## ----setup, include=FALSE-----------------------------------------------------
options(rmarkdown.html_vignette.check_title = FALSE)
knitr::opts_chunk$set(echo = TRUE)

## -----------------------------------------------------------------------------
library(surveysd)

set.seed(1234)
eusilc <- demo.eusilc(prettyNames = TRUE)
dat_boot <- draw.bootstrap(eusilc, REP = 10, hid = "hid", weights = "pWeight",
                           strata = "region", period = "year")
dat_boot_calib <- recalib(dat_boot, conP.var = "gender", conH.var = "region",
                          epsP = 1e-2, epsH = 2.5e-2, verbose = FALSE)
dat_boot_calib[, onePerson := nrow(.SD) == 1, by = .(year, hid)]

## print part of the dataset
dat_boot_calib[1:5, .(year, povertyRisk, eqIncome, onePerson, pWeight, w1, w2, w3, w4, w5)]

## -----------------------------------------------------------------------------
povertyRate <- calc.stError(dat_boot_calib, var = "povertyRisk", fun = weightedRatio)
totalIncome <- calc.stError(dat_boot_calib, var = "eqIncome", fun = weightedSum)

## -----------------------------------------------------------------------------
povertyRate$Estimates
totalIncome$Estimates

## -----------------------------------------------------------------------------
## define custom estimator
myWeightedSum <- function(x, w) {
  sum(x*w)
}

## check if results are equal to the one using `surveysd::weightedSum()`
totalIncome2 <- calc.stError(dat_boot_calib, var = "eqIncome", fun = myWeightedSum)
all.equal(totalIncome$Estimates, totalIncome2$Estimates)

## -----------------------------------------------------------------------------
## use add.arg-argument
fun <- function(x, w, b) {
  sum(x*w*b)
}
add.arg = list(b="onePerson")

err.est <- calc.stError(dat_boot_calib, var = "povertyRisk", fun = fun,
                        period.mean = 0, add.arg=add.arg)
err.est$Estimates

# compare with direct computation
compare.value <- dat_boot_calib[,fun(povertyRisk,pWeight,b=onePerson),
                                 by=c("year")]
all((compare.value$V1-err.est$Estimates$val_povertyRisk)==0)

## -----------------------------------------------------------------------------
# custom estimator to first derive poverty threshold 
# and then estimate a weighted ratio
povmd <- function(x, w) {
 md <- laeken::weightedMedian(x, w)*0.6
 pmd60 <- x < md
 # weighted ratio is directly estimated inside the function
 return(sum(w[pmd60])/sum(w)*100)
}

err.est <- calc.stError(
  dat_boot_calib, var = "povertyRisk", fun = weightedRatio,
  fun.adjust.var = povmd, adjust.var = "eqIncome")
err.est$Estimates


## -----------------------------------------------------------------------------
# using fun.adjust.var and adjust.var to estimate povmd60 indicator
# for each period and bootstrap weight before applying the weightedRatio
povmd2 <- function(x, w) {
 md <- laeken::weightedMedian(x, w)*0.6
 pmd60 <- x < md
 return(as.integer(pmd60))
}

# set adjust.var="eqIncome" so the income vector is used to estimate
# the povmd60 indicator for each bootstrap weight
# and the resulting indicators are passed to function weightedRatio
group <- "gender"
err.est <- calc.stError(
  dat_boot_calib, var = "povertyRisk", fun = weightedRatio, group = "gender",
  fun.adjust.var = povmd2, adjust.var = "eqIncome")
err.est$Estimates

## -----------------------------------------------------------------------------
multipleRates <- calc.stError(dat_boot_calib, var = c("povertyRisk", "onePerson"), fun = weightedRatio)
multipleRates$Estimates

## -----------------------------------------------------------------------------
dat2 <- subset(dat_boot_calib, year == 2010)
for (att  in c("period", "weights", "b.rep"))
  attr(dat2, att) <- attr(dat_boot_calib, att)

## -----------------------------------------------------------------------------
povertyRates <- calc.stError(dat2, var = "povertyRisk", fun = weightedRatio, group = "region")
povertyRates$Estimates

## -----------------------------------------------------------------------------
povertyRates <- calc.stError(dat2, var = "povertyRisk", fun = weightedRatio, 
                             group = c("gender", "region"))
povertyRates$Estimates


## -----------------------------------------------------------------------------
povertyRates <- calc.stError(dat2, var = "povertyRisk", fun = weightedRatio, 
                             group = list(c("gender", "region")))
povertyRates$Estimates

## -----------------------------------------------------------------------------
povertyRates <- calc.stError(dat2, var = "povertyRisk", fun = weightedRatio, 
                             group = list("gender", "region", c("gender", "region")))
povertyRates$Estimates

## -----------------------------------------------------------------------------
povertyRates <- calc.stError(dat2, var = "povertyRisk", fun = weightedRatio, 
                             group = c("gender", "region"),
                             group.diff = TRUE)
povertyRates$Estimates

## -----------------------------------------------------------------------------
povertyRates <- calc.stError(dat2, var = "povertyRisk", fun = weightedRatio, 
                             group = list(c("gender", "region")),
                             group.diff = TRUE)
povertyRates$Estimates[,.N,by=.(estimate_type)]

## -----------------------------------------------------------------------------
povertyRates <- calc.stError(dat_boot_calib[year>2013], var = "povertyRisk", fun = weightedRatio, 
                             period.diff = c("2017 - 2016", "2016 - 2015", "2015 - 2014"))
povertyRates$Estimates

## -----------------------------------------------------------------------------
povertyRates <- calc.stError(dat_boot_calib[year>2013], var = "povertyRisk", fun = weightedRatio, 
                             group = "gender",
                             period.diff = c("2017 - 2016", "2016 - 2015", "2015 - 2014"))
povertyRates$Estimates

## -----------------------------------------------------------------------------
povertyRates <- calc.stError(dat_boot_calib[year>2013], var = "povertyRisk", fun = weightedRatio, 
                             period.mean = 3)
povertyRates$Estimates

## -----------------------------------------------------------------------------
povertyRates <- calc.stError(dat_boot_calib[year>2013], var = "povertyRisk", fun = weightedRatio, 
                             period.mean = 3, period.diff = "2016 - 2015",
                             group = "gender")
povertyRates$Estimates

