Redistributes the weight of the cases whose eligibility was never resolved onto the resolved cases of the same adjustment cell, so the resolved units stand in for the unresolved share of the frame. Reach for it as the first step of the cascade, while the known-ineligible units are still in the data.
Arguments
- spec
a weighting_spec.
- unknown
a 0/1 dummy column (1 = eligibility unknown) or any logical condition (unquoted) that is TRUE for unknown-eligibility cases. Evaluated on the data.
- by
character. Variables defining the adjustment cells (optional).
- cluster
character. Cluster (e.g. household) id column. If given, the redistribution is done at the cluster level: each cluster counts once with its (uniform) weight, the weight of unknown-eligibility clusters is redistributed among the known ones, and the adjusted weight is assigned to every member. Use this when unknown-eligibility units have no roster (one row per address) while resolved units are expanded by person.
- id
optional string: a stable identifier for this step, shown in the recipe print-out and usable to select it in
collect_step_detail(); defaults to a derived"<class>_<k>".
Value
The input weighting_spec with this step appended to its recipe. The
step is recorded only; it is evaluated when prep() is called.
Details
Within each cell \(c\) the resolved cases are scaled up to also carry the unresolved ones, $$w_i^{\mathrm{out}} = w_i \, \frac{\sum_{j \in c} w_j}{\sum_{j \in c,\, \mathrm{resolved}} w_j},$$ with every weight on the right the weight entering the step, so the ratio is one number per cell, the cell total is conserved exactly, and the result does not depend on the order in which units are updated.
See also
Other weighting steps:
step_assert(),
step_calibrate(),
step_cre(),
step_drop_ineligible(),
step_model_calibration(),
step_nonresponse(),
step_nr_sensitivity(),
step_pseudoweight(),
step_rescale(),
step_round(),
step_select_within(),
step_subsample(),
step_trim(),
step_trim_calibrated(),
step_trim_weights()
Examples
weighting_spec(sample_survey, base_weights = pw) |>
step_unknown_eligibility(unknown = unknown_elig, by = "region")
#>
#> == Weighting specification (weightflow) ==
#> Data : 467 cases
#> Base wts: pw
#> Steps :
#> 1. unknown eligibility [unknown_eligibility_1]
#> Status : not estimated
#>
# household-level redistribution (unknown units without roster)
weighting_spec(sample_survey, base_weights = pw) |>
step_unknown_eligibility(unknown = unknown_elig, by = "region",
cluster = "household_id")
#>
#> == Weighting specification (weightflow) ==
#> Data : 467 cases
#> Base wts: pw
#> Steps :
#> 1. unknown eligibility (by household_id) [unknown_eligibility_1]
#> Status : not estimated
#>