# cmdscale

Classical multidimensional scaling

## Description

performs
classical multidimensional scaling on the `Y`

= cmdscale(`D`

)`n`

-by-`n`

distance or dissimilarity matrix `D`

, and returns an
`n`

-by-`p`

configuration matrix. The rows of
`Y`

correspond to the coordinates of `n`

points in a
`p`

-dimensional space, where `p`

<
`n`

.

When `D`

is a Euclidean distance matrix, its elements are the
pairwise distances between the `n`

points, and `p`

is the
dimension of the smallest space in which these points can be embedded.

When `D`

is a non-Euclidean distance matrix or a dissimilarity
matrix, `p`

is the number of positive eigenvalues of
`Y*Y'`

. In this case, the reduction to `p`

or fewer
dimensions provides a reasonable approximation to `D`

only if the
negative eigenvalues of `Y*Y'`

are small in magnitude.

## Examples

## Input Arguments

## Output Arguments

## References

[1] Cox, Trevor
F., and Michael A. A. Cox. *Multidimensional Scaling*. 2nd ed. Monographs
on Statistics and Applied Probability 88. Boca Raton: Chapman & Hall/CRC,
2001.

[2] Davison, Mark L. *Multidimensional Scaling*. Wiley Series in Probability and Mathematical Statistics. New York: Wiley, 1983.

[3] Seber, G. A.
F. *Multivariate Observations*. 1st ed. Wiley Series in Probability and
Statistics. Wiley, 1984.

## Version History

**Introduced before R2006a**