Quick normalisation of a purse — qNormalise.purse • COINr

This is a wrapper function for Normalise(), which offers a simpler syntax but less flexibility. It normalises data sets within a purse using a specified function f_n which is used to normalise each indicator, with additional function arguments passed by f_n_para. By default, f_n = "n_minmax" and f_n_para is set so that the indicators are normalised using the min-max method, between 0 and 100.

Usage

# S3 method for class 'purse'
qNormalise(
  x,
  dset,
  f_n = "n_minmax",
  f_n_para = list(l_u = c(0, 100)),
  directions = NULL,
  global = TRUE,
  ...
)

Arguments

x

A purse

dset

Name of data set to normalise

f_n

Name of a normalisation function (as a string) to apply to each indicator. Default "n_minmax".

f_n_para

Any further arguments to pass to f_n, as a named list.

directions

An optional data frame containing the following columns:

iCode The indicator code, corresponding to the column names of the data frame
Direction numeric vector with entries either -1 or 1 If directions is not specified, the directions will be taken from the iMeta table in the coin, if available.

global

Logical: if TRUE, normalisation is performed "globally" across all coins, by using e.g. the max and min of each indicator in any coin. This effectively makes normalised scores comparable between coins because they are all scaled using the same parameters. Otherwise if FALSE, coins are normalised individually.

...

arguments passed to or from other methods.

Value

An updated purse with normalised data sets

Details

Essentially, this function is similar to Normalise() but brings parameters into the function arguments rather than being wrapped in a list. It also does not allow individual normalisation.

Normalisation can either be performed independently on each coin, or over the entire panel data set simultaneously. See the discussion in Normalise.purse() and vignette("normalise").

Examples

# build example purse
purse <- build_example_purse(up_to = "new_coin", quietly = TRUE)

# normalise using min-max, globally
purse <- qNormalise(purse, dset = "Raw", global = TRUE)
#> Written data set to .$Data$Normalised
#> Written data set to .$Data$Normalised
#> Written data set to .$Data$Normalised
#> Written data set to .$Data$Normalised
#> Written data set to .$Data$Normalised