# superMDS-package: Supervised multidimensional scaling for visualization,... In superMDS: Implements the supervised multidimensional scaling (superMDS) proposal of Witten and Tibshirani (2011)

## Description

A method for implementing the supervised multidimensional scaling proposal of Witten and Tibshirani (2011)

## Details

 Package: superMDS Type: Package Version: 1.0.2 Date: 2013-01-02 License: GPL-2 LazyLoad: yes

Supervised multidimensional scaling (MDS) is a supervised version of least squares MDS. Suppose that we have a nxn dissimilarity matrix D and we want to find a set of n configuration points z1,...,zn, each a vector of length s, so that D is well-approximated by the Euclidean distances between the configuration points. Then least squares MDS can be used. However, suppose that we also have a vector of binary class labels associated with the dissimilarity matrix, yi = 1 or 2 for i=1,...,n. Then we might want configuration points whose Euclidean dsitances approximate D, and also that have the property that zis > zjs when yi > yj. This is the objective of supervised MDS. It leads to a method for visualizing observations, as well as a classification method. Details can be found in the paper below.

## Author(s)

Daniela M. Witten

Maintainer: Daniela Witten <dwitten@u.washington.edu>

## References

Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis 55(1): 789-801.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```########### Generate some data ############ n <- 30 p <- 10 x <- matrix(rnorm(n*p),ncol=p) y <- c(rep(1,n/2),rep(2,n/2)) xte <- matrix(rnorm(n*p),ncol=p) yte <- c(rep(1,n/2),rep(2,n/2)) x[y==1,1:(p)] <- x[y==1,1:(p)] + .4 x[y==2,1:(p)] <- x[y==2,1:(p)] - .4 xte[yte==1,1:(p)] <- xte[yte==1,1:(p)] + .4 xte[yte==2,1:(p)] <- xte[yte==2,1:(p)] - .4 # Done generating data # ########### Perform SuperMDS ############## out <- TrainSuperMDS(x=x,y=y,alpha=.4,S=2, silent=TRUE) # A plot of the training configuration points # par(mfrow=c(1,2)) plot(out\$z, col=yte, main="Training Data", xlab="Dimension 1", ylab="Dimension 2") testout <- TestSuperMDS(trout=out,xte=xte) ytehat <- testout\$ytehat # A table showing the true vs predicted class labels # print(table(ytehat,yte)) # A plot of the test configuration points # plot(testout\$zte, col=yte, main="Test Data", xlab="Dimension 1", ylab="Dimension 2") ```

### Example output ```      yte
ytehat  1  2
1 14  6
2  1  9
```

superMDS documentation built on May 2, 2019, 8:23 a.m.