# eucunivMDS_RPS: Given a n x n distance matrix D (not necessarily Euclidean)... In RPS: Resistant Procrustes Superimposition

## Description

Given a n x n distance matrix D (not necessarily Euclidean) and a initial set X0 of n seeds in k dim (that is, an initial n x k matrix), this function finds a set of n points in k dimensions X (a final n x k matrix) through a least-squares criterion such that the n x n matrix Dk of euclidean distances among these new points X is as close as possible to D.

## Usage

 `1` ```eucunivMDS_RPS(D, k = 2) ```

## Arguments

 `D` distance matrix n x n to be approximated `k` dimension of output results

## Value

X A set of n points in k dimensions

## Author(s)

Guillermo Pacheco, Viviana Ferraggine, Sebastian Torcida

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```source = array(matrix(nrow = 8,ncol = 3),c(8,3,3),dimnames = NULL) source[,,1] <- matrix(c(3,0,0,3,0,1,3,1,1,3,1,0,0,0,0,0,0,1,0,1,1,0,1,0) ,nrow = 8,ncol = 3,byrow = TRUE) source[,,2] <- matrix(c(3, 0 ,0,3, 0, 0.5,3, 1 ,0.75,3 ,1 ,0,0 ,0 ,0,0, 0 ,1,0, 1, 1,0, 1, 0.25) ,nrow = 8,ncol = 3,byrow = TRUE) source[,,3] <- matrix(c(5, 2 ,1,3, 0, 1.5,3.4, 1 ,1.75,3 ,1 ,0,0 ,0 ,0,0, 2 ,1,0, 3, 1,0, 1, 0.75) ,nrow = 8,ncol = 3,byrow = TRUE) result <- RPS::robgit_RPS(source, consenso = FALSE) distance <- RPS::resdistance_RPS(result) RPS::eucunivMDS_RPS(distance,2) ```

RPS documentation built on May 2, 2019, 3:29 p.m.