Convert a tbl_sample to a replicate-weight survey design

Creates a svyrep.design object from a tbl_sample by first converting to a survey::svydesign() object via as_svydesign(), then converting with survey::as.svrepdesign().

Usage

as_svrepdesign(x, ...)

# S3 method for class 'tbl_sample'
as_svrepdesign(
  x,
  ...,
  type = c("auto", "JK1", "JKn", "BRR", "bootstrap", "subbootstrap", "mrbbootstrap",
    "Fay")
)

Arguments

x: A tbl_sample object produced by execute().
...: Additional arguments passed to survey::as.svrepdesign().
type: Replicate method passed to survey::as.svrepdesign(). One of "auto", "JK1", "JKn", "BRR", "bootstrap", "subbootstrap", "mrbbootstrap", or "Fay".

Value

A svyrep.design object from the survey package.

Details

Replicate conversion supports single-phase designs. For unequal-probability designs (PPS or random-size Poisson), "subbootstrap" and "mrbbootstrap" are the supported replicate types. Other types emit a warning and may fail because inclusion probabilities vary within strata. For fixed-size PPS variance estimation, linearization via as_svydesign() is generally preferred. Two-phase designs should be exported with as_svydesign().

Bootstrap escape hatch for random-size Poisson at stage 1

Some designs cannot be expressed as a linearization-based survey::svydesign() object. Specifically, multi-stage designs with a random-size Poisson method (bernoulli or pps_poisson) at stage 1, and single-stage designs with cluster_by() and multiple rows per sampled cluster, are rejected by as_svydesign() for those methods.

For these cases as_svrepdesign(type = "subbootstrap") (or "mrbbootstrap") is the recommended path. The design is exported with a permissive specification (no finite-population correction at the Poisson stage, no pps argument), and the bootstrap resampler supplies the variance through replicate weights.

This is the package's bootstrap approximation for designs that exact Horvitz-Thompson linearization cannot express in survey::svydesign(). The subbootstrap and mrbbootstrap methods were developed for fixed-size PPS sampling (Antal and Tille 2011); their behaviour on random-size Poisson designs, especially at multiple stages, has weaker theoretical backing and should be treated as an approximation.

Examples

sample <- sampling_design() |>
  stratify_by(region, alloc = "proportional") |>
  draw(n = 300) |>
  execute(bfa_eas, seed = 42)

rep_svy <- as_svrepdesign(sample, type = "auto")
survey::svymean(~households, rep_svy)
#>              mean    SE
#> households 71.979 3.679