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Delete-a-PSU jackknife replicate weights that re-apply the recipe

Source: R/variance.R
jackknife_weights.Rd

Builds jackknife replicate weights by deleting one primary sampling unit (PSU) at a time and re-running the whole recipe on each replicate, so the replicate weights carry the variability of every adjustment (like bootstrap_weights(), but with the delete-a-PSU jackknife instead of a resampling bootstrap).

Usage

jackknife_weights(
  object,
  strata = NULL,
  psu = NULL,
  lonely_psu = c("certainty", "collapse"),
  cores = 1L,
  progress = TRUE
)

Arguments

object

a weighting_spec (inert recipe) or a prepped weighting_spec. Pass the recipe before prep(): the jackknife preps it once per replicate.

strata

name of the stratum column, or NULL for a single stratum.

psu

name of the PSU column, or NULL to delete one unit at a time.

lonely_psu

how to treat strata with a single PSU: "certainty" (default) skips them (no variance) and warns; "collapse" merges them into a pseudo-stratum so they yield delete-a-PSU replicates.

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; serial on Windows). For a deterministic recipe the result is identical to the serial run.

progress

print progress every 25 replicates (serial only).

Value

An object of class weightflow_jack with the replicates matrix (units x replicates), the point weights, the per-replicate stratum and stratum size (used by jackknife_estimate()), and the design metadata.

Details

For a stratum \(h\) with \(n_h\) PSUs, the replicate that deletes PSU \(i\) zeros the base weight of that PSU and inflates the remaining PSUs of the stratum by \(n_h/(n_h-1)\); other strata are unchanged. There is one replicate per PSU. Strata with a single PSU contribute no variance and are skipped. This is the stratified jackknife (JKn); with strata = NULL it is the unstratified jackknife (JK1), and with psu = NULL each unit is its own PSU (delete-one-unit jackknife).

Examples

spec <- weighting_spec(sample_one, base_weights = pw) |>
  step_calibrate(method = "raking",
                 margins = list(region = c(table(population$region))))
jk <- jackknife_weights(spec, strata = "region", psu = "psu", progress = FALSE)
jack_total(jk, "employed")
#>   estimate       se ci_lower ci_upper
#> 1 1031.456 85.28049 864.3092 1198.603