Description Usage Arguments Value Author(s) References See Also Examples

Given a nxn dissimilarity matrix D and a n-vector of binary (1,2) class labels y, this function outputs a set of configuration points z1,...,zn, each a S-vector, such that the distances between the configuration points approximate the dissimilarity matrix D, AND such that zis >= zjs tends to occur when yi >= yj.

1 | ```
TrainSuperMDS(d = NULL, y, alpha = 0.5, S = 2, x = NULL, nstarts = 5, silent = FALSE)
``` |

`d` |
A nxn dissimilarity matrix. If NULL, then x, a nxp data matrix, must be input instead. |

`y` |
A n-vector of binary labels, in the form of 1's and 2's. For instance, c(1,1,1,2,2) could be input if D is a 5x5 matrix. |

`alpha` |
A scalar between 0 and 1. If alpha=0 then this is just least squares MDS, and if alpha=1 then it's completely supervised. |

`S` |
The number of dimensions of the configuration points z1,...,zn. Must be at least equal to 1. |

`x` |
A nxp data matrix, to be input only if D is NULL. |

`nstarts` |
The supervised MDS algorithm finds a local minimum for the objective. Here, specify the number of initial values to try. If nstarts>1 then the set of configuration points corresponding to the optimal (smallest) value of the objective will be reported. |

`silent` |
Set to TRUE in order to turn off printing output to screen. |

`z` |
A nxS matrix of the configuration points obtained. |

`crits` |
The values of the criterion obtained at the iterations of the algorithm. |

`stress` |
The portion of the final criterion value that are due to the STRESS component of the objective function. |

`super` |
The portion of the final criterion value that are due to the SUPERVISED component of the objective function. |

Daniela M Witten

Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis.

1 | ```
# Try ?superMDS for examples
``` |

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