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.
linarpr(Y, id = NULL, 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",
kern_method = "gaussian", r = NULL, ro = NULL,
h_breaks = NULL, checking = TRUE)Study variable (for example equalized disposable income). One dimensional object convertible to one-column data.table or variable name as character, column number).
Optional variable for unit ID codes. One dimensional object convertible to one-column data.table or variable name as character, column number or logical vector).
Optional weight variable. One dimensional object convertible to one-column data.table or variable name as character, column number or logical vector).
Variable (for example equalized disposable income) used for computation and linearization of poverty threshold. One dimensional object convertible to one-column data.table or variable name as character, column number. Variable specified for inc is used as income_thres if income_thres is not defined.
Weight variable used for computation and linearization of poverty threshold. One dimensional object convertible to one-column data.table or variable name as character, column number or logical vector. Variable specified for weight is used as wght_thres if wght_thres is not defined.
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.
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.
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.
Optional survey data object convertible to data.table.
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 quantile, \(p\) should be set equal to 60.
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 median income, \(\alpha\) should be set equal to 50.
A character specifying the name of the linearized variable.
A character specifying the method used for density calculation.
Value specifying the smoothing spline degree.
Value specifying the smoothing spline error from histogram.
Value specifying the histogram bins.
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 four objects are returned:
A data.table containing the estimated value of the quintile used for at-risk-of-poverty threshold estimation.
A data.table containing the estimated at-risk-of-poverty threshold.
A data.table containing the estimated at-risk-of-poverty rate (in percentage).
A data.table containing the linearized variables of the at-risk-of-poverty rate (in percentage).
The implementation strictly follows the Eurostat definition.
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://www.statcan.gc.ca/pub/12-001-x/1999002/article/4882-eng.pdf.
# NOT RUN {
data(eusilc)
require(data.table)
dati <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
# Full population
d <- linarpr(Y = "eqIncome", id = "IDd",
weight = "rb050", Dom = NULL,
dataset = dati, percentage = 60,
order_quant = 50L)
d$value
# }
# NOT RUN {
# By domains
dd <- linarpr(Y = "eqIncome", id = "IDd",
weight = "rb050", Dom = "db040",
dataset = dati, percentage = 60,
order_quant=50L)
# }
Run the code above in your browser using DataLab