# Classical Multi-Dimensional Scaling for a distance matrix

### Description

`cmdsFit`

obtains coordinates in a `k`

dimensional space
which best approximate the given distances between objects.

### Usage

1 |

### Arguments

`d` |
Distances between objects |

`k` |
Dimensionality of the reconstructed space, typically set to 2 or 3. |

`type` |
Set to |

`add` |
Logical indicating if an additive constant c* should be
computed, and added to the non-diagonal dissimilarities such
that all n-1 eigenvalues are non-negative in |

`cor.method` |
A character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman", can be abbreviated. |

### Value

The function returns a `cmdsFit`

object.
See help("cmdsFit-class") for details.

### Methods

`signature(d = "matrix")`

Use Classical Multi-Dimensional Scaling to represent points in a k-dimensional space.

### Examples

1 2 3 | ```
### Not run
#d <- matrix(c(0,5,10,5,0,15,10,15,0),byrow=TRUE,ncol=3)
#cmdsFit(d,add=TRUE)
``` |

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