vardpoor (version 0.20.1)

vardchangespoor: Variance estimation for measures of change for sample surveys for indicators on social exclusion and poverty

Description

Computes the variance estimation for measures of change for indicators on social exclusion and poverty.

Usage

vardchangespoor(
  Y,
  age = NULL,
  pl085 = NULL,
  month_at_work = NULL,
  Y_den = NULL,
  Y_thres = NULL,
  wght_thres = NULL,
  H,
  PSU,
  w_final,
  ID_level1,
  ID_level2,
  Dom = NULL,
  country = NULL,
  period,
  sort = NULL,
  period1,
  period2,
  gender = NULL,
  dataset = NULL,
  X = NULL,
  countryX = NULL,
  periodX = NULL,
  X_ID_level1 = NULL,
  ind_gr = NULL,
  g = NULL,
  q = NULL,
  datasetX = NULL,
  percentage = 60,
  order_quant = 50,
  alpha = 20,
  use.estVar = FALSE,
  confidence = 0.95,
  outp_lin = FALSE,
  outp_res = FALSE,
  type = "linrmpg",
  change_type = "absolute"
)

Arguments

Y

Study variable (for example equalized disposable income or gross pension income). One dimensional object convertible to one-column data.table or variable name as character, column number.

age

Age variable. One dimensional object convertible to one-column data.table or variable name as character, column number.

pl085

Retirement variable (Number of months spent in retirement or early retirement). One dimensional object convertible to one-column data.table or variable name as character, column number.

month_at_work

Variable for total number of month at work (sum of the number of months spent at full-time work as employee, number of months spent at part-time work as employee, number of months spent at full-time work as self-employed (including family worker), number of months spent at part-time work as self-employed (including family worker)). One dimensional object convertible to one-column data.table or variable name as character, column number.

Y_den

Denominator variable (for example gross individual earnings). One dimensional object convertible to one-column data.table or variable name as character, column number.

Y_thres

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.

wght_thres

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. Variable specified for weight is used as wght_thres if wght_thres is not defined.

H

The unit stratum variable. One dimensional object convertible to one-column data.table or variable name as character, column number.

PSU

Primary sampling unit variable. One dimensional object convertible to one-column data.table or variable name as character, column number.

w_final

Weight variable. One dimensional object convertible to one-column data.table 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 count of dataset).

ID_level1

Variable for level1 ID codes. One dimensional object convertible to one-column data.table or variable name as character, column number.

ID_level2

Optional variable for unit ID codes. 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, variables are calculated for each domain. An object convertible to data.table or variable names as character vector, column numbers.

country

Variable for the survey countries. The values for each country are computed independently. Object convertible to data.table or variable names as character, column numbers.

period

Variable for the all survey periods. The values for each period are computed independently. Object convertible to data.table or variable names as character, column numbers.

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.

period1

The vector from variable period describes the first period.

period2

The vector from variable period describes the second period.

gender

Numerical variable for gender, where 1 is for males, but 2 is for females. One dimensional object convertible to one-column data.table or variable name as character, column number.

dataset

Optional survey data object convertible to data.frame.

X

Optional matrix of the auxiliary variables for the calibration estimator. Object convertible to data.table or variable names as character, column numbers.

countryX

Optional variable for the survey countries. The values for each country are computed independently. Object convertible to data.table or variable names as character, column numbers.

periodX

Optional variable of the survey periods and countries. If supplied, residual estimation of calibration is done independently for each time period. Object convertible to data.table or variable names as character, column numbers.

X_ID_level1

Variable for level1 ID codes. One dimensional object convertible to one-column data.table or variable name as character, column number.

ind_gr

Optional variable by which divided independently X matrix of the auxiliary variables for the calibration. One dimensional object convertible to one-column data.table or variable name as character, column number.

g

Optional variable of the g weights. One dimensional object convertible to one-column data.table or variable name as character, column number.

q

Variable of the positive values accounting for heteroscedasticity. One dimensional object convertible to one-column data.table or variable name as character, column number.

datasetX

Optional survey data object in household level convertible to data.table.

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 60.

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 set equal to 50.

alpha

a numeric value in range \([0,100]\) for the order of the income quantile share ratio (in percentage).

use.estVar

Logical value. If value is TRUE, then R function estVar is used for the estimation of covariance matrix of the residuals. If value is FALSE, then R function estVar is not used for the estimation of covariance matrix of the residuals.

confidence

optional; either a positive value for confidence interval. This variable by default is 0.95.

outp_lin

Logical value. If TRUE linearized values of the ratio estimator will be printed out.

outp_res

Logical value. If TRUE estimated residuals of calibration will be printed out.

type

a character vector (of length one unless several.ok is TRUE), example "linarpr","linarpt", "lingpg", "linpoormed", "linrmpg", "lingini", "lingini2", "linqsr", "linarr", "linrmir", "all_choices".

change_type

character value net changes type - absolute or relative.

Value

A list with objects are returned by the function:

• cros_lin_out - a data.table containing the linearized values of the ratio estimator with ID_level2 and PSU by periods and countries (if available).

• cros_res_out - a data.table containing the estimated residuals of calibration with ID_level1 and PSU by periods and countries (if available).

• crossectional_results - a data.table containing: period - survey periods, country - survey countries, Dom - optional variable of the population domains, type - type variable, count_respondents - the count of respondents, pop_size - the population size (in numbers of individuals), estim - the estimated value, se - the estimated standard error, var - the estimated variance, rse - the estimated relative standard error (coefficient of variation), cv - the estimated relative standard error (coefficient of variation) in percentage.

• changes_results - a data.table containing: period - survey periods, country - survey countries, Dom - optional variable of the population domains, type - type variable, estim_1 - the estimated value for period1, estim_2 - the estimated value for period2, estim - the estimated value, se - the estimated standard error, var - the estimated variance, rse - the estimated relative standard error (coefficient of variation), cv - the estimated relative standard error (coefficient of variation) in percentage.

References

Guillaume Osier, Yves Berger, Tim Goedeme, (2013), Standard error estimation for the EU-SILC indicators of poverty and social exclusion, Eurostat Methodologies and Working papers, URL http://ec.europa.eu/eurostat/documents/3888793/5855973/KS-RA-13-024-EN.PDF. Eurostat Methodologies and Working papers, Handbook on precision requirements and variance estimation for ESS household surveys, 2013, URL http://ec.europa.eu/eurostat/documents/3859598/5927001/KS-RA-13-029-EN.PDF. Yves G. Berger, Tim Goedeme, Guillame Osier (2013). Handbook on standard error estimation and other related sampling issues in EU-SILC, URL https://ec.europa.eu/eurostat/cros/content/handbook-standard-error-estimation-and-other-related-sampling-issues-ver-29072013_en

See Also

domain, vardchanges, vardcros, vardcrospoor

Examples

# NOT RUN {
 
### Example 
library("laeken")  
library("data.table")
data(eusilc)
set.seed(1)
dataset1 <- data.table(rbind(eusilc, eusilc),
                       year = c(rep(2010, nrow(eusilc)),
                                rep(2011, nrow(eusilc))),
                       country = c(rep("AT", nrow(eusilc)),
                                   rep("AT", nrow(eusilc))))
dataset1[age < 0, age := 0]
PSU <- dataset1[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
PSU$inc <- runif(nrow(PSU), 20, 100000)
dataset1 <- merge(dataset1, PSU, all = TRUE, by = "db030")
PSU <- eusilc <- NULL
dataset1[, strata := c("XXXX")]
dataset1$pl085 <- 12 * trunc(runif(nrow(dataset1), 0, 2))
dataset1$month_at_work <- 12 * trunc(runif(nrow(dataset1), 0, 2))
dataset1[, id_l2 := paste0("V", .I)]
result <- vardchangespoor(Y = "inc", age = "age",
                          pl085 = "pl085", month_at_work = "month_at_work",
                          Y_den = "inc", Y_thres = "inc",
                          wght_thres = "rb050",  H = "strata", 
                          PSU = "PSU", w_final="rb050",
                          ID_level1 = "db030",  ID_level2 = "id_l2",
                          Dom = c("rb090"), country = "country",
                          period = "year", sort = NULL,  
                          period1 = c(2010, 2011),
                          period2 = c(2011, 2010),
                          gender = NULL, dataset = dataset1,
                          percentage = 60, order_quant = 50L,
                          alpha = 20, confidence = 0.95,
                          type = "linrmpg")
result


# }