vardpoor (version 0.8.4)

vardchanges: Variance estimation for measures of change for single and multistage stage cluster sampling designs

Description

Computes the variance estimation for measures of change for single and multistage stage cluster sampling designs.

Usage

vardchanges(Y, H, PSU, w_final,  ID_level1, ID_level2, Dom = NULL, Z = NULL, country = NULL, period, dataset = NULL, period1, period2, X = NULL, countryX = NULL, periodX = NULL, X_ID_level1 = NULL, ind_gr = NULL, g = NULL, q = NULL, datasetX = NULL, annual = FALSE, linratio = FALSE, percentratio = 1, use.estVar = FALSE, outp_res = FALSE, confidence = 0.95, change_type = "absolute")

Arguments

Y

Variables of interest. Object convertible to data.table or variable names as character, column numbers.

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.

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.

Z

Optional variables of denominator for ratio estimation. If supplied, the ratio estimation is computed. Object convertible to data.table or variable names as character, column numbers. This variable is NULL by default.

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.

dataset

Optional survey data object convertible to data.table.

period1

The vector of periods from variable periods describes the first period.

period2

The vector of periods from variable periods describes the second period.

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 all survey periods. 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.

annual

Logical value. If value is TRUE, then calculate for annual net changes.

linratio

Logical value. If value is TRUE, then the linearized variables for the ratio estimator is used for variance estimation. If value is FALSE, then the gradients is used for variance estimation.

percentratio

Positive numeric value. All linearized variables are multiplied with percentratio value, by default - 1.

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.

outp_res

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

confidence

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

change_type

character value net changes type - absolute or relative.

Value

A list with objects are returned by the function:

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, vardcros, vardchangespoor

Examples

### Example 

data("eusilc")
set.seed(1)
eusilc1 <- eusilc[1:40,]

set.seed(1)
data <- data.table(rbind(eusilc1, eusilc1),
                   year = c(rep(2010, nrow(eusilc1)),
                            rep(2011, nrow(eusilc1))))
data[age < 0, age := 0]
PSU <- data[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 5))]
data <- merge(data, PSU, all = TRUE, by = "db030")
PSU <- eusilc <- NULL
data[, strata := c("XXXX")]

data[, t_pov := trunc(runif(nrow(data), 0, 2))]
data[, exp := 1]

# At-risk-of-poverty (AROP)
data[, pov := ifelse (t_pov == 1, 1, 0)]
data[, id_lev2 := .I]

result <- vardchanges(Y = "pov", H = "strata", 
                      PSU = "PSU", w_final = "rb050",
                      ID_level1 = "db030", ID_level2 = "id_lev2",
                      Dom = NULL, Z = NULL, period = "year",
                      dataset = data, period1 = 2010,
                      period2 = 2011, change_type = "absolute")
 
## Not run: 
# data("eusilc")
# data <- data.table(rbind(eusilc, eusilc),
#                    year=c(rep(2010, nrow(eusilc)),
#                           rep(2011, nrow(eusilc))))
# data[age < 0, age := 0]
# PSU <- data[,.N, keyby = "db030"][, N := NULL]
# PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
# data <- merge(data, PSU, all = TRUE, by = "db030")
# PSU <- eusilc <- NULL
# data[, strata := "XXXX"]
# 
# data[, t_pov := trunc(runif(nrow(data), 0, 2))]
# data[, t_dep := trunc(runif(nrow(data), 0, 2))]
# data[, t_lwi := trunc(runif(nrow(data), 0, 2))]
# data[, exp := 1]
# data[, exp2 := 1 * (age < 60)]
# 
# # At-risk-of-poverty (AROP)
# data[, pov := ifelse (t_pov == 1, 1, 0)]
#  
# # Severe material deprivation (DEP)
# data[, dep := ifelse (t_dep == 1, 1, 0)]
# 
# # Low work intensity (LWI)
# data[, lwi := ifelse (t_lwi == 1 & exp2 == 1, 1, 0)]
# 
# # At-risk-of-poverty or social exclusion (AROPE)
# data[, arope := ifelse (pov == 1 | dep == 1 | lwi == 1, 1, 0)]
# data[, dom := 1]
# data[, id_lev2 := .I]
# 
# result <- vardchanges(Y = c("pov", "dep", "lwi", "arope"),
#                       H = "strata", PSU = "PSU", w_final = "rb050",
#                       ID_level1 = "db030", ID_level2 = "id_lev2",
#                       Dom = "rb090", Z = NULL, period = "year",
#                       dataset = data, period1 = 2010, 
#                       period2 = 2011, change_type = "absolute")
# ## End(Not run)