hddplot (version 0.59)

hddplot-package: tools:::Rd_package_title("hddplot")

Description

tools:::Rd_package_description("hddplot")

Author

tools:::Rd_package_author("hddplot")

Maintainer: tools:::Rd_package_maintainer("hddplot")

Details

The DESCRIPTION file: tools:::Rd_package_DESCRIPTION("hddplot") tools:::Rd_package_indices("hddplot") 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.

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

References

J. H. Maindonald, C. J. Burden, 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., Proceedings of 12th Computational Techniques and Applications Conference CTAC-2004, volume 46, pp. C59--C74.

http://journal.austms.org.au/V46/CTAC2004/Main/home.html [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) 
if (FALSE) demo(biasedPlots)
if (FALSE) demo(CVscoreplot)