Implements a variety of functions to approximate expected ranks for partial rankings.

## Usage

approx_rank_expected(P, method = "lpom")

## Arguments

P

A partial ranking as matrix object calculated with neighborhood_inclusion or positional_dominance.

method

String indicating which method to be used. see Details.

## Value

A vector containing approximated expected ranks.

## Details

The method parameter can be set to

lpom

local partial order model

glpom

extension of the local partial order model.

loof1

based on a connection with relative rank probabilities.

loof2

extension of the previous method.

Which of the above methods performs best depends on the structure and size of the partial ranking. See vignette("benchmarks",package="netrankr") for more details.

## References

Brüggemann R., Simon, U., and Mey,S, 2005. Estimation of averaged ranks by extended local partial order models. MATCH Commun. Math. Comput. Chem., 54:489-518.

Brüggemann, R. and Carlsen, L., 2011. An improved estimation of averaged ranks of partial orders. MATCH Commun. Math. Comput. Chem., 65(2):383-414.

De Loof, L., De Baets, B., and De Meyer, H., 2011. Approximation of Average Ranks in Posets. MATCH Commun. Math. Comput. Chem., 66:219-229.

David Schoch

## Examples

P <- matrix(c(0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, rep(0, 10)), 5, 5, byrow = TRUE)
# Exact result
exact_rank_prob(P)\$expected.rank
#>       V1       V2       V3       V4       V5
#> 1.333333 2.111111 2.888889 4.222222 4.444444

approx_rank_expected(P, method = "lpom")
#> [1] 1.2 2.0 3.0 4.5 4.5
approx_rank_expected(P, method = "glpom")
#> [1] 1.250000 2.166667 2.833333 4.333333 4.416667