GreedyLocal: Estimate the MLE of a Mallows-Binomial distribution using the...
In rankrate: Joint Statistical Models for Preference Learning with Rankings and Ratings
GreedyLocal R Documentation
Estimate the MLE of a Mallows-Binomial distribution using the "Greedy Local" method
Description
This function estimates the MLE of a Mallows-Binomial distribution using the GreedyLocal method,
which is identical to the Greedy method but includes an automatic and targeted post-hoc local search.
Usage
GreedyLocal(rankings, ratings, M)
Arguments
rankings
A matrix of rankings, potentially with attribute "assignments" to signify separate
reviewer assignments. One ranking per row.
ratings
A matrix of ratings, one row per judge and one column per object.
M
Numeric specifying maximum (=worst quality) integer rating.
Value
A list with elements pi0, the estimated consensus ranking MLE, p, the
estimated object quality parameter MLE, theta, the estimated scale parameter MLE, and
numnodes, number of nodes traversed during algorithm and a measure of computational complexity.
If multiple MLEs are found, pi0, p, and theta are returned a matrix elements, with
one row per MLE.
Examples
data("ToyData1")
GreedyLocal(ToyData1$rankings,ToyData1$ratings,ToyData1$M)
rankrate documentation built on April 12, 2025, 1:46 a.m.
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| GreedyLocal | R Documentation |
Estimate the MLE of a Mallows-Binomial distribution using the "Greedy Local" method
Description
This function estimates the MLE of a Mallows-Binomial distribution using the GreedyLocal method, which is identical to the Greedy method but includes an automatic and targeted post-hoc local search.
Usage
GreedyLocal(rankings, ratings, M)
Arguments
rankings |
A matrix of rankings, potentially with attribute "assignments" to signify separate reviewer assignments. One ranking per row. |
ratings |
A matrix of ratings, one row per judge and one column per object. |
M |
Numeric specifying maximum (=worst quality) integer rating. |
Value
A list with elements pi0, the estimated consensus ranking MLE, p, the
estimated object quality parameter MLE, theta, the estimated scale parameter MLE, and
numnodes, number of nodes traversed during algorithm and a measure of computational complexity.
If multiple MLEs are found, pi0, p, and theta are returned a matrix elements, with
one row per MLE.
Examples
data("ToyData1")
GreedyLocal(ToyData1$rankings,ToyData1$ratings,ToyData1$M)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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