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

Description

Computes the variance estimation for measures of change for any 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 convertable 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 dimentional object convertable 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 dimentional object convertable 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 dimentional object convertable 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

optional; either 1 column data.frame, matrix, data.table with column names giving the IDs, or (if dataset is not NULL) a character string, an integer or a logical vector (length is the same as 'dataset' column count) specifying t

Dom1

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

country1

optional; either a data.frame, matrix, data.table with column names giving different countries, or (if dataset is not NULL) character strings, integers or a logical vectors (length is the same as 'dataset' column count) specifyin

period1

Optional variable for survey period. If supplied, variables is calculated for each time period. One dimentional object convertable to one-column data.frame or variable name as character, column number or logical vector with only one TRU

dataset1

Optional survey data object convertable to data.frame.

PSU2

w_final2

id2

Dom2

country2

period2

dataset2

Optional survey data object convertable 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 standart error, rse - the estiamted relative standart error (coefficient of variation), cv - the estimated relative standart 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 <- 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)