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Implements Plug-in Bias-Corrected Estimators for Ranking Data (Rcpp)

Source: R/imprr_direct_rcpp.R
imprr_direct_rcpp.Rd

This function implements the bias correction of the ranking distribution using a paired anchor question. This is a fast Rcpp-based implementation that is approximately 200-300x faster than the tidyverse version.

Usage

imprr_direct_rcpp(
  data,
  J = NULL,
  main_q,
  anc_correct = NULL,
  population = "non-random",
  assumption = "contaminated",
  n_bootstrap = 200,
  seed = 123456,
  weight = NULL,
  verbose = FALSE,
  p_random = NULL
)

Arguments

data

The input dataset with ranking data.

J

The number of items in the ranking question. Defaults to NULL, in which case it will be inferred from the data.

main_q

Ranking question to be analyzed. When `main_q` is a single column name or unquoted symbol such as `app_identity`, the function looks for `app_identity_1`, `app_identity_2`, `app_identity_3`, and so on. You may also supply `main_q` directly as a character vector or unquoted `c(...)` expression of ranking columns such as `c(party, gender, race, religion)`.

anc_correct

Optional indicator for passing the anchor question. If `NULL`, `p_random` is used when supplied; otherwise the function defaults to `p_random = 0` and applies no correction.

population

Choice of the target population out of non-random respondents (default) or all respondents.

assumption

Choice of identifying assumption when `population = "all"`: `uniform` assumes random respondents would have uniform counterfactual preferences, while `contaminated` assumes their counterfactual preferences match those of non-random respondents.

n_bootstrap

Number of bootstraps. Defaults to 200.

seed

Seed for set.seed for reproducibility.

weight

The name of the weight column in `data`. Defaults to `NULL`, which uses equal weights. This can also be supplied as a numeric vector or as an unquoted column name.

verbose

Indicator for verbose output. Defaults to FALSE.

p_random

Optional fixed proportion of random/inattentive respondents. When supplied, this overrides `anc_correct` and a message is shown if both are provided.

Value

A list with two elements:

est_p_random

Summary statistics for the estimated proportion of random respondents (mean, lower, upper)

results

A tibble with bias-corrected estimates for all items, including average ranks, pairwise probabilities, top-k probabilities, and marginal probabilities

Examples

out <- imprr_direct_rcpp(
  identity,
  main_q = "app_identity",
  anc_correct = "anc_correct_identity",
  n_bootstrap = 1,
  seed = 123
)
#> No weight column supplied; using equal weights for all observations.
out$est_p_random
#> # A tibble: 1 × 3
#>    mean lower upper
#>   <dbl> <dbl> <dbl>
#> 1 0.322 0.322 0.322
head(out$results)
#> # A tibble: 6 × 6
#>   item           qoi              outcome               mean  lower  upper
#>   <chr>          <chr>            <chr>                <dbl>  <dbl>  <dbl>
#> 1 app_identity_1 average rank     Avg: app_identity_1 3.30   3.30   3.30  
#> 2 app_identity_1 pairwise ranking v. app_identity_2   0.349  0.349  0.349 
#> 3 app_identity_1 pairwise ranking v. app_identity_3   0.0924 0.0924 0.0924
#> 4 app_identity_1 pairwise ranking v. app_identity_4   0.256  0.256  0.256 
#> 5 app_identity_1 top-k ranking    Top-1               0.0298 0.0298 0.0298
#> 6 app_identity_1 top-k ranking    Top-2               0.171  0.171  0.171