Collect replicate weights into a data frame ready for srvyr

Returns the data with the point weight and the replicate weights as columns, so it can be fed directly to srvyr::as_survey_rep() (or survey::svrepdesign()). Works for both a bootstrap (weightflow_boot) and a delete-a-PSU jackknife (weightflow_jack). Replicate columns are full (combined) weights, so use combined.weights = TRUE and mse = TRUE; the correct type, scale and rscales for the object are attached as attributes ("type", "scale", "rscales") – for a bootstrap scale = 1 / R, rscales = 1; for the jackknife scale = 1, rscales = (n_h - 1)/n_h per replicate.

Usage

collect_replicate_weights(
  object,
  weight_name = ".weight",
  prefix = "rep_",
  drop_zero = TRUE
)

Arguments

object

a weightflow_boot or weightflow_jack object.

weight_name

name of the point-weight column to add.

prefix

prefix for the replicate-weight columns (rep_1, rep_2, ...).

drop_zero

keep only active units (point weight > 0).

Value

A data frame: the original columns, weight_name, and one column per replicate. The number of replicates is in attribute "R", and the replication design in attributes "type", "scale" and "rscales".

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 = 30, strata = "region",
                          psu = "psu", seed = 1, progress = FALSE)
df <- collect_replicate_weights(boot)   # or a weightflow_jack object
# \donttest{
if (requireNamespace("srvyr", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  srvyr::as_survey_rep(df, weights = .weight,
                       repweights = dplyr::starts_with("rep_"),
                       type = attr(df, "type"), combined.weights = TRUE,
                       scale = attr(df, "scale"), rscales = attr(df, "rscales"),
                       mse = TRUE)
}
#> Call: Called via srvyr
#> Survey bootstrap with 30 replicates and MSE variances.
#> Sampling variables:
#>   - repweights: `rep_1 + rep_2 + rep_3 + rep_4 + rep_5 + rep_6 + rep_7 + rep_8
#>     + rep_9 + rep_10 + rep_11 + rep_12 + rep_13 + rep_14 + rep_15 + rep_16 +
#>     rep_17 + rep_18 + rep_19 + rep_20 + rep_21 + rep_22 + rep_23 + rep_24 +
#>     rep_25 + rep_26 + rep_27 + rep_28 + rep_29 + rep_30` 
#>   - weights: .weight 
#> Data variables: 
#>   - person_id (int), household_id (int), psu (int), region (fct), sex (fct),
#>     age (dbl), pw (dbl), unknown_elig (int), responded (dbl), income (dbl),
#>     employed (int), .weight (dbl), rep_1 (dbl), rep_2 (dbl), rep_3 (dbl), rep_4
#>     (dbl), rep_5 (dbl), rep_6 (dbl), rep_7 (dbl), rep_8 (dbl), rep_9 (dbl),
#>     rep_10 (dbl), rep_11 (dbl), rep_12 (dbl), rep_13 (dbl), rep_14 (dbl),
#>     rep_15 (dbl), rep_16 (dbl), rep_17 (dbl), rep_18 (dbl), rep_19 (dbl),
#>     rep_20 (dbl), rep_21 (dbl), rep_22 (dbl), rep_23 (dbl), rep_24 (dbl),
#>     rep_25 (dbl), rep_26 (dbl), rep_27 (dbl), rep_28 (dbl), rep_29 (dbl),
#>     rep_30 (dbl)
# }