hddplot-package {hddplot} | R Documentation

## For high-dimensional data with known groups, derive scores for plotting

### Description

Cross-validated linear discriminant calculations determine the optimum number of features. Test and training scores from successive cross-validation steps determine, via a principal components calculation, a low-dimensional global space onto which test scores are projected, in order to plot them. Further functions are included for didactic purposes.

### Details

Package: | hddplot
Type: | Package
Version: | 1.0
Date: | 2006-01-09
License: | GPL Version 2 or later.

The most important functions are

`cvdisc`: Determine variation in cross-validated accuracy with number of features

`cvscores`: For a specific choice of number of features, determine scores that can be used for plotting

Note also `scoreplot` (plot scores), `qqthin` (qqplots, designed to avoid generating large files when there are many points), and functions that are intended to illustrate issues that arise in the plotting of expression array and other high-dimensional data

### Author(s)

John Maindonald

Maintainer: John Maindonald <john.maindonald@anu.edu.au>

### References

Maindonald, J.H. and Burden, C.J., 2005. Selection bias in plots of microarray or other data that have been sampled from a high-dimensional space. In R. May and A.J. Roberts, eds., {em Proceedings of 12th Computational Techniques and Applications Conference CTAC-2004}, volume 46, pp. C59–C74.

http://anziamj.austms.org.au/V46/CTAC2004/Main [March 15, 2005].

### See Also

`cvscores`, `scoreplot`

### Examples

    ## Use first 500 rows (expression values) of Golub, for demonstration.
    data(Golub)
    data(golubInfo)
    attach(golubInfo)
    miniG.BM <- Golub[1:500, BM.PB=="BM"]  # 1st 500 rows only
    cancer.BM <- cancer[BM.PB=="BM"]
    miniG.cv <- cvdisc(miniG.BM, cl=cancer.BM, nfeatures=1:10,
                        nfold=c(10,4))
    miniG.scores <- cvscores(cvlist=miniG.cv, nfeatures=4, cl.other=NULL)
    subsetB <- (cancer=="allB") & (tissue.mf %in% c("BM:f","BM:m","PB:m"))
    tissue.mfB <- tissue.mf[subsetB, drop=TRUE]
    scoreplot(scorelist=miniG.scores, cl.circle=tissue.mfB,
           circle=tissue.mfB%in%c("BM:f","BM:m"),
           params=list(circle=list(col=c("cyan","gray"))),
           prefix="BM samples -")
    detach(golubInfo)
    ## Not run: demo(biasedPlots)
    ## Not run: demo(CVscoreplot)

[Package hddplot version 0.51 Index]