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predict_top_k

Predict Top-k Rankings with Pairwise Preferences

Description

Predict the posterior probability, per item, of being ranked among the top-k for each assessor. This is useful when the data take the form of pairwise preferences.

Usage

predict_top_k(model_fit, burnin = model_fit$burnin, k = 3)

Arguments

model_fit

An object of type BayesMallows, returned from compute_mallows.
burnin

A numeric value specifying the number of iterations to discard as burn-in. Defaults to model_fit$burnin, and must be provided if model_fit$burnin does not exist. See assess_convergence.
k

Integer specifying the k in top-k.

Value

A dataframe with columns assessor, item, and prob, where each row states the probability that the given assessor rates the given item among top-k.

See Also

BayesMallows

Bayesian Preference Learning with the Mallows Rank Model

v1.0.1
GPL-3
Authors
Oystein Sorensen [aut, cre] (<https://orcid.org/0000-0003-0724-3542>), Valeria Vitelli [aut] (<https://orcid.org/0000-0002-6746-0453>), Marta Crispino [aut], Qinghua Liu [aut], Cristina Mollica [aut], Luca Tardella [aut]
Initial release

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