ci_factor {Compind} | R Documentation |

## Weighting method based on Factor Analysis

### Description

Factor analysis groups together collinear simple indicators to estimate a composite indicator that captures as much as possible of the information common to individual indicators.

### Usage

```
ci_factor(x,indic_col,method="ONE",dim)
```

### Arguments

`x` |
A data.frame containing score of the simple indicators. |

`indic_col` |
Simple indicators column number. |

`method` |
If method = "ONE" (default) the composite indicator estimated values are equal to first component scores; if method = "ALL" the composite indicator estimated values are equal to component score multiplied by its proportion variance; if method = "CH" it can be choose the number of the component to take into account. |

`dim` |
Number of chosen component (if method = "CH", default is 3). |

### Value

An object of class "CI". This is a list containing the following elements:

`ci_factor_est` |
Composite indicator estimated values. |

`loadings_fact` |
Variance explained by principal factors (in percentage terms). |

`ci_method` |
Method used; for this function ci_method="factor". |

### Author(s)

Vidoli F.

### References

OECD (2008) "*Handbook on constructing composite indicators: methodology and user guide*".

### See Also

### Examples

```
i1 <- seq(0.3, 0.5, len = 100) - rnorm (100, 0.2, 0.03)
i2 <- seq(0.3, 1, len = 100) - rnorm (100, 0.2, 0.03)
Indic = data.frame(i1, i2)
CI = ci_factor(Indic)
data(EU_NUTS1)
CI = ci_factor(EU_NUTS1,c(2:3), method="ALL")
data(EU_2020)
data_norm = normalise_ci(EU_2020,c(47:51),polarity = c("POS","POS","POS","POS","POS"), method=2)
CI3 = ci_factor(data_norm$ci_norm,c(1:5),method="CH", dim=3)
```

*Compind*version 3.1 Index]