Introduction to surveysd

2020-12-10

The goal of surveysd is to combine all necessary steps to use calibrated bootstrapping with custom estimation functions. This vignette will cover the usage of the most important functions. For insights in the theory used in this package, refer to vignette("methodology").

Load dummy data

A test data set based on data(eusilc, package = "laeken") can be created with demo.eusilc()

library(surveysd)

set.seed(1234)
eusilc <- demo.eusilc(n = 2, prettyNames = TRUE)

eusilc[1:5, .(year, povertyRisk, gender, pWeight)]
year povertyRisk gender pWeight
2010 FALSE female 504.5696
2010 FALSE male 504.5696
2010 FALSE male 504.5696
2010 FALSE female 493.3824
2010 FALSE male 493.3824

Draw bootstrap replicates

Use stratified resampling without replacement to generate 10 samples. Those samples are consistent with respect to the reference periods.

dat_boot <- draw.bootstrap(eusilc, REP = 10, hid = "hid", weights = "pWeight", 
                           strata = "region", period = "year")

Calibrate bootstrap replicates

Calibrate each sample according to the distribution of gender (on a personal level) and region (on a household level).

dat_boot_calib <- recalib(dat_boot, conP.var = "gender", conH.var = "region",
                          epsP = 1e-2, epsH = 2.5e-2, verbose = FALSE)
dat_boot_calib[1:5, .(year, povertyRisk, gender, pWeight, w1, w2, w3, w4)]
year povertyRisk gender pWeight w1 w2 w3 w4
2010 FALSE female 504.5696 1025.360 0.4581938 0.4456302 0.4520549
2010 FALSE male 504.5696 1025.360 0.4581938 0.4456302 0.4520549
2010 FALSE male 504.5696 1025.360 0.4581938 0.4456302 0.4520549
2011 FALSE female 504.5696 1024.862 0.4721126 0.4582807 0.4608312
2011 FALSE male 504.5696 1024.862 0.4721126 0.4582807 0.4608312

Estimate with respect to a grouping variable

Estimate relative amount of persons at risk of poverty per period and gender.

err.est <- calc.stError(dat_boot_calib, var = "povertyRisk", fun = weightedRatio, group = "gender")
err.est$Estimates
year n N gender val_povertyRisk stE_povertyRisk
2010 7267 3979572 male 12.02660 0.5882841
2010 7560 4202650 female 16.73351 0.7473909
2010 14827 8182222 NA 14.44422 0.6626295
2011 7267 3979572 male 12.81921 0.6059190
2011 7560 4202650 female 16.62488 0.7355060
2011 14827 8182222 NA 14.77393 0.6631967

The output contains estimates (val_povertyRisk) as well as standard errors (stE_povertyRisk) measured in percent. The rows with gender = NA denotes the aggregate over all genders for the corresponding year.

Estimate with respect to several variables

Estimate relative amount of persons at risk of poverty per period for each region, gender, and combination of both.

group <- list("gender", "region", c("gender", "region"))
err.est <- calc.stError(dat_boot_calib, var = "povertyRisk", fun = weightedRatio, group = group)
head(err.est$Estimates)
year n N gender region val_povertyRisk stE_povertyRisk
2010 261 122741.8 male Burgenland 17.414524 3.831697
2010 288 137822.2 female Burgenland 21.432598 3.243412
2010 359 182732.9 male Vorarlberg 12.973259 1.869263
2010 374 194622.1 female Vorarlberg 19.883637 3.112974
2010 440 253143.7 male Salzburg 9.156964 1.809600
2010 484 282307.3 female Salzburg 17.939382 2.587059 
## skipping 54 more rows