varpoord: Estimation of the variance and deff for sample surveys for...
In vardpoor: Variance Estimation for Sample Surveys by the Ultimate Cluster Method

Description Usage Arguments Value References See Also Examples

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

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"
)

`Y`	Study variable (for example equalized disposable income or gross pension income). 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.
`age`	Age variable. One dimensional object convertible to one-column `data.frame` or variable name as character, column number.
`pl085`	Retirement variable (Number of months spent in retirement or early retirement). One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`Y_den`	Denominator variable (for example gross individual earnings). One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`Y_thres`	Variable (for example equalized disposable income) used for computation and linearization of poverty threshold. One dimensional object convertible to one-column `data.table` or variable name as character, column number. Variable specified for `inc` is used as `income_thres` if `income_thres` is not defined.
`wght_thres`	Weight variable used for computation and linearization of poverty threshold. One dimensional object convertible to one-column `data.table` or variable name as character, column number. Variable specified for `weight` is used as `wght_thres` if `wght_thres` is not defined.
`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.
`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.
`N_h`	Number of primary sampling units in population for each stratum (and period if `period` is not `NULL`). If `N_h = NULL` and `fh_zero = FALSE` (default), `N_h` is estimated from sample data as sum of weights (`w_final`) in each stratum (and period if `period` is not `NULL`). Optional for single-stage sampling design as it will be estimated from sample data. Recommended for multi-stage sampling design as `N_h` can not be correctly estimated from the sample data in this case. If `N_h` is not used in case of multi-stage sampling design (for example, because this information is not available), it is advisable to set `fh_zero = TRUE`. If `period` is `NULL`. A two-column data object convertible to `data.table` with rows for each stratum. The first column should contain stratum code. The second column - the number of primary sampling units in the population of each stratum. If `period` is not `NULL`. A three-column data object convertible to `data.table` with rows for each intersection of strata and period. The first column should contain period. The second column should contain stratum code. The third column - the number of primary sampling units in the population of each stratum and period.
`PSU_sort`	optional; if PSU_sort is defined, then variance is calculated for systematic sample.
`fh_zero`	by default FALSE; `fh` is calculated as division of n_h and N_h in each strata, if TRUE, `fh` value is zero in each strata.
`PSU_level`	by default TRUE; if PSU_level is TRUE, in each strata `fh` is calculated as division of count of PSU in sample (n_h) and count of PSU in frame(N_h). if PSU_level is FALSE, in each strata `fh` is calculated as division of count of units in sample (n_h) and count of units in frame(N_h), which calculated as sum of weights.
`sort`	Optional variable to be used as tie-breaker for sorting. 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 is calculated for each domain. An object convertible to `data.table` or variable names as character vector, column numbers.
`period`	Optional variable for survey period. If supplied, variables is calculated for each time period. Object convertible to `data.table` or variable names as character, column numbers.
`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.
`dataset`	Optional survey data object convertible to `data.frame`.
`X`	Optional matrix of the auxiliary variables for the calibration estimator. Object convertible to `data.table` or variable names as character, column numbers.
`periodX`	Optional variable of the 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`.
`percentage`	A numeric value in range [0,100] for p in the formula for poverty threshold computation: p/100 Z(α/100).* For example, to compute poverty threshold equal to 60% of some income quantile, p should be set equal to 60.
`order_quant`	A numeric value in range [0,100] for α in the formula for poverty threshold computation: p/100 Z(α/100).* For example, to compute poverty threshold equal to some percentage of median income, α should be set equal to 50.
`alpha`	a numeric value in range [0,100] for the order of the income quantile share ratio (in percentage).
`confidence`	Optional positive value for confidence interval. This variable by default is 0.95.
`outp_lin`	Logical value. If `TRUE` linearized values of the ratio estimator will be printed out.
`outp_res`	Logical value. If `TRUE` estimated residuals of calibration will be printed out.
`type`	a character vector (of length one unless several.ok is TRUE), example "linarpr","linarpt", "lingpg", "linpoormed", "linrmpg", "lingini", "lingini2", "linqsr", "linarr", "linrmir".
`month_at_work`	Variable

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.

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

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.

vardom, vardomh, linarpt

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)