Comparison Vector Distribution

Computes the distribution of gamma patterns (agreement vectors) across record pairs. Each unique combination of gamma values across comparisons is a "comparison vector". This function counts how often each pattern occurs.

Usage

il_comparison_vectors(model, blocking = NULL, limit = NULL)

Arguments

model

A trained il_model.

blocking

A blocking rule created by block_on(). If NULL, uses all blocking rules from the model spec.

limit

Maximum number of pairs to sample. Defaults to NULL (all pairs).

Value

A tibble::tibble() with one row per unique comparison vector and columns gamma_<col> for each comparison plus count (number of pairs with that pattern) and proportion. Class il_comparison_vectors.

Details

On DuckDB, the computation runs entirely in SQL.

Examples

con <- DBI::dbConnect(duckdb::duckdb())
#> duckdb keeps downloaded extensions and secrets in a temporary directory:
#> ℹ /tmp/Rtmpye6umx/duckdb
#> This is removed when the R session ends.
#> • Extensions are re-downloaded each session.
#> • Secrets are lost.
#> ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
#> ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
#> ℹ See ?duckdb_storage for details and alternatives.
spec <- il_spec() |>
  il_compare(first_name, cl_exact()) |>
  il_compare(surname, cl_exact())
model <- il_model(fake_20, spec = spec, con = con)
vectors <- il_comparison_vectors(model)
ggplot2::autoplot(vectors)

il_cleanup(model)
DBI::dbDisconnect(con, shutdown = TRUE)