Given a matrix, and a distance measure, an embedding of the rows into desired Euclidean space is performed using non-Metric Multi-Dimensional Scaling.

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`data` |
matrix whose rows shall be embedded. |

`embed.dim` |
Dimensionality of Euclidean space into which embedding shall be performed. |

`n.iters` |
Number of iterations of the nMDS scheme |

`metric` |
The distance metric used to compare rows. Currently only "pearson" and "euclidean" ae supported. |

`random.seed` |
A random seed used by nMDS. Use of this option allows reproducability of nMDS results |

non-Metric Multi-Dimensional Scaling is performed using the scheme proposed by Taguchi and Oono.

If an element is missing (NA) in a particular row, all distance comparisons to that row shall ignore that particular element.

An object of class "nMDS" containing:

`x` |
matrix with the same number of rows and row names as |

Satwik Rajaram and Yoshi Oono

Relational patterns of gene expression via non-metric multidimensional scaling analysis: Y.-h. Taguchi and Y. Oono, Bioinformatics, 2005 21(6):730-740.

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