COINr (version 1.1.14)

Normalise.purse: Create normalised data sets in a purse of coins

Description

This creates normalised data sets for each coin in the purse. In most respects, this works in a similar way to normalising on a coin, for which reason please see Normalise.coin() for most documentation. There is however a special case in terms of operating on a purse of coins. This is because, when dealing with time series data, it is often desirable to normalise over the whole panel data set at once rather than independently for each time point. This makes the resulting index and aggregates comparable over time. Here, the global argument controls whether to normalise each coin independently or to normalise across all data at once. In other respects, this function behaves the same as Normalise.coin().

Usage

# S3 method for purse
Normalise(
  x,
  dset,
  global_specs = NULL,
  indiv_specs = NULL,
  directions = NULL,
  global = TRUE,
  write_to = NULL,
  ...
)

Value

An updated purse with new normalised data sets added at .$Data$Normalised in each coin

Arguments

x

A purse object

dset

The data set to normalise in each coin

global_specs

Default specifications

indiv_specs

Individual specifications

directions

An optional data frame containing the following columns:

• iCode The indicator code, corresponding to the column names of the data set

• 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.

write_to

Optional character string for naming the data set in each coin. Data will be written to .$Data[[write_to]]. Default is write_to == "Normalised".

...

arguments passed to or from other methods.

Details

The same specifications are passed to each coin in the purse. This means that each coin is normalised using the same set of specifications and directions. If you need control over individual coins, you will have to normalise coins individually.

Examples

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

# normalise raw data set
purse <- Normalise(purse, dset = "Raw", global = TRUE)