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Within-cluster selection adjustment

Source: R/spec-steps-cascade.R
step_select_within.Rd

Undoes one stage of subsampling inside a cluster: when only some of the eligible units of a household (or dwelling, or area segment) were selected, the selected ones must represent the whole cluster, so their weight is multiplied by the inverse of the within-cluster selection probability. Apply it after the cluster-level eligibility and nonresponse steps and before the person-level nonresponse step.

Usage

step_select_within(
  spec,
  prob = NULL,
  n_eligible = NULL,
  n_selected = NULL,
  id = NULL
)

Arguments

spec

a weighting_spec.

prob

unquoted column with the within-household selection probability of the selected person (need not be 1/n_eligible). The weight is multiplied by 1/prob.

n_eligible

unquoted column with the number of eligible persons in the household, for simple random selection within the household. When a single person is selected (the default), the weight is multiplied by n_eligible (equivalent to prob = 1/n_eligible).

n_selected

optional number of persons selected per household under simple random selection, when more than one person is subsampled. Either a single number (same subsample size in every household) or an unquoted column (subsample size varying by household). The weight is multiplied by n_eligible / n_selected (equivalent to prob = n_selected/n_eligible). Defaults to 1. Only used together with n_eligible.

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

Despite the name, the cluster need not be a household and the unit need not be a person: the step is the generic within-cluster subsampling adjustment. In a multi-stage design it can appear more than once – e.g. dwellings selected within sampled area segments, then persons selected within dwellings – each occurrence undoing one stage of subsampling.

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_subsample(), step_trim(), step_trim_calibrated(), step_trim_weights(), step_unknown_eligibility()

Examples

# simple random selection of one eligible person per household
df <- transform(sample_survey,
                n_elig = ave(person_id, household_id, FUN = length))
weighting_spec(df, base_weights = pw) |>
  step_select_within(n_eligible = n_elig)
#> 
#> == Weighting specification (weightflow) ==
#> Data    : 467 cases
#> Base wts: pw
#> Steps   :
#>   1. within-cluster selection  [select_within_1]
#> Status  : not estimated
#> 

# simple random selection of two eligible persons per household
weighting_spec(df, base_weights = pw) |>
  step_select_within(n_eligible = n_elig, n_selected = 2)
#> 
#> == Weighting specification (weightflow) ==
#> Data    : 467 cases
#> Base wts: pw
#> Steps   :
#>   1. within-cluster selection  [select_within_1]
#> Status  : not estimated
#>