vardom_othstr: Variance estimation for sample surveys in domain by the two stratification

Description

Computes the variance estimation for sample surveys in domain by the two stratification.

Usage

vardom_othstr(Y, H, H2, PSU, w_final, id=NULL,
       Dom = NULL, period=NULL, N_h = NULL,
       N_h2, Z = NULL,
       X = NULL, g = NULL, dataset = NULL, 
       q = rep(1, if (is.null(dataset)) nrow(data.frame(X)) else nrow(dataset)),
       confidence = .95,  outp_lin=FALSE, outp_res=FALSE)

Arguments

Variables of interest. Object convertible to data.frame or variable names as character, column numbers or logical vector with only one TRUE value (length of the vector has to be the same as the column count of dataset

The unit stratum variable. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as the column

The unit new stratum variable. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as the co

PSU

Primary sampling unit variable. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as the c

w_final

Weight variable. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as the column count of

Optional variable for unit ID codes. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as

Dom

Optional variables used to define population domains. If supplied, linearization of the at-risk-of-poverty rate is done for each domain. An object convertible to data.frame or variable names as character vector, column numbers or logical vect

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 data.frame.

N_h

optional; either a data.frame giving the first column - stratum, but the second column - the total of the population in each stratum.

N_h2

optional; either a data.frame giving the first column - new stratum, but the second column - the total of the population in each new stratum.

optional variables of denominator for ratio estimation. Object convertible to data.frame or variable names as character, column numbers or logical vector with only one TRUE value (length of the vector has to be the same as the co

Optional matrix of the auxiliary variables for the calibration estimator. Object convertible to data.frame or variable names as character, column numbers or logical vector with only one TRUE value (length of the vector has to be

Optional variable of the g weights. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as t

Variable of the positive values accounting for heteroscedasticity. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the

dataset

Optional survey data object convertible to data.frame.

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.

Value

A list with objects are returned by the function:
lin_outA data.table containing the linearized values of the ratio estimator with id and PSU.
res_outA data.table containing the estimated residuals of calibration with id and PSU.
s2gA data.table containing the s^2g value.
all_resultA 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, 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, CI_lower - the estimated confidence interval lower bound, CI_upper - the estimated confidence interval upper bound, 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

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. M. Liberts. (2004) Non-response Analysis and Bias Estimation in a Survey on Transportation of Goods by Road.

Examples

Run this code

data(eusilc)

# Example 1
eusilc1 <- eusilc[1:1000, ]
dataset <- data.frame(1:nrow(eusilc1),eusilc1)
colnames(dataset)[1] <- "IDd"
Nh <- data.table(db040=eusilc$db040, rb050=eusilc$rb050)
setkeyv(Nh, names(Nh)[1])
N_h2 <- Nh[, sum(rb050, na.rm = FALSE), keyby="db040"]

aa<-vardom_othstr(Y="eqIncome", H="db040",H2="db040", PSU="db030", w_final="rb050",
           id="rb030", Dom = "db040", period=NULL, N_h=NULL, N_h2=N_h2, Z = NULL,
           X = NULL, g = NULL, dataset = dataset,
           q = rep(1, if (is.null(dataset)) 
                       nrow(as.data.frame(H)) else nrow(dataset)),
           confidence = .95, outp_lin=TRUE, outp_res=TRUE)


# Example 2
dataset <- data.frame(1:nrow(eusilc),eusilc)
colnames(dataset)[1] <- "IDd"
Nh <- data.table(db040=eusilc$db040, rb050=eusilc$rb050)
setkeyv(Nh, names(Nh)[1])
N_h2 <- Nh[, sum(rb050, na.rm = FALSE), keyby="db040"]

aa<-vardom_othstr(Y="eqIncome", H="db040",H2="db040", PSU="db030", w_final="rb050",
           id="rb030", Dom = "db040", period=NULL, N_h=NULL, N_h2=N_h2, Z = NULL,
           X = NULL, g = NULL, dataset = dataset,
           q = rep(1, if (is.null(dataset)) 
                       nrow(as.data.frame(H)) else nrow(dataset)),
           confidence = .95, outp_lin=TRUE, outp_res=TRUE)

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