nonlinear_MMDS: Metric Multidimensional Scaling

In Rdimtools: Dimension Reduction and Estimation Methods

Metric Multidimensional Scaling

Description

Metric MDS is a nonlinear method that is solved iteratively. We adopt a well-known SMACOF algorithm for updates with uniform weights over all pairwise distances after initializing the low-dimensional configuration via classical MDS.

Usage

do.mmds(X, ndim = 2, ...)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables.
ndim	an integer-valued target dimension (default: 2).
...	extra parameters including maxiter maximum number of iterations for metric MDS updates (default: 100). abstol stopping criterion for metric MDS iterations (default: 1e-8).

Value

a named Rdimtools S3 object containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

algorithm

name of the algorithm.

References

\insertRef

leeuw_applications_1977Rdimtools

\insertRef

borg_modern_2010Rdimtools

Examples

## load iris data
data(iris)
X     = as.matrix(iris[,1:4])
lab   = as.factor(iris[,5])

## compare with other methods
pca2d <- do.pca(X, ndim=2)
cmd2d <- do.mds(X, ndim=2)
mmd2d <- do.mmds(X, ndim=2)

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(pca2d$Y, col=lab, pch=19, main="PCA")
plot(cmd2d$Y, col=lab, pch=19, main="Classical MDS")
plot(mmd2d$Y, col=lab, pch=19, main="Metric MDS")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.