Description Usage Arguments Details Value References See Also Examples
Computes the variance estimation of the sample surveys in domain by the ultimate cluster method.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 |
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 |
Calculate variance estimation in domains based on book of Hansen, Hurwitz and Madow.
A list with objects is returned by the function:
lin_out
- a data.table
containing the linearized values of the ratio estimator with id and PSU.
res_out
- a data.table
containing the estimated residuals of calibration with id and PSU.
betas
- a numeric data.table
containing the estimated coefficients of calibration.
all_result
- a data.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.
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.
domain
,
lin.ratio
,
residual_est
,
vardomh
,
var_srs
,
variance_est
,
variance_othstr
1 2 3 4 5 6 7 8 9 10 11 | 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)
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