vardpoor (version 0.3.2)

linpoormed: Linearization of the median income of individuals below the At Risk of Poverty Threshold

Description

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.

Usage

linpoormed(Y, id, weight=NULL, sort=NULL,
           Dom=NULL, period=NULL, dataset = NULL,
           percentage=60, order_quant=50,
           var_name="lin_poormed")

Arguments

Y

Study variable (for example equalized disposable income). One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector h

id

Optional variable for unit ID codes. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as

weight

Optional weight variable. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as the column

sort

Optional variable to be used as tie-breaker for sorting. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector ha

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.frame or variable names as character vector, col

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.frame or variable names as character, column numbers or logical

dataset

Optional survey data object convertible to data.frame.

percentage

A numeric value in range $[0,100]$ for $p$ in the formula for poverty threshold computation: $$\frac{p}{100} \cdot Z_{\frac{\alpha}{100}}.$$ For example, to compute poverty threshold equal to 60% of some income quantile, $p$ should be set equal to 6

order_quant

A numeric value in range $[0,100]$ for $\alpha$ in the formula for poverty threshold computation: $$\frac{p}{100} \cdot Z_{\frac{\alpha}{100}}.$$ For example, to compute poverty threshold equal to some percentage of median income, $\alpha$ should be

var_name

A character specifying the name of the linearized variable.

Value

• 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.

References

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 http://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 http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=12-001-X19990024882.

See Also

linarpt, linrmpg, varpoord

Examples

data(eusilc)
dati=data.frame(1:nrow(eusilc),eusilc)
colnames(dati)[1] <- "IDd"

# Full population
d <- linpoormed(Y="eqIncome", id="IDd", weight = "rb050", Dom=NULL,
                dataset=dati, percentage=60, order_quant=50)

# Domains by location of houshold
dd <- linpoormed(Y="eqIncome", id="IDd", weight="rb050", Dom="db040",
                dataset=dati, percentage=60, order_quant=50)