lgmm: Learn a Generalized Mallows Model

In PerMallows: Permutations and Mallows Distributions

Description

Learn the parameter of the distribution of a sample of n permutations comming from a Generalized Mallows Model (GMM).

Usage

lgmm(data, sigma_0_ini = identity.permutation(dim(data)[2]),
  dist.name = "kendall", estimation = "approx")

Arguments

data	the matrix with the permutations to estimate
sigma_0_ini	optional the initial guess for the consensus permutation
dist.name	optional name of the distance used by the GMM. One of: kendall (default), cayley, hamming
estimation	optional select the approximated or the exact. One of: approx, exact

Value

A list with the parameters of the estimated distribution: the mode and the dispersion parameter vector

References

"Ekhine Irurozki, Borja Calvo, Jose A. Lozano (2016). PerMallows: An R Package for Mallows and Generalized Mallows Models. Journal of Statistical Software, 71(12), 1-30. doi:10.18637/jss.v071.i12"

Examples

data <- matrix(c(1,2,3,4, 1,4,3,2, 1,2,4,3), nrow = 3, ncol = 4, byrow = TRUE)
lgmm(data, dist.name="kendall", estimation="approx")
lgmm(data, dist.name="cayley", estimation="approx")
lgmm(data, dist.name="cayley", estimation="exact")
lgmm(data, dist.name="hamming", estimation="approx")

Example output

Loading required package: Rcpp
$mode
[1] 1 2 4 3

$theta
[1] 4.9999993 0.5224429 0.6931465

$mode
[1] 1 2 3 4

$theta
[1] 1.7917595 1.3862944 0.6931472

$mode
[1] 1 2 4 3

$theta
[1]  1.7917595  1.3862944 -0.6931472

$mode
[1] 1 2 3 4

$theta
[1] 2.5344050 1.4142606 1.4142606 0.2941161

PerMallows documentation built on May 2, 2019, 6:14 a.m.