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(Y1, H1, PSU1, w_final1, id1,
            Dom1 = NULL, Z1 = NULL,
            country1, period1, dataset1 = NULL,
            Y2, H2, PSU2, w_final2, id2,
            Dom2 = NULL, Z2 = NULL, country2,
            period2, dataset2 = NULL, confidence=0.95)

Arguments

Variables of interest. Object convertible to data.frame or variable names as character, column numbers or logical vector with only one TRUE value (length of the vector has to be the same as the column count of dataset1

The unit stratum variable. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as the column

PSU1

Primary sampling unit variable. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as the c

w_final1

Weight variable. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as the column count of

id1

variable for unit ID codes (for household surveys - secondary unit id number). One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (l

Dom1

Optional variables used to define population domains. If supplied, variables are calculated for each domain. An object convertible to data.frame or variable names as character vector, column numbers or logical vector (length of the vector has

Optional variables of denominator for ratio estimation. If supplied, the ratio estimation is computed. Object convertible to data.frame or variable names as character, column numbers or logical vector (length of the vector has to be the same

country1

Variable for the survey countries. The values for each country are computed independently. Object convertible to data.frame or variable names as character, column numbers or logical vector (length of the vector has to be the same as the colum

period1

Variable for the survey periods. The values for each period are computed independently. Object convertible to data.frame or variable names as character, column numbers or logical vector (length of the vector has to be the same as the column c

dataset1

Optional survey data object convertible to data.frame.

PSU2

w_final2

id2

Dom2

country2

period2

dataset2

Optional survey data object convertible to data.frame.

confidence

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

Value

A data.table is returned by the function and containing: 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, CI_lower - the estimated confidence interval lower bound, CI_upper - the estimated confidence interval upper bound.

References

Eurostat Methodologies and Working papers, Standard error estimation for the EU-SILC indicators of poverty and social exclusion, 2013, URL http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-13-024/EN/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 http://www.cros-portal.eu/content/handbook-standard-error-estimation-and-other-related-sampling-issues-ver-29072013

Examples

Run this code

### Example 

data("eusilc")

set.seed(1)

data("eusilc")
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))]
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[, exp:= 1]

# At-risk-of-poverty (AROP)
data[, pov:= ifelse (t_pov == 1, 1, 0)]
 
dataset1 <- data[year==2010]
dataset2 <- data[year==2011]

result <- vardchanges(Y1="pov",
                   H1="strata", PSU1="PSU", w_final1="rb050",
                   id1="db030", Dom1=NULL, Z1=NULL,
                   country1="country", period1="year",
                   dataset1=dataset1,
                   Y2="pov",
                   H2="strata", PSU2="PSU", w_final2="rb050",
                   id2="db030", Dom2=NULL, Z2=NULL,
                   country2="country", period2="year",
                   dataset2=dataset2) 

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]
dataset1 <- data[year==2010]
dataset2 <- data[year==2011]

result <- vardchanges(Y1=c("pov", "dep", "lwi", "arope"),
                   H1="strata", PSU1="PSU", w_final1="rb050",
                   id1="db030", Dom1="rb090", Z1=NULL,
                   country1="country", period1="year",
                   dataset1=dataset1,
                   Y2=c("pov", "dep", "lwi", "arope"),
                   H2="strata", PSU2="PSU", w_final2="rb050",
                   id2="db030", Dom2="rb090", Z2=NULL,
                   country2="country", period2="year",
                   dataset2=dataset2)