Description Usage Arguments Value References See Also Examples
View source: R/vardchangespoor.R
Computes the variance estimation for measures of change for indicators on social exclusion and poverty.
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 32 33 34 35 36 37 38 39 | vardchangespoor(
Y,
age = NULL,
pl085 = NULL,
month_at_work = NULL,
Y_den = NULL,
Y_thres = NULL,
wght_thres = NULL,
H,
PSU,
w_final,
ID_level1,
ID_level2,
Dom = NULL,
country = NULL,
period,
sort = NULL,
period1,
period2,
gender = NULL,
dataset = NULL,
X = NULL,
countryX = NULL,
periodX = NULL,
X_ID_level1 = NULL,
ind_gr = NULL,
g = NULL,
q = NULL,
datasetX = NULL,
percentage = 60,
order_quant = 50,
alpha = 20,
use.estVar = FALSE,
confidence = 0.95,
outp_lin = FALSE,
outp_res = FALSE,
type = "linrmpg",
change_type = "absolute"
)
|
Y |
Study variable (for example equalized disposable income or gross pension income). One dimensional object convertible to one-column |
age |
Age variable. One dimensional object convertible to one-column |
pl085 |
Retirement variable (Number of months spent in retirement or early retirement). One dimensional object convertible to one-column |
month_at_work |
Variable for total number of month at work (sum of the number of months spent at full-time work as employee, number of months spent at part-time work as employee, number of months spent at full-time work as self-employed (including family worker), number of months spent at part-time work as self-employed (including family worker)). One dimensional object convertible to one-column |
Y_den |
Denominator variable (for example gross individual earnings). One dimensional object convertible to one-column |
Y_thres |
Variable (for example equalized disposable income) used for computation and linearization of poverty threshold. One dimensional object convertible to one-column |
wght_thres |
Weight variable used for computation and linearization of poverty threshold. One dimensional object convertible to one-column |
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 |
country |
Variable for the survey countries. The values for each country are computed independently. Object convertible to |
period |
Variable for the all survey periods. The values for each period are computed independently. Object convertible to |
sort |
Optional variable to be used as tie-breaker for sorting. One dimensional object convertible to one-column |
period1 |
The vector from variable |
period2 |
The vector from variable |
gender |
Numerical variable for gender, where 1 is for males, but 2 is for females. One dimensional object convertible to one-column |
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 |
percentage |
A numeric value in range [0,100] for p in the formula for poverty threshold computation: p/100 * Z(α/100). For example, to compute poverty threshold equal to 60% of some income quantile, p should be set equal to 60. |
order_quant |
A numeric value in range [0,100] for α in the formula for poverty threshold computation: p/100 * Z(α/100). For example, to compute poverty threshold equal to some percentage of median income, α should be set equal to 50. |
alpha |
a numeric value in range [0,100] for the order of the income quantile share ratio (in percentage). |
use.estVar |
Logical value. If value is |
confidence |
optional; either a positive value for confidence interval. This variable by default is 0.95. |
outp_lin |
Logical value. If |
outp_res |
Logical value. If |
type |
a character vector (of length one unless several.ok is TRUE), example "linarpr","linarpt", "lingpg", "linpoormed", "linrmpg", "lingini", "lingini2", "linqsr", "linarr", "linrmir", "all_choices". |
change_type |
character value net changes type - absolute or relative. |
A list with objects are returned by the function:
cros_lin_out
- a data.table
containing the linearized values of the ratio estimator with ID_level2 and PSU by periods and countries (if available).
cros_res_out
- a data.table
containing the estimated residuals of calibration with ID_level1 and PSU by periods and countries (if available).
crossectional_results
- a data.table
containing:
period
- survey periods,
country
- survey countries,
Dom
- optional variable of the population domains,
type
- type variable,
count_respondents
- the count of respondents,
pop_size
- the population size (in numbers of individuals),
estim
- the estimated value,
se
- the estimated standard error,
var
- the estimated variance,
rse
- the estimated relative standard error (coefficient of variation),
cv
- the estimated relative standard error (coefficient of variation) in percentage.
changes_results
- a data.table
containing:
period
- survey periods,
country
- survey countries,
Dom
- optional variable of the population domains,
type
- type variable,
estim_1
- the estimated value for period1,
estim_2
- the estimated value for period2,
estim
- the estimated value,
se
- the estimated standard error,
var
- the estimated variance,
rse
- the estimated relative standard error (coefficient of variation),
cv
- the estimated relative standard error (coefficient of variation) in percentage.
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
domain
,
vardchanges
,
vardcros
,
vardcrospoor
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 32 33 34 35 36 |
### Example
library("laeken")
library("data.table")
data(eusilc)
set.seed(1)
dataset1 <- data.table(rbind(eusilc, eusilc),
year = c(rep(2010, nrow(eusilc)),
rep(2011, nrow(eusilc))),
country = c(rep("AT", nrow(eusilc)),
rep("AT", nrow(eusilc))))
dataset1[age < 0, age := 0]
PSU <- dataset1[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
PSU$inc <- runif(nrow(PSU), 20, 100000)
dataset1 <- merge(dataset1, PSU, all = TRUE, by = "db030")
PSU <- eusilc <- NULL
dataset1[, strata := c("XXXX")]
dataset1$pl085 <- 12 * trunc(runif(nrow(dataset1), 0, 2))
dataset1$month_at_work <- 12 * trunc(runif(nrow(dataset1), 0, 2))
dataset1[, id_l2 := paste0("V", .I)]
result <- vardchangespoor(Y = "inc", age = "age",
pl085 = "pl085", month_at_work = "month_at_work",
Y_den = "inc", Y_thres = "inc",
wght_thres = "rb050", H = "strata",
PSU = "PSU", w_final="rb050",
ID_level1 = "db030", ID_level2 = "id_l2",
Dom = c("rb090"), country = "country",
period = "year", sort = NULL,
period1 = c(2010, 2011),
period2 = c(2011, 2010),
gender = NULL, dataset = dataset1,
percentage = 60, order_quant = 50L,
alpha = 20, confidence = 0.95,
type = "linrmpg")
result
|
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