Recover the fitted response propensities of a nonresponse step

A step_nonresponse(method = "propensity") step fits a response-propensity model and adjusts the weights by \(1/\hat p\). prep() keeps the full per-unit propensity vector \(\hat p\) (out-of-fold when cross-fitting is used) on the step, but it is not returned by collect_weights(). This accessor extracts it aligned to the sample, so you can inspect its distribution and confirm the nonresponse model is well fitted before trusting the adjusted weights. It works the same way whether the adjustment was made at the unit level or, through cluster, at the household level (there the household propensity is broadcast to its members).

Usage

collect_propensities(object, step = NULL)

Arguments

object

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

step

optional integer, which step to read when the recipe has more than one propensity step. If NULL (default) and there is a single propensity step it is used; with several, the last one is used with a message.

Value

The sample data.frame with columns appended: .propensity (the fitted response propensity \(\hat p\), NA for units outside the model, i.e. ineligible / already dropped), .responded (the response indicator the model used), .weight_in (the weight reaching the step, see below), .factor (the multiplier the step actually applied to the unit), .status (a factor that labels each unit as "eligible respondent", "eligible nonrespondent" or "not in propensity model"), and, when the step uses propensity classes (num_classes), .class (the assigned class). Units not in the propensity model carry NA in the per-unit columns. .weight_in is the weight reaching the nonresponse step – it already carries any earlier adjustment (unknown-eligibility redistribution, within-cluster selection), not the raw base weight. At the unit level it is also the weight the propensity model is fitted with (unless weight_model = FALSE); with cluster, the model is fitted at the household level with the household weight, which equals .weight_in only when weights are uniform within the household. .factor equals \(1/\hat p\) only when num_classes = NULL; with propensity classes it is the class-level adjustment, so 1/.propensity does not reconstruct the applied factor – use .factor. The stage-by-stage weights are available through weight_factors() and domain_summary().

See also

step_nonresponse(), collect_weights()

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

Examples

fit <- weighting_spec(sample_survey, base_weights = pw) |>
  step_nonresponse(respondent = responded, method = "propensity",
                   formula = ~ sex + region, engine = "logit") |>
  prep()
p <- collect_propensities(fit)
summary(p$.propensity)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.5111  0.5304  0.5846  0.5782  0.6460  0.6654