Add IPW Weights to the Original Data — add_ipw

This function is a thin convenience wrapper around imprr_weights() for users who want respondent-level inverse probability weights attached to the original data and do not necessarily need the full ranking-profile output.

Usage

add_ipw_weights(
  data,
  J = NULL,
  main_q,
  anc_correct = NULL,
  population = "non-random",
  assumption = "contaminated",
  weight = NULL,
  weight_col = "ipw_weights",
  keep_ranking = FALSE,
  ranking_col = "ranking",
  keep_rankings = 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.
weight: Optional weight specification for the estimation step. This can be the name of a weight column in data, a numeric vector with one weight per row, or an unquoted column name. Defaults to NULL, which uses equal weights.
weight_col: Name of the respondent-level IPW weight column to add to the returned data. Defaults to "ipw_weights".
keep_ranking: Logical; if TRUE, keep the unified ranking-profile column in the returned augmented data. Defaults to FALSE.
ranking_col: Name of the unified ranking-profile column used in the augmented data and ranking summary. Defaults to "ranking".
keep_rankings: Logical; if TRUE, also return the permutation- level ranking summary and estimated random-response rate. 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

If keep_rankings = FALSE, a data frame equal to the original data augmented with weight_col. If keep_rankings = TRUE, a list with three elements:

data: The augmented original data with respondent-level IPW weights.
rankings: The permutation-level ranking summary returned by imprr_weights().
est_p_random: The estimated proportion of random responses.

Examples

dat_w <- add_ipw_weights(
  identity,
  main_q = "app_identity",
  anc_correct = "anc_correct_identity"
)
#> No weight column supplied; using equal weights for all observations.
head(dat_w)
#> # A tibble: 6 × 17
#>   s_weight app_identity app_identity_1 app_identity_2 app_identity_3
#>      <dbl> <chr>                 <dbl>          <dbl>          <dbl>
#> 1    0.844 1423                      1              4              2
#> 2    0.886 1423                      1              4              2
#> 3    2.96  3412                      3              4              1
#> 4    0.987 1423                      1              4              2
#> 5    1.76  4132                      4              1              3
#> 6    0.469 3124                      3              1              2
#> # ℹ 12 more variables: app_identity_4 <dbl>, anc_identity <chr>,
#> #   anc_identity_1 <dbl>, anc_identity_2 <dbl>, anc_identity_3 <dbl>,
#> #   anc_identity_4 <dbl>, anc_correct_identity <dbl>,
#> #   app_identity_recorded <chr>, anc_identity_recorded <chr>,
#> #   app_identity_row_rnd <chr>, anc_identity_row_rnd <chr>, ipw_weights <dbl>

out <- add_ipw_weights(
  identity,
  main_q = "app_identity",
  anc_correct = "anc_correct_identity",
  keep_rankings = TRUE
)
#> No weight column supplied; using equal weights for all observations.
head(out$data)
#> # A tibble: 6 × 17
#>   s_weight app_identity app_identity_1 app_identity_2 app_identity_3
#>      <dbl> <chr>                 <dbl>          <dbl>          <dbl>
#> 1    0.844 1423                      1              4              2
#> 2    0.886 1423                      1              4              2
#> 3    2.96  3412                      3              4              1
#> 4    0.987 1423                      1              4              2
#> 5    1.76  4132                      4              1              3
#> 6    0.469 3124                      3              1              2
#> # ℹ 12 more variables: app_identity_4 <dbl>, anc_identity <chr>,
#> #   anc_identity_1 <dbl>, anc_identity_2 <dbl>, anc_identity_3 <dbl>,
#> #   anc_identity_4 <dbl>, anc_correct_identity <dbl>,
#> #   app_identity_recorded <chr>, anc_identity_recorded <chr>,
#> #   app_identity_row_rnd <chr>, anc_identity_row_rnd <chr>, ipw_weights <dbl>
head(out$rankings)
#>   ranking  n    prop_obs     prop_bc   weights   prop_bc_raw prop_bc_adj
#> 1    1234 14 0.012939002 0.000000000 0.0000000 -0.0003526508 0.000000000
#> 2    1243 11 0.010166359 0.000000000 0.0000000 -0.0044081345 0.000000000
#> 3    1324 14 0.012939002 0.000000000 0.0000000 -0.0003526508 0.000000000
#> 4    1342  7 0.006469501 0.000000000 0.0000000 -0.0098154461 0.000000000
#> 5    1423 50 0.046210721 0.046944603 1.0158812  0.0483131539 0.048313154
#> 6    1432 20 0.018484288 0.007538549 0.4078355  0.0077583167 0.007758317