Introduction to surveysd

2025-08-26

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 0.4486785 1008.6905620 0.4486785 0.4486785
2010 FALSE male 504.5696 0.4486785 1008.6905620 0.4486785 0.4486785
2010 FALSE male 504.5696 0.4486785 1008.6905620 0.4486785 0.4486785
2010 FALSE female 493.3824 0.4387304 0.4387304 0.4387304 986.3259754
2010 FALSE male 493.3824 0.4387304 0.4387304 0.4387304 986.3259754

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 estimate_type val_povertyRisk stE_povertyRisk
2010 7267 3979572 male direct 12.02660 0.5591015
2010 7560 4202650 female direct 16.73351 0.7715705
2010 14827 8182222 NA direct 14.44422 0.6072575
2011 7267 3979572 male direct 12.81921 0.5643889
2011 7560 4202650 female direct 16.62488 0.6980746
2011 14827 8182222 NA direct 14.77393 0.5833305

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 estimate_type val_povertyRisk stE_povertyRisk
2010 261 122741.8 male Burgenland direct 17.414524 4.208485
2010 288 137822.2 female Burgenland direct 21.432598 3.730093
2010 359 182732.9 male Vorarlberg direct 12.973259 2.475398
2010 374 194622.1 female Vorarlberg direct 19.883637 3.331928
2010 440 253143.7 male Salzburg direct 9.156964 2.337213
2010 484 282307.3 female Salzburg direct 17.939382 2.633282 
## skipping 54 more rows