mean_seed: Mean Seed Ranking

In RankAggSIgFUR: Polynomially Bounded Rank Aggregation under Kemeny's Axiomatic Approach

Mean Seed Ranking

Description

Determine the mean seed ranking of the given input rankings. The average rank of an object is the sum of its various rankings from each input ranking divided by the total number of rankings. The mean seed ranking is formed by ranking the objects based on their average ranks, and ties are broken by ranking the first tied object with a higher rank.

Usage

mean_seed(input_rkgs, wt = c())

Arguments

input_rkgs	a k by n matrix of k rankings of n objects, where each row is a complete ranking. Note that this is a transpose of matrix used for functions like fur, sigfur, rap_greedy_alg, and subit_convergence.
wt	a k-length vector containing weights for each judge or attribute. An optional parameter.

Value

A vector containing the mean seed ranking of the input rankings.

See Also

rank, subit_convergence, fur, sigfur

Examples

## Four input rankings of five objects
input_rkgs <- matrix(c(3, 2, 5, 4, 1, 2, 3, 1, 5, 4, 5, 1, 3, 4, 2, 1, 2, 4, 5, 3),
    byrow = FALSE, ncol = 4)
mean_seed(t(input_rkgs)) # Found the mean seed ranking

## Five input rankings with five objects 
## 2nd ranking == 3rd ranking, so if a third object is weighted as zero,
## we should get the same answer as the first examples
input_rkgs <- matrix(c(3, 2, 5, 4, 1, 2, 3, 1, 5, 4, 2, 3, 1, 5, 4, 5, 1, 3, 4, 2, 1, 
                       2, 4, 5, 3),byrow = FALSE, ncol = 5)
wt = c(1,1,0,1,1)
mean_seed(t(input_rkgs),wt=wt) # Found the mean seed ranking

## Included dataset of 15 input rankings of 50 objects
data(data50x15)
input_rkgs <- t(as.matrix(data50x15[, -1]))
mean_seed(input_rkgs)

RankAggSIgFUR documentation built on July 9, 2023, 7:26 p.m.