linarpr: Linearization of at-risk-of-poverty rate

Description

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.

Usage

linarpr(Y, id,
  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")

Arguments

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

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

Y_thres

Variable (for example equalized disposable income) used for computation and linearization of poverty threshold. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with on

wght_thres

Weight variable used for computation and linearization of poverty threshold. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (len

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 at-risk-of-poverty threshold is done for each domain. An object convertible to data.frame or variable names as character vector, column numbers as numeric vec

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.frame or variable names as character, column numbers as numeric vector or logical vecto

dataset

Optional survey data object convertible to data.frame.

percentage

A numeric value in range $\left[ 0,100 \right]$ for $p$ in the formula for at-risk-of-poverty threshold computation: $$\frac{p}{100} \cdot Z_{\frac{\alpha}{100}}.$$ For example, to compute at-risk-of-poverty threshold equal to 60% of some income quantil

order_quant

A numeric value in range $\left[ 0,100 \right]$ for $\alpha$ in the formula for at-risk-of-poverty threshold computation: $$\frac{p}{100} \cdot Z_{\frac{\alpha}{100}}.$$ For example, to compute at-risk-of-poverty threshold equal to some percentage of me

var_name

A character specifying the name of the linearized variable.

Value

A list with four objects are returned:
quantileA data.table containing the estimated value of the quintile used for at-risk-of-poverty threshold estimation.
thresholdA data.table containing the estimated at-risk-of-poverty threshold.
valueA data.table containing the estimated at-risk-of-poverty rate (in percentage).
linA data.table containing the linearized variables of the at-risk-of-poverty rate (in percentage).

Details

The implementation strictly follows the Eurostat definition.

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.

Examples

Run this code

data(eusilc)

dati <- data.frame(IDd = 1:nrow(eusilc), eusilc)

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

# By domains
dd <- linarpr(Y="eqIncome", id="IDd", weight="rb050", Dom="db040",
              dataset=dati, percentage=60, order_quant=50)

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