vardpoor (version 0.6.2)

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

Description

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

Usage

vardchanges(Y, H, PSU, w_final, id,
            Dom = NULL, Z = NULL,
            country, periods, dataset = NULL,
            period1, period2,
            annual = FALSE,
            linratio = FALSE,
            percentratio = 1,
            use.estVar = FALSE,
            confidence=0.95,
            change_type = "absolute")

Arguments

Y

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

H

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

PSU

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

w_final

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

id

variable for unit ID codes (for household surveys - secondary unit id number). One dimensional object convertible to one-column data.table or variable name as character, column number.

Dom

Optional variables used to define population domains. If supplied, variables are calculated for each domain. An object convertible to data.table or variable names as character vector, column numbers.

Z

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

country

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

periods

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

dataset

Optional survey data object convertible to data.table.

period1

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

period2

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

annual

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

linratio

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

percentratio

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

use.estVar

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

confidence

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

change_type

character value net changes type - absolute or relative.

Value

• A list with objects are returned by the function:
• 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 

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

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

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

# At-risk-of-poverty (AROP)
data[, pov:= ifelse (t_pov == 1, 1, 0)]
 
result <- vardchanges(Y="pov",
                   H="strata", PSU="PSU", w_final="rb050",
                   id="db030", Dom=NULL, Z=NULL,
                   country="country", periods="year",
                   dataset=data,
                   period1=2010, period2=2011,
                   change_type="absolute")
 
data("eusilc")
data <- 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))))
data[age<0, age:=0]
PSU <- data[,.N, keyby="db030"]
PSU[, N:=NULL]
PSU[, PSU:=trunc(runif(nrow(PSU), 0, 100))]
setkeyv(PSU, "db030")
setkeyv(data, "db030")
data <- merge(data, PSU, all=TRUE)
PSU <- eusilc <- NULL
data[, strata:=c("XXXX")]
data[, strata:=as.character(strata)]

data[, t_pov:=trunc(runif(nrow(data), 0, 2))]
data[, t_dep:=trunc(runif(nrow(data), 0, 2))]
data[, t_lwi:=trunc(runif(nrow(data), 0, 2))]
data[, exp:= 1]
data[, exp2:= 1 * (age < 60)]

# At-risk-of-poverty (AROP)
data[, pov:= ifelse (t_pov == 1, 1, 0)]
 
# Severe material deprivation (DEP)
data[, dep:= ifelse (t_dep == 1, 1, 0)]

# Low work intensity (LWI)
data[, lwi:= ifelse (t_lwi == 1 & exp2 == 1, 1, 0)]

# At-risk-of-poverty or social exclusion (AROPE)
data[, arope:= ifelse (pov == 1 | dep == 1 | lwi == 1, 1, 0)]
data[, dom:=1]

result <- vardchanges(Y=c("pov", "dep", "lwi", "arope"),
                   H="strata", PSU="PSU", w_final="rb050",
                   id="db030", Dom="rb090", Z=NULL,
                   country="country", periods="year",
                   dataset=data,
                   period1=2010, period2=2011,
                   change_type="absolute")