val1, arg2 = val2)`

, where `val1`

and `val2`

are the
values assigned to the arguments `arg1`

and `arg2`

respectively.

See also `vignette("normalise")`

for more details.

### Value

A normalised numeric vector

### Examples

```
# example vector
x <- runif(10)
# normalise using distance to reference (5th data point)
x_norm <- Normalise(x, f_n = "n_dist2ref", f_n_para = list(iref = 5))
# view side by side
data.frame(x, x_norm)
```

*COINr*version 1.1.14 Index]