Per-unit detail of one step of the cascade

A generic companion to collect_weights() that returns, for a single step, the weight it received and the multiplier it applied to every unit, plus any quantities that step computed internally (for a propensity step, the fitted propensity and its class). .weight_in and .factor are read from the stage-by-stage weights that prep() already stores, so .weight_in * .factor equals the weight leaving the step by construction, for any step. Native columns (those a step exposes on its own) are NA for units the step did not touch.

Usage

collect_step_detail(object, step = NULL)

Arguments

object

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

step

optional: which step to inspect, as an integer position (1 for the first piped step) or a step id string (e.g. "calibrate_1"; see the recipe print-out). If NULL (default): a single step exposing native detail is used; if several do, or if none do and the recipe has more than one step, an error lists the steps so you can choose.

Value

The sample data.frame with .weight_in (the weight reaching the step, carrying every earlier adjustment) and .factor (the multiplier the step applied to each unit, NA where the incoming weight is zero) appended, plus any native columns of the chosen step (for a propensity step: .propensity, .responded, and .class when propensity classes are used), which are NA outside the units the step covers. Here .factor is defined for every unit with a nonzero incoming weight, so an active unit the step did not touch reports .factor = 1; this differs from collect_propensities(), where .factor is NA outside the propensity model (see its .status).

See also

collect_weights(), collect_propensities(), weight_factors()

Other cascade audit: as_sae_input(), collect_propensities(), 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()
d <- collect_step_detail(fit, step = 1)
head(d[!is.na(d$.factor), c(".weight_in", ".factor", ".propensity")])
#>   .weight_in  .factor .propensity
#> 1       12.5 1.526316   0.6653748
#> 2       12.5 0.000000   0.6653748
#> 3       12.5 0.000000   0.6460371
#> 4       12.5 0.000000   0.6460371
#> 5       12.5 1.614706   0.6460371
#> 6       12.5 1.614706   0.6460371