Collect replicate weights into a data frame ready for srvyr

Returns the data with the point weight and every replicate weight as ordinary columns, plus the replication design as attributes. This is the form srvyr::as_survey_rep() and survey::svrepdesign() expect, and the form to write out when the analysis continues in another session, another script or another language.

Usage

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

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).

scramble

disclosure control for a public-use file. When TRUE, the replicate columns are randomly permuted (their rscales move with them, so the variance is unchanged) and the design identifier columns (the strata and psu columns used to build the replicates) are dropped from the output, so the exported weights do not reveal the sampling design. The point weights and the variance estimate are unaffected. Set a seed beforehand for a reproducible permutation. The result carries attribute "scrambled" = TRUE.

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".

See also

Other variance estimation: as_svydesign(), bootstrap_estimate(), bootstrap_weights(), jackknife_estimate(), jackknife_weights()

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
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)