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Bootstrap replicate weights that re-apply the recipe

Source: R/variance.R
bootstrap_weights.Rd

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,
  lonely_psu = c("certainty", "collapse"),
  seed = NULL,
  cores = 1L,
  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 psu is NULL each unit is its own PSU; if strata is NULL a single stratum is assumed.

m

PSUs drawn per stratum (default n - 1).

lonely_psu

how to treat strata with a single PSU (which a with-replacement bootstrap cannot resample): "certainty" (default) treats them as self-representing, so they contribute no bootstrap variance, and warns; "collapse" merges the single-PSU strata into a pseudo-stratum (with the smallest other stratum if there is only one), so they are resampled and do contribute a (conservative) variance. For full control, build your own collapsed stratum column and pass it as strata.

seed

optional RNG seed.

cores

number of parallel workers for the replicates (default 1 = serial). With cores > 1 the replicate re-preps run in parallel via parallel::mclapply (forking; on Windows it falls back to serial). Results are identical to the serial run: the resampling is drawn up front with the seed and only the deterministic re-prep is parallelised.

progress

print progress every 25 replicates (serial only).

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 90.4319 2486.034  2840.52