# 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

 ```1 2``` ```lgmm(data, sigma_0_ini = identity.permutation(dim(data)), 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

 ```1 2 3 4 5``` ```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 2 4 3

\$theta
 4.9999993 0.5224429 0.6931465

\$mode
 1 2 3 4

\$theta
 1.7917595 1.3862944 0.6931472

\$mode
 1 2 4 3

\$theta
  1.7917595  1.3862944 -0.6931472

\$mode
 1 2 3 4

\$theta
 2.5344050 1.4142606 1.4142606 0.2941161
```

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