# Multidimensional Scaling

**R** provides functions for both classical and nonmetric multidimensional scaling. Assume that we have N objects measured on p numeric variables. We want to represent the distances among the objects in a parsimonious (and visual) way (i.e., a lower k-dimensional space).

## Classical MDS

You can perform a classical MDS using the **cmdscale( )** function.

`# Classical MDS`

# N rows (objects) x p columns (variables)

# each row identified by a unique row name

d <- dist(mydata) # euclidean distances between the rows

fit <- cmdscale(d,eig=TRUE, k=2) # k is the number of dim

fit # view results

# plot solution

x <- fit$points[,1]

y <- fit$points[,2]

plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2",

main="Metric MDS", type="n")

text(x, y, labels = row.names(mydata), cex=.7)

## Nonmetric MDS

Nonmetric MDS is performed using the** isoMDS( ) **function in the **MASS **package.

`# Nonmetric MDS`

# N rows (objects) x p columns (variables)

# each row identified by a unique row name

library(MASS)

d <- dist(mydata) # euclidean distances between the rows

fit <- isoMDS(d, k=2) # k is the number of dim

fit # view results

# plot solution

x <- fit$points[,1]

y <- fit$points[,2]

plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2",

main="Nonmetric MDS", type="n")

text(x, y, labels = row.names(mydata), cex=.7)

## Individual Difference Scaling

3-way or individual difference scaling can be completed using the **indscal()** function in the SensoMineR package. The smacof package offers a three way analysis of individual differences based on stress minimization of means of majorization.

## To practice

This tutorial on ggplot3 includes exercises on Distance matrices and Multi-Dimensional Scaling (MDS).