linarpt: Linearization of at-risk-of-poverty threshold
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 threshold (defined as percentage (usually 60%) of equalised disposable income after social transfers quantile (usually median)) and computes linearized variable for variance estimation.

linarpt(
  Y,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  percentage = 60,
  order_quant = 50,
  var_name = "lin_arpt",
  checking = TRUE
)

`Y`	Study variable (for example equalised disposable income after social transfers). 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.
`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`.
`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 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 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 three objects are returned:

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

linarpr, incPercentile, varpoord , vardcrospoor, vardchangespoor

library("data.table") 
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)

# Full population
d1 <- linarpt(Y = "eqIncome", id = "IDd",
              weight = "rb050", Dom = NULL,
              dataset = dataset1, percentage = 60,
              order_quant = 50L)
d1$value

## Not run: 
# By domains
d2 <- linarpt(Y = "eqIncome", id = "IDd",
              weight = "rb050", Dom = "db040",
              dataset = dataset1, percentage = 60,
              order_quant = 50L)
d2$value
## End(Not run)