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Fit an EloChoice model to pairwise comparison data

Source: R/elo_model.R
fit_elo_model.Rd

This function fits an Elo-based paired-comparison model using the EloChoice package. It is intended to complement fit_bt_model by providing an alternative scoring framework based on Elo ratings rather than Bradley–Terry models.

Usage

fit_elo_model(elo_data, runs = 5, verbose = FALSE, ...)

Arguments

elo_data

A data frame or tibble containing winner and loser columns. Typically produced using build_elo_data.

runs

Integer number of randomizations to use in EloChoice::elochoice. Default is 5.

verbose

Logical. If TRUE (default), show any messages/warnings emitted by the underlying fitting functions. If FALSE, suppress noisy output to keep examples and reports clean.

...

Additional arguments passed to EloChoice::elochoice().

Value

A named list with components:

engine

Character scalar identifying the scoring engine ("EloChoice").

fit

The "elochoice" model object.

elo

A tibble with columns ID and elo.

reliability

Numeric scalar: mean unweighted reliability index.

reliability_weighted

Numeric scalar: mean weighted reliability index.

Details

The input elo_data must contain two columns:

  1. winner: ID of the winning sample in each pairwise trial

  2. loser: ID of the losing sample in each trial

These can be created from standard pairwise comparison output using build_elo_data.

Internally, this function calls:

  • elochoice — to estimate Elo ratings using repeated randomization of trial order;

  • reliability — to compute unweighted and weighted reliability indices as described in Clark et al. (2018).

If the EloChoice package is not installed, a helpful error message is shown telling the user how to install it.

The returned object mirrors the structure of fit_bt_model for consistency across scoring engines:

  • engine — always "EloChoice".

  • fit — the raw "elochoice" object returned by EloChoice::elochoice().

  • elo — a tibble with columns:

    • ID: sample identifier

    • elo: estimated Elo rating

    (Unlike Bradley–Terry models, EloChoice does not provide standard errors for these ratings, so none are returned.)

  • reliability — the mean unweighted reliability index (mean proportion of “upsets” across randomizations).

  • reliability_weighted — the mean weighted reliability index (weighted version of the upset measure).

References

Clark AP, Howard KL, Woods AT, Penton-Voak IS, Neumann C (2018). "Why rate when you could compare? Using the 'EloChoice' package to assess pairwise comparisons of perceived physical strength." PLOS ONE, 13(1), e0190393. doi:10.1371/journal.pone.0190393 .

See also

build_bt_data(), fit_bt_model()

Other frequentist models: build_bt_data(), build_elo_data(), fit_bt_model(), summarize_bt_fit()

Examples

data("example_writing_pairs", package = "pairwiseLLM")

elo_data <- build_elo_data(example_writing_pairs)

if (requireNamespace("EloChoice", quietly = TRUE)) {
  fit <- fit_elo_model(elo_data, runs = 5, verbose = FALSE)
  fit$elo
  fit$reliability
  fit$reliability_weighted
}
#> [1] 0.9212787