Extra variables for domain estimation

Description

The function computes extra variables for domain estimation. Each unique D row defines a domain. Extra variables are computed for each Y variable.

Usage

domain(Y, D, dataset = NULL, checking = TRUE)

Arguments

Value

Numeric data.table containing extra variables for domain estimation.

References

Carl-Erik Sarndal, Bengt Swensson, Jan Wretman. Model Assisted Survey Sampling. Springer-Verlag, 1992, p.70.

See Also

vardom, vardomh

Examples

### Example 0
 
domain(Y = 1, D = "A")
 
  
### Example 1

Y1 <- as.matrix(1 : 10)
colnames(Y1) <- "Y1"
D1 <- as.matrix(rep(1, 10))
colnames(D1) <- "D1"
domain(Y = Y1, D = D1)
  
### Example 2
Y <- matrix(1 : 20, 10, 2)
colnames(Y) <- paste0("Y", 1 : 2)
D <- matrix(rep(1 : 2, each = 5), 10, 1)
colnames(D) <- "D"
domain(Y, D)

### Example 3
Y <- matrix(1 : 20, 10, 2)
colnames(Y) <- paste0("Y", 1 : 2)
D <- matrix(rep(1 : 4, each = 5), 10, 2)
colnames(D) <- paste0("D", 1 : 2)
domain(Y, D)
  
### Example 4
Y <- matrix(1 : 20, 10, 2)
colnames(Y) <- paste0("Y", 1 : 2)
D <- matrix(c(rep(1 : 2, each = 5), rep(3, 10)), 10, 2)
colnames(D) <- paste0("D", 1 : 2)
domain(Y, D)

 

Estimation of weighted percentiles

Description

The function computes the estimates of weighted percentiles.

Usage

incPercentile(
  Y,
  weights = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  k = c(20, 80),
  dataset = NULL,
  checking = TRUE
)

Arguments

Value

A data.table containing the estimates of weighted income percentiles specified by k.

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.

See Also

linarpt, linarpr, linqsr

Examples

library("laeken")
data("eusilc")
incPercentile(Y = "eqIncome", weights = "rb050", Dom = "db040", dataset = eusilc)

Linearization of the ratio estimator

Description

Computes linearized variable for the ratio estimator.

Usage

lin.ratio(
  Y,
  Z,
  weight,
  Dom = NULL,
  dataset = NULL,
  percentratio = 1,
  checking = TRUE
)

Arguments

Value

The function returns the data.table of the linearized variables for the ratio estimator.

References

Carl-Erik Sarndal, Bengt Swensson, Jan Wretman. Model Assisted Survey Sampling. Springer-Verlag, 1992, p.178.

See Also

domain, vardom, vardomh, vardcros, vardchanges, vardannual

Examples

library("data.table")
Y <- data.table(Y = rchisq(10, 3))
Z <- data.table(Z = rchisq(10, 3))
weights <- rep(2, 10)
data.table(Y, Z, weights,
           V1 = lin.ratio(Y, Z, weights, percentratio = 1),
           V10 = lin.ratio(Y, Z, weights, percentratio = 10),
           V100 = lin.ratio(Y, Z, weights, percentratio = 100))

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 = 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",
  checking = TRUE
)

Arguments

Details

The implementation strictly follows the Eurostat definition.

Value

A list with four objects are returned:

• quantile - a data.table containing the estimated value of the quantile used for at-risk-of-poverty threshold estimation.

• threshold - a data.table containing the estimated at-risk-of-poverty threshold.

• value - a data.table containing the estimated at-risk-of-poverty rate (in percentage).

• lin - a data.table containing the linearized variables of the at-risk-of-poverty rate (in percentage).

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 https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

linarpt, varpoord, vardcrospoor, vardchangespoor

Examples

library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
    
# Full population
d <- linarpr(Y = "eqIncome", id = "IDd",
             weight = "rb050", Dom = NULL,
             dataset = dataset1, percentage = 60,
             order_quant = 50L)
d$value
    
## Not run: 
# By domains
dd <- linarpr(Y = "eqIncome", id = "IDd",
              weight = "rb050", Dom = "db040",
              dataset = dataset1, percentage = 60,
              order_quant = 50L)
dd
## End(Not run)

Linearization of at-risk-of-poverty threshold

Description

Estimates the at-risk-of-poverty threshold (defined as percentage (usually 60%) of equalised disposable income after social transfers quantile (usually median)) and computes linearized variable for variance estimation.

Usage

linarpt(
  Y,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  percentage = 60,
  order_quant = 50,
  var_name = "lin_arpt",
  checking = TRUE
)

Arguments

Details

The implementation strictly follows the Eurostat definition.

Value

A list with three objects are returned:

• quantile - a data.table containing the estimated value of the quantile used for at-risk-of-poverty threshold estimation.

• value - a data.table containing the estimated at-risk-of-poverty threshold (in percentage).

• lin - a data.table containing the linearized variables of the at-risk-of-poverty threshold (in percentage).

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 https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

linarpr, incPercentile, varpoord , vardcrospoor, vardchangespoor

Examples

library("data.table") 
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)

# Full population
d1 <- linarpt(Y = "eqIncome", id = "IDd",
              weight = "rb050", Dom = NULL,
              dataset = dataset1, percentage = 60,
              order_quant = 50L)
d1$value

## Not run: 
# By domains
d2 <- linarpt(Y = "eqIncome", id = "IDd",
              weight = "rb050", Dom = "db040",
              dataset = dataset1, percentage = 60,
              order_quant = 50L)
d2$value
## End(Not run)
 

Linearization of the aggregate replacement ratio

Description

Estimates the aggregate replacement ratio (defined as the gross median individual pension income of the population aged 65-74 relative to the gross median individual earnings from work of the population aged 50-59, excluding other social benefits) and computes linearized variable for variance estimation.

Usage

linarr(
  Y,
  Y_den,
  id = NULL,
  age,
  pl085,
  month_at_work,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  order_quant = 50,
  var_name = "lin_arr",
  checking = TRUE
)

Arguments

Details

The implementation strictly follows the Eurostat definition.

Value

A list with four objects are returned:

• value - a data.table containing the estimated the aggregate replacement ratio.

• lin - a data.table containing the linearized variables of the aggregate replacement ratio.

References

Working group on Statistics on Income and Living Conditions (2015) Task 5 - Improvement and optimization of calculation of net change. LC- 139/15/EN, Eurostat.
Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

varpoord, vardcrospoor, vardchangespoor

Examples

library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
dataset1$pl085 <- 12 * trunc(runif(nrow(dataset1), 0, 2))
dataset1$month_at_work <- 12 * trunc(runif(nrow(dataset1), 0, 2))
    
# Full population
d <- linarr(Y = "eqIncome", Y_den = "eqIncome",
            id = "IDd", age = "age",  
            pl085 = "pl085", month_at_work = "month_at_work",
            weight = "rb050",  Dom = NULL,
            dataset = dataset1, order_quant = 50L)
d$value
    
## Not run: 
# By domains
dd <- linarr(Y = "eqIncome", Y_den = "eqIncome",
             id = "IDd", age = "age",  
             pl085 = "pl085", month_at_work = "month_at_work",
             weight = "rb050",  Dom = "db040",
             dataset = dataset1, order_quant = 50L)
 dd
## End(Not run) 

Linearization of the Gini coefficient I

Description

Estimate the Gini coefficient, which is a measure for inequality, and its linearization.

Usage

lingini(
  Y,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  var_name = "lin_gini",
  checking = TRUE
)

Arguments

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 https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

lingini2, linqsr, varpoord, vardcrospoor, vardchangespoor

Examples

library("laeken")
library("data.table")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)[1 : 3,]
 
# Full population
dat1 <- lingini(Y = "eqIncome", id = "IDd",
                weight = "rb050", dataset = dataset1)
dat1$value
  
## Not run: 
# By domains
dat2 <- lingini(Y = "eqIncome", id = "IDd", weight = "rb050",
                Dom = c("db040"), dataset = dataset1)
dat2$value
## End(Not run)

Linearization of the Gini coefficient II

Description

Estimate the Gini coefficient, which is a measure for inequality, and its linearization.

Usage

lingini2(
  Y,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  var_name = "lin_gini2",
  checking = TRUE
)

Arguments

Value

A list with two objects are returned by the function:

• value - a data.table containing the estimated Gini coefficients (in percentage) by Langel and Tille (2012) and Eurostat.

• lin - a data.table containing the linearized variables of the Gini coefficients (in percentage) by Langel and Tille (2012).

References

Eric Graf, Yves Tille, Variance Estimation Using Linearization for Poverty and Social Exclusion Indicators, Survey Methodology, June 2014 61 Vol. 40, No. 1, pp. 61-79, Statistics Canada, Catalogue no. 12-001-X, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/12-001-x2014001-eng.pdf
Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.
Matti Langel, Yves Tille, Corrado Gini, a pioneer in balanced sampling and inequality theory. Metron - International Journal of Statistics, 2011, vol. LXIX, n. 1, pp. 45-65, URL http://dx.doi.org/10.1007/BF03263549.
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.

See Also

lingini, linqsr, varpoord, vardcrospoor, vardchangespoor

Examples

library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
    
# Full population
dat1 <- lingini2(Y = "eqIncome", id = "IDd",
                 weight = "rb050",  dataset = dataset1)
dat1$value
    
## Not run: 
# By domains
dat2 <- lingini2(Y = "eqIncome", id = "IDd",
                 weight = "rb050", Dom = c("db040"),
                 dataset = dataset1)
dat2$value
## End(Not run)

Linearization of the gender pay (wage) gap.

Description

Estimation of gender pay (wage) gap and computation of linearized variables for variance estimation.

Usage

lingpg(
  Y,
  gender = NULL,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  var_name = "lin_gpg",
  checking = TRUE
)

Arguments

Value

A list with two objects are returned:

• value - a data.table containing the estimated gender pay (wage) gap (in percentage).

• lin - a data.table containing the linearized variables of the gender pay (wage) gap (in percentage) for variance estimation.

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 https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

linqsr, lingini, varpoord , vardcrospoor, vardchangespoor

Examples

library("data.table")
library("laeken")
data("ses")
dataset1 <- data.table(ID = paste0("V", 1 : nrow(ses)), ses)

dataset1[, IDnum := .I]

setnames(dataset1, "sex", "sexf")
dataset1[sexf == "male", sex:= 1]
dataset1[sexf == "female", sex:= 2]
  
# Full population
gpgs1 <- lingpg(Y = "earningsHour", gender = "sex",
                id = "IDnum", weight = "weights",
                dataset = dataset1)
gpgs1$value
  
## Not run: 
# Domains by education
gpgs2 <- lingpg(Y = "earningsHour", gender = "sex",
                id = "IDnum", weight = "weights",
                Dom = "education", dataset = dataset1)
gpgs2$value
    
# Sort variable
gpgs3 <- lingpg(Y = "earningsHour", gender = "sex",
                id = "IDnum", weight = "weights",
                sort = "IDnum", Dom = "education",
                dataset = dataset1)
gpgs3$value
    
# Two survey periods
dataset1[, year := 2010]
dataset2 <- copy(dataset1)
dataset2[, year := 2011]
dataset1 <- rbind(dataset1, dataset2)

gpgs4 <- lingpg(Y = "earningsHour", gender = "sex",
                id = "IDnum", weight = "weights", 
                sort = "IDnum", Dom = "education",
                period = "year", dataset = dataset1)
gpgs4$value
names(gpgs4$lin)
## End(Not run)
  

Linearization of the median income of individuals below the At Risk of Poverty Threshold

Description

Estimation of the median income of individuals below At Risk of Poverty Threshold and computation of linearized variable for variance estimation. The At Risk of Poverty Threshold is estimated for the whole population always. The median income is estimated for the whole population or for each domain.

Usage

linpoormed(
  Y,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  percentage = 60,
  order_quant = 50,
  var_name = "lin_poormed",
  checking = TRUE
)

Arguments

Value

A list with two objects are returned by the function:

• value - a data.table containing the estimated median income of individuals below the At Risk of Poverty Threshold.

• lin - a data.table containing the linearized variables of the median income below the At Risk of Poverty Threshold.

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 https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

linarpt, linrmpg, varpoord, vardcrospoor, vardchangespoor

Examples

library("laeken")
library("data.table")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
 
# Full population
d <- linpoormed(Y = "eqIncome", id = "IDd",
                weight = "rb050", Dom = NULL,
                dataset = dataset1, percentage = 60,
                order_quant = 50L)
  
## Not run: 
# Domains by location of houshold
dd <- linpoormed(Y = "eqIncome", id = "IDd",
                 weight = "rb050", Dom = "db040",
                 dataset = dataset1, percentage = 60,
                 order_quant = 50L)
dd
## End(Not run)

Linearization of the Quintile Share Ratio

Description

Estimate the Quintile Share Ratio, which is defined as the ratio of the sum of equalized disposable income received by the top 20% to the sum of equalized disposable income received by the bottom 20%, and its linearization.

Usage

linqsr(
  Y,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  alpha = 20,
  var_name = "lin_qsr",
  checking = TRUE
)

Arguments

Value

A list with two objects are returned by the function:

• value - a data.table containing the estimated Quintile Share Ratio by G. Osier and Eurostat papers.

• lin - a data.table containing the linearized variables of the Quintile Share Ratio by G. Osier paper.

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 https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

incPercentile, varpoord, vardcrospoor, vardchangespoor

Examples

library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)

# Full population
dd <- linqsr(Y = "eqIncome", id = "IDd",
             weight = "rb050", Dom = NULL,
             dataset = dataset1, alpha = 20)
dd$value
 
## Not run: 
# By domains
dd <- linqsr(Y = "eqIncome", id = "IDd",
             weight = "rb050", Dom = "db040",
             dataset = dataset1, alpha = 20)
dd$value
## End(Not run)

Linearization of the relative median income ratio

Description

Estimates the relative median income ratio (defined as the ratio of the median equivalised disposable income of people aged above age to the median equivalised disposable income of those aged below 65) and computes linearized variable for variance estimation.

Usage

linrmir(
  Y,
  id = NULL,
  age,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  order_quant = 50,
  var_name = "lin_rmir",
  checking = TRUE
)

Arguments

Details

The implementation strictly follows the Eurostat definition.

Value

A list with four objects are returned:

• value - a data.table containing the estimated relative median income ratio.

• lin - a data.table containing the linearized variables of the relative median income ratio.

References

Working group on Statistics on Income and Living Conditions (2015) Task 5 - Improvement and optimization of calculation of net change. LC- 139/15/EN, Eurostat.
Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

varpoord, vardcrospoor, vardchangespoor

Examples

library("laeken")
library("data.table")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
 
# Full population
d <- linrmir(Y = "eqIncome", id = "IDd",  age = "age",
             weight = "rb050", Dom = NULL,  
             dataset = dataset1, order_quant = 50L)
 
## Not run: 
 # By domains
 dd <- linrmir(Y = "eqIncome", id = "IDd", age = "age",
               weight = "rb050", Dom = "db040",
               dataset = dataset1, order_quant = 50L)
 dd
## End(Not run)

Linearization of the relative median at-risk-of-poverty gap

Description

Estimate the relative median at-risk-of-poverty gap, which is defined as the relative difference between the median equalized disposable income of persons below the At Risk of Poverty Threshold and the At Risk of Poverty Threshold itself (expressed as a percentage of the at-risk-of-poverty threshold) and its linearization.

Usage

linrmpg(
  Y,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  percentage = 60,
  order_quant = 50,
  var_name = "lin_rmpg",
  checking = TRUE
)

Arguments

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 https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

linarpt, linarpr, linpoormed, varpoord, vardcrospoor, vardchangespoor

Examples

library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)

# Full population
d <- linrmpg(Y = "eqIncome", id = "IDd",
             weight = "rb050", Dom = NULL,
             dataset = dataset1, percentage = 60,
             order_quant = 50L)
d$value
d$threshold
  
## Not run: 
# By domains
dd <- linrmpg(Y = "eqIncome", id = "IDd",
              weight = "rb050", Dom = "db040",
              dataset = dataset1, percentage = 60,
              order_quant = 50L)
dd$value
## End(Not run)

Residual estimation of calibration

Description

Computes the estimation residuals of calibration.

Usage

residual_est(Y, X, weight, q, dataset = NULL, checking = TRUE)

Arguments

Details

The function implements the following estimator:

e_k=Y_k-X_k^{'}B

where

\hat{B} = \left(\sum_{s} weight_k q_k X_k X^{'}_{k} \right)^{-1} \left(\sum_{s} weight_k q_k X_k Y_k \right)

.

Value

A list with objects are returned by the function:

• residuals - a numeric data.table containing the estimated residuals of calibration.

• betas - a numeric data.table containing the estimated coefficients of calibration.

References

Sixten Lundstrom and Carl-Erik Sarndal. Estimation in the presence of Nonresponse and Frame Imperfections. Statistics Sweden, 2001, p. 43-44.

See Also

domain, lin.ratio, linarpr, linarpt, lingini, lingini2, lingpg, linpoormed, linqsr, linrmpg, vardom, vardomh, varpoord, variance_est, variance_othstr

Examples

Y <- matrix(rchisq(10, 3), 10, 1)
X <- matrix(rchisq(20, 3), 10, 2)
w <- rep(2, 10)
q <- rep(1, 10)
residual_est(Y, X, w, q)

### Test2
Y <- matrix(rchisq(10, 3), 10, 1)
X <- matrix(c(rchisq(10, 2), rchisq(10, 2) + 10), 10, 2)
w <- rep(2, 10)
q <- rep(1, 10)
residual_est(Y, X, w, q)
as.matrix(lm(Y ~ X - 1, weights = w * q)$residuals)

The estimation of the simple random sampling.

Description

Computes the estimation of the simple random sampling.

Usage

var_srs(Y, w = rep(1, length(Y)))

Arguments

Value

A list with objects are returned by the function:

• S2p - a data.table containing the values of the variance estimation of the population.

• varsrs - a data.table containing the values of the variance estimation of the simple random sampling.

References

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

vardom, vardomh, varpoord

Examples

Ys <- matrix(rchisq(10, 3), 10, 1)
ws <- c(rep(2, 5), rep(3, 5))
var_srs(Ys, ws)

Variance estimation for measures of annual net change or annual for single and multistage stage cluster sampling designs

Description

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

Usage

vardannual(
  Y,
  H,
  PSU,
  w_final,
  ID_level1,
  ID_level2,
  Dom = NULL,
  Z = NULL,
  gender = NULL,
  country = NULL,
  years,
  subperiods,
  dataset = NULL,
  year1 = NULL,
  year2 = NULL,
  X = NULL,
  countryX = NULL,
  yearsX = NULL,
  subperiodsX = NULL,
  X_ID_level1 = NULL,
  ind_gr = NULL,
  g = NULL,
  q = NULL,
  datasetX = NULL,
  frate = 0,
  percentratio = 1,
  use.estVar = FALSE,
  use.gender = FALSE,
  confidence = 0.95,
  method = "cros"
)

Arguments

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

survey data object convertible to data.table.

Value

A list with objects are returned by the function:

• crossectional_results - a data.table containing:
  year - survey years,
  subperiods - survey sub-periods,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  sample_size - the sample size (in numbers of individuals),
  pop_size - the population size (in numbers of individuals),
  total - the estimated totals,
  variance - the estimated variance of cross-sectional or longitudinal measures,
  sd_w - the estimated weighted variance of simple random sample,
  sd_nw - the estimated variance estimation of simple random sample,
  pop - the population size (in numbers of households),
  sampl_siz - the sample size (in numbers of households),
  stderr_w - the estimated weighted standard error of simple random sample,
  stderr_nw - the estimated standard error of simple random sample,
  se - the estimated standard error of cross-sectional or longitudinal,
  rse - the estimated relative standard error (coefficient of variation),
  cv - the estimated relative standard error (coefficient of variation) in percentage,
  absolute_margin_of_error - the estimated absolute margin of error,
  relative_margin_of_error - the estimated relative margin of error,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound,
  confidence_level - the positive value for confidence interval.

• crossectional_var_grad - a data.table containing:
  year - survey years,
  subperiods - survey sub-periods,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  grad - the estimated gradient,
  var - the estimated a design-based variance.

• vardchanges_grad_var - a data.table containing:
  year_1 - survey years of years1,
  subperiods_1 - survey sub-periods of years1,
  year_2 - survey years of years2,
  subperiods_2 - survey sub-periods of years2,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  nams - gradient names, numerator (num) and denominator (den), for each year,
  grad - the estimated gradient,
  cros_var - the estimated a design-based variance.

• vardchanges_rho - a data.table containing:
  year - survey years of years for cross-sectional estimates,
  subperiods - survey sub-periods of years for cross-sectional estimates,
  year_1 - survey years of years1,
  subperiods_1 - survey sub-periods of years1,
  year_2 - survey years of years2,
  subperiods_2 - survey sub-periods of years2,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  nams - gradient names, numerator (num) and denominator (den), for each year,
  rho - the estimated correlation matrix.

• vardchanges_var_tau - a data.table containing:
  year_1 - survey years of years1,
  subperiods_1 - survey sub-periods of years1,
  year_2 - survey years of years2,
  subperiods_2 - survey sub-periods of years2,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  nams - gradient names, numerator (num) and denominator (den), for each year,
  var_tau - the estimated covariance matrix.

• vardchanges_results - a data.table containing:
  year - survey years of years for measures of annual,
  subperiods - survey sub-periods of years for measures of annual,
  year_1 - survey years of years1 for measures of annual net change,
  subperiods_1 - survey sub-periods of years1 for measures of annual net change,
  year_2 - survey years of years2 for measures of annual net change,
  subperiods_2 - survey sub-periods of years2 for measures of annual net change,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  estim_1 - the estimated value for period1,
  estim_2 - the estimated value for period2,
  estim - the estimated value,
  var - the estimated variance,
  se - the estimated standard error,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound,
  confidence_level - the positive value for confidence interval,
  significant - is the the difference significant

• X_annual - a data.table containing:
  year - survey years of years for measures of annual,
  year_1 - survey years of years1 for measures of annual net change,
  year_2 - survey years of years2 for measures of annual net change,
  period - period1 and period2 together,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  cros_se - the estimated cross-sectional standard error.

• A_matrix - a data.table containing:
  year - survey years of years1 for measures of annual,
  year_1 - survey years of years1 for measures of annual net change,
  year_2 - survey years of years2 for measures of annual net change,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  cols - the estimated matrix_A columns,
  matrix_A - the estimated matrix A.

• annual_sum - a data.table containing:
  year - survey years,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  totalY - the estimated value of variables of interest for period1,
  totalZ - optional the estimated value of denominator for period2,
  estim - the estimated value for year.

• annual_results - a data.table containing:
  year - survey years of years for measures of annual,
  year_1 - survey years of years1 for measures of annual net change,
  year_2 - survey years of years2 for measures of annual net change,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  estim_1 - the estimated value for period1 for measures of annual net change,
  estim_2 - the estimated value for period2 for measures of annual net change,
  estim - the estimated value,
  var - the estimated variance,
  se - the estimated standard error,
  rse - the estimated relative standard error (coefficient of variation),
  cv - the estimated relative standard error (coefficient of variation) in percentage,
  absolute_margin_of_error - the estimated absolute margin of error for period1 for measures of annual,
  relative_margin_of_error - the estimated relative margin of error in percentage for measures of annual,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound,
  confidence_level - the positive value for confidence interval,
  significant - is the the difference significant

References

Guillaume Osier, Virginie Raymond, (2015), Development of methodology for the estimate of variance of annual net changes for LFS-based indicators. Deliverable 1 - Short document with derivation of the methodology.
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, vardchanges

Examples

 
### Example
library("data.table")

set.seed(1)

data("eusilc", package = "laeken")
eusilc1 <- eusilc[1:20, ]
rm(eusilc)

dataset1 <- data.table(rbind(eusilc1, eusilc1),
                       year = c(rep(2010, nrow(eusilc1)),
                                rep(2011, nrow(eusilc1))))
rm(eusilc1)

dataset1[, country := "AT"]
dataset1[, half := .I - 2 * trunc((.I - 1) / 2)]
dataset1[, quarter := .I - 4 * trunc((.I - 1) / 4)]
dataset1[age < 0, age := 0]

PSU <- dataset1[, .N, keyby = "db030"][, N := NULL][]
PSU[, PSU := trunc(runif(.N, 0, 5))]

dataset1 <- merge(dataset1, PSU, all = TRUE, by = "db030")
rm(PSU)

dataset1[, strata := "XXXX"]
dataset1[, employed := trunc(runif(.N, 0, 2))]
dataset1[, unemployed := trunc(runif(.N, 0, 2))]
dataset1[, labour_force := employed + unemployed]
dataset1[, id_lv2 := paste0("V", .I)]

vardannual(Y = "employed", H = "strata",
           PSU = "PSU", w_final = "rb050",
           ID_level1 = "db030", ID_level2 = "id_lv2",
           Dom = NULL, Z = NULL, years = "year",
           subperiods = "half", dataset = dataset1,
           percentratio = 100, confidence = 0.95,
           method = "cros")
  
## Not run: 
vardannual(Y = "employed", H = "strata",
           PSU = "PSU", w_final = "rb050",
           ID_level1 = "db030", ID_level2 = "id_lv2",
           Dom = NULL, Z = NULL, country = "country",
           years = "year", subperiods = "quarter",
           dataset = dataset1, year1 = 2010, year2 = 2011,
           percentratio = 100, confidence = 0.95,
           method = "netchanges")
    
vardannual(Y = "unemployed", H = "strata",
           PSU = "PSU", w_final = "rb050",
           ID_level1 = "db030", ID_level2 = "id_lv2", 
           Dom = NULL, Z = "labour_force",
           country = "country", years = "year",
           subperiods = "quarter", dataset = dataset1,
           year1 = 2010, year2 = 2011,
           percentratio = 100, confidence = 0.95,
           method = "netchanges")

## End(Not run)

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,
  gender = 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,
  linratio = FALSE,
  percentratio = 1,
  use.estVar = FALSE,
  outp_res = FALSE,
  confidence = 0.95,
  change_type = "absolute",
  checking = TRUE
)

Arguments

Value

A list with objects are returned by the function:

• 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,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  sample_size - the sample size (in numbers of individuals),
  pop_size - the population size (in numbers of individuals),
  total - the estimated totals,
  variance - the estimated variance of cross-sectional or longitudinal measures,
  sd_w - the estimated weighted variance of simple random sample,
  sd_nw - the estimated variance estimation of simple random sample,
  pop - the population size (in numbers of households),
  sampl_siz - the sample size (in numbers of households),
  stderr_w - the estimated weighted standard error of simple random sample,
  stderr_nw - the estimated standard error of simple random sample,
  se - the estimated standard error of cross-sectional or longitudinal,
  rse - the estimated relative standard error (coefficient of variation),
  cv - the estimated relative standard error (coefficient of variation) in percentage,
  absolute_margin_of_error - the estimated absolute margin of error,
  relative_margin_of_error - the estimated relative margin of error,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound. #'

• crossectional_var_grad - a data.table containing:
  periods - survey periods,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  grad - the estimated gradient,
  var - the estimated a design-based variance.

• rho - a data.table containing:
  periods_1 - survey periods of periods1,
  periods_2 - survey periods of periods2,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  nams - the variable names in correlation matrix,
  rho - the estimated correlation matrix.

• var_tau - a data.table containing:
  periods_1 - survey periods of periods1,
  periods_2 - survey periods of periods2,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  nams - the variable names in correlation matrix,
  var_tau - the estimated covariance matrix.

• changes_results - a data.table containing:
  periods_1 - survey periods of periods1,
  periods_2 - survey periods of periods2,
  country - survey countries,
  Dom - optional variable of the population domains,
  namesY - variable with names of variables of interest,
  namesZ - optional variable with names of denominator for ratio estimation,
  estim_1 - the estimated value for period1,
  estim_2 - the estimated value for period2,
  estim - the estimated value,
  var - the estimated variance,
  se - the estimated standard error,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound.
  significant - is the the difference significant.

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 
library("data.table")
library("laeken")
data("eusilc")
set.seed(1)
eusilc1 <- eusilc[1:40,]
set.seed(1)
dataset1 <- data.table(rbind(eusilc1, eusilc1),
                       year = c(rep(2010, nrow(eusilc1)),
                                rep(2011, nrow(eusilc1))))
dataset1[age < 0, age := 0]
PSU <- dataset1[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 5))]
dataset1 <- merge(dataset1, PSU, all = TRUE, by = "db030")
PSU <- eusilc <- NULL
dataset1[, strata := c("XXXX")]

dataset1[, t_pov := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, exp := 1]

# At-risk-of-poverty (AROP)
dataset1[, pov := ifelse (t_pov == 1, 1, 0)]
dataset1[, id_lev2 := paste0("V", .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 = dataset1, period1 = 2010,
                      period2 = 2011, change_type = "absolute")
result

## Not run: 
data("eusilc")
dataset1 <- data.table(rbind(eusilc, eusilc),
                       year = c(rep(2010, nrow(eusilc)),
                                rep(2011, nrow(eusilc))))
dataset1[age < 0, age := 0]
PSU <- dataset1[,.N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
dataset1 <- merge(dataset1, PSU, all = TRUE, by = "db030")
PSU <- eusilc <- NULL
dataset1[, strata := "XXXX"]
  
dataset1[, t_pov := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_dep := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_lwi := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, exp := 1]
dataset1[, exp2 := 1 * (age < 60)]
  
# At-risk-of-poverty (AROP)
dataset1[, pov := ifelse (t_pov == 1, 1, 0)]
  
# Severe material deprivation (DEP)
dataset1[, dep := ifelse (t_dep == 1, 1, 0)]
  
# Low work intensity (LWI)
dataset1[, lwi := ifelse (t_lwi == 1 & exp2 == 1, 1, 0)]
  
# At-risk-of-poverty or social exclusion (AROPE)
dataset1[, arope := ifelse (pov == 1 | dep == 1 | lwi == 1, 1, 0)]
dataset1[, dom := 1]
dataset1[, 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 = dataset1, period1 = 2010, 
                      period2 = 2011, change_type = "absolute")
result
## End(Not run)

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

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

 
### 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

Variance estimation for measures of annual net change or annual for single stratified sampling designs

Description

Computes the variance estimation for measures of annual net change or annual for single stratified sampling designs.

Usage

vardchangstrs(
  Y,
  H,
  PSU,
  w_final,
  Dom = NULL,
  periods = NULL,
  dataset,
  periods1,
  periods2,
  in_sample,
  in_frame,
  confidence = 0.95,
  percentratio = 1
)

Arguments

Value

A list with objects are returned by the function:

• crossectional_results - a data.table containing:
  year - survey years,
  subperiods - survey sub-periods,
  variable - names of variables of interest,
  Dom - optional variable of the population domains,
  estim - the estimated value,
  var - the estimated variance of cross-sectional and longitudinal measures,
  sd_w - the estimated weighted variance of simple random sample,
  se - the estimated standard error of cross-sectional or longitudinal,
  rse - the estimated relative standard error (coefficient of variation),
  cv - the estimated relative standard error (coefficient of variation) in percentage,
  absolute_margin_of_error - the estimated absolute margin of error,
  relative_margin_of_error - the estimated relative margin of error,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound,
  confidence_level - the positive value for confidence interval.

• annual_results - a data.table containing: year_1 - survey years of years1 for measures of annual net change,
  year_2 - survey years of years2 for measures of annual net change,
  Dom - optional variable of the population domains,
  variable - names of variables of interest,
  estim_2 - the estimated value for period2 for measures of annual net change,
  estim_1 - the estimated value for period1 for measures of annual net change,
  estim - the estimated value,
  var - the estimated variance,
  se - the estimated standard error,
  rse - the estimated relative standard error (coefficient of variation),
  cv - the estimated relative standard error (coefficient of variation) in percentage,
  absolute_margin_of_error - the estimated absolute margin of error for period1 for measures of annual,
  relative_margin_of_error - the estimated relative margin of error in percentage for measures of annual,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound,
  confidence_level - the positive value for confidence interval,
  significant - is the the difference significant

References

Guillaume OSIER, Virginie RAYMOND, (2015), Development of methodology for the estimate of variance of annual net changes for LFS-based indicators. Deliverable 1 - Short document with derivation of the methodology.

See Also

vardchanges, vardannual

Variance estimation for cross-sectional, longitudinal measures for single and multistage stage cluster sampling designs

Description

Computes the variance estimation for cross-sectional and longitudinal measures for any stage cluster sampling designs.

Usage

vardcros(
  Y,
  H,
  PSU,
  w_final,
  ID_level1,
  ID_level2,
  Dom = NULL,
  Z = NULL,
  gender = NULL,
  country = NULL,
  period,
  dataset = NULL,
  X = NULL,
  countryX = NULL,
  periodX = NULL,
  X_ID_level1 = NULL,
  ind_gr = NULL,
  g = NULL,
  q = NULL,
  datasetX = NULL,
  linratio = FALSE,
  percentratio = 1,
  use.estVar = FALSE,
  ID_level1_max = TRUE,
  outp_res = FALSE,
  withperiod = TRUE,
  netchanges = TRUE,
  confidence = 0.95,
  checking = TRUE
)

Arguments

Value

A list with four objects are returned by the function:

• res_out - a data.table containing the estimated residuals of calibration with ID_level1 and PSU.

• data_net_changes - a data.table containing aggregation of weighted data by period (if available) and countries (if available), country, strata, PSU.

• var_grad - a data.table containing estimation for Y, the variance, gradient for numerator and denominator by period, country (if available) and population domains (if available).

• results A data.table containing:
  period - survey periods,
  country - survey countries (if available),
  Dom - optional variable of the population domains,
  namesY - names of variables of interest,
  namesZ - optional variable for names of denominator for ratio estimation,
  sample_size - the sample size (in numbers of individuals),
  pop_size - the population size (in numbers of individuals),
  total - the estimated totals,
  variance - the estimated variance of cross-sectional or longitudinal measures,
  sd_w - the estimated weighted variance of simple random sample,
  sd_nw - the estimated variance estimation of simple random sample,
  pop - the population size (in numbers of households),
  sampl_siz - the sample size (in numbers of households),
  stderr_w - the estimated weighted standard error of simple random sample,
  stderr_nw - the estimated standard error of simple random sample,
  se - the estimated standard error of cross-sectional or longitudinal,
  rse - the estimated relative standard error (coefficient of variation),
  cv - the estimated relative standard error (coefficient of variation) in percentage,
  absolute_margin_of_error - the estimated absolute margin of error,
  relative_margin_of_error - the estimated relative margin of error,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound,
  confidence_level - the positive value for confidence interval.

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.
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
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.

See Also

domain, lin.ratio

Examples

library("data.table")
library("laeken")

# Example 1
data(eusilc)
set.seed(1)
dataset1 <- data.table(eusilc)
dataset1[, year := 2010]
dataset1[, country := "AT"]
dataset1[age < 0, age := 0]
PSU <- dataset1[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
dataset1 <- merge(dataset1, PSU, by = "db030", all = TRUE)
PSU <- eusilc <- 0
  
dataset1[, strata := "XXXX"]
dataset1[, t_pov := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_dep := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_lwi := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, exp := 1]
dataset1[, exp2 := 1 * (age < 60)]
  
# At-risk-of-poverty (AROP)
dataset1[, pov := ifelse (t_pov == 1, 1, 0)]
  
# Severe material deprivation (DEP)
dataset1[, dep := ifelse (t_dep == 1, 1, 0)]
  
# Low work intensity (LWI)
dataset1[, lwi := ifelse (t_lwi == 1 & exp2 == 1, 1, 0)]
  
# At-risk-of-poverty or social exclusion (AROPE)
dataset1[, arope := ifelse (pov == 1 | dep == 1 | lwi == 1, 1, 0)]

result11 <- vardcros(Y="arope", H = "strata",
                     PSU = "PSU", w_final = "rb050",
                     ID_level1 = "db030", ID_level2 = "rb030",
                     Dom = "rb090", Z = NULL, country = "country",
                     period = "year", dataset = dataset1,
                     linratio = FALSE, withperiod = TRUE,
                     netchanges = TRUE, confidence = .95)
   
## Not run: 
# Example 2
data(eusilc)
set.seed(1)
dataset1 <- data.table(rbind(eusilc, eusilc),
                       year = c(rep(2010, nrow(eusilc)),
                                rep(2011, nrow(eusilc))))
dataset1[, country := "AT"]
dataset1[age < 0, age := 0]
PSU <- dataset1[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
dataset1 <- merge(dataset1, PSU, by = "db030", all = TRUE)
PSU <- eusilc <- 0
dataset1[, strata := "XXXX"]
dataset1[, strata := as.character(strata)]
dataset1[, t_pov := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_dep := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_lwi := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, exp := 1]
dataset1[, exp2 := 1 * (age < 60)]
    
# At-risk-of-poverty (AROP)
dataset1[, pov := ifelse(t_pov == 1, 1, 0)]
    
# Severe material deprivation (DEP)
dataset1[, dep := ifelse(t_dep == 1, 1, 0)]
    
# Low work intensity (LWI)
dataset1[, lwi := ifelse(t_lwi == 1 & exp2 == 1, 1, 0)]
    
# At-risk-of-poverty or social exclusion (AROPE)
dataset1[, arope := ifelse(pov == 1 | dep == 1 | lwi == 1, 1, 0)]
    
result11 <- vardcros(Y = c("pov", "dep", "arope"),
                     H = "strata", PSU = "PSU", w_final = "rb050",
                     ID_level1 = "db030", ID_level2 = "rb030",
                     Dom = "rb090", Z = NULL, country = "country",
                     period = "year", dataset = dataset1,
                     linratio = FALSE, withperiod = TRUE,
                     netchanges = TRUE, confidence = .95)
    
dataset2 <- dataset1[exp2 == 1]
result12 <- vardcros(Y = c("lwi"), H = "strata",
                     PSU = "PSU", w_final = "rb050",
                     ID_level1 = "db030", ID_level2 = "rb030",
                     Dom = "rb090", Z = NULL,
                     country = "country", period = "year",
                     dataset = dataset2, linratio = FALSE, 
                     withperiod = TRUE, netchanges = TRUE,
                     confidence = .95)
    
### Example 3
data(eusilc)
set.seed(1)
year <- 2011
dataset1 <- data.table(rbind(eusilc, eusilc, eusilc, eusilc),
                       rb010 = c(rep(2008, nrow(eusilc)),
                                 rep(2009, nrow(eusilc)),
                                 rep(2010, nrow(eusilc)),
                                 rep(2011, nrow(eusilc))))
dataset1[, rb020 := "AT"]
        
dataset1[, u := 1]
dataset1[age < 0, age := 0]
dataset1[, strata := "XXXX"]
PSU <- dataset1[, .N, keyby = "db030"][, N:=NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
dataset1 <- merge(dataset1, PSU, by = "db030", all = TRUE)
thres <- data.table(rb020 = as.character(rep("AT", 4)),
                   thres = c(11406, 11931, 12371, 12791),
                   rb010 = 2008:2011)
dataset1 <- merge(dataset1, thres, all.x = TRUE, by = c("rb010", "rb020"))
dataset1[is.na(u), u := 0]
dataset1 <- dataset1[u == 1]
    
#############
# T3        #
#############
    
T3 <- dataset1[rb010 == year - 3]
T3[, strata1 := strata]
T3[, PSU1 := PSU]
T3[, w1 := rb050]
T3[, inc1 := eqIncome]
T3[, rb110_1 := db030]
T3[, pov1 := inc1 <= thres]
T3 <- T3[, c("rb020", "rb030", "strata", "PSU", "inc1", "pov1"), with = FALSE]
    
#############
# T2        #
#############
T2 <- dataset1[rb010 == year - 2]
T2[, strata2 := strata]
T2[, PSU2 := PSU]
T2[, w2 := rb050]
T2[, inc2 := eqIncome]
T2[, rb110_2 := db030]
setnames(T2, "thres", "thres2")
T2[, pov2 := inc2 <= thres2]
T2 <- T2[, c("rb020", "rb030", "strata2", "PSU2", "inc2", "pov2"), with = FALSE]
    
#############
# T1        #
#############
T1 <- dataset1[rb010 == year - 1]
T1[, strata3 := strata]
T1[, PSU3 := PSU]
T1[, w3 := rb050]
T1[, inc3 := eqIncome]
T1[, rb110_3 := db030]
setnames(T1, "thres", "thres3")
T1[, pov3 := inc3 <= thres3]
T1 <- T1[, c("rb020", "rb030", "strata3", "PSU3", "inc3", "pov3"), with = FALSE]
    
#############
# T0        #
#############
T0 <- dataset1[rb010 == year]
T0[, PSU4 := PSU]
T0[, strata4 := strata]
T0[, w4 := rb050]
T0[, inc4 := eqIncome]
T0[, rb110_4 := db030]
setnames(T0, "thres", "thres4")
T0[, pov4 := inc4 <= thres4]
T0 <- T0[, c("rb010", "rb020", "rb030", "strata4", "PSU4", "w4", "inc4", "pov4"), with = FALSE]
apv <- merge(T3, T2, all = TRUE, by = c("rb020", "rb030"))
apv <- merge(apv, T1, all = TRUE, by = c("rb020", "rb030"))
apv <- merge(apv, T0, all = TRUE, by = c("rb020", "rb030"))
apv <- apv[(!is.na(inc1)) & (!is.na(inc2)) & (!is.na(inc3)) & (!is.na(inc4))]
apv[, ppr := ifelse(((pov4 == 1) & ((pov1 == 1 & pov2 == 1 & pov3 == 1) 
                                  | (pov1 == 1 & pov2 == 1 & pov3 == 0)
                                  | (pov1 == 1 & pov2 == 0 & pov3 == 1)
                                  | (pov1 == 0 & pov2 ==1 & pov3 == 1))), 1, 0)]
                                  
result20 <- vardcros(Y = "ppr", H = "strata", PSU = "PSU",
                     w_final = "w4", ID_level1 = "rb030",
                     ID_level2 = "rb030", Dom = NULL,
                     Z = NULL, country = "rb020",
                     period = "rb010", dataset = apv,
                     linratio = FALSE, 
                     withperiod = TRUE,
                     netchanges = FALSE,
                     confidence = .95)
result20
## End(Not run)

Variance estimation for cross-sectional, longitudinal measures for indicators on social exclusion and poverty

Description

Computes the variance estimation for cross-sectional and longitudinal measures for indicators on social exclusion and poverty.

Usage

vardcrospoor(
  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,
  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,
  withperiod = TRUE,
  netchanges = TRUE,
  confidence = 0.95,
  outp_lin = FALSE,
  outp_res = FALSE,
  type = "linrmpg",
  checking = TRUE
)

Arguments

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.

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.

Value

A list with objects are returned by the function:

• lin_out - a data.table containing the linearized values of the ratio estimator with ID_level2 and PSU.

• res_out - a data.table containing the estimated residuals of calibration with ID_level1 and PSU.

• data_net_changes - a data.table containing aggregation of weighted data by period (if available), country, strata, PSU.

• 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.

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. 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 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

See Also

linrmir, linarr, vardchanges

Examples

library("data.table")
library("laeken")
data(eusilc)
set.seed(1)
dataset1 <- data.table(rbind(eusilc, eusilc),
                       year = c(rep(2010, nrow(eusilc)),
                                rep(2011, nrow(eusilc))))
dataset1[age < 0, age := 0]
PSU <- dataset1[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
PSU$inc <- runif(nrow(PSU), 20, 100000)
dataset1 <- merge(dataset1, PSU, all = TRUE, by = "db030")
PSU <- eusilc <- NULL
dataset1[, strata := "XXXX"]
dataset1[, strata := as.character(strata)]
dataset1$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 <- vardcrospoor(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", "db040"),
                       country = NULL, period = "year",
                       sort = NULL, gender = NULL,
                       dataset = dataset1,
                       percentage = 60,
                       order_quant = 50L,
                       alpha = 20,
                       confidence = 0.95,
                       type = "linrmpg")
 
 ## Not run: 
 result2 <- vardcrospoor(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", "db040"),
                         period = "year", sort = NULL,
                         gender = NULL, dataset = dataset1,
                         percentage = 60,
                         order_quant = 50L,
                         alpha = 20,
                         confidence = 0.95,
                         type = "linrmpg")
result2
## End(Not run)
  

Variance estimation of the sample surveys in domain by the ultimate cluster method

Description

Computes the variance estimation of the sample surveys in domain by the ultimate cluster method.

Usage

vardom(
  Y,
  H,
  PSU,
  w_final,
  id = NULL,
  Dom = NULL,
  period = NULL,
  PSU_sort = NULL,
  N_h = NULL,
  fh_zero = FALSE,
  PSU_level = TRUE,
  Z = NULL,
  X = NULL,
  ind_gr = NULL,
  g = NULL,
  q = NULL,
  dataset = NULL,
  confidence = 0.95,
  percentratio = 1,
  outp_lin = FALSE,
  outp_res = FALSE
)

Arguments

Details

Calculate variance estimation in domains based on book of Hansen, Hurwitz and Madow.

Value

A list with objects is returned by the function:

• lin_out - a data.table containing the linearized values of the ratio estimator with id and PSU.

• res_out - a data.table containing the estimated residuals of calibration with id and PSU.

• betas - a numeric data.table containing the estimated coefficients of calibration.

• all_result - a data.table, which containing variables: variable - names of variables of interest,
  Dom - optional variable of the population domains,
  period - optional variable of the survey periods,
  respondent_count - the count of respondents,
  pop_size - the estimated size of population,
  n_nonzero - the count of respondents, who answers are larger than zero,
  estim - the estimated value,
  var - the estimated variance,
  se - the estimated standard error,
  rse - the estimated relative standard error (coefficient of variation),
  cv - the estimated relative standard error (coefficient of variation) in percentage,
  absolute_margin_of_error - the estimated absolute margin of error,
  relative_margin_of_error - the estimated relative margin of error in percentage,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound,
  confidence_level - the positive value for confidence interval,
  S2_y_HT - the estimated variance of the y variable in case of total or the estimated variance of the linearised variable in case of the ratio of two totals using non-calibrated weights,
  S2_y_ca - the estimated variance of the y variable in case of total or the estimated variance of the linearised variable in case of the ratio of two totals using calibrated weights,
  S2_res - the estimated variance of the regression residuals,
  var_srs_HT - the estimated variance of the HT estimator under SRS,
  var_cur_HT - the estimated variance of the HT estimator under current design,
  var_srs_ca - the estimated variance of the calibrated estimator under SRS,
  deff_sam - the estimated design effect of sample design,
  deff_est - the estimated design effect of estimator,
  deff - the overall estimated design effect of sample design and estimator,
  n_eff - the effective sample size.

References

Morris H. Hansen, William N. Hurwitz, William G. Madow, (1953), Sample survey methods and theory Volume I Methods and applications, 257-258, Wiley.
Guillaume Osier and Emilio Di Meglio. The linearisation approach implemented by Eurostat for the first wave of EU-SILC: what could be done from the second wave onwards? 2012
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
Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

domain, lin.ratio, residual_est, vardomh, var_srs, variance_est, variance_othstr

Examples

library("data.table")
library("laeken")
data(eusilc)
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)

aa <- vardom(Y = "eqIncome", H = "db040", PSU = "db030",
             w_final = "rb050", id = "rb030", Dom = "db040",
             period = NULL, N_h = NULL, Z = NULL,
             X = NULL, g = NULL, q = NULL, dataset = dataset1,
             confidence = .95, percentratio = 100, 
             outp_lin = TRUE, outp_res = TRUE)

Variance estimation for sample surveys in domain by the two stratification

Description

Computes the variance estimation for sample surveys in domain by the two stratification.

Usage

vardom_othstr(
  Y,
  H,
  H2,
  PSU,
  w_final,
  id = NULL,
  Dom = NULL,
  period = NULL,
  N_h = NULL,
  N_h2 = NULL,
  Z = NULL,
  X = NULL,
  ind_gr = NULL,
  g = NULL,
  q = NULL,
  dataset = NULL,
  confidence = 0.95,
  percentratio = 1,
  outp_lin = FALSE,
  outp_res = FALSE
)

Arguments

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

Value

A list with objects are returned by the function:

• lin_out - a data.table containing the linearized values of the ratio estimator with id and PSU.

• res_out - a data.table containing the estimated residuals of calibration with id and PSU.

• betas - a numeric data.table containing the estimated coefficients of calibration.

• s2g - a data.table containing the s^2g value.

• all_result - a data.table, which containing variables:
  respondent_count - the count of respondents,
  pop_size - the estimated size of population,
  n_nonzero - the count of respondents, who answers are larger than zero,
  estim - the estimated value,
  var - the estimated variance,
  se - the estimated standard error,
  rse - the estimated relative standard error (coefficient of variation),
  cv - the estimated relative standard error (coefficient of variation) in percentage,
  absolute_margin_of_error - the estimated absolute margin of error,
  relative_margin_of_error - the estimated relative margin of error in percentage,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound,
  confidence_level - the positive value for confidence interval,
  var_srs_HT - the estimated variance of the HT estimator under SRS,
  var_cur_HT - the estimated variance of the HT estimator under current design,
  var_srs_ca - the estimated variance of the calibrated estimator under SRS,
  deff_sam - the estimated design effect of sample design,
  deff_est - the estimated design effect of estimator,
  deff - the overall estimated design effect of sample design and estimator.
  

References

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.
M. Liberts. (2004) Non-response Analysis and Bias Estimation in a Survey on Transportation of Goods by Road.

See Also

domain, lin.ratio, residual_est, vardomh, var_srs, variance_est, variance_othstr

Examples

library("laeken")
library("data.table")
data("eusilc")
  
# Example 1
eusilc1 <- eusilc[1:1000, ]
dataset1 <- data.table(IDd = paste0("V", 1:nrow(eusilc1)), eusilc1)
dataset1[, db040_2 := get("db040")]
N_h2 <- dataset1[, sum(rb050, na.rm = FALSE), keyby = "db040_2"]
  
aa <- vardom_othstr(Y = "eqIncome", H = "db040", H2 = "db040_2",  
                    PSU = "db030", w_final = "rb050", id = "rb030",
                    Dom = "db040", period = NULL, N_h = NULL,
                    N_h2 = N_h2, Z = NULL, X = NULL, g = NULL,
                    q = NULL, dataset = dataset1, confidence = .95,           
                    outp_lin = TRUE, outp_res = TRUE)
  
## Not run: 
# Example 2
dataset1 <- data.table(IDd = 1:nrow(eusilc), eusilc)
dataset1[, db040_2 := get("db040")]
N_h2 <- dataset1[, sum(rb050, na.rm = FALSE), keyby = "db040_2"]
    
aa <- vardom_othstr(Y = "eqIncome", H = "db040", H2 = "db040_2",
                    PSU = "db030", w_final = "rb050", id = "rb030",
                    Dom = "db040", period = NULL, N_h2 = N_h2,
                    Z = NULL, X = NULL, g = NULL, dataset = dataset1,
                    q = NULL, confidence = .95, outp_lin = TRUE,
                    outp_res = TRUE)
 aa
## End(Not run)

Variance estimation for sample surveys in domain for one or two stage surveys by the ultimate cluster method

Description

Computes the variance estimation in domain for ID_level1.

Usage

vardomh(
  Y,
  H,
  PSU,
  w_final,
  ID_level1,
  ID_level2,
  Dom = NULL,
  period = NULL,
  N_h = NULL,
  PSU_sort = NULL,
  fh_zero = FALSE,
  PSU_level = TRUE,
  Z = NULL,
  dataset = NULL,
  X = NULL,
  periodX = NULL,
  X_ID_level1 = NULL,
  ind_gr = NULL,
  g = NULL,
  q = NULL,
  datasetX = NULL,
  confidence = 0.95,
  percentratio = 1,
  outp_lin = FALSE,
  outp_res = FALSE
)

Arguments

Details

Calculate variance estimation in domains for household surveys based on book of Hansen, Hurwitz and Madow.

Value

A list with objects are returned by the function:

• lin_out A data.table containing the linearized values of the ratio estimator with ID_level2 and PSU.

• res_out A data.table containing the estimated residuals of calibration with ID_level1 and PSU.

• betas A numeric data.table containing the estimated coefficients of calibration.

• all_result A data.table, which containing variables: variable - names of variables of interest,
  Dom - optional variable of the population domains,
  period - optional variable of the survey periods,
  respondent_count - the count of respondents,
  pop_size - the estimated size of population,
  n_nonzero - the count of respondents, who answers are larger than zero,
  estim - the estimated value,
  var - the estimated variance,
  se - the estimated standard error,
  rse - the estimated relative standard error (coefficient of variation),
  cv - the estimated relative standard error (coefficient of variation) in percentage,
  absolute_margin_of_error - the estimated absolute margin of error,
  relative_margin_of_error - the estimated relative margin of error in percentage,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound,
  confidence_level - the positive value for confidence interval,
  S2_y_HT - the estimated variance of the y variable in case of total or the estimated variance of the linearised variable in case of the ratio of two totals using non-calibrated weights,
  S2_y_ca - the estimated variance of the y variable in case of total or the estimated variance of the linearised variable in case of the ratio of two totals using calibrated weights,
  S2_res - the estimated variance of the regression residuals,
  S2_res - the estimated variance of the regression residuals,
  var_srs_HT - the estimated variance of the HT estimator under SRS for household,
  var_cur_HT - the estimated variance of the HT estimator under current design for household,
  var_srs_ca - the estimated variance of the calibrated estimator under SRS for household,
  deff_sam - the estimated design effect of sample design for household,
  deff_est - the estimated design effect of estimator for household,
  deff - the overall estimated design effect of sample design and estimator for household

References

Morris H. Hansen, William N. Hurwitz, William G. Madow, (1953), Sample survey methods and theory Volume I Methods and applications, 257-258, Wiley.
Guillaume Osier and Emilio Di Meglio. The linearisation approach implemented by Eurostat for the first wave of EU-SILC: what could be done from the second wave onwards? 2012
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
Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

domain, lin.ratio, residual_est, var_srs, variance_est

Examples

library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
aa <- vardomh(Y = "eqIncome", H = "db040", PSU = "db030",
             w_final = "rb050", ID_level1 = "db030",
             ID_level2 = "rb030", Dom = "db040", period = NULL,
             N_h = NULL, Z = NULL, dataset = dataset1, X = NULL,
             X_ID_level1 = NULL, g = NULL, q = NULL, 
             datasetX = NULL, confidence = 0.95, percentratio = 1,
             outp_lin = TRUE, outp_res = TRUE)

## Not run: 
dataset2 <- copy(dataset1)
dataset1$period <- 1
dataset2$period <- 2
dataset1 <- data.table(rbind(dataset1, dataset2))

# by default without using fh_zero (finite population correction)
aa2 <- vardomh(Y = "eqIncome", H = "db040", PSU = "db030",
               w_final = "rb050", ID_level1 = "db030",
               ID_level2 = "rb030", Dom = "db040", period = "period",
               N_h = NULL, Z = NULL, dataset = dataset1,
               X = NULL, X_ID_level1 = NULL,  
               g = NULL, q = NULL, datasetX = NULL,
               confidence = .95, percentratio = 1,
               outp_lin = TRUE, outp_res = TRUE)
aa2

# without using fh_zero (finite population correction)
aa3 <- vardomh(Y = "eqIncome", H = "db040", PSU = "db030",
               w_final = "rb050", ID_level1 = "db030", 
               ID_level2 = "rb030", Dom = "db040",
               period = "period", N_h = NULL, fh_zero = FALSE, 
               Z = NULL, dataset = dataset1, X = NULL,
               X_ID_level1 = NULL, g = NULL, q = NULL,
               datasetX = NULL, confidence = .95,
               percentratio = 1, outp_lin = TRUE,
               outp_res = TRUE)
aa3

# with using fh_zero (finite population correction)
aa4 <- vardomh(Y = "eqIncome", H = "db040", PSU = "db030",
               w_final = "rb050", ID_level1 = "db030",
               ID_level2 = "rb030", Dom = "db040",
               period = "period", N_h = NULL, fh_zero = TRUE, 
               Z = NULL, dataset = dataset1,
               X = NULL, X_ID_level1 = NULL, 
               g = NULL, q = NULL, datasetX = NULL,
               confidence = .95, percentratio = 1,
               outp_lin = TRUE, outp_res = TRUE)
aa4
## End(Not run)

Variance estimation for sample surveys by the ultimate cluster method

Description

Computes the variance estimation by the ultimate cluster method.

Usage

variance_est(
  Y,
  H,
  PSU,
  w_final,
  N_h = NULL,
  fh_zero = FALSE,
  PSU_level = TRUE,
  PSU_sort = NULL,
  period = NULL,
  dataset = NULL,
  msg = "",
  checking = TRUE
)

Arguments

Details

If we assume that n_h \geq 2 for all h, that is, two or more PSUs are selected from each stratum, then the variance of \hat{\theta} can be estimated from the variation among the estimated PSU totals of the variable Z:

\hat{V} \left(\hat{\theta} \right)=\sum\limits_{h=1}^{H} \left(1-f_h \right) \frac{n_h}{n_{h}-1} \sum\limits_{i=1}^{n_h} \left( z_{hi\bullet}-\bar{z}_{h\bullet\bullet}\right)^2,

where \bullet z_{hi\bullet}=\sum\limits_{j=1}^{m_{hi}} \omega_{hij} z_{hij}

\bullet \bar{z}_{h\bullet\bullet}=\frac{\left( \sum\limits_{i=1}^{n_h} z_{hi\bullet} \right)}{n_h}

\bullet f_h is the sampling fraction of PSUs within stratum

\bullet h is the stratum number, with a total of H strata

\bullet i is the primary sampling unit (PSU) number within stratum h, with a total of n_h PSUs

\bullet j is the household number within cluster i of stratum h, with a total of m_{hi} household

\bullet w_{hij} is the sampling weight for household j in PSU i of stratum h

\bullet z_{hij} denotes the observed value of the analysis variable z for household j in PSU i of stratum h

Value

a data.table containing the values of the variance estimation by totals.

References

Morris H. Hansen, William N. Hurwitz, William G. Madow, (1953), Sample survey methods and theory Volume I Methods and applications, 257-258, Wiley.
Guillaume Osier and Emilio Di Meglio. The linearisation approach implemented by Eurostat for the first wave of EU-SILC: what could be done from the second onwards? 2012
Eurostat Methodologies and Working papers, Standard error estimation for the EU-SILC indicators of poverty and social exclusion, 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
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.

See Also

domain, lin.ratio, linarpr, linarpt, lingini, lingini2, lingpg, linpoormed, linqsr, linrmpg, residual_est, vardom, vardomh, varpoord, variance_othstr

Examples

Ys <- rchisq(10, 3)
w <- rep(2, 10)
PSU <- 1 : length(Ys)
H <- rep("Strata_1", 10)

# by default without using fh_zero (finite population correction)
variance_est(Y = Ys, H = H, PSU = PSU, w_final = w)


## Not run: 
 # without using fh_zero (finite population correction)
 variance_est(Y = Ys, H = H, PSU = PSU, w_final = w, fh_zero = FALSE)
 
 # with using fh_zero (finite population correction)
 variance_est(Y = Ys, H = H, PSU = PSU, w_final = w, fh_zero = TRUE)
 
## End(Not run)

Variance estimation for sample surveys by the new stratification

Description

Computes s2g and the variance estimation by the new stratification.

Usage

variance_othstr(
  Y,
  H,
  H2,
  w_final,
  N_h = NULL,
  N_h2,
  period = NULL,
  dataset = NULL,
  checking = TRUE
)

Arguments

Details

It is possible to compute population size M_g from sampling frame. The standard deviation of g-th stratum is

S_g^2 =\frac{1}{M_g-1} \sum\limits_{k=1}^{M_g} \left(y_{gk}-\bar{Y}_g \right)^2= \frac{1}{M_g-1} \sum\limits_{k=1}^{M_g} y_{gk}^2 - \frac{M_g}{M_g-1}\bar{Y}_g^2

\sum\limits_{k=1}^{M_g} y_{gk} ^2 and \bar{Y}_g^2 have to be estimated to estimate S_g^2. Estimate of \sum\limits_{k=1}^{M_g} y_{gk}^2 is \sum\limits_{h=1}^{H} \frac{N_h}{n_h} \sum\limits_{i=1}^{n_h} y_{gi}^2 z_{hi}, where

z_{hi} = \left\{ \begin{array}{ll} 0, & h_i \notin \theta_g \\ 1, & h_i \in \theta_g \end{array} \right. , \theta_g is the index group of successfully surveyed units belonging to g-th stratum. #'Estimate of \bar{Y}_g^2 is

\hat{\bar{Y}}_g^2=\left( \hat{\bar{Y}}_g \right)^2-\hat{Var} \left(\hat{\bar{Y}} \right)

\hat{\bar{Y}}_g =\frac{\hat{Y}_g}{M_g}= \frac{1}{M_g} \sum\limits_{h=1}^{H} \frac{N_h}{n_h} \sum\limits_{i=1}^{n_h} y_{hi} z_{hi}

So the estimate of S_g^2 is

s_g^2=\frac{1}{M_g-1} \sum\limits_{h=1}^{H} \frac{N_h}{n_h} \sum\limits_{i=1}^{n_h} y_{hi}^2 z_{hi} -

-\frac{M_g}{M_g-1} \left( \left( \frac{1}{M_g} \sum\limits_{h=1}^{H} \frac{N_h}{n_h} \sum\limits_{i=1}^{n_h} y_{hi} z_{hi} \right)^2 - \frac{1}{M_g^2} \sum\limits_{h=1}^{H} N_h^2 \left(\frac{1}{n_h} - \frac{1}{N_h}\right) \frac{1}{n_h-1} \sum\limits_{i=1}^{n_h} \left(y_{hi} z_{hi} - \frac{1}{n_h} \sum\limits_{t=1}^{n_h} y_{ht} z_{ht} \right)^2 \right)

Two conditions have to realize to estimate S_g^2: n_h>1, \forall g and \theta_g \ne 0, \forall g.

Variance of \hat{Y} is

Var\left( \hat{Y} \right) = \sum\limits_{g=1}^{G} M_g^2 \left( \frac{1}{m_g} - \frac{1}{M_g} \right) S_g^2

Estimate of \hat{Var}\left( \hat{Y} \right) is

\hat{Var}\left( \hat{Y} \right) = \sum\limits_{g=1}^{G} M_g^2 \left( \frac{1}{m_g} - \frac{1}{M_g} \right)s_g^2

Value

A list with objects are returned by the function:

• betas A numeric data.table containing the estimated coefficients of calibration.

• s2g A data.table containing the s^2g value.

• var_est A data.table containing the values of the variance estimation.

References

M. Liberts. (2004) Non-response Analysis and Bias Estimation in a Survey on Transportation of Goods by Road.

See Also

domain, lin.ratio, linarpr, linarpt, lingini, lingini2, lingpg, linpoormed, linqsr, linrmpg, residual_est, vardom, vardom_othstr, vardomh, varpoord

Examples

library("data.table")
Y <- data.table(matrix(runif(50) * 5, ncol = 5))
   
H <- data.table(H = as.integer(trunc(5 * runif(10))))
H2 <- data.table(H2 = as.integer(trunc(3 * runif(10))))
   
N_h <- data.table(matrix(0 : 4, 5, 1))
setnames(N_h, names(N_h), "H")
N_h[, sk:= 10]
   
N_h2 <- data.table(matrix(0 : 2, 3, 1))
setnames(N_h2, names(N_h2), "H2")
N_h2[, sk2:= 4]
   
w_final <- rep(2, 10)
   
vo <- variance_othstr(Y = Y, H = H, H2 = H2,
                      w_final = w_final,
                      N_h = N_h, N_h2 = N_h2,
                      period = NULL,
                      dataset = NULL)
vo

Estimation of the variance and deff for sample surveys for indicators on social exclusion and poverty

Description

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

Usage

varpoord(
  Y,
  w_final,
  age = NULL,
  pl085 = NULL,
  month_at_work = NULL,
  Y_den = NULL,
  Y_thres = NULL,
  wght_thres = NULL,
  ID_level1,
  ID_level2 = NULL,
  H,
  PSU,
  N_h,
  PSU_sort = NULL,
  fh_zero = FALSE,
  PSU_level = TRUE,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  gender = NULL,
  dataset = NULL,
  X = NULL,
  periodX = NULL,
  X_ID_level1 = NULL,
  ind_gr = NULL,
  g = NULL,
  q = NULL,
  datasetX = NULL,
  percentage = 60,
  order_quant = 50,
  alpha = 20,
  confidence = 0.95,
  outp_lin = FALSE,
  outp_res = FALSE,
  type = "linrmpg"
)

Arguments

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.

Value

A list with objects are returned by the function:

• lin_out - a data.table containing the linearized values of the ratio estimator with ID_level2 and PSU.

• res_out - a data.table containing the estimated residuals of calibration with ID_level1 and PSU.

• betas - a numeric data.table containing the estimated coefficients of calibration.

• all_result - a data.table, which containing variables:
  respondent_count - the count of respondents,
  pop_size - the estimated size of population,
  n_nonzero - the count of respondents, who answers are larger than zero,
  value - the estimated value,
  var - the estimated variance,
  se - the estimated standard error,
  rse - the estimated relative standard error (coefficient of variation),
  cv - the estimated relative standard error (coefficient of variation) in percentage,
  absolute_margin_of_error - the estimated absolute margin of error,
  relative_margin_of_error - the estimated relative margin of error in percentage,
  CI_lower - the estimated confidence interval lower bound,
  CI_upper - the estimated confidence interval upper bound,
  confidence_level - the positive value for confidence interval,
  S2_y_HT - the estimated variance of the y variable in case of total or the estimated variance of the linearised variable in case of the ratio of two totals using non-calibrated weights,
  S2_y_ca - the estimated variance of the y variable in case of total or the estimated variance of the linearised variable in case of the ratio of two totals using calibrated weights,
  S2_res - the estimated variance of the regression residuals,
  var_srs_HT - the estimated variance of the HT estimator under SRS for household,
  var_cur_HT - the estimated variance of the HT estimator under current design for household,
  var_srs_ca - the estimated variance of the calibrated estimator under SRS for household,
  deff_sam - the estimated design effect of sample design for household,
  deff_est - the estimated design effect of estimator for household,
  deff - the overall estimated design effect of sample design and estimator for household

References

Eric Graf and Yves Tille, Variance Estimation Using Linearization for Poverty and Social Exclusion Indicators, Survey Methodology, June 2014 61 Vol. 40, No. 1, pp. 61-79, Statistics Canada, Catalogue no. 12-001-X, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/12-001-x2014001-eng.pdf
Guillaume Osier and Emilio Di Meglio. The linearisation approach implemented by Eurostat for the first wave of EU-SILC: what could be done from the second wave onwards? 2012
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.
Eurostat Methodologies and Working papers, Standard error estimation for the EU-SILC indicators of poverty and social exclusion, 2013, URL http://ec.europa.eu/eurostat/documents/3859598/5927001/KS-RA-13-029-EN.PDF.
Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.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.
Matti Langel, Yves Tille, Corrado Gini, a pioneer in balanced sampling and inequality theory. Metron - International Journal of Statistics, 2011, vol. LXIX, n. 1, pp. 45-65, URL http://dx.doi.org/10.1007/BF03263549.
Morris H. Hansen, William N. Hurwitz, William G. Madow, (1953), Sample survey methods and theory Volume I Methods and applications, 257-258, Wiley.
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
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.

See Also

vardom, vardomh, linarpt

Examples

library("data.table")
library("laeken")
data("eusilc")
dataset <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
dataset1 <- dataset[1 : 1000]
 
#use dataset1 by default without using fh_zero (finite population correction)
aa <- varpoord(Y = "eqIncome", w_final = "rb050",
               Y_thres = NULL, wght_thres = NULL,
               ID_level1 = "db030", ID_level2 = "IDd", 
               H = "db040", PSU = "rb030", N_h = NULL,
               sort = NULL, Dom = NULL,
               gender = NULL, X = NULL,
               X_ID_level1 = NULL, g = NULL,
               q = NULL, datasetX = NULL,             
               dataset = dataset1, percentage = 60,
               order_quant = 50L, alpha = 20, 
               confidence = .95, outp_lin = FALSE,
               outp_res = FALSE, type = "linarpt")
aa
 
## Not run: 
 # use dataset1 by default with using fh_zero (finite population correction)
 aa2 <- varpoord(Y = "eqIncome", w_final = "rb050",
                 Y_thres = NULL, wght_thres = NULL,
                 ID_level1 = "db030", ID_level2 = "IDd", 
                 H = "db040", PSU = "rb030", N_h = NULL,
                 fh_zero = TRUE, sort = NULL, Dom = "db040",
                 gender = NULL, X = NULL, X_ID_level1 = NULL,
                 g = NULL, datasetX = NULL, dataset =  dataset1,
                 percentage = 60, order_quant = 50L,
                 alpha = 20, confidence = .95, outp_lin = FALSE,
                 outp_res = FALSE, type = "linarpt")
 aa2
 aa2$all_result
 
 
 # using dataset1
 aa4 <- varpoord(Y = "eqIncome", w_final = "rb050",
                 Y_thres = NULL, wght_thres = NULL,
                 ID_level1 = "db030", ID_level2 = "IDd", 
                 H = "db040", PSU = "rb030", N_h = NULL,
                 sort = NULL, Dom = "db040",
                 gender = NULL, X = NULL,
                 X_ID_level1 = NULL, g = NULL,
                 datasetX = NULL, dataset =  dataset,
                 percentage = 60, order_quant = 50L,
                 alpha = 20, confidence = .95,
                 outp_lin = TRUE, outp_res = TRUE,
                 type = "linarpt")
 aa4$lin_out[20 : 40]
## End(Not run)