vardom {vardpoor} | R Documentation |
Variance estimation of the sample surveys in domain by the ultimate cluster method
Description
Computes the variance estimation of the sample surveys in domain by the ultimate cluster method.
Usage
vardom(
Y,
H,
PSU,
w_final,
id = NULL,
Dom = NULL,
period = NULL,
PSU_sort = NULL,
N_h = NULL,
fh_zero = FALSE,
PSU_level = TRUE,
Z = NULL,
X = NULL,
ind_gr = NULL,
g = NULL,
q = NULL,
dataset = NULL,
confidence = 0.95,
percentratio = 1,
outp_lin = FALSE,
outp_res = FALSE
)
Arguments
Y |
Variables of interest. Object convertible to |
H |
The unit stratum variable. One dimensional object convertible to one-column |
PSU |
Primary sampling unit variable. One dimensional object convertible to one-column |
w_final |
Weight variable. One dimensional object convertible to one-column |
id |
Optional variable for unit ID codes. One dimensional object convertible to one-column |
Dom |
Optional variables used to define population domains. If supplied, variables of interest are calculated for each domain. An object convertible to |
period |
Optional variable for survey period. If supplied, residual estimation of calibration is done independently for each time period. One dimensional object convertible to one-column |
PSU_sort |
optional; if PSU_sort is defined, then variance is calculated for systematic sample. |
N_h |
Number of primary sampling units in population for each stratum (and period if |
fh_zero |
by default FALSE; |
PSU_level |
by default TRUE; if PSU_level is TRUE, in each strata |
Z |
Optional variables of denominator for ratio estimation. Object convertible to |
X |
Optional matrix of the auxiliary variables for the calibration estimator. Object convertible to |
ind_gr |
Optional variable by which divided independently X matrix of the auxiliary variables for the calibration. One dimensional object convertible to one-column |
g |
Optional variable of the g weights. One dimensional object convertible to one-column |
q |
Variable of the positive values accounting for heteroscedasticity. One dimensional object convertible to one-column |
dataset |
Optional survey data object convertible to |
confidence |
Optional positive value for confidence interval. This variable by default is 0.95. |
percentratio |
Positive numeric value. All linearized variables are multiplied with |
outp_lin |
Logical value. If |
outp_res |
Logical value. If |
Details
Calculate variance estimation in domains based on book of Hansen, Hurwitz and Madow.
Value
A list with objects is returned by the function:
-
lin_out
- adata.table
containing the linearized values of the ratio estimator with id and PSU. -
res_out
- adata.table
containing the estimated residuals of calibration with id and PSU. -
betas
- a numericdata.table
containing the estimated coefficients of calibration. -
all_result
- adata.table
, which containing variables:variable
- names of variables of interest,
Dom
- optional variable of the population domains,
period
- optional variable of the survey periods,
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,
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 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,
var_cur_HT
- the estimated variance of the HT estimator under current design,
var_srs_ca
- the estimated variance of the calibrated estimator under SRS,
deff_sam
- the estimated design effect of sample design,
deff_est
- the estimated design effect of estimator,
deff
- the overall estimated design effect of sample design and estimator,
n_eff
- the effective sample size.
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 https://www150.statcan.gc.ca/n1/pub/12-001-x/1999002/article/4882-eng.pdf.
See Also
domain
,
lin.ratio
,
residual_est
,
vardomh
,
var_srs
,
variance_est
,
variance_othstr
Examples
library("data.table")
library("laeken")
data(eusilc)
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
aa <- vardom(Y = "eqIncome", H = "db040", PSU = "db030",
w_final = "rb050", id = "rb030", Dom = "db040",
period = NULL, N_h = NULL, Z = NULL,
X = NULL, g = NULL, q = NULL, dataset = dataset1,
confidence = .95, percentratio = 100,
outp_lin = TRUE, outp_res = TRUE)