linrmir {vardpoor} | R Documentation |
Linearization of the relative median income ratio
Description
Estimates the relative median income ratio (defined as the ratio of the median equivalised disposable income of people aged above age to the median equivalised disposable income of those aged below 65) and computes linearized variable for variance estimation.
Usage
linrmir(
Y,
id = NULL,
age,
weight = NULL,
sort = NULL,
Dom = NULL,
period = NULL,
dataset = NULL,
order_quant = 50,
var_name = "lin_rmir",
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 |
age |
Age variable. 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 at-risk-of-poverty threshold is done for each domain. An object convertible to |
period |
Optional variable for survey period. If supplied, linearization of at-risk-of-poverty threshold is done for each survey period. Object convertible to |
dataset |
Optional survey data object convertible to |
order_quant |
A numeric value in range
For example, to compute the relative median income ratio to some percentage of median income, |
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. |
Details
The implementation strictly follows the Eurostat definition.
Value
A list with four objects are returned:
-
value
- adata.table
containing the estimated relative median income ratio. -
lin
- adata.table
containing the linearized variables of the relative median income ratio.
References
Working group on Statistics on Income and Living Conditions (2015) Task 5 - Improvement and optimization of calculation of net change. LC- 139/15/EN, Eurostat.
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
varpoord
,
vardcrospoor
,
vardchangespoor
Examples
library("laeken")
library("data.table")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
# Full population
d <- linrmir(Y = "eqIncome", id = "IDd", age = "age",
weight = "rb050", Dom = NULL,
dataset = dataset1, order_quant = 50L)
## Not run:
# By domains
dd <- linrmir(Y = "eqIncome", id = "IDd", age = "age",
weight = "rb050", Dom = "db040",
dataset = dataset1, order_quant = 50L)
dd
## End(Not run)