Description Usage Arguments Value Author(s) Examples

View source: R/eucunivMDS_RPS.R

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.

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

`D` |
distance matrix n x n to be approximated |

`k` |
dimension of output results |

X A set of n points in k dimensions

Guillermo Pacheco, Viviana Ferraggine, Sebastian Torcida

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)
``` |

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