Description Usage Arguments Value References See Also Examples

Estimate the Gini coefficient, which is a measure for inequality, and its linearization.

1 2 3 4 5 |

`Y` |
Study variable (for example equalized disposable income). One dimensional object convertible to one-column |

`id` |
Optional variable for unit ID codes. One dimensional object convertible to one-column |

`weight` |
Optional weight variable. One dimensional object convertible to one-column |

`sort` |
Optional variable to be used as tie-breaker for sorting. One dimensional object convertible to one-column |

`Dom` |
Optional variables used to define population domains. If supplied, linearization of the GINI is done for each domain. An object convertible to |

`period` |
Optional variable for survey period. If supplied, linearization of the GINI is done for each time period. Object convertible to |

`dataset` |
Optional survey data object convertible to |

`var_name` |
A character specifying the name of the linearized variable. |

`checking` |
Optional variable if this variable is TRUE, then function checks data preparation errors, otherwise not checked. This variable by default is TRUE. |

A list with two objects are returned by the function:

`value` |
A |

`lin` |
A |

Eric Graf and Yves Tille, Variance Estimation Using Linearization for Poverty and Social Exclusion Indicators, Survey Methodology, June 2014 61 Vol. 40, No. 1, pp. 61-79, Statistics Canada, Catalogue no. 12-001-X,
URL http://www.statcan.gc.ca/pub/12-001-x/12-001-x2014001-eng.pdf

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL http://www.statcan.gc.ca/pub/12-001-x/1999002/article/4882-eng.pdf.

MATTI LANGEL - YVES TILLE, Corrado Gini, a pioneer in balanced sampling and inequality theory. *METRON - International Journal of Statistics*, 2011, vol. LXIX, n. 1, pp. 45-65, URL ftp://metron.sta.uniroma1.it/RePEc/articoli/2011-1-3.pdf.

Working group on Statistics on Income and Living Conditions (2004) Common cross-sectional EU indicators based on EU-SILC; the gender pay gap. *EU-SILC 131-rev/04*, Eurostat.

`lingini`

, `linqsr`

, `varpoord`

, `vardcrospoor`

, `vardchangespoor`

1 2 3 4 5 6 7 8 9 10 11 12 | ```
data(eusilc)
dati <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
# Full population
dat1 <- lingini2(Y = "eqIncome", id = "IDd", weight = "rb050", dataset = dati)
dat1$value
## Not run:
# By domains
dat2 <- lingini2(Y = "eqIncome", id = "IDd", weight = "rb050", Dom = c("db040"), dataset = dati)
dat2$value
## End(Not run)
``` |

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