Caps the current weights at a multiple of a reference (each unit's base
weight, the group median, or an absolute value) and, by default, redistributes
the removed mass among the untrimmed units so the weighted total survives the
trim. With by, the reference and the cap are computed separately within each
subgroup. An optional step that can appear anywhere in the recipe, more than
once.
Usage
step_trim(
spec,
max_ratio,
min_ratio = NULL,
reference = c("base", "median", "value"),
redistribute = TRUE,
by = NULL,
maxit = 50L,
id = NULL
)Arguments
- spec
a weighting_spec.
- max_ratio
number. Upper cap. Its meaning depends on
reference. E.g. with reference = "base" and max_ratio = 4, no weight may exceed 4 times its design weight. Must be greater than 1 for reference = "base"/"median" (a multiplier) and greater than 0 for reference = "value" (an absolute weight).- min_ratio
number or NULL. Lower floor (same units as max_ratio); if supplied, must be greater than 0 and below
max_ratio.- reference
"base" (multiple of each unit's base weight), "median" (multiple of the median of current weights) or "value" (absolute weight value).
- redistribute
logical. If TRUE, redistributes the trimmed excess among the uncapped weights to preserve the total (iterating). If you calibrate afterwards you can use FALSE: calibration restores the totals.
- by
character. Groups within which to redistribute (optional).
- maxit
integer. Maximum cap+redistribution iterations.
- 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
There is no standard threshold: max_ratio is an analyst decision, a
bias-variance trade-off. Use Kish's design effect (see summary) to judge
whether trimming is worth it.
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_calibrated(),
step_trim_weights(),
step_unknown_eligibility()
Examples
weighting_spec(sample_survey, base_weights = pw) |>
step_trim(max_ratio = 3, reference = "base")
#>
#> == Weighting specification (weightflow) ==
#> Data : 467 cases
#> Base wts: pw
#> Steps :
#> 1. trimming (base, cap 3) [trim_1]
#> Status : not estimated
#>