hddplot: Use known groups in high-dimensional data to derive scores for plots

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 that serve didactic purposes.

Version: 0.56
Depends: R (≥ 3.0.0)
Imports: MASS, multtest
Suggests: knitr
Published: 2013-12-05
Author: John Maindonald
Maintainer: John Maindonald <john.maindonald at anu.edu.au>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.maths.anu.edu.au/~johnm
NeedsCompilation: no
Citation: hddplot citation info
Materials: README
In views: Multivariate
CRAN checks: hddplot results

Downloads:

Reference manual: hddplot.pdf
Vignettes: Feature Selection Bias in Classification of High Dimensional Data
Package source: hddplot_0.56.tar.gz
Windows binaries: r-devel: hddplot_0.56.zip, r-release: hddplot_0.56.zip, r-oldrel: hddplot_0.56.zip
OS X Snow Leopard binaries: r-release: hddplot_0.56.tgz, r-oldrel: hddplot_0.56.tgz
OS X Mavericks binaries: r-release: hddplot_0.56.tgz
Old sources: hddplot archive