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 |
| 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: not available, r-oldrel: hddplot_0.56.tgz |
| OS X Mavericks binaries: | r-release: hddplot_0.56.tgz |
| Old sources: | hddplot archive |