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: 0.57-2
Depends: R (≥ 3.0.0)
Imports: MASS, multtest
Suggests: knitr
Published: 2016-11-03
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


Reference manual: hddplot.pdf
Vignettes: Feature Selection Bias in Classification of High Dimensional Data
Package source: hddplot_0.57-2.tar.gz
Windows binaries: r-devel: hddplot_0.57-2.zip, r-release: hddplot_0.57-2.zip, r-oldrel: hddplot_0.57-2.zip
OS X Mavericks binaries: r-release: hddplot_0.57-2.tgz, r-oldrel: hddplot_0.57-2.tgz
Old sources: hddplot archive


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