linarpr: Linearization of at-risk-of-poverty rate
In vardpoor: Variance Estimation for Sample Surveys by the Ultimate Cluster Method

Description Usage Arguments Details Value References See Also Examples

Estimates the at-risk-of-poverty rate (defined as the proportion of persons with equalized disposable income below at-risk-of-poverty threshold) and computes linearized variable for variance estimation.

linarpr(
  Y,
  id = NULL,
  weight = NULL,
  Y_thres = NULL,
  wght_thres = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  percentage = 60,
  order_quant = 50,
  var_name = "lin_arpr",
  checking = TRUE
)

`Y`	Study variable (for example equalized disposable income). 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 or logical vector).
`weight`	Optional weight variable. One dimensional object convertible to one-column `data.table` or variable name as character, column number or logical vector).
`Y_thres`	Variable (for example equalized disposable income) used for computation and linearization of poverty threshold. One dimensional object convertible to one-column `data.table` or variable name as character, column number. Variable specified for `inc` is used as `income_thres` if `income_thres` is not defined.
`wght_thres`	Weight variable used for computation and linearization of poverty threshold. One dimensional object convertible to one-column `data.table` or variable name as character, column number or logical vector. Variable specified for `weight` is used as `wght_thres` if `wght_thres` is not defined.
`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`.
`percentage`	A numeric value in range [0,100] for p 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 60% of some income quantile, p #'should be set equal to 60.
`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:

quantile - a data.table containing the estimated value of the quantile used for at-risk-of-poverty threshold estimation.
threshold - a data.table containing the estimated at-risk-of-poverty threshold.
value - a data.table containing the estimated at-risk-of-poverty rate (in percentage).
lin - a data.table containing the linearized variables of the at-risk-of-poverty rate (in percentage).

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.

linarpt, varpoord, vardcrospoor, vardchangespoor

library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
    
# Full population
d <- linarpr(Y = "eqIncome", id = "IDd",
             weight = "rb050", Dom = NULL,
             dataset = dataset1, percentage = 60,
             order_quant = 50L)
d$value
    
## Not run: 
# By domains
dd <- linarpr(Y = "eqIncome", id = "IDd",
              weight = "rb050", Dom = "db040",
              dataset = dataset1, percentage = 60,
              order_quant = 50L)
dd
## End(Not run)