# Variance estimation for measures of change for single and multistage stage cluster sampling designs Computes the variance estimation for measures of change for single and multistage stage cluster sampling designs. ```r 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 - `Y`: Variables of interest. Object convertible to `data.table` or variable names as character, column numbers. - `H`: The unit stratum variable. One dimensional object convertible to one-column `data.table` or variable name as character, column number. - `PSU`: Primary sampling unit variable. One dimensional object convertible to one-column `data.table` or variable name as character, column number. - `w_final`: Weight variable. One dimensional object convertible to one-column `data.table` or variable name as character, column number. - `ID_level1`: Variable for level1 ID codes. One dimensional object convertible to one-column `data.table` or variable name as character, column number. - `ID_level2`: Optional variable for unit ID codes. One dimensional object convertible to one-column `data.table` or variable name as character, column number. - `Dom`: Optional variables used to define population domains. If supplied, variables are calculated for each domain. An object convertible to `data.table` or variable names as character vector, column numbers. - `Z`: Optional variables of denominator for ratio estimation. If supplied, the ratio estimation is computed. Object convertible to `data.table` or variable names as character, column numbers. This variable is `NULL` by default. - `gender`: Numerical variable for gender, where 1 is for males, but 2 is for females. One dimensional object convertible to one-column `data.table` or variable name as character, column number. - `country`: Variable for the survey countries. The values for each country are computed independently. Object convertible to `data.table` or variable names as character, column numbers. - `period`: Variable for the all survey periods. The values for each period are computed independently. Object convertible to `data.table` or variable names as character, column numbers. - `dataset`: Optional survey data object convertible to `data.table`. - `period1`: The vector of periods from variable `periods` describes the first period. - `period2`: The vector of periods from variable `periods` describes the second period. - `X`: Optional matrix of the auxiliary variables for the calibration estimator. Object convertible to `data.table` or variable names as character, column numbers. - `countryX`: Optional variable for the survey countries. The values for each country are computed independently. Object convertible to `data.table` or variable names as character, column numbers. - `periodX`: Optional variable of the all survey periods. If supplied, residual estimation of calibration is done independently for each time period. Object convertible to `data.table` or variable names as character, column numbers. - `X_ID_level1`: Variable for level1 ID codes. One dimensional object convertible to one-column `data.table` or variable name as character, column number. - `ind_gr`: Optional variable by which divided independently X matrix of the auxiliary variables for the calibration. One dimensional object convertible to one-column `data.table` or variable name as character, column number. - `g`: Optional variable of the g weights. One dimensional object convertible to one-column `data.table` or variable name as character, column number. - `q`: Variable of the positive values accounting for heteroscedasticity. One dimensional object convertible to one-column `data.table` or variable name as character, column number. - `datasetX`: Optional survey data object in household level convertible to `data.table`. - `linratio`: Logical value. If value is `TRUE`, then the linearized variables for the ratio estimator is used for variance estimation. If value is `FALSE`, then the gradients is used for variance estimation. - `percentratio`: Positive numeric value. All linearized variables are multiplied with `percentratio` value, by default - 1. - `use.estVar`: Logical value. If value is `TRUE`, then `R` function `estVar` is used for the estimation of covariance matrix of the residuals. If value is `FALSE`, then `R` function `estVar` is not used for the estimation of covariance matrix of the residuals. - `outp_res`: Logical value. If `TRUE` estimated residuals of calibration will be printed out. - `confidence`: optional; either a positive value for confidence interval. This variable by default is 0.95 . - `change_type`: character value net changes type - absolute or relative. - `checking`: Optional variable if this variable is TRUE, then function checks data preparation errors, otherwise not checked. This variable by default is TRUE. ## Returns 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. ## Examples ```r ### 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) ``` ## 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](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](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](https://ec.europa.eu/eurostat/cros/content/handbook-standard-error-estimation-and-other-related-sampling-issues-ver-29072013_en) ## See Also `domain`, `vardcros`, `vardchangespoor`

vardchanges function