Prepare rank or preference data for further analyses.

## Arguments

- rankings
A matrix of ranked items, of size

`n_assessors x n_items`

. See`create_ranking()`

if you have an ordered set of items that need to be converted to rankings. If`preferences`

is provided,`rankings`

is an optional initial value of the rankings. If`rankings`

has column names, these are assumed to be the names of the items.`NA`

values in rankings are treated as missing data and automatically augmented; to change this behavior, see the`na_action`

argument to`set_model_options()`

. A vector length`n_items`

is silently converted to a matrix of length`1 x n_items`

, and names (if any), are used as column names.- preferences
A data frame with one row per pairwise comparison, and columns

`assessor`

,`top_item`

, and`bottom_item`

. Each column contains the following:`assessor`

is a numeric vector containing the assessor index.`bottom_item`

is a numeric vector containing the index of the item that was disfavored in each pairwise comparison.`top_item`

is a numeric vector containing the index of the item that was preferred in each pairwise comparison.

So if we have two assessors and five items, and assessor 1 prefers item 1 to item 2 and item 1 to item 5, while assessor 2 prefers item 3 to item 5, we have the following

`df`

:**assessor****bottom_item****top_item**1 2 1 1 5 1 2 5 3 - user_ids
Optional

`numeric`

vector of user IDs. Only only used by`update_mallows()`

. If provided, new data can consist of updated partial rankings from users already in the dataset, as described in Section 6 of Stein (2023) .- observation_frequency
A vector of observation frequencies (weights) to apply do each row in

`rankings`

. This can speed up computation if a large number of assessors share the same rank pattern. Defaults to`NULL`

, which means that each row of`rankings`

is multiplied by 1. If provided,`observation_frequency`

must have the same number of elements as there are rows in`rankings`

, and`rankings`

cannot be`NULL`

. See`compute_observation_frequency()`

for a convenience function for computing it.- validate_rankings
Logical specifying whether the rankings provided (or generated from

`preferences`

) should be validated. Defaults to`TRUE`

. Turning off this check will reduce computing time with a large number of items or assessors.- na_action
Character specifying how to deal with

`NA`

values in the`rankings`

matrix, if provided. Defaults to`"augment"`

, which means that missing values are automatically filled in using the Bayesian data augmentation scheme described in Vitelli et al. (2018) . The other options for this argument are`"fail"`

, which means that an error message is printed and the algorithm stops if there are`NA`

s in`rankings`

, and`"omit"`

which simply deletes rows with`NA`

s in them.- cl
Optional computing cluster used for parallelization when generating transitive closure based on preferences, returned from

`parallel::makeCluster()`

. Defaults to`NULL`

.- max_topological_sorts
When preference data are provided, multiple rankings will be consistent with the preferences stated by each users. These rankings are the topological sorts of the directed acyclic graph corresponding to the transitive closure of the preferences. This number defaults to one, which means that the algorithm stops when it finds a single initial ranking which is compatible with the rankings stated by the user. By increasing this number, multiple rankings compatible with the pairwise preferences will be generated, and one initial value will be sampled from this set.

- timepoint
Integer vector specifying the timepoint. Defaults to

`NULL`

, which means that a vector of ones, one for each observation, is generated. Used by`update_mallows()`

to identify data with a given iteration of the sequential Monte Carlo algorithm. If not`NULL`

, must contain one integer for each row in`rankings`

.- n_items
Integer specifying the number of items. Defaults to

`NULL`

, which means that the number of items is inferred from`rankings`

or from`preferences`

. Setting`n_items`

manually can be useful with pairwise preference data in the SMC algorithm, i.e., when`rankings`

is`NULL`

and`preferences`

is non-`NULL`

, and contains a small number of pairwise preferences for a subset of users and items.

## Value

An object of class `"BayesMallowsData"`

, to be provided in the `data`

argument to `compute_mallows()`

.

## Note

Setting `max_topological_sorts`

larger than 1 means that many possible
orderings of each assessor's preferences are generated, and one of them is
picked at random. This can be useful when experiencing convergence issues,
e.g., if the MCMC algorithm does not mix properly.

It is assumed that the items are labeled starting from 1. For example, if a
single comparison of the following form is provided, it is assumed that
there is a total of 30 items (`n_items=30`

), and the initial ranking is a
permutation of these 30 items consistent with the preference 29<30.

assessor | bottom_item | top_item |

1 | 29 | 30 |

If in reality there are only two items, they should be relabeled to 1 and 2, as follows:

assessor | bottom_item | top_item |

1 | 1 | 2 |

## References

Stein A (2023).
*Sequential Inference with the Mallows Model*.
Ph.D. thesis, Lancaster University.

Vitelli V, Sørensen, Crispino M, Arjas E, Frigessi A (2018).
“Probabilistic Preference Learning with the Mallows Rank Model.”
*Journal of Machine Learning Research*, **18**(1), 1–49.
https://jmlr.org/papers/v18/15-481.html.

## See also

Other preprocessing:
`get_transitive_closure()`

,
`set_compute_options()`

,
`set_initial_values()`

,
`set_model_options()`

,
`set_priors()`

,
`set_progress_report()`

,
`set_smc_options()`