# cmdscale

##### Classical (Metric) Multidimensional Scaling

Classical multidimensional scaling (MDS) of a data matrix.
Also known as *principal coordinates analysis* (Gower, 1966).

- Keywords
- multivariate

##### Usage

```
cmdscale(d, k = 2, eig = FALSE, add = FALSE, x.ret = FALSE,
list. = eig || add || x.ret)
```

##### Arguments

- d
a distance structure such as that returned by

`dist`

or a full symmetric matrix containing the dissimilarities.- k
the maximum dimension of the space which the data are to be represented in; must be in \(\{1, 2, \ldots, n-1\}\).

- eig
indicates whether eigenvalues should be returned.

- add
logical indicating if an additive constant \(c*\) should be computed, and added to the non-diagonal dissimilarities such that the modified dissimilarities are Euclidean.

- x.ret
indicates whether the doubly centred symmetric distance matrix should be returned.

- list.
logical indicating if a

`list`

should be returned or just the \(n \times k\) matrix, see ‘Value:’.

##### Details

Multidimensional scaling takes a set of dissimilarities and returns a set of points such that the distances between the points are approximately equal to the dissimilarities. (It is a major part of what ecologists call ‘ordination’.)

A set of Euclidean distances on \(n\) points can be represented
exactly in at most \(n - 1\) dimensions. `cmdscale`

follows
the analysis of Mardia (1978), and returns the best-fitting
\(k\)-dimensional representation, where \(k\) may be less than the
argument `k`

.

The representation is only determined up to location (`cmdscale`

takes the column means of the configuration to be at the origin),
rotations and reflections. The configuration returned is given in
principal-component axes, so the reflection chosen may differ between
R platforms (see `prcomp`

).

When `add = TRUE`

, a minimal additive constant \(c*\) is
computed such that the dissimilarities \(d_{ij} + c*\) are Euclidean and hence can be represented in `n - 1`

dimensions. Whereas S (Becker *et al*, 1988) computes this
constant using an approximation suggested by Torgerson, R uses the
analytical solution of Cailliez (1983), see also Cox and Cox (2001).
Note that because of numerical errors the computed eigenvalues need
not all be non-negative, and even theoretically the representation
could be in fewer than `n - 1`

dimensions.

##### Value

If `.list`

is false (as per default), a matrix with `k`

columns whose rows give the coordinates of the points chosen to
represent the dissimilarities.

Otherwise, a `list`

containing the following components.

a matrix with up to `k`

columns whose rows give the
coordinates of the points chosen to represent the dissimilarities.

the \(n\) eigenvalues computed during the scaling process if
`eig`

is true. **NB**: versions of R before 2.12.1
returned only `k`

but were documented to return \(n - 1\).

the doubly centered distance matrix if `x.ret`

is true.

the additive constant \(c*\), `0`

if `add = FALSE`

.

a numeric vector of length 2, equal to say \((g_1,g_2)\), where \(g_i = (\sum_{j=1}^k \lambda_j)/ (\sum_{j=1}^n T_i(\lambda_j))\), where \(\lambda_j\) are the eigenvalues (sorted in decreasing order), \(T_1(v) = \left| v \right|\), and \(T_2(v) = max( v, 0 )\).

##### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988).
*The New S Language*.
Wadsworth & Brooks/Cole.

Cailliez, F. (1983).
The analytical solution of the additive constant problem.
*Psychometrika*, **48**, 343--349.
10.1007/BF02294026.

Cox, T. F. and Cox, M. A. A. (2001).
*Multidimensional Scaling*. Second edition.
Chapman and Hall.

Gower, J. C. (1966).
Some distance properties of latent root and vector
methods used in multivariate analysis.
*Biometrika*, **53**, 325--328.
10.2307/2333639.

Krzanowski, W. J. and Marriott, F. H. C. (1994).
*Multivariate Analysis. Part I. Distributions, Ordination and
Inference.*
London: Edward Arnold.
(Especially pp.108--111.)

Mardia, K.V. (1978).
Some properties of classical multidimensional scaling.
*Communications on Statistics -- Theory and Methods*, **A7**,
1233--41.
10.1080/03610927808827707

Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979).
Chapter 14 of *Multivariate Analysis*, London: Academic Press.

Seber, G. A. F. (1984).
*Multivariate Observations*.
New York: Wiley.

Torgerson, W. S. (1958).
*Theory and Methods of Scaling*.
New York: Wiley.

##### See Also

`dist`

.

`isoMDS`

and `sammon`

in package MASS provide alternative methods of
multidimensional scaling.

##### Examples

`library(stats)`

```
# NOT RUN {
require(graphics)
loc <- cmdscale(eurodist)
x <- loc[, 1]
y <- -loc[, 2] # reflect so North is at the top
## note asp = 1, to ensure Euclidean distances are represented correctly
plot(x, y, type = "n", xlab = "", ylab = "", asp = 1, axes = FALSE,
main = "cmdscale(eurodist)")
text(x, y, rownames(loc), cex = 0.6)
# }
```

*Documentation reproduced from package stats, version 3.6.2, License: Part of R 3.6.2*