linpoormed: Linearization of the median income of individuals below the...
In vardpoor: Variance Estimation for Sample Surveys by the Ultimate Cluster Method

Description Usage Arguments Value References See Also Examples

Estimation of the median income of individuals below At Risk of Poverty Threshold and computation of linearized variable for variance estimation. The At Risk of Poverty Threshold is estimated for the whole population always. The median income is estimated for the whole population or for each domain.

linpoormed(
  Y,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  percentage = 60,
  order_quant = 50,
  var_name = "lin_poormed",
  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.
`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 the median income of persons below a poverty threshold is done for each domain. An object convertible to `data.table` or variable names as character vector, column numbers.
`period`	Optional variable for survey period. If supplied, linearization of the median income of persons below a poverty threshold is done for each time period. Object convertible to `data.table` or variable names as character, column numbers.
`dataset`	Optional survey data object convertible to `data.table`.
`percentage`	A numeric value in range [0,100] for p in the formula for 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 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.

A list with two objects are returned by the function:

value - a data.table containing the estimated median income of individuals below the At Risk of Poverty Threshold.
lin - a data.table containing the linearized variables of the median income below the At Risk of Poverty Threshold.

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, linrmpg, varpoord, vardcrospoor, vardchangespoor

library("laeken")
library("data.table")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
 
# Full population
d <- linpoormed(Y = "eqIncome", id = "IDd",
                weight = "rb050", Dom = NULL,
                dataset = dataset1, percentage = 60,
                order_quant = 50L)
  
## Not run: 
# Domains by location of houshold
dd <- linpoormed(Y = "eqIncome", id = "IDd",
                 weight = "rb050", Dom = "db040",
                 dataset = dataset1, percentage = 60,
                 order_quant = 50L)
dd
## End(Not run)