Per-domain weight summary at every stage of the cascade

For quality control by study domain (for example a department / DAM), this summarises how the weights move within each domain at every stage of the recipe: the base weights, then the weights after each step. It reads the stage-by-stage weights that prep() already stores, so it adds no computation to the cascade and never changes a weight.

Usage

domain_summary(object, by, min_n_eff = NULL)

Arguments

object

a prepped weighting_spec (the output of prep()).

by

the name(s) of one or more domain columns in the data (e.g. "region", or c("region", "area") to cross them). Domains are ordered by the factor levels of the column (or numerically for a numeric column); units with a missing domain value are shown as a "(missing)" domain rather than dropped silently.

min_n_eff

optional publication threshold. When set to a positive number, the result gains a logical publishable column (whether the domain's final-stage effective sample size reaches the threshold) and a warning names the domains that fall below it, turning the implicit reliability read into an explicit gate. Domains below the threshold are candidates for small-area estimation (see as_sae_input()) rather than direct estimation.

Value

A data.frame with one row per stage x domain and the columns stage (an ordered factor: base weights, then 1. <step>, 2. <step>, ...), domain (an ordered factor), n_active (active units in the domain at that stage), sum_w (sum of the active weights), mean_w, deff (the Kish design effect within the domain) and n_eff; and, when min_n_eff is given, publishable. Reading down a domain shows how its weight total and dispersion evolve step by step.

See also

design_effect(), weight_factors(), summary.prepped_weighting_spec()

Other cascade audit: as_sae_input(), collect_propensities(), collect_step_detail(), collect_weights(), weight_factors(), weighting_alerts()

Examples

fit <- weighting_spec(sample_survey, base_weights = pw) |>
  step_nonresponse(respondent = responded, method = "weighting_class", by = "region") |>
  step_calibrate(method = "raking", margins = list(region = c(table(population$region)))) |>
  prep()
domain_summary(fit, by = "region")
#>             stage domain n_active     sum_w    mean_w deff n_eff
#> 1    base weights  North      119 1487.5000 12.500000    1   119
#> 2    base weights  South      121 1210.0000 10.000000    1   121
#> 3    base weights   East       96  800.0000  8.333333    1    96
#> 4    base weights   West      131  873.3333  6.666667    1   131
#> 5  1. nonresponse  North       78 1487.5000 19.070513    1    78
#> 6  1. nonresponse  South       72 1210.0000 16.805556    1    72
#> 7  1. nonresponse   East       52  800.0000 15.384615    1    52
#> 8  1. nonresponse   West       68  873.3333 12.843137    1    68
#> 9    2. calibrate  North       78 1570.0000 20.128205    1    78
#> 10   2. calibrate  South       72 1250.0000 17.361111    1    72
#> 11   2. calibrate   East       52  927.0000 17.826923    1    52
#> 12   2. calibrate   West       68  748.0000 11.000000    1    68