linear_MDS: (Classical) Multidimensional Scaling

In Rdimtools: Dimension Reduction and Estimation Methods

(Classical) Multidimensional Scaling

Description

do.mds performs a classical Multidimensional Scaling (MDS) using Rcpp and RcppArmadillo package to achieve faster performance than cmdscale.

Usage

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

Arguments

X	an (n\times p) matrix whose rows are observations and columns represent independent variables.
ndim	an integer-valued target dimension.
...	extra parameters including preprocess an additional option for preprocessing the data. Default is "null". See also aux.preprocess for more details.

Value

a named Rdimtools S3 object containing

Y

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

projection

a (p\times ndim) whose columns are basis for projection.

trfinfo

a list containing information for out-of-sample prediction.

algorithm

name of the algorithm.

References

\insertRef

kruskal_multidimensional_1964Rdimtools

Examples

## use iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X     = as.matrix(iris[subid,1:4])
lab   = as.factor(iris[subid,5])

## compare with PCA
Rmds <- do.mds(X, ndim=2)
Rpca <- do.pca(X, ndim=2)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(Rmds$Y, pch=19, col=lab, main="MDS")
plot(Rpca$Y, pch=19, col=lab, main="PCA")
par(opar)

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