Automatic weight trimming (survey-style)

Caps weights into [lower, upper] and redistributes the change among the untrimmed units to preserve the total, mirroring survey::trimWeights(). By default no weight may fall below 1, and the upper cap is chosen by an automatic rule: the Tukey far-out fence (Q3 + 3*IQR) or, with method = "potter", Potter's MSE-optimal cutoff.

Usage

step_trim_weights(
  spec,
  lower = 1,
  upper = NULL,
  method = c("tukey", "potter"),
  strict = TRUE,
  maxit = 50L
)

Arguments

spec

a weighting_spec.

lower

numeric. Lower floor (default 1: no weight below 1).

upper

numeric or NULL. Upper cap. If NULL, the cap is chosen automatically by method.

method

rule for the automatic cap when upper = NULL: "tukey" (default, Q3 + 3*IQR far-out fence) or "potter" (Potter's MSE-optimal cutoff, which over a grid of candidate cutoffs minimizes an estimate of bias^2 + variance and so balances the bias of trimming against the variance from extreme weights). Ignored when upper is supplied.

strict

logical. If TRUE (default), iterate cap+redistribution until no weight is outside [lower, upper] (like survey's strict = TRUE). If FALSE, a single pass (redistribution may push some weights slightly past the cap).

maxit

integer. Maximum iterations when strict = TRUE.

Examples

weighting_spec(sample_survey, base_weights = pw) |>
  step_nonresponse(respondent = responded, method = "weighting_class", by = "region") |>
  step_trim_weights(lower = 1, strict = TRUE) |> prep()
#> 
#> == Weighting specification (weightflow) ==
#> Data    : 467 cases
#> Base wts: pw
#> Steps   :
#>   1. nonresponse (weighting class)
#>   2. auto weight trimming
#> Status  : estimated (prep)
#> 
#> Stage summary:
#>                      stage n_active sum_wts cv_wts deff_kish n_eff
#>                       base      467    4371  0.236     1.056   442
#>   stage_1_step_nonresponse      270    4371  0.144     1.021   265
#>  stage_2_step_trim_weights      270    4371  0.144     1.021   265
#> 
#> deff_kish = 1 + CV^2 (Kish design effect from unequal weighting);
#> n_eff = n_active / deff_kish. Both worsen with each adjustment and
#> improve with trimming.
#> 

# Potter MSE-optimal cutoff chosen from the data
weighting_spec(sample_survey, base_weights = pw) |>
  step_nonresponse(respondent = responded, method = "weighting_class", by = "region") |>
  step_trim_weights(method = "potter") |> prep()
#> 
#> == Weighting specification (weightflow) ==
#> Data    : 467 cases
#> Base wts: pw
#> Steps   :
#>   1. nonresponse (weighting class)
#>   2. auto weight trimming (Potter MSE)
#> Status  : estimated (prep)
#> 
#> Stage summary:
#>                      stage n_active sum_wts cv_wts deff_kish n_eff
#>                       base      467    4371  0.236     1.056   442
#>   stage_1_step_nonresponse      270    4371  0.144     1.021   265
#>  stage_2_step_trim_weights      270    4371  0.137     1.019   265
#> 
#> deff_kish = 1 + CV^2 (Kish design effect from unequal weighting);
#> n_eff = n_active / deff_kish. Both worsen with each adjustment and
#> improve with trimming.
#>