cmdscale {mva} | R Documentation |
Classical multidimensional scaling of a data matrix.
cmdscale(d, k = 2, eig = FALSE)
d |
a distance structure such as that returned by dist
or a full symmetric matrix containing the dissimilarities. |
k |
the dimension of the space which the data are to be represented in; must be in {1,2, .., n-1}. |
eig |
indicates whether eigenvalues should be returned. |
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.
The functions isoMDS
and sammon
in package
`MASS' provide alternative ordination techniques.
If eig = FALSE
, 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.
points |
a matrix with k columns whose rows give the
coordinates of the points chosen to represent the dissimilarities. |
eig |
the eigenvalues computed during the scaling process. |
The S version of this function provides for computing an additional ``fiddle'' factor suggested by Torgerson. R will provide this option from version 1.5.0 on.
Cox, F.C. and Cox, M.A.A. (1994) Multidimensional Scaling. Chapman and Hall.
Mardia, K. V., J. T. Kent and J. M. Bibby (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.
dist
. Also
isoMDS
and sammon
in package `MASS'.
data(eurodist) loc <- cmdscale(eurodist) x <- loc[,1] y <- -loc[,2] plot(x, y, type="n", xlab="", ylab="", main="cmdscale(eurodist)") text(x, y, names(eurodist), cex=0.8)