vardcros: Variance estimation for cross-sectional, longitudinal...
In vardpoor: Variance Estimation for Sample Surveys by the Ultimate Cluster Method
Description
Usage
Arguments
Value
References
See Also
Examples
Description
Computes the variance estimation for cross-sectional and longitudinal measures for any stage cluster sampling designs.
Usage
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vardcros(
Y,
H,
PSU,
w_final,
ID_level1,
ID_level2,
Dom = NULL,
Z = NULL,
gender = NULL,
country = NULL,
period,
dataset = NULL,
X = NULL,
countryX = NULL,
periodX = NULL,
X_ID_level1 = NULL,
ind_gr = NULL,
g = NULL,
q = NULL,
datasetX = NULL,
linratio = FALSE,
percentratio = 1,
use.estVar = FALSE,
ID_level1_max = TRUE,
outp_res = FALSE,
withperiod = TRUE,
netchanges = TRUE,
confidence = 0.95,
checking = TRUE
)
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.
Dom
Optional variables used to define population domains. If supplied, variables are calculated for each domain. An object convertible to data.table or variable names as character vector, column numbers.
Z
Optional variables of denominator for ratio estimation. If supplied, the ratio estimation is computed. Object convertible to data.table or variable names as character, column numbers. This variable is NULL by default.
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.
country
Variable for the survey countries. The values for each country are computed independently. Object convertible to data.table or variable names as character, column numbers.
period
Variable for the survey periods. The values for each period are computed independently. Object convertible to data.table or variable names as character, column numbers.
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.
countryX
Optional variable for the survey countries. The values for each country are computed independently. Object convertible to data.table or variable names as character, column numbers.
periodX
Optional variable of the survey periods and countries. 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.
linratio
Logical value. If value is TRUE, then the linearized variables for the ratio estimator is used for variance estimation. If value is FALSE, then the gradients is used for variance estimation.
percentratio
Positive numeric value. All linearized variables are multiplied with percentratio value, by default - 1.
use.estVar
Logical value. If value is TRUE, then R function estVar is used for the estimation of covariance matrix of the residuals. If value is FALSE, then R function estVar is not used for the estimation of covariance matrix of the residuals.
ID_level1_max
Logical value. If value is TRUE, then the size of sample for variance under simple random sampling is taken as maximum value of size in ID_level1 . If value is FALSE, then the size of sample for variance under simple random sampling is taken as count of ID_level2 in ID_level1.
outp_res
Logical value. If TRUE estimated residuals of calibration will be printed out.
withperiod
Logical value. If TRUE is value, the results is with period, if FALSE, without period.
netchanges
Logical value. If value is TRUE, then produce two objects: the first object is aggregation of weighted data by period (if available), country, strata and PSU, the second object is an estimation for Y, the variance, gradient for numerator and denominator by country and period (if available). If value is FALSE, then both objects containing NULL.
confidence
Optional positive value for confidence interval. This variable by default is 0.95.
checking
Optional variable if this variable is TRUE, then function checks data preparation errors, otherwise not checked. This variable by default is TRUE.
Value
A list with four objects are returned by the function:
-
res_out - a data.table containing the estimated residuals of calibration with ID_level1 and PSU.
-
data_net_changes - a data.table containing aggregation of weighted data by period (if available) and countries (if available), country, strata, PSU.
-
var_grad - a data.table containing estimation for Y, the variance, gradient for numerator and denominator by period, country (if available) and population domains (if available).
results A data.table containing:
period - survey periods,
country - survey countries (if available),
Dom - optional variable of the population domains,
namesY - names of variables of interest,
namesZ - optional variable for names of denominator for ratio estimation,
sample_size - the sample size (in numbers of individuals),
pop_size - the population size (in numbers of individuals),
total - the estimated totals,
variance - the estimated variance of cross-sectional or longitudinal measures,
sd_w - the estimated weighted variance of simple random sample,
sd_nw - the estimated variance estimation of simple random sample,
pop - the population size (in numbers of households),
sampl_siz - the sample size (in numbers of households),
stderr_w - the estimated weighted standard error of simple random sample,
stderr_nw - the estimated standard error of simple random sample,
se - the estimated standard error of cross-sectional or longitudinal,
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,
confidence_level - the positive value for confidence interval.
References
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.
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
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.
See Also
Examples
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library("data.table")
library("laeken")
# Example 1
data(eusilc)
set.seed(1)
dataset1 <- data.table(eusilc)
dataset1[, year := 2010]
dataset1[, country := "AT"]
dataset1[age < 0, age := 0]
PSU <- dataset1[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
dataset1 <- merge(dataset1, PSU, by = "db030", all = TRUE)
PSU <- eusilc <- 0
dataset1[, strata := "XXXX"]
dataset1[, t_pov := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_dep := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_lwi := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, exp := 1]
dataset1[, exp2 := 1 * (age < 60)]
# At-risk-of-poverty (AROP)
dataset1[, pov := ifelse (t_pov == 1, 1, 0)]
# Severe material deprivation (DEP)
dataset1[, dep := ifelse (t_dep == 1, 1, 0)]
# Low work intensity (LWI)
dataset1[, lwi := ifelse (t_lwi == 1 & exp2 == 1, 1, 0)]
# At-risk-of-poverty or social exclusion (AROPE)
dataset1[, arope := ifelse (pov == 1 | dep == 1 | lwi == 1, 1, 0)]
result11 <- vardcros(Y="arope", H = "strata",
PSU = "PSU", w_final = "rb050",
ID_level1 = "db030", ID_level2 = "rb030",
Dom = "rb090", Z = NULL, country = "country",
period = "year", dataset = dataset1,
linratio = FALSE, withperiod = TRUE,
netchanges = TRUE, confidence = .95)
## Not run:
# Example 2
data(eusilc)
set.seed(1)
dataset1 <- data.table(rbind(eusilc, eusilc),
year = c(rep(2010, nrow(eusilc)),
rep(2011, nrow(eusilc))))
dataset1[, country := "AT"]
dataset1[age < 0, age := 0]
PSU <- dataset1[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
dataset1 <- merge(dataset1, PSU, by = "db030", all = TRUE)
PSU <- eusilc <- 0
dataset1[, strata := "XXXX"]
dataset1[, strata := as.character(strata)]
dataset1[, t_pov := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_dep := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_lwi := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, exp := 1]
dataset1[, exp2 := 1 * (age < 60)]
# At-risk-of-poverty (AROP)
dataset1[, pov := ifelse(t_pov == 1, 1, 0)]
# Severe material deprivation (DEP)
dataset1[, dep := ifelse(t_dep == 1, 1, 0)]
# Low work intensity (LWI)
dataset1[, lwi := ifelse(t_lwi == 1 & exp2 == 1, 1, 0)]
# At-risk-of-poverty or social exclusion (AROPE)
dataset1[, arope := ifelse(pov == 1 | dep == 1 | lwi == 1, 1, 0)]
result11 <- vardcros(Y = c("pov", "dep", "arope"),
H = "strata", PSU = "PSU", w_final = "rb050",
ID_level1 = "db030", ID_level2 = "rb030",
Dom = "rb090", Z = NULL, country = "country",
period = "year", dataset = dataset1,
linratio = FALSE, withperiod = TRUE,
netchanges = TRUE, confidence = .95)
dataset2 <- dataset1[exp2 == 1]
result12 <- vardcros(Y = c("lwi"), H = "strata",
PSU = "PSU", w_final = "rb050",
ID_level1 = "db030", ID_level2 = "rb030",
Dom = "rb090", Z = NULL,
country = "country", period = "year",
dataset = dataset2, linratio = FALSE,
withperiod = TRUE, netchanges = TRUE,
confidence = .95)
### Example 3
data(eusilc)
set.seed(1)
year <- 2011
dataset1 <- data.table(rbind(eusilc, eusilc, eusilc, eusilc),
rb010 = c(rep(2008, nrow(eusilc)),
rep(2009, nrow(eusilc)),
rep(2010, nrow(eusilc)),
rep(2011, nrow(eusilc))))
dataset1[, rb020 := "AT"]
dataset1[, u := 1]
dataset1[age < 0, age := 0]
dataset1[, strata := "XXXX"]
PSU <- dataset1[, .N, keyby = "db030"][, N:=NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
dataset1 <- merge(dataset1, PSU, by = "db030", all = TRUE)
thres <- data.table(rb020 = as.character(rep("AT", 4)),
thres = c(11406, 11931, 12371, 12791),
rb010 = 2008:2011)
dataset1 <- merge(dataset1, thres, all.x = TRUE, by = c("rb010", "rb020"))
dataset1[is.na(u), u := 0]
dataset1 <- dataset1[u == 1]
#############
# T3 #
#############
T3 <- dataset1[rb010 == year - 3]
T3[, strata1 := strata]
T3[, PSU1 := PSU]
T3[, w1 := rb050]
T3[, inc1 := eqIncome]
T3[, rb110_1 := db030]
T3[, pov1 := inc1 <= thres]
T3 <- T3[, c("rb020", "rb030", "strata", "PSU", "inc1", "pov1"), with = FALSE]
#############
# T2 #
#############
T2 <- dataset1[rb010 == year - 2]
T2[, strata2 := strata]
T2[, PSU2 := PSU]
T2[, w2 := rb050]
T2[, inc2 := eqIncome]
T2[, rb110_2 := db030]
setnames(T2, "thres", "thres2")
T2[, pov2 := inc2 <= thres2]
T2 <- T2[, c("rb020", "rb030", "strata2", "PSU2", "inc2", "pov2"), with = FALSE]
#############
# T1 #
#############
T1 <- dataset1[rb010 == year - 1]
T1[, strata3 := strata]
T1[, PSU3 := PSU]
T1[, w3 := rb050]
T1[, inc3 := eqIncome]
T1[, rb110_3 := db030]
setnames(T1, "thres", "thres3")
T1[, pov3 := inc3 <= thres3]
T1 <- T1[, c("rb020", "rb030", "strata3", "PSU3", "inc3", "pov3"), with = FALSE]
#############
# T0 #
#############
T0 <- dataset1[rb010 == year]
T0[, PSU4 := PSU]
T0[, strata4 := strata]
T0[, w4 := rb050]
T0[, inc4 := eqIncome]
T0[, rb110_4 := db030]
setnames(T0, "thres", "thres4")
T0[, pov4 := inc4 <= thres4]
T0 <- T0[, c("rb010", "rb020", "rb030", "strata4", "PSU4", "w4", "inc4", "pov4"), with = FALSE]
apv <- merge(T3, T2, all = TRUE, by = c("rb020", "rb030"))
apv <- merge(apv, T1, all = TRUE, by = c("rb020", "rb030"))
apv <- merge(apv, T0, all = TRUE, by = c("rb020", "rb030"))
apv <- apv[(!is.na(inc1)) & (!is.na(inc2)) & (!is.na(inc3)) & (!is.na(inc4))]
apv[, ppr := ifelse(((pov4 == 1) & ((pov1 == 1 & pov2 == 1 & pov3 == 1)
| (pov1 == 1 & pov2 == 1 & pov3 == 0)
| (pov1 == 1 & pov2 == 0 & pov3 == 1)
| (pov1 == 0 & pov2 ==1 & pov3 == 1))), 1, 0)]
result20 <- vardcros(Y = "ppr", H = "strata", PSU = "PSU",
w_final = "w4", ID_level1 = "rb030",
ID_level2 = "rb030", Dom = NULL,
Z = NULL, country = "rb020",
period = "rb010", dataset = apv,
linratio = FALSE,
withperiod = TRUE,
netchanges = FALSE,
confidence = .95)
result20
## End(Not run)
vardpoor documentation built on Nov. 30, 2020, 5:08 p.m.
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Description Usage Arguments Value References See Also Examples
Description
Computes the variance estimation for cross-sectional and longitudinal measures for any stage cluster sampling designs.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | vardcros(
Y,
H,
PSU,
w_final,
ID_level1,
ID_level2,
Dom = NULL,
Z = NULL,
gender = NULL,
country = NULL,
period,
dataset = NULL,
X = NULL,
countryX = NULL,
periodX = NULL,
X_ID_level1 = NULL,
ind_gr = NULL,
g = NULL,
q = NULL,
datasetX = NULL,
linratio = FALSE,
percentratio = 1,
use.estVar = FALSE,
ID_level1_max = TRUE,
outp_res = FALSE,
withperiod = TRUE,
netchanges = TRUE,
confidence = 0.95,
checking = TRUE
)
|
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_level1 |
Variable for level1 ID codes. One dimensional object convertible to one-column |
ID_level2 |
Optional variable for unit ID codes. One dimensional object convertible to one-column |
Dom |
Optional variables used to define population domains. If supplied, variables are calculated for each domain. An object convertible to |
Z |
Optional variables of denominator for ratio estimation. If supplied, the ratio estimation is computed. Object convertible to |
gender |
Numerical variable for gender, where 1 is for males, but 2 is for females. One dimensional object convertible to one-column |
country |
Variable for the survey countries. The values for each country are computed independently. Object convertible to |
period |
Variable for the survey periods. The values for each period are computed independently. Object convertible to |
dataset |
Optional survey data object convertible to |
X |
Optional matrix of the auxiliary variables for the calibration estimator. Object convertible to |
countryX |
Optional variable for the survey countries. The values for each country are computed independently. Object convertible to |
periodX |
Optional variable of the survey periods and countries. If supplied, residual estimation of calibration is done independently for each time period. Object convertible to |
X_ID_level1 |
Variable for level1 ID codes. One dimensional object convertible to one-column |
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 |
datasetX |
Optional survey data object in household level convertible to |
linratio |
Logical value. If value is |
percentratio |
Positive numeric value. All linearized variables are multiplied with |
use.estVar |
Logical value. If value is |
ID_level1_max |
Logical value. If value is |
outp_res |
Logical value. If |
withperiod |
Logical value. If |
netchanges |
Logical value. If value is TRUE, then produce two objects: the first object is aggregation of weighted data by period (if available), country, strata and PSU, the second object is an estimation for Y, the variance, gradient for numerator and denominator by country and period (if available). If value is FALSE, then both objects containing |
confidence |
Optional positive value for confidence interval. This variable by default is 0.95. |
checking |
Optional variable if this variable is TRUE, then function checks data preparation errors, otherwise not checked. This variable by default is TRUE. |
Value
A list with four objects are returned by the function:
-
res_out- adata.tablecontaining the estimated residuals of calibration with ID_level1 and PSU. -
data_net_changes- adata.tablecontaining aggregation of weighted data by period (if available) and countries (if available), country, strata, PSU. -
var_grad- adata.tablecontaining estimation for Y, the variance, gradient for numerator and denominator by period, country (if available) and population domains (if available). results A
data.tablecontaining:
period- survey periods,
country- survey countries (if available),
Dom- optional variable of the population domains,
namesY- names of variables of interest,
namesZ- optional variable for names of denominator for ratio estimation,
sample_size- the sample size (in numbers of individuals),
pop_size- the population size (in numbers of individuals),
total- the estimated totals,
variance- the estimated variance of cross-sectional or longitudinal measures,
sd_w- the estimated weighted variance of simple random sample,
sd_nw- the estimated variance estimation of simple random sample,
pop- the population size (in numbers of households),
sampl_siz- the sample size (in numbers of households),
stderr_w- the estimated weighted standard error of simple random sample,
stderr_nw- the estimated standard error of simple random sample,
se- the estimated standard error of cross-sectional or longitudinal,
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,
confidence_level- the positive value for confidence interval.
References
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.
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
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.
See Also
Examples
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library("laeken")
# Example 1
data(eusilc)
set.seed(1)
dataset1 <- data.table(eusilc)
dataset1[, year := 2010]
dataset1[, country := "AT"]
dataset1[age < 0, age := 0]
PSU <- dataset1[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
dataset1 <- merge(dataset1, PSU, by = "db030", all = TRUE)
PSU <- eusilc <- 0
dataset1[, strata := "XXXX"]
dataset1[, t_pov := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_dep := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_lwi := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, exp := 1]
dataset1[, exp2 := 1 * (age < 60)]
# At-risk-of-poverty (AROP)
dataset1[, pov := ifelse (t_pov == 1, 1, 0)]
# Severe material deprivation (DEP)
dataset1[, dep := ifelse (t_dep == 1, 1, 0)]
# Low work intensity (LWI)
dataset1[, lwi := ifelse (t_lwi == 1 & exp2 == 1, 1, 0)]
# At-risk-of-poverty or social exclusion (AROPE)
dataset1[, arope := ifelse (pov == 1 | dep == 1 | lwi == 1, 1, 0)]
result11 <- vardcros(Y="arope", H = "strata",
PSU = "PSU", w_final = "rb050",
ID_level1 = "db030", ID_level2 = "rb030",
Dom = "rb090", Z = NULL, country = "country",
period = "year", dataset = dataset1,
linratio = FALSE, withperiod = TRUE,
netchanges = TRUE, confidence = .95)
## Not run:
# Example 2
data(eusilc)
set.seed(1)
dataset1 <- data.table(rbind(eusilc, eusilc),
year = c(rep(2010, nrow(eusilc)),
rep(2011, nrow(eusilc))))
dataset1[, country := "AT"]
dataset1[age < 0, age := 0]
PSU <- dataset1[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
dataset1 <- merge(dataset1, PSU, by = "db030", all = TRUE)
PSU <- eusilc <- 0
dataset1[, strata := "XXXX"]
dataset1[, strata := as.character(strata)]
dataset1[, t_pov := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_dep := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, t_lwi := trunc(runif(nrow(dataset1), 0, 2))]
dataset1[, exp := 1]
dataset1[, exp2 := 1 * (age < 60)]
# At-risk-of-poverty (AROP)
dataset1[, pov := ifelse(t_pov == 1, 1, 0)]
# Severe material deprivation (DEP)
dataset1[, dep := ifelse(t_dep == 1, 1, 0)]
# Low work intensity (LWI)
dataset1[, lwi := ifelse(t_lwi == 1 & exp2 == 1, 1, 0)]
# At-risk-of-poverty or social exclusion (AROPE)
dataset1[, arope := ifelse(pov == 1 | dep == 1 | lwi == 1, 1, 0)]
result11 <- vardcros(Y = c("pov", "dep", "arope"),
H = "strata", PSU = "PSU", w_final = "rb050",
ID_level1 = "db030", ID_level2 = "rb030",
Dom = "rb090", Z = NULL, country = "country",
period = "year", dataset = dataset1,
linratio = FALSE, withperiod = TRUE,
netchanges = TRUE, confidence = .95)
dataset2 <- dataset1[exp2 == 1]
result12 <- vardcros(Y = c("lwi"), H = "strata",
PSU = "PSU", w_final = "rb050",
ID_level1 = "db030", ID_level2 = "rb030",
Dom = "rb090", Z = NULL,
country = "country", period = "year",
dataset = dataset2, linratio = FALSE,
withperiod = TRUE, netchanges = TRUE,
confidence = .95)
### Example 3
data(eusilc)
set.seed(1)
year <- 2011
dataset1 <- data.table(rbind(eusilc, eusilc, eusilc, eusilc),
rb010 = c(rep(2008, nrow(eusilc)),
rep(2009, nrow(eusilc)),
rep(2010, nrow(eusilc)),
rep(2011, nrow(eusilc))))
dataset1[, rb020 := "AT"]
dataset1[, u := 1]
dataset1[age < 0, age := 0]
dataset1[, strata := "XXXX"]
PSU <- dataset1[, .N, keyby = "db030"][, N:=NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
dataset1 <- merge(dataset1, PSU, by = "db030", all = TRUE)
thres <- data.table(rb020 = as.character(rep("AT", 4)),
thres = c(11406, 11931, 12371, 12791),
rb010 = 2008:2011)
dataset1 <- merge(dataset1, thres, all.x = TRUE, by = c("rb010", "rb020"))
dataset1[is.na(u), u := 0]
dataset1 <- dataset1[u == 1]
#############
# T3 #
#############
T3 <- dataset1[rb010 == year - 3]
T3[, strata1 := strata]
T3[, PSU1 := PSU]
T3[, w1 := rb050]
T3[, inc1 := eqIncome]
T3[, rb110_1 := db030]
T3[, pov1 := inc1 <= thres]
T3 <- T3[, c("rb020", "rb030", "strata", "PSU", "inc1", "pov1"), with = FALSE]
#############
# T2 #
#############
T2 <- dataset1[rb010 == year - 2]
T2[, strata2 := strata]
T2[, PSU2 := PSU]
T2[, w2 := rb050]
T2[, inc2 := eqIncome]
T2[, rb110_2 := db030]
setnames(T2, "thres", "thres2")
T2[, pov2 := inc2 <= thres2]
T2 <- T2[, c("rb020", "rb030", "strata2", "PSU2", "inc2", "pov2"), with = FALSE]
#############
# T1 #
#############
T1 <- dataset1[rb010 == year - 1]
T1[, strata3 := strata]
T1[, PSU3 := PSU]
T1[, w3 := rb050]
T1[, inc3 := eqIncome]
T1[, rb110_3 := db030]
setnames(T1, "thres", "thres3")
T1[, pov3 := inc3 <= thres3]
T1 <- T1[, c("rb020", "rb030", "strata3", "PSU3", "inc3", "pov3"), with = FALSE]
#############
# T0 #
#############
T0 <- dataset1[rb010 == year]
T0[, PSU4 := PSU]
T0[, strata4 := strata]
T0[, w4 := rb050]
T0[, inc4 := eqIncome]
T0[, rb110_4 := db030]
setnames(T0, "thres", "thres4")
T0[, pov4 := inc4 <= thres4]
T0 <- T0[, c("rb010", "rb020", "rb030", "strata4", "PSU4", "w4", "inc4", "pov4"), with = FALSE]
apv <- merge(T3, T2, all = TRUE, by = c("rb020", "rb030"))
apv <- merge(apv, T1, all = TRUE, by = c("rb020", "rb030"))
apv <- merge(apv, T0, all = TRUE, by = c("rb020", "rb030"))
apv <- apv[(!is.na(inc1)) & (!is.na(inc2)) & (!is.na(inc3)) & (!is.na(inc4))]
apv[, ppr := ifelse(((pov4 == 1) & ((pov1 == 1 & pov2 == 1 & pov3 == 1)
| (pov1 == 1 & pov2 == 1 & pov3 == 0)
| (pov1 == 1 & pov2 == 0 & pov3 == 1)
| (pov1 == 0 & pov2 ==1 & pov3 == 1))), 1, 0)]
result20 <- vardcros(Y = "ppr", H = "strata", PSU = "PSU",
w_final = "w4", ID_level1 = "rb030",
ID_level2 = "rb030", Dom = NULL,
Z = NULL, country = "rb020",
period = "rb010", dataset = apv,
linratio = FALSE,
withperiod = TRUE,
netchanges = FALSE,
confidence = .95)
result20
## End(Not run)
|
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