linqsr {vardpoor} | R Documentation |
Linearization of the Quintile Share Ratio
Description
Estimate the Quintile Share Ratio, which is defined as the ratio of the sum of equalized disposable income received by the top 20% to the sum of equalized disposable income received by the bottom 20%, and its linearization.
Usage
linqsr(
Y,
id = NULL,
weight = NULL,
sort = NULL,
Dom = NULL,
period = NULL,
dataset = NULL,
alpha = 20,
var_name = "lin_qsr",
checking = TRUE
)
Arguments
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 income quantile share ratio is done for each domain. An object convertible to |
period |
Optional variable for survey period. If supplied, linearization of the income quantile share ratio is done for each time period. Object convertible to |
dataset |
Optional survey data object convertible to |
alpha |
a numeric value in range |
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. |
Value
A list with two objects are returned by the function:
-
value
- adata.table
containing the estimated Quintile Share Ratio by G. Osier and Eurostat papers. -
lin
- adata.table
containing the linearized variables of the Quintile Share Ratio by G. Osier paper.
References
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.
See Also
incPercentile
,
varpoord
,
vardcrospoor
,
vardchangespoor
Examples
library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
# Full population
dd <- linqsr(Y = "eqIncome", id = "IDd",
weight = "rb050", Dom = NULL,
dataset = dataset1, alpha = 20)
dd$value
## Not run:
# By domains
dd <- linqsr(Y = "eqIncome", id = "IDd",
weight = "rb050", Dom = "db040",
dataset = dataset1, alpha = 20)
dd$value
## End(Not run)