Rdimtools (version 1.0.6)

do.mds: (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,
  preprocess = c("center", "cscale", "decorrelate", "whiten")
)

Arguments

X

an \((n\times p)\) matrix whose rows are observations and columns represent independent variables.

ndim

an integer-valued target dimension.

preprocess

an option for preprocessing the data. Default is "center". See also aux.preprocess for more details.

Value

a named list containing

Y

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

trfinfo

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

projection

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

References

kruskal_multidimensional_1964Rdimtools

Examples

# NOT RUN {
## 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])

## try different preprocessing
out1 <- do.mds(X,ndim=2)
out2 <- do.mds(X,ndim=2,preprocess="cscale")
out3 <- do.mds(X,ndim=2,preprocess="whiten")

## extract embeddings for each procedure
Y1 <- out1$Y; Y2 <- out2$Y; Y3 <- out3$Y

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(Y1, pch=19, col=lab, main="MDS::center")
plot(Y2, pch=19, col=lab, main="MDS::decorrelate")
plot(Y3, pch=19, col=lab, main="MDS::whiten")
par(opar)

# }