# NOT RUN {
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
# }
# NOT RUN {
# }
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