Builds bootstrap replicate weights by resampling primary sampling units (PSUs) with replacement within strata and re-running the whole recipe on each replicate. Because every adjustment (nonresponse, calibration, ...) is recomputed per replicate, the resulting replicate weights propagate the variability introduced by each weighting stage.
Usage
bootstrap_weights(
object,
replicates = 200L,
strata = NULL,
psu = NULL,
m = NULL,
seed = NULL,
progress = TRUE
)Arguments
- object
a
weighting_spec(or a prepped one) holding the recipe.- replicates
number of bootstrap replicates.
- strata, psu
column names of the stratum and the PSU. If
psuis NULL each unit is its own PSU; ifstratais NULL a single stratum is assumed.- m
PSUs drawn per stratum (default
n - 1).- seed
optional RNG seed.
- progress
print progress every 25 replicates.
Value
An object of class weightflow_boot with the replicates matrix
(units x replicates), the point weights, and the design metadata.
Details
The multiplier is the Rao-Wu rescaling bootstrap: within a stratum with \(n\) PSUs, \(m\) PSUs are drawn with replacement (default \(m = n - 1\)) and unit \(i\) in PSU \(k\) gets \(\lambda = 1 - \sqrt{m/(n-1)} + \sqrt{m/(n-1)}\,(n/m)\,t_k\), with \(t_k\) the number of times its PSU was drawn.
Examples
spec <- weighting_spec(sample_survey, base_weights = pw) |>
step_calibrate(method = "raking",
margins = list(region = c(table(population$region))))
boot <- bootstrap_weights(spec, replicates = 50, strata = "region",
psu = "psu", seed = 1)
#> bootstrap replicate 25/50
#> bootstrap replicate 50/50
boot_total(boot, "responded")
#> estimate se ci_lower ci_upper
#> 1 2663.277 99.4537 2468.351 2858.203