linarr: Linearization of the aggregate replacement ratio
In vardpoor: Variance Estimation for Sample Surveys by the Ultimate Cluster Method

Description Usage Arguments Details Value References See Also Examples

Estimates the aggregate replacement ratio (defined as the gross median individual pension income of the population aged 65-74 relative to the gross median individual earnings from work of the population aged 50-59, excluding other social benefits) and computes linearized variable for variance estimation.

linarr(
  Y,
  Y_den,
  id = NULL,
  age,
  pl085,
  month_at_work,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  order_quant = 50,
  var_name = "lin_arr",
  checking = TRUE
)

`Y`	Numerator variable (for gross pension income). One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`Y_den`	Denominator variable (for example gross individual earnings). One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`id`	Optional variable for unit ID codes. One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`age`	Age variable. One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`pl085`	Retirement variable (Number of months spent in retirement or early retirement). One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`month_at_work`	Variable for total number of month at work (sum of the number of months spent at full-time work as employee, number of months spent at part-time work as employee, number of months spent at full-time work as self-employed (including family worker), number of months spent at part-time work as self-employed (including family worker)). One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`weight`	Optional weight variable. One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`sort`	Optional variable to be used as tie-breaker for sorting. One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`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 `data.table` or variable names as character vector, column numbers as numeric vector.
`period`	Optional variable for survey period. If supplied, linearization of at-risk-of-poverty threshold is done for each survey period. Object convertible to `data.table` or variable names as character, column numbers as numeric vector.
`dataset`	Optional survey data object convertible to `data.table`.
`order_quant`	A numeric value in range [0,100] for α in the formula #'for at-risk-of-poverty threshold computation: p/100 Z(α/100).* For example, to compute at-risk-of-poverty threshold equal to some percentage of median income, α #'should be set equal to 50.
`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.

The implementation strictly follows the Eurostat definition.

A list with four objects are returned:

value - a data.table containing the estimated the aggregate replacement ratio.
lin - a data.table containing the linearized variables of the aggregate replacement ratio.

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.

varpoord, vardcrospoor, vardchangespoor

library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
dataset1$pl085 <- 12 * trunc(runif(nrow(dataset1), 0, 2))
dataset1$month_at_work <- 12 * trunc(runif(nrow(dataset1), 0, 2))
    
# Full population
d <- linarr(Y = "eqIncome", Y_den = "eqIncome",
            id = "IDd", age = "age",  
            pl085 = "pl085", month_at_work = "month_at_work",
            weight = "rb050",  Dom = NULL,
            dataset = dataset1, order_quant = 50L)
d$value
    
## Not run: 
# By domains
dd <- linarr(Y = "eqIncome", Y_den = "eqIncome",
             id = "IDd", age = "age",  
             pl085 = "pl085", month_at_work = "month_at_work",
             weight = "rb050",  Dom = "db040",
             dataset = dataset1, order_quant = 50L)
 dd
## End(Not run)