vardpoor (version 0.2.0.9.2)

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

Description

Estimate the at-risk-of-poverty rate, which is defined as the proportion of persons with equivalized disposable income below the at-risk-of-poverty threshold, and its linearization.

Usage

linarpr(inc, id, weight = NULL,
  income_thres = NULL, wght_thres = NULL,
  sort = NULL, Dom = NULL, period = NULL, dataset = NULL,
  percentage = 60, order_quant = 50, na.rm = FALSE,
  var_name = "lin_arpr")

Arguments

inc

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

id

Optional variable for unit ID codes. One dimentional object convertable 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 dimentional object convertable 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

income_thres

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

wght_thres

Weight variable used for computation and linearization of poverty threshold. One dimentional object convertable 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 dimentional object convertable 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 at-risk-of-poverty rate is done for each domain. An object convertable to data.frame or variable names as character vector, column numbers or logical vect

period

Optional variable for survey period. If supplied, linearization of the at-risk-of-poverty rate is done for each time period. Object convertable to data.frame or variable names as character, column numbers or logical vector (length of the vect

dataset

Optional survey data object convertable 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

na.rm

A logical value indicating whether missing values in study variable should be removed.

var_name

A character specifying the name of the linearized variable.

Value

• A list with four objects are returned by the function:
• quantile
  A data.frame containing the estimated value of the quintale used for at-risk-of-poverty threshold estimation.
• threshold
  A data.frame containing the estimated at-risk-of-poverty threshold.
• value
  A data.frame containing the estimated at-risk-of-poverty rate (in percentage).
• lin
  A data.frame containing the linearized variables of the 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 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 http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=12-001-X19990024882.

See Also

linarpt, vardom, varpoord

Examples

data(eusilc)
dati=data.frame(1:nrow(eusilc),eusilc)
colnames(dati)[1] <- "IDd"
d<-linarpr("eqIncome", id="IDd", weight = "rb050", Dom = NULL,
         dataset = dati, percentage = 60, order_quant=50, na.rm = FALSE)

dd<-linarpr("eqIncome", id="IDd", weight = "rb050", Dom = "db040",
         dataset = dati, percentage = 60, order_quant=50, na.rm = FALSE)