vardpoor (version 0.8.4)

vardomh: Variance estimation for sample surveys in domain for one or two stage surveys by the ultimate cluster method

Description

Computes the variance estimation in domain for ID_level1.

Usage

vardomh(Y, H, PSU, w_final, ID_level1, ID_level2 = NULL, Dom = NULL, period = NULL, N_h = NULL, PSU_sort = NULL, fh_zero = FALSE, PSU_level = TRUE, Z = NULL, dataset = NULL, X = NULL, periodX = NULL, X_ID_level1 = NULL, ind_gr = NULL, g = NULL, q = NULL, datasetX = NULL, confidence = .95,   percentratio = 1, outp_lin = FALSE, outp_res = FALSE)

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.

period

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

Dom

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

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.

Z

Optional variables of denominator for ratio estimation. Object convertible to data.table or variable names as character, column numbers or logical vector (length of the vector has to be the same as the column count of dataset).

dataset

Optional survey data object convertible to data.table.

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 level1 convertible to data.table.

confidence

Optional positive value for confidence interval. This variable by default is 0.95.

percentratio

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

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.

Value

A list with objects are returned by the function:

Details

Calculate variance estimation in domains for household surveys based on book of Hansen, Hurwitz and Madow.

References

Morris H. Hansen, William N. Hurwitz, William G. Madow, (1953), Sample survey methods and theory Volume I Methods and applications, 257-258, Wiley.

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, 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

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=12-001-X19990024882.

See Also

domain, lin.ratio, residual_est, var_srs, variance_est

Examples

data(eusilc)
dataset <- data.table(IDd = 1 : nrow(eusilc), eusilc)

aa <- vardomh(Y = "eqIncome", H = "db040", PSU = "db030",
              w_final = "rb050", ID_level1 = "db030",
              ID_level2 = "rb030", Dom = "db040", period = NULL,
              N_h = NULL, Z = NULL, dataset = dataset, X = NULL,
              X_ID_level1 = NULL, g = NULL, q = NULL, 
              datasetX = NULL, confidence = 0.95, percentratio = 1,
              outp_lin = TRUE, outp_res = TRUE)

## Not run: 
# dataset2 <- copy(dataset)
# dataset$period <- 1
# dataset2$period <- 2
# dataset <- data.table(rbind(dataset, dataset2))
# 
# # by default without using fh_zero (finite population correction)
# aa2 <- vardomh(Y = "eqIncome", H = "db040", PSU = "db030",
#                w_final = "rb050", ID_level1 = "db030",
#                ID_level2 = "rb030", Dom = "db040", period = "period",
#                N_h = NULL, Z = NULL, dataset = dataset,
#                X = NULL, X_ID_level1 = NULL,  
#                g = NULL, q = NULL, datasetX = NULL,
#                confidence = .95, percentratio = 1,
#                outp_lin = TRUE, outp_res = TRUE)
# aa2
# 
# # without using fh_zero (finite population correction)
# aa3 <- vardomh(Y = "eqIncome", H = "db040", PSU = "db030",
#                w_final = "rb050", ID_level1 = "db030", 
#                ID_level2 = "rb030", Dom = "db040",
#                period = "period", N_h = NULL, fh_zero=FALSE, 
#                Z = NULL, dataset = dataset, X = NULL,
#                X_ID_level1 = NULL, g = NULL, q = NULL,
#                datasetX = NULL, confidence = .95,
#                percentratio = 1, outp_lin = TRUE,
#                outp_res = TRUE)
# aa3
# 
# # with using fh_zero (finite population correction)
# aa4 <- vardomh(Y="eqIncome", H="db040", PSU="db030", w_final="rb050",
#                ID_level1="db030", ID_level2="rb030", Dom = "db040",
#                period = "period", N_h = NULL, fh_zero=TRUE, 
#                Z = NULL, dataset = dataset,
#                X = NULL, X_ID_level1 = NULL, 
#                g = NULL, q = NULL, datasetX = NULL,
#                confidence = .95, percentratio = 1,
#                outp_lin = TRUE, outp_res = TRUE)
# aa4
# ## End(Not run)