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  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 = 50L,
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 onecolumn 
age 
Age variable. One dimensional object convertible to onecolumn 
pl085 
Retirement variable (Number of months spent in retirement or early retirement). One dimensional object convertible to onecolumn 
month_at_work 
Variable for total number of month at work (sum of the number of months spent at fulltime work as employee, number of months spent at parttime work as employee, number of months spent at fulltime work as selfemployed (including family worker), number of months spent at parttime work as selfemployed (including family worker)). One dimensional object convertible to onecolumn 
Y_den 
Denominator variable (for example gross individual earnings). One dimensional object convertible to onecolumn 
Y_thres 
Variable (for example equalized disposable income) used for computation and linearization of poverty threshold. One dimensional object convertible to onecolumn 
wght_thres 
Weight variable used for computation and linearization of poverty threshold. One dimensional object convertible to onecolumn 
H 
The unit stratum variable. One dimensional object convertible to onecolumn 
PSU 
Primary sampling unit variable. One dimensional object convertible to onecolumn 
w_final 
Weight variable. One dimensional object convertible to onecolumn 
ID_level1 
Variable for level1 ID codes. One dimensional object convertible to onecolumn 
ID_level2 
Optional variable for unit ID codes. One dimensional object convertible to onecolumn 
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 tiebreaker for sorting. One dimensional object convertible to onecolumn 
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 onecolumn 
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 onecolumn 
ind_gr 
Optional variable by which divided independently X matrix of the auxiliary variables for the calibration. One dimensional object convertible to onecolumn 
g 
Optional variable of the g weights. One dimensional object convertible to onecolumn 
q 
Variable of the positive values accounting for heteroscedasticity. One dimensional object convertible to onecolumn 
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 integer 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 
cros_res_out 
A 
crossectional_results 
A

changes_results 
A

Guillaume Osier, Yves Berger, Tim Goedeme, (2013), Standard error estimation for the EUSILC indicators of poverty and social exclusion, Eurostat Methodologies and Working papers, URL http://ec.europa.eu/eurostat/documents/3888793/5855973/KSRA13024EN.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/KSRA13029EN.PDF.
Yves G. Berger, Tim Goedeme, Guillame Osier (2013). Handbook on standard error estimation and other related sampling issues in EUSILC,
URL https://ec.europa.eu/eurostat/cros/content/handbookstandarderrorestimationandotherrelatedsamplingissuesver29072013_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  ### Example
data(eusilc)
set.seed(1)
data < 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))))
data[age < 0, age := 0]
PSU < data[, .N, keyby = "db030"][, N := NULL]
PSU[, PSU := trunc(runif(nrow(PSU), 0, 100))]
PSU$inc < runif(nrow(PSU), 20, 100000)
data < merge(data, PSU, all = TRUE, by = "db030")
PSU < eusilc < NULL
data[, strata:=c("XXXX")]
data$pl085 < 12 * trunc(runif(nrow(data), 0, 2))
data$month_at_work < 12 * trunc(runif(nrow(data), 0, 2))
data[, 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 = data,
percentage = 60, order_quant = 50L,
alpha = 20, confidence = 0.95,
type = "linrmpg")

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