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Assess weight quality with diagnostic tables

weight_assess.Rd

Produces a per-variable diagnostic table comparing target distributions to unweighted and weighted distributions.

Usage

weight_assess(
  data,
  targets,
  weights,
  base_weights = NULL,
  na_method = c("exclude", "bucket")
)

Arguments

data

Data frame.

targets

Named list of named numeric target vectors (proportions).

weights

Final raked weight vector.

base_weights

Original base weights before raking. If NULL, uses uniform weights.

na_method

How to handle NA values. "exclude" skips NA cases from that margin. "bucket" treats missing values as an implicit extra category.

Value

Named list of tibbles, one per variable.

Examples

data <- data.frame(
  gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4))
)
targets <- list(gender = c(M = 0.5, F = 0.5))
result <- rake(data, targets)
weight_assess(data, targets, result$weights)
#> $gender
#> # A tibble: 3 × 9
#>   level target unweighted_n unweighted_pct weighted_n weighted_pct change_pct
#>   <chr>  <dbl>        <dbl>          <dbl>      <dbl>        <dbl>      <dbl>
#> 1 M        0.5           59           0.59         50          0.5      -0.09
#> 2 F        0.5           41           0.41         50          0.5       0.09
#> 3 Total    1            100           1           100          1         0.18
#> # ℹ 2 more variables: residual_disc <dbl>, original_disc <dbl>
#>