Package 'RankAggregator'

Title: Aggregation of (Partial) Ordinal Rankings
Description: Easily compute an aggregate ranking (also called a median ranking or a consensus ranking) according to the axiomatic approach presented by Cook et al. (2007). This approach minimises the number of violations between all candidate consensus rankings and all input (partial) rankings, and draws on a branch and bound algorithm and a heuristic algorithm to drastically improve speed. The package also provides an option to bootstrap a consensus ranking based on resampling input rankings (with replacement). Input rankings can be either incomplete (partial) or complete. Reference: Cook, W.D., Golany, B., Penn, M. and Raviv, T. (2007) <doi:10.1016/j.cor.2005.05.030>.
Authors: Jay Burns [aut, cre], Adam Butler [aut]
Maintainer: Jay Burns <[email protected]>
License: GPL-3
Version: 0.0.1
Built: 2026-05-21 10:50:34 UTC
Source: https://github.com/cranhaven/cranhaven.r-universe.dev

Help Index

Rank aggregation of partial rankings

Description

This function is the core function for the RankAggregator package. This function uses a branch and bound algorithm, described by Cook et al. (2007), to return a best consensus (or median) ranking for a set of (partial) rankings.

Usage

consensusRanking(x)

Arguments

x

a data.frame containing columns titled Reviewer, Item, Ranking. On data structure, Reviewer and Item must be character, and Ranking must be numeric. Each row of x identifes the rank position that a single Reviewer awarded a single Item

Value

A data.frame is returned, with two columns: Item and Rank, where Item is a Factor containing all unique Item's from the input data.frame x, and where Rank is the estimated (numeric) rank position based on the branch and bound rank aggregation procedure.

See Also

This function calls internal functions evaluationMatrix, extendRanking, lowerBound, and upperBound

Examples

consensusRanking(cook_example)

Rank aggregation of partial rankings with optonal bootstrapping

Description

This funciton calls RankAggregator::consensusRanking to return a best consensus (or median) ranking for a set of (partial) rankings.

This function also provides an optional bootstrap resampling procedure to give user-defined confidence intervals and average rank positions with the consensus ranking.

Usage

consensusRankingBoot(
  x,
  bootstrap,
  nboot = 10000,
  conf.int = 0.95,
  prog.upd = TRUE
)

Arguments

x

a data.frame containing columns titled Reviewer, Item, Ranking. On data structure, Reviewer and Item must be character, and Ranking must be numeric. Each row of x identifes the rank position that a single Reviewer awarded a single Item
bootstrap

a logical value indicating whether to bootstrap the rank aggregation procedure.
nboot

a numeric value for bootstrap replicates. Default value is 10000.
conf.int

a numeric value >0 and <1. Default value is 0.95, which sets confidence interval at 95% level.
prog.upd

a logical value indicating whether the user wants progress updates on the bootstrap procedure.

Value

If bootstrap is FALSE, a data.frame is returned, with two columns: Item and Rank.est, where Item is a Factor containing all unique Item's from the input data.frame x, and where Rank.est is the estimated (numeric) rank position based on the consensusRanking() rank aggregation procedure.#'

If bootstrap is TRUE, a list is returned, with two elements:

  • $summaryTable is a data.frame with six columns: Item Rank.est, Rank.cilo, Rank.cihi, Rank.median, Rank.mean. Where Item and Rank.est are as described above, Rank.cilo and Rank.cihi are the estimates for the low and high confidence intervals, respectively. Rank.median and Rank.mean both describe the average rank positions.

  • $bootstrapData is an array containing estimated (numeric) rank positions based on the consensusRanking() rank aggregation procedure with resampled data. NA denotes estimated rankings that were discarded due to not containing all Items.

References

Cook, W.D., Golany, B., Penn, M. and Raviv, T., 2007. Creating a consensus ranking of proposals from reviewers partial ordinal rankings. Computers & Operations Research, 34, pp.954-965.

Marshall, E.C., Sanderson, C., Spiegelhalter, D.J. and McKee, M., 1998. Reliability of league tables of in vitro fertilisation clinics: retrospective analysis of live birth ratesCommentary: How robust are rankings? The implications of confidence intervals. Bmj, 316, pp.1701-1705.

See Also

Calls the internal function consensusRanking, which calls the other internal functions evaluationMatrix, consensusRanking, extendRanking, lowerBound, upperBound

Example data: partial rankings

Description

A dataset containing 5 partial rankings of 6 items. This is the example used by Cook et al (2007).

Usage

cook_example

Format

A data frame of 20 rows and 3 columns

Item

Character values giving one of 6 items

Reviewer

Character values giving one of 5 reviewers

Ranking

Numeric values giving a rank position

Source

Cook, W.D., Golany, B., Penn, M. and Raviv, T., 2007. Creating a consensus ranking of proposals from reviewers’ partial ordinal rankings. Computers & Operations Research, 34, pp.954-965.

Evaluation matrix

Description

This function is called by RankAggregator::consensusRanking. For each pair of Items, whenever both Items are ranked by the same Reviewer, this function sums the occurances when each of the two Items is preferred to the other.

Usage

evaluationMatrix(x)

Arguments

x

a data.frame containing columns titled Reviewer, Item, Ranking. On data structure, Reviewer and Item must be character, and Ranking must be numeric. Each row of x identifes the rank position that a single Reviewer awarded a single Item

Value

An m x n pairwise matrix giving the number of times Item[m] is preferred to (i.e. receives a ranking value lower than) Item[n] across all Reviewer Rankings

Examples

evaluationMatrix(cook_example)

Fully extend a partial ranking

Description

This function is called by RankAggregator::consensusRanking. The heuristic procedure orders unranked Items according the proportion of times an item was preferred in all pairwise comparisons with other unranked Items.

Usage

extendRanking(umat, node)

Arguments

umat

a matrix, which is either the output of evaluationMatrix, or a subset of the output of evaluationMatrix.
node

a list of elements, containing information about a node in the branch and bound search space. The relevant elements here are $partial.ranking, $included, and $prl. Where, $partial.ranking is a vector of rank positions for each Item in umat that is ranked so far; partial rankings may contain some - or all - NA values. $included is a logical vector denoting if an Item in umat is ranked in $partial.ranking. And $prl is a numeric value denoting how many of the Items in umat are ranked in $partial.ranking.

Value

A vector of rank positions.

Lower bound value

Description

This function is called by RankAggregator::consensusRanking. The lower bound is the absolute lowest value a complete candidate ranking could attain. Note, this value is not always achievable, so may differ from the value returned by upperBound.

     For each pair of \code{Item}s, there are three possible calculations, depending
     on whether both \code{Item}s are in the \code{partial.ranking}, one is in
     and the other is out the \code{partial.ranking}, or both are not in
     the \code{partial.ranking}.

Usage

lowerBound(umat, partial.ranking)

Arguments

umat

a matrix, which is either the output of evaluationMatrix, or a subset of the output of evaluationMatrix.
partial.ranking

a vector of rank positions for each Item in umat that is ranked so far; partial rankings may contain some - or all - NA values.

Value

A numeric value for the lower bound of a partial.ranking

RankAggregator

Description

This package provides a set of functions to easily compute an aggregate ranking (also called a median ranking or a compromise ranking) according to the axiomatic approach presented by Cook et al. (2007). This approach minimises the number of violations between all candidate consensus rankings and all input (partial) rankings, and draws on a branch and bound algorithm, and a heuristic algorithm to drastically improve speed. Input rankings can be either incomplete (partial) or complete.

The package also provides an option to bootstrap resulting consensus ranking based on resampling input rankings (with replacement). This approach was inspired by Marshall et al. (1998).

Author(s)

Jay Burns [email protected], Adam Butler [email protected]

References

Cook, W.D., Golany, B., Penn, M. and Raviv, T., 2007. Creating a consensus ranking of proposals from reviewers partial ordinal rankings. Computers & Operations Research, 34, pp.954-965.

Marshall, E.C., Sanderson, C., Spiegelhalter, D.J. and McKee, M., 1998. Reliability of league tables of in vitro fertilisation clinics: retrospective analysis of live birth ratesCommentary: How robust are rankings? The implications of confidence intervals. Bmj, 316, pp.1701-1705.

Upper bound value

Description

This function is called by RankAggregator::consensusRanking. The upper bound value is the value used by the branch and bound algorithm in determining whether or not to replace the current incumbent solution.

Usage

upperBound(ccr, umat)

Arguments

ccr

a vector of rank positions that is a candidate complete ranking
umat

a matrix, which is either the output of evaluationMatrix, or a subset of the output of evaluationMatrix.

Value

A numeric value for the upper bound of a candidate complete ranking