ci_factor_mixed {Compind} | R Documentation |

## Weighting method based on Factor analysis of mixed data (FAMD)

### Description

Factor analysis of mixed data (FAMD) can be seen as a principal component method dedicated to analyze a data set containing both quantitative and qualitative variables making possible to compute composite indicators taking into account continous, dummy, or factor variables

### Usage

```
ci_factor_mixed(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_mixed". |

### Author(s)

Luis Carlos Castillo Tellez

### 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)
i3 <- seq(0, 1, len = 100)
i3 = as.factor(ifelse(i3>0.5,1,0))
Indic = data.frame(i1, i2, i3)
CI = ci_factor_mixed(Indic,c(1:3))
CI2 = ci_factor_mixed(Indic,c(1:3), method="ALL")
CI3 = ci_factor_mixed(Indic,c(1:3), method="CH", dim=2)
```

*Compind*version 3.1 Index]