normalise_ci {Compind} R Documentation

## Normalisation and polarity functions

### Description

This function lets to normalise simple indicators according to the polarity of each one.

### Usage

normalise_ci(x, indic_col, polarity, method=1, z.mean=0, z.std=1, ties.method ="average")


### Arguments

 x A data frame containing simple indicators. indic_col Simple indicators column number. method Normalisation methods: 1 (default) = standardization or z-scores using the following formulation: z_{ij}=z.mean \pm \frac{x_{ij}-M_{x_j}}{S_{x_j}}\cdot z.std where \pm depends on polarity parameter and z.mean and z.std represent the shifting parameters. 2 = Min-max method using the following formulation: if polarity="POS": \frac{x-min(x)}{max(x)-min(x)} if polarity="NEG": \frac{max(x)-x}{max(x)-min(x)} 3 = Ranking method. If polarity="POS" ranking is increasing, while if polarity="NEG" ranking is decreasing. polarity Polarity vector: "POS" = positive, "NEG" = negative. The polarity of a individual indicator is the sign of the relationship between the indicator and the phenomenon to be measured (e.g., in a well-being index, "GDP per capita" has 'positive' polarity and "Unemployment rate" has 'negative' polarity). z.mean If method=1, Average shifting parameter. Default is 0. z.std If method=1, Standard deviation expansion parameter. Default is 1. ties.method If method=3, A character string specifying how ties are treated, see rank for details. Default is "average".

### Value

 ci_norm A data.frame containing normalised score of the choosen simple indicators. norm_method Normalisation method used.

Vidoli F.

### References

OECD, "Handbook on constructing composite indicators: methodology and user guide", 2008, pag.30.

ci_bod, ci_mpi

### Examples

data(EU_NUTS1)

# Standard z-scores normalisation #
data_norm = normalise_ci(EU_NUTS1,c(2:3),c("NEG","POS"),method=1,z.mean=0, z.std=1)
summary(data_norm$ci_norm) # Normalisation for MPI index # data_norm = normalise_ci(EU_NUTS1,c(2:3),c("NEG","POS"),method=1,z.mean=100, z.std=10) summary(data_norm$ci_norm)

data_norm = normalise_ci(EU_NUTS1,c(2:3),c("NEG","POS"),method=2)
summary(data_norm\$ci_norm)


[Package Compind version 2.8 Index]