Description Usage Arguments References See Also Examples
Estimate the Gini coefficient, which is a measure for inequality, and its linearization.
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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. return A list with two objects are returned by the function:
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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.
Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.
Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.
lingini2
,
linqsr
,
varpoord
,
vardcrospoor
,
vardchangespoor
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | library("laeken")
library("data.table")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)[1 : 3,]
# Full population
dat1 <- lingini(Y = "eqIncome", id = "IDd",
weight = "rb050", dataset = dataset1)
dat1$value
## Not run:
# By domains
dat2 <- lingini(Y = "eqIncome", id = "IDd", weight = "rb050",
Dom = c("db040"), dataset = dataset1)
dat2$value
## End(Not run)
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